system

Abstract

The system according to the embodiment aims to determine the baby's emotions and provide appropriate advice. [Solution] A system according to an embodiment includes a device, an emotion engine, and a generation AI. The device reads vocalizations, facial expressions, or body movements. The emotion engine determines the baby's emotion based on the data read by the device. The generation AI generates advice based on the emotion determined by the emotion engine.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to accurately assess a baby's emotions and provide appropriate advice.

[0005] The system according to the embodiment aims to determine the baby's emotions and provide appropriate advice. [Means for solving the problem]

[0006] A system according to an embodiment includes a device, an emotion engine, and a generation AI. The device reads vocalizations, facial expressions, or body movements. The emotion engine determines the baby's emotion based on the data read by the device. The generation AI generates advice based on the emotion determined by the emotion engine. [Effects of the Invention]

[0007] The system according to the embodiment can determine the baby's emotions and provide appropriate advice. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention reads a baby's vocalizations, facial expressions, and body movements to determine the baby's emotions, and then generates appropriate advice using a generation AI to provide it to the guardian. This system includes a device (smartphone) that reads the baby's vocalizations, facial expressions, and body movements; an emotion engine that determines the baby's emotions based on the read data; and a generation AI that generates appropriate advice based on the emotions determined by the emotion engine. For example, the system uses a smartphone to read the baby's vocalizations, facial expressions, and body movements. Next, the recorded data is input into a baby's emotion engine to determine the baby's emotions. The generation AI then generates appropriate advice for the guardian based on the determination result of the emotion engine. Finally, the generated advice is provided to the guardian via the smartphone screen or audio. This allows the guardian to understand the baby's emotions and take appropriate action. The system thereby stimulates communication between the baby and the guardian, making it easier for the guardian to understand the baby's emotions.

[0029] A baby emotion determination system according to an embodiment includes a device, an emotion engine, and a generation AI. The device reads the baby's vocalizations, facial expressions, or body movements. For example, the device collects the baby's crying using a microphone. The device can also capture the baby's smiling face using a camera. The device can also detect the baby's limb movements using a sensor. The emotion engine analyzes the read data to determine the baby's emotion. For example, the emotion engine can analyze the crying pattern using a machine learning algorithm to determine whether the baby is sad. The emotion engine can also analyze the baby's smiling face using facial expression recognition technology to determine whether the baby is happy. The emotion engine can also analyze the body movement data to determine whether the baby is excited. The generation AI generates advice based on the determination result of the emotion engine. For example, if the baby is crying, the generation AI can generate advice to the guardian, such as "Hold the baby close." If the baby is smiling, the generation AI can generate advice to the guardian, such as "Play with the baby." Furthermore, if the baby is excited, the generation AI can generate advice such as, "Create a quiet environment to calm the baby." In this way, the baby emotion determination system according to the embodiment can stimulate communication between the baby and the caregiver by determining the baby's emotion and generating appropriate advice.

[0030] The device can read the baby's vocalizations, facial expressions, or body movements. For example, the device can collect the baby's crying sound using a microphone. The device can also capture the baby's smile using a camera. The device can also detect the baby's limb movements using a sensor. In this way, by reading the baby's vocalizations, facial expressions, and body movements, data for determining emotions can be obtained.

[0031] The emotion engine can analyze the read data and determine the baby's emotions. For example, the emotion engine can use a machine learning algorithm to analyze the crying pattern and determine whether the baby is sad. The emotion engine can also use facial expression recognition technology to analyze the baby's smile and determine whether the baby is happy. The emotion engine can also analyze body movement data and determine whether the baby is excited. In this way, the baby's emotions can be accurately determined by analyzing the read data.

[0032] The generation AI can generate advice based on the judgment results of the emotion engine. For example, if a baby is crying, the generation AI can generate advice to the guardian such as "Hold the baby close." If the baby is laughing, the generation AI can also generate advice such as "Play with the baby." If the baby is excited, the generation AI can also generate advice such as "Create a quiet environment to calm the baby." In this way, appropriate advice can be generated based on the judgment results of the emotion engine, providing support to guardians.

[0033] The generation AI can provide the generated advice via smartphone screen or voice. For example, the generation AI displays the generated advice on a smartphone screen. The generation AI can also provide the generated advice via voice. This allows parents to easily receive the advice by providing the generated advice on a smartphone screen or voice.

[0034] The device can estimate the baby's emotions and adjust the sensitivity of the device's readings based on the estimated baby's emotions. For example, if the baby is crying, the device can increase the microphone sensitivity of the device to read the vocalizations in detail. If the baby is smiling, the device can increase the camera resolution to capture the facial expressions more clearly. If the baby is moving around, the device can improve the accuracy of motion tracking to read the body movements more accurately. This allows for more accurate data to be obtained by adjusting the sensitivity of the device's readings based on the baby's emotions.

[0035] The device can analyze data on the baby's past vocalizations, facial expressions, and body movements to select the optimal reading method. For example, the device can learn the baby's crying patterns from past data and select the optimal microphone settings for detecting crying. The device can also select the optimal camera settings for detecting smiles based on past facial expression data. The device can also analyze past movement data to select the optimal sensor settings for detecting specific movements. In this way, by analyzing past data, the optimal reading method can be selected and the accuracy of the data can be improved.

[0036] When reading data, the device can filter it based on the baby's current environment. For example, if the room temperature is high, the device can filter facial expression data taking into account the baby's sweating. The device can also increase the sensitivity of the camera to accurately read facial expressions when the lighting is low. The device can also perform noise cancellation to accurately read vocalizations when the ambient volume is high. This allows for filtering based on the current environment, improving the accuracy of the data.

[0037] When reading, the device can select the optimal reading means according to the baby's movement pattern. For example, if the baby rolls over, the device can detect the movement pattern and adjust the camera angle. Also, if the baby waves their hands, the device can select the optimal sensor settings for tracking their hand movements. Also, if the baby is crawling, the device can adjust the camera frame rate according to the speed of their movement. This allows the accuracy of data to be improved by selecting the optimal reading means according to the movement pattern.

[0038] The device can estimate the baby's emotions and determine the priority of data to be read based on the estimated emotions of the baby. For example, if the baby is crying, the device can prioritize reading vocalization data. If the baby is smiling, the device can also prioritize reading facial expression data. If the baby is moving around, the device can also prioritize reading body movement data. Thus, by prioritizing data based on emotions, important data can be acquired preferentially.

[0039] When reading, the device can prioritize reading data that is highly relevant based on the baby's geographical location information. For example, if the baby is in a park, the device can filter vocalization data taking into account ambient sounds. If the baby is at home, the device can read facial expression data taking into account indoor lighting conditions. If the baby is in a car, the device can read body movement data taking into account vibrations. This allows the device to prioritize obtaining data that is highly relevant by taking into account the geographical location information.

[0040] When reading, the device can analyze the baby's social media activity and read related data. For example, the device can analyze the baby's social media photos and complement facial expression data. The device can also analyze the baby's social media videos and complement movement patterns. The device can also analyze the baby's social media audio posts and complement vocalization data. In this way, the device can complement related data by analyzing social media activity.

[0041] When reading, the device can customize the reading method by reflecting the baby's past feedback. For example, if the past feedback shows that the baby reacts to a particular sound, the device can prioritize reading that sound. Also, if the past feedback shows that the baby often makes a particular facial expression, the device can prioritize reading that facial expression. Also, if the past feedback shows that the baby often makes a particular movement, the device can prioritize reading that movement. In this way, the reading method can be optimized by reflecting past feedback.

[0042] The emotion engine can estimate the baby's emotion and adjust the emotion determination algorithm based on the estimated emotion of the baby. For example, when the baby is crying, the emotion engine adjusts the emotion determination algorithm by placing emphasis on the crying pattern. When the baby is smiling, the emotion engine can also adjust the emotion determination algorithm by placing emphasis on the smiling pattern. When the baby is moving around, the emotion engine can also adjust the emotion determination algorithm by placing emphasis on the movement pattern. In this way, by adjusting the algorithm based on the emotion, the accuracy of emotion determination can be improved.

[0043] When determining an emotion, the emotion engine can adjust the level of detail of the determination based on the importance of the baby's vocalizations, facial expressions, and body movements. For example, if vocalizations are important, the emotion engine determines the emotion by increasing the level of detail of the vocalization data. Also, if facial expressions are important, the emotion engine can determine the emotion by increasing the level of detail of the facial expression data. Also, if body movements are important, the emotion engine can determine the emotion by increasing the level of detail of the movement data. In this way, by adjusting the level of detail of the determination based on the importance, the accuracy of emotion determination can be improved.

[0044] When determining emotions, the emotion engine can apply different determination algorithms depending on the baby category. For example, the emotion engine can apply an algorithm that emphasizes crying patterns to younger babies. The emotion engine can also apply an algorithm that emphasizes facial expression patterns to older babies. The emotion engine can also apply an algorithm that emphasizes specific emotion patterns depending on the gender. In this way, by applying an algorithm depending on the category, the accuracy of emotion determination can be improved.

[0045] The emotion engine can improve the accuracy of emotion determination by referring to the baby's past emotion data. For example, the emotion engine can learn the baby's crying pattern from the past emotion data to improve the accuracy of the determination. The emotion engine can also learn the baby's laughing pattern from the past emotion data to improve the accuracy of the determination. The emotion engine can also learn the baby's movement pattern from the past emotion data to improve the accuracy of the determination. In this way, by referring to the past emotion data, the accuracy of emotion determination can be improved.

[0046] The emotion engine can estimate the baby's emotion and adjust the order in which emotion determination results are displayed based on the estimated baby's emotion. For example, if the baby is crying, the emotion engine can first display the emotion determination result for crying. Also, if the baby is smiling, the emotion engine can first display the emotion determination result for smiling. Also, if the baby is moving around, the emotion engine can first display the emotion determination result for movement. In this way, adjusting the display order of the results based on emotion allows parents to prioritize checking important information.

[0047] The emotion engine can make emotion determinations taking into account the geographical distribution of the baby. For example, if the baby is at home, the emotion engine determines the emotion taking into account the home environment. Also, if the baby is in a park, the emotion engine can determine the emotion taking into account the external environment. Also, if the baby is in a nursery school, the emotion engine can determine the emotion taking into account the group environment. In this way, by taking geographical distribution into account, emotion determination according to the environment becomes possible.

[0048] When determining an emotion, the emotion engine can improve the accuracy of the determination by referring to literature related to babies. For example, the emotion engine can improve the accuracy of the determination by referring to literature on babies' crying. The emotion engine can also improve the accuracy of the determination by referring to literature on babies' smiling. The emotion engine can also improve the accuracy of the determination by referring to literature on babies' movements. In this way, by referring to related literature, the accuracy of the emotion determination can be improved.

[0049] The emotion engine can take the market value of the baby into consideration when determining emotions. For example, if the market value of the baby is high, the emotion engine collects additional data to improve the accuracy of emotion determination. Alternatively, if the market value of the baby is low, the emotion engine can apply a basic emotion determination algorithm. The emotion engine can also adjust emotion determination resources according to the market value of the baby. This allows for optimal resource allocation by taking market value into consideration.

[0050] The generation AI can estimate the baby's emotions and adjust the way the advice is expressed based on the estimated emotions of the baby. For example, if the baby is crying, the generation AI can generate advice in gentle words. If the baby is smiling, the generation AI can also generate advice in cheerful words. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to increase the effectiveness of advice for parents by adjusting the way the advice is expressed based on the baby's emotions.

[0051] When generating advice, the generation AI can adjust the level of detail of the advice based on the importance of the baby's emotions. For example, if the baby is crying, the generation AI will generate detailed advice. If the baby is smiling, the generation AI can also generate concise advice. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to provide appropriate advice by adjusting the level of detail of the advice based on the importance of the emotion.

[0052] When generating advice, the generation AI can apply different advice algorithms depending on the baby's category. For example, the generation AI can apply an algorithm that generates basic advice to younger babies. The generation AI can also apply an algorithm that generates detailed advice to older babies. The generation AI can also apply an algorithm that generates specific advice depending on the baby's gender. This makes it possible to provide appropriate advice by applying an algorithm depending on the category.

[0053] When generating advice, the generation AI can improve the accuracy of the advice by referring to past advice results for the baby. For example, the generation AI can learn the patterns that make the baby stop crying from past advice results, and improve the accuracy of advice in similar situations. The generation AI can also learn the patterns that make the baby smile from past advice results, and improve the accuracy of advice in similar situations. The generation AI can also learn the patterns that make the baby calm from past advice results, and improve the accuracy of advice in similar situations. In this way, the accuracy of advice can be improved by referring to past advice results.

[0054] The generation AI can estimate the baby's emotions and adjust the length of advice based on the estimated baby's emotions. For example, if the baby is crying, the generation AI can generate short, to-the-point advice. If the baby is laughing, the generation AI can also generate detailed advice. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to increase the effectiveness of advice for parents by adjusting the length of advice based on the baby's emotions.

[0055] When generating advice, the generation AI can determine the priority of advice based on when the baby's emotions were submitted. For example, if the baby is crying, the generation AI will generate advice as a top priority. The generation AI can also generate advice as a second priority if the baby is laughing. The generation AI can also generate advice as a last priority if the baby is moving around. This allows advice to be provided at the appropriate time by determining the priority of advice based on when it was submitted.

[0056] When generating advice, the generation AI can adjust the order of advice based on the relevance of the baby's emotions. For example, if the baby is crying, the generation AI will first generate advice to stop crying. If the baby is smiling, the generation AI can also first generate advice to maintain a smile. If the baby is moving around, the generation AI can also first generate advice to calm the baby. This allows parents to prioritize important information by adjusting the order of advice based on relevance.

[0057] When generating advice, the generation AI can adjust the use of technical terminology in the advice depending on the parent's level of expertise. For example, if the parent is a beginner, the generation AI can generate advice in simple language, avoiding technical jargon. Alternatively, if the parent is an intermediate learner, the generation AI can generate advice using a moderate amount of technical terminology. Alternatively, if the parent is an advanced learner, the generation AI can generate detailed advice using a lot of technical terminology. In this way, by adjusting the use of technical terminology depending on the parent's level of expertise, it is possible to provide advice that is easy for parents to understand.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] In addition to determining a baby's emotions, the baby emotion detection system can also monitor the baby's health. For example, the device can measure the baby's temperature and notify parents if there is an abnormality. The device can also monitor the baby's heart rate and issue an alert if an abnormal pattern is detected. Furthermore, the device can track the baby's sleep patterns and generate advice on providing an appropriate sleeping environment. This can help manage the baby's health comprehensively and provide better support to parents.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The device reads the baby's vocalizations, facial expressions, or body movements. For example, the device collects the baby's crying sound with a microphone, captures the baby's smile with a camera, and detects the baby's limb movements with sensors. Step 2: The emotion engine analyzes the data read by the device and determines the baby's emotions. For example, it can use machine learning algorithms to analyze crying patterns to determine whether the baby is sad. It can also use facial expression recognition technology to analyze the baby's smile to determine whether the baby is happy. It can also analyze body movement data to determine whether the baby is excited. Step 3: The generation AI generates advice based on the emotion engine's judgment. For example, if the baby is crying, the AI ​​generates advice to the parent such as "Hold the baby close." If the baby is laughing, the AI ​​can also generate advice such as "Play with the baby." If the baby is excited, the AI ​​can also generate advice such as "Create a quiet environment to calm the baby."

[0062] (Example 2) A system according to an embodiment of the present invention reads a baby's vocalizations, facial expressions, and body movements to determine the baby's emotions, and then generates appropriate advice using a generation AI to provide it to the guardian. This system includes a device (smartphone) that reads the baby's vocalizations, facial expressions, and body movements; an emotion engine that determines the baby's emotions based on the read data; and a generation AI that generates appropriate advice based on the emotions determined by the emotion engine. For example, the system uses a smartphone to read the baby's vocalizations, facial expressions, and body movements. Next, the recorded data is input into a baby's emotion engine to determine the baby's emotions. The generation AI then generates appropriate advice for the guardian based on the determination result of the emotion engine. Finally, the generated advice is provided to the guardian via the smartphone screen or audio. This allows the guardian to understand the baby's emotions and take appropriate action. The system thereby stimulates communication between the baby and the guardian, making it easier for the guardian to understand the baby's emotions.

[0063] A baby emotion determination system according to an embodiment includes a device, an emotion engine, and a generation AI. The device reads the baby's vocalizations, facial expressions, or body movements. For example, the device collects the baby's crying using a microphone. The device can also capture the baby's smiling face using a camera. The device can also detect the baby's limb movements using a sensor. The emotion engine analyzes the read data to determine the baby's emotion. For example, the emotion engine can analyze the crying pattern using a machine learning algorithm to determine whether the baby is sad. The emotion engine can also analyze the baby's smiling face using facial expression recognition technology to determine whether the baby is happy. The emotion engine can also analyze the body movement data to determine whether the baby is excited. The generation AI generates advice based on the determination result of the emotion engine. For example, if the baby is crying, the generation AI can generate advice to the guardian, such as "Hold the baby close." If the baby is smiling, the generation AI can generate advice to the guardian, such as "Play with the baby." Furthermore, if the baby is excited, the generation AI can generate advice such as, "Create a quiet environment to calm the baby." In this way, the baby emotion determination system according to the embodiment can stimulate communication between the baby and the caregiver by determining the baby's emotion and generating appropriate advice.

[0064] The device can read the baby's vocalizations, facial expressions, or body movements. For example, the device can collect the baby's crying sound using a microphone. The device can also capture the baby's smile using a camera. The device can also detect the baby's limb movements using a sensor. In this way, by reading the baby's vocalizations, facial expressions, and body movements, data for determining emotions can be obtained.

[0065] The emotion engine can analyze the read data and determine the baby's emotions. For example, the emotion engine can use a machine learning algorithm to analyze the crying pattern and determine whether the baby is sad. The emotion engine can also use facial expression recognition technology to analyze the baby's smile and determine whether the baby is happy. The emotion engine can also analyze body movement data and determine whether the baby is excited. In this way, the baby's emotions can be accurately determined by analyzing the read data.

[0066] The generation AI can generate advice based on the judgment results of the emotion engine. For example, if a baby is crying, the generation AI can generate advice to the guardian such as "Hold the baby close." If the baby is laughing, the generation AI can also generate advice such as "Play with the baby." If the baby is excited, the generation AI can also generate advice such as "Create a quiet environment to calm the baby." In this way, appropriate advice can be generated based on the judgment results of the emotion engine, providing support to guardians.

[0067] The generation AI can provide the generated advice via smartphone screen or voice. For example, the generation AI displays the generated advice on a smartphone screen. The generation AI can also provide the generated advice via voice. This allows parents to easily receive the advice by providing the generated advice on a smartphone screen or voice.

[0068] The device can estimate the baby's emotions and adjust the sensitivity of the device's readings based on the estimated baby's emotions. For example, if the baby is crying, the device can increase the microphone sensitivity of the device to read the vocalizations in detail. If the baby is smiling, the device can increase the camera resolution to capture the facial expressions more clearly. If the baby is moving around, the device can improve the accuracy of motion tracking to read the body movements more accurately. This allows for more accurate data to be obtained by adjusting the sensitivity of the device's readings based on the baby's emotions.

[0069] The device can analyze data on the baby's past vocalizations, facial expressions, and body movements to select the optimal reading method. For example, the device can learn the baby's crying patterns from past data and select the optimal microphone settings for detecting crying. The device can also select the optimal camera settings for detecting smiles based on past facial expression data. The device can also analyze past movement data to select the optimal sensor settings for detecting specific movements. In this way, by analyzing past data, the optimal reading method can be selected and the accuracy of the data can be improved.

[0070] When reading data, the device can filter it based on the baby's current environment. For example, if the room temperature is high, the device can filter facial expression data taking into account the baby's sweating. The device can also increase the sensitivity of the camera to accurately read facial expressions when the lighting is low. The device can also perform noise cancellation to accurately read vocalizations when the ambient volume is high. This allows for filtering based on the current environment, improving the accuracy of the data.

[0071] When reading, the device can select the optimal reading means according to the baby's movement pattern. For example, if the baby rolls over, the device can detect the movement pattern and adjust the camera angle. Also, if the baby waves their hands, the device can select the optimal sensor settings for tracking their hand movements. Also, if the baby is crawling, the device can adjust the camera frame rate according to the speed of their movement. This allows the accuracy of data to be improved by selecting the optimal reading means according to the movement pattern.

[0072] The device can estimate the baby's emotions and determine the priority of data to be read based on the estimated emotions of the baby. For example, if the baby is crying, the device can prioritize reading vocalization data. If the baby is smiling, the device can also prioritize reading facial expression data. If the baby is moving around, the device can also prioritize reading body movement data. Thus, by prioritizing data based on emotions, important data can be acquired preferentially.

[0073] When reading, the device can prioritize reading data that is highly relevant based on the baby's geographical location information. For example, if the baby is in a park, the device can filter vocalization data taking into account ambient sounds. If the baby is at home, the device can read facial expression data taking into account indoor lighting conditions. If the baby is in a car, the device can read body movement data taking into account vibrations. This allows the device to prioritize obtaining data that is highly relevant by taking into account the geographical location information.

[0074] When reading, the device can analyze the baby's social media activity and read related data. For example, the device can analyze the baby's social media photos and complement facial expression data. The device can also analyze the baby's social media videos and complement movement patterns. The device can also analyze the baby's social media audio posts and complement vocalization data. In this way, the device can complement related data by analyzing social media activity.

[0075] When reading, the device can customize the reading method by reflecting the baby's past feedback. For example, if the past feedback shows that the baby reacts to a particular sound, the device can prioritize reading that sound. Also, if the past feedback shows that the baby often makes a particular facial expression, the device can prioritize reading that facial expression. Also, if the past feedback shows that the baby often makes a particular movement, the device can prioritize reading that movement. In this way, the reading method can be optimized by reflecting past feedback.

[0076] The emotion engine can estimate the baby's emotion and adjust the emotion determination algorithm based on the estimated emotion of the baby. For example, when the baby is crying, the emotion engine adjusts the emotion determination algorithm by placing emphasis on the crying pattern. When the baby is smiling, the emotion engine can also adjust the emotion determination algorithm by placing emphasis on the smiling pattern. When the baby is moving around, the emotion engine can also adjust the emotion determination algorithm by placing emphasis on the movement pattern. In this way, by adjusting the algorithm based on the emotion, the accuracy of emotion determination can be improved.

[0077] When determining an emotion, the emotion engine can adjust the level of detail of the determination based on the importance of the baby's vocalizations, facial expressions, and body movements. For example, if vocalizations are important, the emotion engine determines the emotion by increasing the level of detail of the vocalization data. Also, if facial expressions are important, the emotion engine can determine the emotion by increasing the level of detail of the facial expression data. Also, if body movements are important, the emotion engine can determine the emotion by increasing the level of detail of the movement data. In this way, by adjusting the level of detail of the determination based on the importance, the accuracy of emotion determination can be improved.

[0078] When determining emotions, the emotion engine can apply different determination algorithms depending on the baby category. For example, the emotion engine can apply an algorithm that emphasizes crying patterns to younger babies. The emotion engine can also apply an algorithm that emphasizes facial expression patterns to older babies. The emotion engine can also apply an algorithm that emphasizes specific emotion patterns depending on the gender. In this way, by applying an algorithm depending on the category, the accuracy of emotion determination can be improved.

[0079] The emotion engine can improve the accuracy of emotion determination by referring to the baby's past emotion data. For example, the emotion engine can learn the baby's crying pattern from the past emotion data to improve the accuracy of the determination. The emotion engine can also learn the baby's laughing pattern from the past emotion data to improve the accuracy of the determination. The emotion engine can also learn the baby's movement pattern from the past emotion data to improve the accuracy of the determination. In this way, by referring to the past emotion data, the accuracy of emotion determination can be improved.

[0080] The emotion engine can estimate the baby's emotion and adjust the order in which emotion determination results are displayed based on the estimated baby's emotion. For example, if the baby is crying, the emotion engine can first display the emotion determination result for crying. Also, if the baby is smiling, the emotion engine can first display the emotion determination result for smiling. Also, if the baby is moving around, the emotion engine can first display the emotion determination result for movement. In this way, adjusting the display order of the results based on emotion allows parents to prioritize checking important information.

[0081] The emotion engine can make emotion determinations taking into account the geographical distribution of the baby. For example, if the baby is at home, the emotion engine determines the emotion taking into account the home environment. Also, if the baby is in a park, the emotion engine can determine the emotion taking into account the external environment. Also, if the baby is in a nursery school, the emotion engine can determine the emotion taking into account the group environment. In this way, by taking geographical distribution into account, emotion determination according to the environment becomes possible.

[0082] When determining an emotion, the emotion engine can improve the accuracy of the determination by referring to literature related to babies. For example, the emotion engine can improve the accuracy of the determination by referring to literature on babies' crying. The emotion engine can also improve the accuracy of the determination by referring to literature on babies' smiling. The emotion engine can also improve the accuracy of the determination by referring to literature on babies' movements. In this way, by referring to related literature, the accuracy of the emotion determination can be improved.

[0083] The emotion engine can take the market value of the baby into consideration when determining emotions. For example, if the market value of the baby is high, the emotion engine collects additional data to improve the accuracy of emotion determination. Alternatively, if the market value of the baby is low, the emotion engine can apply a basic emotion determination algorithm. The emotion engine can also adjust emotion determination resources according to the market value of the baby. This allows for optimal resource allocation by taking market value into consideration.

[0084] The generation AI can estimate the baby's emotions and adjust the way the advice is expressed based on the estimated emotions of the baby. For example, if the baby is crying, the generation AI can generate advice in gentle words. If the baby is smiling, the generation AI can also generate advice in cheerful words. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to increase the effectiveness of advice for parents by adjusting the way the advice is expressed based on the baby's emotions.

[0085] When generating advice, the generation AI can adjust the level of detail of the advice based on the importance of the baby's emotions. For example, if the baby is crying, the generation AI will generate detailed advice. If the baby is smiling, the generation AI can also generate concise advice. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to provide appropriate advice by adjusting the level of detail of the advice based on the importance of the emotion.

[0086] When generating advice, the generation AI can apply different advice algorithms depending on the baby's category. For example, the generation AI can apply an algorithm that generates basic advice to younger babies. The generation AI can also apply an algorithm that generates detailed advice to older babies. The generation AI can also apply an algorithm that generates specific advice depending on the baby's gender. This makes it possible to provide appropriate advice by applying an algorithm depending on the category.

[0087] When generating advice, the generation AI can improve the accuracy of the advice by referring to past advice results for the baby. For example, the generation AI can learn the patterns that make the baby stop crying from past advice results, and improve the accuracy of advice in similar situations. The generation AI can also learn the patterns that make the baby smile from past advice results, and improve the accuracy of advice in similar situations. The generation AI can also learn the patterns that make the baby calm from past advice results, and improve the accuracy of advice in similar situations. In this way, the accuracy of advice can be improved by referring to past advice results.

[0088] The generation AI can estimate the baby's emotions and adjust the length of advice based on the estimated baby's emotions. For example, if the baby is crying, the generation AI can generate short, to-the-point advice. If the baby is laughing, the generation AI can also generate detailed advice. If the baby is moving around, the generation AI can also generate advice that matches the baby's movements. This makes it possible to increase the effectiveness of advice for parents by adjusting the length of advice based on the baby's emotions.

[0089] When generating advice, the generation AI can determine the priority of advice based on when the baby's emotions were submitted. For example, if the baby is crying, the generation AI will generate advice as a top priority. The generation AI can also generate advice as a second priority if the baby is laughing. The generation AI can also generate advice as a last priority if the baby is moving around. This allows advice to be provided at the appropriate time by determining the priority of advice based on when it was submitted.

[0090] When generating advice, the generation AI can adjust the order of advice based on the relevance of the baby's emotions. For example, if the baby is crying, the generation AI will first generate advice to stop crying. If the baby is smiling, the generation AI can also first generate advice to maintain a smile. If the baby is moving around, the generation AI can also first generate advice to calm the baby. This allows parents to prioritize important information by adjusting the order of advice based on relevance.

[0091] When generating advice, the generation AI can adjust the use of technical terminology in the advice depending on the parent's level of expertise. For example, if the parent is a beginner, the generation AI can generate advice in simple language, avoiding technical jargon. Alternatively, if the parent is an intermediate learner, the generation AI can generate advice using a moderate amount of technical terminology. Alternatively, if the parent is an advanced learner, the generation AI can generate detailed advice using a lot of technical terminology. In this way, by adjusting the use of technical terminology depending on the parent's level of expertise, it is possible to provide advice that is easy for parents to understand. === Hard Collateral 1-1 === Each of the multiple elements including the device, emotion engine, and generation AI described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the device reads the baby's vocalizations, facial expressions, and body movements using the camera 42 and microphone 38B of the smart device 14. The emotion engine is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read data to determine the baby's emotion. The generation AI is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate advice for the guardian based on the determination result of the emotion engine. The generated advice is provided to the guardian, for example, via the output device 40 of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the device, emotion engine, and generation AI described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the device reads the baby's vocalizations, facial expressions, and body movements using the camera 42 and microphone 238 of the smart glasses 214. The emotion engine is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read data to determine the baby's emotion. The generation AI is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate advice for the guardian based on the determination result of the emotion engine. The generated advice is provided to the guardian, for example, via the speaker 240 of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the device, emotion engine, and generation AI described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the device reads the baby's vocalizations, facial expressions, and body movements using the camera 42 and microphone 238 of the headset-type terminal 314. The emotion engine is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read data to determine the baby's emotion. The generation AI is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate advice for the guardian based on the determination result of the emotion engine. The generated advice is provided to the guardian, for example, via the speaker 240 of the headset-type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned device, emotion engine, and generation AI is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the device reads the baby's vocalizations, facial expressions, and body movements using the camera 42 and microphone 238 of the robot 414. The emotion engine is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the read data to determine the baby's emotions. The generation AI is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates appropriate advice for the guardian based on the determination result of the emotion engine. The generated advice is provided to the guardian, for example, via the speaker 240 of the robot 414.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] In addition to determining a baby's emotions, the baby emotion detection system can also monitor the baby's health. For example, the device can measure the baby's temperature and notify parents if there is an abnormality. The device can also monitor the baby's heart rate and issue an alert if an abnormal pattern is detected. Furthermore, the device can track the baby's sleep patterns and generate advice on providing an appropriate sleeping environment. This can help manage the baby's health comprehensively and provide better support to parents.

[0094] The baby emotion determination system can take into account the individual personality traits of the baby when determining the baby's emotion. For example, if the baby is prone to crying, the emotion engine can analyze the crying patterns in more detail. Also, if the baby is prone to smiling, the emotion engine can determine the emotion by placing emphasis on the smiling patterns. Furthermore, if the baby is active, the emotion engine can determine the emotion by placing emphasis on the data of the baby's body movements. In this way, the accuracy of emotion determination can be improved by taking into account the individual personality traits of the baby.

[0095] The baby emotion determination system can analyze the environmental sounds around the baby when determining the baby's emotions. For example, if there is a loud noise around the baby, the emotion engine can take into account the impact of that sound on the baby's emotions. Also, if the baby's surroundings are quiet, the emotion engine can take into account the impact of that environment on the baby's emotions. Furthermore, if specific music is playing around the baby, the emotion engine can take into account the impact of that music on the baby's emotions. In this way, analyzing the surrounding environmental sounds can improve the accuracy of emotion determination.

[0096] The baby emotion determination system can refer to the baby's past emotion data when determining the baby's emotion. For example, the emotion engine can learn the baby's crying pattern from past data and determine the emotion in similar situations. It can also learn the baby's laughing pattern from past data and determine the emotion in similar situations. It can also learn the baby's excitement pattern from past data and determine the emotion in similar situations. In this way, by referring to past emotion data, the accuracy of emotion determination can be improved.

[0097] The baby emotion determination system can take the baby's physical condition into consideration when determining the baby's emotion. For example, if the baby has a cold, the emotion engine can take into consideration the effect of that physical condition on the emotion. Also, if the baby is tired, the emotion engine can take into consideration the effect of that physical condition on the emotion. Furthermore, if the baby is hungry, the emotion engine can take into consideration the effect of that physical condition on the emotion. In this way, by taking the physical condition into consideration, the accuracy of emotion determination can be improved.

[0098] The baby emotion determination system can take the baby's developmental stage into consideration when determining the baby's emotion. For example, the emotion engine can determine the emotion of a newborn baby by placing emphasis on the crying pattern. It can also determine the emotion of an infant baby by placing emphasis on the facial expression pattern. It can also determine the emotion of an infant baby by placing emphasis on the body movement pattern. In this way, by taking the developmental stage into consideration, the accuracy of emotion determination can be improved.

[0099] The baby emotion determination system can take into account the baby's eating situation when determining the baby's emotion. For example, if the baby has just eaten, the emotion engine considers the effect of that situation on the emotion. It can also consider the effect of that situation on the emotion if the baby has not eaten. Furthermore, if the baby has eaten a specific food, it can also consider the effect that food has on the emotion. In this way, by considering the eating situation, the accuracy of emotion determination can be improved.

[0100] The baby emotion determination system can take into account the baby's sleep status when determining the baby's emotion. For example, if the baby is getting enough sleep, the emotion engine can consider the impact of that status on the emotion. Also, if the baby is sleep-deprived, the emotion engine can consider the impact of that status on the emotion. Furthermore, if the baby has just taken a nap, the emotion engine can consider the impact of that status on the emotion. In this way, by taking the sleep status into account, the accuracy of emotion determination can be improved.

[0101] The baby emotion determination system can take into account the influence of people around the baby when determining the baby's emotion. For example, if the baby is around a parent, the emotion engine will determine the emotion taking their influence into account. Also, if the baby has siblings around, it can determine the emotion taking their influence into account. Furthermore, if there are other adults around the baby, it can determine the emotion taking their influence into account. In this way, by taking into account the influence of surrounding people, it is possible to improve the accuracy of emotion determination.

[0102] The baby emotion determination system can take into account the activity level of the baby when determining the emotion of the baby. For example, if the baby is moving around actively, the emotion engine can consider the effect of that activity level on the emotion. Also, if the baby is quiet, the emotion engine can consider the effect of that activity level on the emotion. Furthermore, if the baby is playing, the emotion engine can consider the effect of that activity level on the emotion. In this way, by taking the activity level into account, the accuracy of emotion determination can be improved.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The device reads the baby's vocalizations, facial expressions, or body movements. For example, the device collects the baby's crying sound with a microphone, captures the baby's smile with a camera, and detects the baby's limb movements with sensors. Step 2: The emotion engine analyzes the data read by the device and determines the baby's emotions. For example, it can use machine learning algorithms to analyze crying patterns to determine whether the baby is sad. It can also use facial expression recognition technology to analyze the baby's smile to determine whether the baby is happy. It can also analyze body movement data to determine whether the baby is excited. Step 3: The generation AI generates advice based on the emotion engine's judgment. For example, if the baby is crying, the AI ​​generates advice to the parent such as "Hold the baby close." If the baby is laughing, the AI ​​can also generate advice such as "Play with the baby." If the baby is excited, the AI ​​can also generate advice such as "Create a quiet environment to calm the baby."

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0150] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] 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.

[0168] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system comprising: a device that reads vocalizations, facial expressions, or body movements; an emotion engine that determines a baby's emotions based on the data read by the device; and a generation AI that generates advice based on the emotions determined by the emotion engine.

2. The system of claim 1 , wherein the device reads the baby's vocalizations, facial expressions, or body movements.

3. The emotion engine Analyze the data read and determine the baby's emotions 2. The system of claim 1.

4. The generated AI is Generate advice based on the emotion engine's judgment results 2. The system of claim 1.

5. The generated AI is The generated advice is provided on the smartphone screen or via voice.

2. The system of claim 1.

6. The device comprises: Estimate the baby's emotions and adjust the device's reading sensitivity based on the estimated emotions 2. The system of claim 1.

7. 2. The system of claim 1, further comprising a reading method selection unit.

8. The system of claim 1 , wherein the device, upon reading, filters based on the baby's current environment.

Images