System

Abstract

An object of the system according to the embodiment is to generate a voice of a baby by analyzing an echo photograph and provide specific advice based on a living environment and preferences of a user.SOLUTION: A system includes a generation unit, a collection unit, and a provision unit. The generation part analyzes the echo photograph to generate the voice of the baby. The collection unit collects the living environment and preferences of the user. The providing unit analyzes the information collected by the collecting unit and provides specific advice content.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately utilize ultrasound images to generate a baby's voice or provide specific advice based on the user's living environment and preferences, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze ultrasound images to generate a baby's voice and provide specific advice based on the user's living environment and preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a generating unit, a collecting unit, and a providing unit. The generating unit analyzes an ultrasound image to generate a baby's voice. The collecting unit collects information about the user's living environment and preferences. The providing unit analyzes the information collected by the collecting unit and provides specific advice content. [Effects of the Invention]

[0007] The system according to the embodiment can analyze ultrasound images to generate a baby's voice and provide specific advice based on the user's living environment and preferences. [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 generates a baby's voice from ultrasound images and suggests a future lifestyle. This system inputs ultrasound images into an AI, which analyzes the images to generate the baby's voice. The AI ​​then analyzes the user's living environment, preferences, and needs and provides customized advice. For example, the AI ​​collects and analyzes information about the user's living environment, family structure, hobbies, and lifestyle. This allows it to suggest optimal child-rearing methods and lifestyles for the user. Furthermore, based on the analysis results, the AI ​​makes suggestions for optimal child-rearing methods tailored to the baby's development and for improving the lifestyle of the entire family. This allows users to realize their optimal child-rearing methods and lifestyles. This system not only allows users to hear the baby's future voice, but also allows them to realize better child-rearing methods and lifestyles through customized advice provided by the AI. For example, following the child-rearing methods suggested by the AI ​​can support the baby's development and improve the quality of life for the entire family. Furthermore, the AI ​​suggests lifestyles tailored to the user's preferences and needs, allowing users to live the lifestyle that best suits them.

[0029] A lifestyle suggestion system according to an embodiment includes a generation unit, a collection unit, and a provision unit. The generation unit analyzes an ultrasound image to generate a baby's voice. The generation unit generates the baby's voice using, for example, voice synthesis technology. Furthermore, when analyzing the ultrasound image, the generation unit can customize a voice generation method based on the baby's health condition and developmental stage. Furthermore, when generating the baby's voice, the generation unit can incorporate characteristics of the parent's voice to generate a more familiar voice. The collection unit collects information about the user's living environment and preferences. The collection unit collects information about the user's living environment and preferences using, for example, a questionnaire or a sensor. Furthermore, the collection unit can estimate the user's emotions and adjust a method for collecting information about the living environment and preferences based on the estimated user emotions. Furthermore, the collection unit can select an optimal collection method by referring to the user's past data during collection. The provision unit analyzes the information collected by the collection unit and provides specific advice. For example, the provision unit suggests childcare methods tailored to the baby's development. Furthermore, the provision unit can make suggestions to improve the lifestyle of the entire family. Furthermore, the providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. This allows the lifestyle suggestion system according to the embodiment to generate a baby's voice from an ultrasound image and provide advice customized based on the user's living environment and preferences.

[0030] The generation unit can generate the baby's voice using a speech synthesis technology. Examples of speech synthesis technology include, but are not limited to, text-to-speech synthesis and concatenative speech synthesis. The generation unit can generate the baby's voice using, for example, text-to-speech synthesis technology. The generation unit can also generate the baby's voice using concatenative speech synthesis technology. Furthermore, the generation unit can also generate the baby's voice using speech synthesis technology that uses deep learning. As a result, by using speech synthesis technology, it is possible to generate a more realistic baby's voice.

[0031] The collection unit can collect information about the user's living environment and preferences using a questionnaire or a sensor. The questionnaire can be, for example, an online questionnaire or a paper-based questionnaire, but is not limited to these examples. The collection unit can collect information about the user's living environment and preferences using, for example, an online questionnaire. The collection unit can also collect information about the user's living environment and preferences using a paper-based questionnaire. The collection unit can also collect information about the user's living environment and preferences using a sensor. The sensor can be, for example, a temperature sensor or a motion sensor, but is not limited to these examples. The collection unit can collect information about the user's living environment using, for example, a temperature sensor. The collection unit can also collect information about the user's preferences using a motion sensor. In this way, the user's living environment and preferences can be collected in detail using the questionnaire or the sensor.

[0032] The providing unit can suggest a childcare method suited to the baby's growth. A childcare method suited to the baby's growth includes, but is not limited to, suggestions for meals and play according to the baby's age, for example. The providing unit, for example, suggests meals according to the baby's age. The providing unit can also suggest play according to the baby's age. Furthermore, the providing unit can also suggest sleep according to the baby's growth. This makes it possible to improve the quality of childcare by suggesting optimal childcare methods suited to the baby's growth.

[0033] The providing unit can make suggestions to improve the lifestyle of the entire family. Methods for improving the lifestyle of the entire family include, but are not limited to, methods for dividing up housework and managing health, for example. The providing unit can also suggest methods for managing health, for example. The providing unit can also suggest methods for communicating with the entire family. As a result, by making suggestions to improve the lifestyle of the entire family, the quality of life of the entire family can be improved.

[0034] The providing unit can suggest meal menus or games. The suggested content of the meal menu includes, for example, nutritional balance and allergy-friendly, but is not limited to these examples. The providing unit can, for example, suggest a nutritionally balanced meal menu. The providing unit can also suggest an allergy-friendly meal menu. Furthermore, the providing unit can also suggest a meal menu according to the season. The suggested content of games includes, for example, suggestions of games according to age and educational toys, but is not limited to these examples. The providing unit can, for example, suggest games according to age. The providing unit can also suggest educational toys. Furthermore, the providing unit can also suggest games according to the season. In this way, by suggesting meal menus and games, the user's life can be enriched.

[0035] When analyzing the ultrasound image, the generator can customize the voice generation method based on the baby's health condition and growth stage. Methods for evaluating the baby's health condition and growth stage include, but are not limited to, medical data and growth curves. The generator can, for example, analyze the baby's heart rate and movements to generate a voice tone appropriate to the baby's health condition. The generator can also adjust the pitch and rhythm of the voice according to the baby's growth stage. Furthermore, the generator can reflect changes in the voice as the baby grows based on data on the baby's weight and height. This allows for a more realistic voice to be provided by generating a voice appropriate to the baby's health condition and growth stage.

[0036] When generating the baby's voice, the generation unit can incorporate characteristics of the parent's voice to generate a friendly voice. Methods for incorporating characteristics of the parent's voice include, but are not limited to, voice tone, pitch, and accent. For example, the generation unit analyzes the tone and pitch of the parent's voice and reflects them in the baby's voice. The generation unit can also incorporate the rhythm and intonation of the parent's voice to generate the baby's voice. Furthermore, the generation unit can add a friendly feel to the baby's voice based on the characteristics of the parent's voice. In this way, by incorporating the characteristics of the parent's voice, a friendly feel can be added to the baby's voice.

[0037] When generating a baby voice, the generation unit can generate a voice corresponding to different languages ​​or dialects. Methods for supporting different languages ​​or dialects include, but are not limited to, multilingual speech synthesis and a dialect database. For example, the generation unit generates a baby voice that matches the user's native language. The generation unit can also generate a baby voice that matches the dialect of the user's region. Furthermore, the generation unit can generate baby voices that match multiple languages ​​and allow the user to select one. This makes it possible to meet the diverse needs of users by generating voices that match different languages ​​or dialects.

[0038] When analyzing the ultrasound image, the generator can generate a voice taking into account the baby's movements and facial expressions. Methods for analyzing the baby's movements and facial expressions include, but are not limited to, image analysis and motion detection algorithms. For example, when the baby is moving, the generator generates a voice that matches the baby's movements. The generator can also analyze the baby's facial expressions and generate a bright voice if the baby is smiling. Furthermore, the generator can generate a calm voice if the baby is standing still. This allows for a more realistic audio experience by generating a voice that matches the baby's movements and facial expressions.

[0039] When generating the baby's voice, the generation unit can incorporate background sounds and environmental sounds to provide a realistic audio experience. Methods for incorporating background sounds and environmental sounds include, but are not limited to, an environmental sound database and audio mixing technology. For example, the generation unit generates the baby's voice by incorporating natural sounds (birds chirping, wind, etc.) into the background. The generation unit can also generate the baby's voice by incorporating household sounds (television, appliance sounds, etc.) into the background. Furthermore, the generation unit can generate the baby's voice by incorporating sounds from a park or city into the background. In this way, by incorporating background sounds and environmental sounds, a more realistic audio experience can be provided.

[0040] When generating a baby voice, the generation unit can improve the voice generation method by reflecting the user's past responses. Methods for collecting the user's past responses include, but are not limited to, feedback data and behavior logs. The generation unit generates a baby voice, for example, based on the user's preferred voice tones and pitches in the past. The generation unit can also analyze the user's past responses and suggest an optimal voice generation method. Furthermore, the generation unit can continuously improve the baby voice generation method based on the user's feedback. In this way, a more appropriate voice generation method can be provided by reflecting the user's past responses.

[0041] The collection unit can select the optimal collection means by referring to the user's past data when collecting data. Methods for referring to past data include, but are not limited to, database searches and past survey results. The collection unit selects the optimal collection means, for example, based on the user's preferred collection means in the past (surveys, sensors, etc.). The collection unit can also analyze the user's past data and suggest the most effective collection means. Furthermore, the collection unit can customize the collection means based on the user's past responses. This allows the optimal collection means to be selected by referring to the user's past data.

[0042] The collection unit can adjust the timing of data collection based on the user's lifestyle rhythm and activity pattern during collection. Methods for collecting lifestyle rhythm and activity pattern include, but are not limited to, wearable devices and behavior logs. For example, the collection unit collects data during times when the user is active. The collection unit can also collect data during times when the user is relaxed. Furthermore, the collection unit can collect data at optimal timing in accordance with the user's lifestyle rhythm. This allows data to be collected at more appropriate times by adjusting the timing of data collection according to the user's lifestyle rhythm and activity pattern.

[0043] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is at home, the collection unit prioritizes collecting data within the home. Also, when the user is out, the collection unit can prioritize collecting data while the user is out. Furthermore, the collection unit can suggest an optimal data collection method based on the user's geographical location information. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0044] During collection, the collection unit can analyze the user's social media activity and collect related data. Methods for analyzing social media activity include, but are not limited to, analyzing the content of posts and followers. The collection unit collects related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and suggest an optimal data collection method. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activity.

[0045] The providing unit can adjust the level of detail of the advice based on the user's living environment and preferences when providing the advice. Methods for adjusting the level of detail of the advice include, but are not limited to, adjusting the level of detail based on the user's preferences, for example. For example, if the user prefers detailed information, the providing unit can provide detailed advice. Also, if the user prefers concise information, the providing unit can provide concise advice. Furthermore, the providing unit can adjust the optimal level of detail of the advice to suit the user's living environment. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice according to the user's living environment and preferences.

[0046] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. Methods for referring to past advice results include, but are not limited to, advice history and feedback data. For example, the providing unit provides optimal advice based on the results of advice the user has received in the past. The providing unit can also analyze the user's past advice results to improve the accuracy of the advice. Furthermore, the providing unit can improve the advice method based on the user's feedback. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0047] The providing unit can customize advice based on the user's family structure and lifestyle rhythm when providing the advice. Methods for collecting the family structure and lifestyle rhythm include, but are not limited to, information about family members and daily schedules, for example. The providing unit can, for example, suggest an optimal child-rearing method in accordance with the user's family structure. The providing unit can also suggest an optimal lifestyle in accordance with the user's lifestyle rhythm. Furthermore, the providing unit can customize the advice taking into account the lifestyle rhythm of the user's entire family. This allows the advice to be customized according to the user's family structure and lifestyle rhythm, thereby providing more appropriate advice.

[0048] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is at home, the providing unit can provide advice that can be practiced at home. Furthermore, when the user is out, the providing unit can also provide advice that can be practiced while away from home. Furthermore, the providing unit can suggest optimal advice based on the user's geographical location information. In this way, optimal advice can be provided by taking into account the user's geographical location information.

[0049] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing the advice. Methods for analyzing social media activity include, but are not limited to, analyzing the content of posts and followers, for example. The providing unit can provide relevant advice based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity patterns and suggest optimal advice. Furthermore, the providing unit can provide relevant advice by referring to the activities of the user's friends on social media. In this way, relevant advice can be efficiently provided by analyzing the user's social media activity.

[0050] The providing unit can improve the advice method by reflecting the user's past feedback when providing the advice. Methods for collecting past feedback include, but are not limited to, feedback data and behavior logs. The providing unit can improve the content of the advice, for example, based on the user's past feedback. The providing unit can also analyze the user's feedback and suggest an optimal advice method. Furthermore, the providing unit can also continuously improve the advice method by referring to the user's past feedback. In this way, the advice method can be continuously improved by reflecting the user's past feedback.

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

[0052] When generating a baby's voice, the generation unit can refer to the user's past voice data to generate a more personalized voice. For example, the generation unit can analyze the user's previously recorded voice or the voices of family members and reflect the characteristics of the voice in the baby's voice. The generation unit can also adjust the tone and rhythm of the baby's voice based on the user's past voice data. Furthermore, the generation unit can also use the user's past voice data to impart a specific emotion to the baby's voice. This makes it possible to generate a baby's voice that is more familiar to the user.

[0053] When collecting information about a user's living environment and preferences, the collection unit can analyze the user's social media activities and collect related data. For example, the living environment and preferences can be estimated based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and suggest an optimal data collection method. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, more detailed information about the living environment and preferences can be collected by analyzing the user's social media activities.

[0054] When generating a baby voice, the generation unit can generate a voice corresponding to different languages ​​or dialects. For example, a baby voice can be generated to match the user's native language. A baby voice corresponding to the user's local dialect can also be generated. Furthermore, the generation unit can generate baby voices corresponding to multiple languages ​​and allow the user to select one. This makes it possible to meet the diverse needs of users by generating voices corresponding to different languages ​​or dialects.

[0055] The providing unit can improve the method of giving advice by reflecting the user's past feedback. For example, the content of the advice can be improved based on the user's past feedback. The providing unit can also analyze the user's feedback and propose an optimal method of giving advice. Furthermore, the providing unit can also continuously improve the method of giving advice by referring to the user's past feedback. In this way, the method of giving advice can be continuously improved by reflecting the user's past feedback.

[0056] When generating a baby's voice, the generation unit can incorporate background sounds and environmental sounds to provide a more realistic audio experience. For example, a baby's voice can be generated by incorporating natural sounds (birds chirping, wind, etc.) into the background. A baby's voice can also be generated by incorporating household sounds (television, home appliances, etc.) into the background. Furthermore, the generation unit can generate a baby's voice by incorporating park or city sounds into the background. In this way, by incorporating background sounds and environmental sounds, a more realistic audio experience can be provided.

[0057] The providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is at home, advice that can be practiced at home can be provided. Also, if the user is out, advice that can be practiced while away from home can be provided. Furthermore, the providing unit can also suggest optimal advice based on the user's geographical location information. In this way, optimal advice can be provided by taking into account the user's geographical location information.

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

[0059] Step 1: The generator analyzes the ultrasound image and generates the baby's voice. The generator uses voice synthesis technology to generate the baby's voice, and can customize the voice generation method based on the baby's health condition and developmental stage. It can also incorporate the characteristics of the parent's voice to create a more familiar voice. Step 2: The collection unit collects information about the user's living environment and preferences. The collection unit uses questionnaires and sensors to collect information about the user's living environment and preferences, and can estimate the user's emotions and adjust the collection method. It can also select the optimal collection method by referring to past data. Step 3: The providing unit analyzes the information collected by the collecting unit and provides specific advice. The providing unit can suggest childcare methods that are tailored to the baby's development and make suggestions to improve the lifestyle of the entire family. It can also estimate the user's emotions and adjust the way the advice is presented.

[0060] (Example 2) A system according to an embodiment of the present invention generates a baby's voice from ultrasound images and suggests a future lifestyle. This system inputs ultrasound images into an AI, which analyzes the images to generate the baby's voice. The AI ​​then analyzes the user's living environment, preferences, and needs and provides customized advice. For example, the AI ​​collects and analyzes information about the user's living environment, family structure, hobbies, and lifestyle. This allows it to suggest optimal child-rearing methods and lifestyles for the user. Furthermore, based on the analysis results, the AI ​​makes suggestions for optimal child-rearing methods tailored to the baby's development and for improving the lifestyle of the entire family. This allows users to realize their optimal child-rearing methods and lifestyles. This system not only allows users to hear the baby's future voice, but also allows them to realize better child-rearing methods and lifestyles through customized advice provided by the AI. For example, following the child-rearing methods suggested by the AI ​​can support the baby's development and improve the quality of life for the entire family. Furthermore, the AI ​​suggests lifestyles tailored to the user's preferences and needs, allowing users to live the lifestyle that best suits them.

[0061] A lifestyle suggestion system according to an embodiment includes a generation unit, a collection unit, and a provision unit. The generation unit analyzes an ultrasound image to generate a baby's voice. The generation unit generates the baby's voice using, for example, voice synthesis technology. Furthermore, when analyzing the ultrasound image, the generation unit can customize a voice generation method based on the baby's health condition and developmental stage. Furthermore, when generating the baby's voice, the generation unit can incorporate characteristics of the parent's voice to generate a more familiar voice. The collection unit collects information about the user's living environment and preferences. The collection unit collects information about the user's living environment and preferences using, for example, a questionnaire or a sensor. Furthermore, the collection unit can estimate the user's emotions and adjust a method for collecting information about the living environment and preferences based on the estimated user emotions. Furthermore, the collection unit can select an optimal collection method by referring to the user's past data during collection. The provision unit analyzes the information collected by the collection unit and provides specific advice. For example, the provision unit suggests childcare methods tailored to the baby's development. Furthermore, the provision unit can make suggestions to improve the lifestyle of the entire family. Furthermore, the providing unit can estimate the user's emotions and adjust the way the advice is presented based on the estimated user's emotions. This allows the lifestyle suggestion system according to the embodiment to generate a baby's voice from an ultrasound image and provide advice customized based on the user's living environment and preferences.

[0062] The generation unit can generate the baby's voice using a speech synthesis technology. Examples of speech synthesis technology include, but are not limited to, text-to-speech synthesis and concatenative speech synthesis. The generation unit can generate the baby's voice using, for example, text-to-speech synthesis technology. The generation unit can also generate the baby's voice using concatenative speech synthesis technology. Furthermore, the generation unit can also generate the baby's voice using speech synthesis technology that uses deep learning. As a result, by using speech synthesis technology, it is possible to generate a more realistic baby's voice.

[0063] The collection unit can collect information about the user's living environment and preferences using a questionnaire or a sensor. The questionnaire can be, for example, an online questionnaire or a paper-based questionnaire, but is not limited to these examples. The collection unit can collect information about the user's living environment and preferences using, for example, an online questionnaire. The collection unit can also collect information about the user's living environment and preferences using a paper-based questionnaire. The collection unit can also collect information about the user's living environment and preferences using a sensor. The sensor can be, for example, a temperature sensor or a motion sensor, but is not limited to these examples. The collection unit can collect information about the user's living environment using, for example, a temperature sensor. The collection unit can also collect information about the user's preferences using a motion sensor. In this way, the user's living environment and preferences can be collected in detail using the questionnaire or the sensor.

[0064] The providing unit can suggest a childcare method suited to the baby's growth. A childcare method suited to the baby's growth includes, but is not limited to, suggestions for meals and play according to the baby's age, for example. The providing unit, for example, suggests meals according to the baby's age. The providing unit can also suggest play according to the baby's age. Furthermore, the providing unit can also suggest sleep according to the baby's growth. This makes it possible to improve the quality of childcare by suggesting optimal childcare methods suited to the baby's growth.

[0065] The providing unit can make suggestions to improve the lifestyle of the entire family. Methods for improving the lifestyle of the entire family include, but are not limited to, methods for dividing up housework and managing health, for example. The providing unit can also suggest methods for managing health, for example. The providing unit can also suggest methods for communicating with the entire family. As a result, by making suggestions to improve the lifestyle of the entire family, the quality of life of the entire family can be improved.

[0066] The providing unit can suggest meal menus or games. The suggested content of the meal menu includes, for example, nutritional balance and allergy-friendly, but is not limited to these examples. The providing unit can, for example, suggest a nutritionally balanced meal menu. The providing unit can also suggest an allergy-friendly meal menu. Furthermore, the providing unit can also suggest a meal menu according to the season. The suggested content of games includes, for example, suggestions of games according to age and educational toys, but is not limited to these examples. The providing unit can, for example, suggest games according to age. The providing unit can also suggest educational toys. Furthermore, the providing unit can also suggest games according to the season. In this way, by suggesting meal menus and games, the user's life can be enriched.

[0067] The generation unit can estimate the user's emotion and adjust the tone and pitch of the baby's voice based on the estimated user's emotion. Methods for estimating the user's emotion include, but are not limited to, facial expression recognition and voice analysis. For example, the generation unit can estimate the user's emotion using facial expression recognition technology. The generation unit can also estimate the user's emotion using voice analysis technology. The generation unit can also estimate the user's emotion using text analysis technology. Methods for adjusting the tone and pitch include, but are not limited to, adjustment based on a voice synthesis algorithm or user preferences. For example, the generation unit can adjust the tone and pitch of the baby's voice using a voice synthesis algorithm. The generation unit can also adjust the tone and pitch of the baby's voice based on the user's preferences. In this way, by adjusting the tone and pitch of the baby's voice according to the user's emotion, a more friendly voice can be generated.

[0068] When analyzing the ultrasound image, the generator can customize the voice generation method based on the baby's health condition and growth stage. Methods for evaluating the baby's health condition and growth stage include, but are not limited to, medical data and growth curves. The generator can, for example, analyze the baby's heart rate and movements to generate a voice tone appropriate to the baby's health condition. The generator can also adjust the pitch and rhythm of the voice according to the baby's growth stage. Furthermore, the generator can reflect changes in the voice as the baby grows based on data on the baby's weight and height. This allows for a more realistic voice to be provided by generating a voice appropriate to the baby's health condition and growth stage.

[0069] When generating the baby's voice, the generation unit can incorporate characteristics of the parent's voice to generate a friendly voice. Methods for incorporating characteristics of the parent's voice include, but are not limited to, voice tone, pitch, and accent. For example, the generation unit analyzes the tone and pitch of the parent's voice and reflects them in the baby's voice. The generation unit can also incorporate the rhythm and intonation of the parent's voice to generate the baby's voice. Furthermore, the generation unit can add a friendly feel to the baby's voice based on the characteristics of the parent's voice. In this way, by incorporating the characteristics of the parent's voice, a friendly feel can be added to the baby's voice.

[0070] When generating a baby voice, the generation unit can generate a voice corresponding to different languages ​​or dialects. Methods for supporting different languages ​​or dialects include, but are not limited to, multilingual speech synthesis and a dialect database. For example, the generation unit generates a baby voice that matches the user's native language. The generation unit can also generate a baby voice that matches the dialect of the user's region. Furthermore, the generation unit can generate baby voices that match multiple languages ​​and allow the user to select one. This makes it possible to meet the diverse needs of users by generating voices that match different languages ​​or dialects.

[0071] The generation unit can estimate the user's emotions and adjust the frequency of generating the baby voice based on the estimated user's emotions. Methods for adjusting the generation frequency include, but are not limited to, frequency adjustment based on the user's emotional state. For example, the generation unit can reduce the frequency of generating the baby voice when the user is stressed. The generation unit can also increase the frequency of generating the baby voice when the user is relaxed. Furthermore, the generation unit can adjust the frequency of generating the baby voice to a medium level when the user is excited. In this way, by adjusting the frequency of generating the baby voice according to the user's emotions, it is possible to provide the voice at a more appropriate time.

[0072] When analyzing the ultrasound image, the generator can generate a voice taking into account the baby's movements and facial expressions. Methods for analyzing the baby's movements and facial expressions include, but are not limited to, image analysis and motion detection algorithms. For example, when the baby is moving, the generator generates a voice that matches the baby's movements. The generator can also analyze the baby's facial expressions and generate a bright voice if the baby is smiling. Furthermore, the generator can generate a calm voice if the baby is standing still. This allows for a more realistic audio experience by generating a voice that matches the baby's movements and facial expressions.

[0073] When generating the baby's voice, the generation unit can incorporate background sounds and environmental sounds to provide a realistic audio experience. Methods for incorporating background sounds and environmental sounds include, but are not limited to, an environmental sound database and audio mixing technology. For example, the generation unit generates the baby's voice by incorporating natural sounds (birds chirping, wind, etc.) into the background. The generation unit can also generate the baby's voice by incorporating household sounds (television, appliance sounds, etc.) into the background. Furthermore, the generation unit can generate the baby's voice by incorporating sounds from a park or city into the background. In this way, by incorporating background sounds and environmental sounds, a more realistic audio experience can be provided.

[0074] When generating a baby voice, the generation unit can improve the voice generation method by reflecting the user's past responses. Methods for collecting the user's past responses include, but are not limited to, feedback data and behavior logs. The generation unit generates a baby voice, for example, based on the user's preferred voice tones and pitches in the past. The generation unit can also analyze the user's past responses and suggest an optimal voice generation method. Furthermore, the generation unit can continuously improve the baby voice generation method based on the user's feedback. In this way, a more appropriate voice generation method can be provided by reflecting the user's past responses.

[0075] The collection unit can estimate the user's emotions and adjust the collection method of the living environment and preferences based on the estimated user emotions. Examples of methods for adjusting the collection method of the living environment and preferences include, but are not limited to, adjusting a questionnaire based on the user's emotional state. For example, when the user is relaxed, the collection unit can collect the living environment and preferences by providing a detailed questionnaire. When the user is stressed, the collection unit can also collect the living environment and preferences in the form of simple questions. Furthermore, when the user is excited, the collection unit can collect the living environment and preferences in an interactive manner. This allows more appropriate data to be collected by adjusting the collection method according to the user's emotions.

[0076] The collection unit can select the optimal collection means by referring to the user's past data when collecting data. Methods for referring to past data include, but are not limited to, database searches and past survey results. The collection unit selects the optimal collection means, for example, based on the user's preferred collection means in the past (surveys, sensors, etc.). The collection unit can also analyze the user's past data and suggest the most effective collection means. Furthermore, the collection unit can customize the collection means based on the user's past responses. This allows the optimal collection means to be selected by referring to the user's past data.

[0077] The collection unit can adjust the timing of data collection based on the user's lifestyle rhythm and activity pattern during collection. Methods for collecting lifestyle rhythm and activity pattern include, but are not limited to, wearable devices and behavior logs. For example, the collection unit collects data during times when the user is active. The collection unit can also collect data during times when the user is relaxed. Furthermore, the collection unit can collect data at optimal timing in accordance with the user's lifestyle rhythm. This allows data to be collected at more appropriate times by adjusting the timing of data collection according to the user's lifestyle rhythm and activity pattern.

[0078] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. Methods for determining the priority of data include, but are not limited to, prioritizing based on the user's emotional state. For example, the collection unit can prioritize collecting detailed data when the user is relaxed. Furthermore, the collection unit can prioritize collecting simple data when the user is stressed. Furthermore, the collection unit can prioritize collecting interactive data when the user is excited. Thus, by determining the priority of data to be collected according to the user's emotions, more important data can be collected preferentially.

[0079] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information during collection. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is at home, the collection unit prioritizes collecting data within the home. Also, when the user is out, the collection unit can prioritize collecting data while the user is out. Furthermore, the collection unit can suggest an optimal data collection method based on the user's geographical location information. This makes it possible to prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0080] During collection, the collection unit can analyze the user's social media activity and collect related data. Methods for analyzing social media activity include, but are not limited to, analyzing the content of posts and followers. The collection unit collects related data based on, for example, information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and suggest an optimal data collection method. Furthermore, the collection unit can collect related data by referring to the activities of the user's friends on social media. This allows for efficient collection of related data by analyzing the user's social media activity.

[0081] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. Methods for adjusting the way in which advice is expressed include, but are not limited to, text format, audio format, visual format, and the like. For example, when the user is relaxed, the providing unit can provide advice in a calm and gentle way. Furthermore, when the user is feeling stressed, the providing unit can provide advice in a concise and easy-to-understand way. Furthermore, when the user is excited, the providing unit can provide advice in a bright and cheerful way. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more effective advice can be provided.

[0082] The providing unit can adjust the level of detail of the advice based on the user's living environment and preferences when providing the advice. Methods for adjusting the level of detail of the advice include, but are not limited to, adjusting the level of detail based on the user's preferences, for example. For example, if the user prefers detailed information, the providing unit can provide detailed advice. Also, if the user prefers concise information, the providing unit can provide concise advice. Furthermore, the providing unit can adjust the optimal level of detail of the advice to suit the user's living environment. In this way, more appropriate advice can be provided by adjusting the level of detail of the advice according to the user's living environment and preferences.

[0083] When providing advice, the providing unit can improve the accuracy of the advice by referring to the user's past advice results. Methods for referring to past advice results include, but are not limited to, advice history and feedback data. For example, the providing unit provides optimal advice based on the results of advice the user has received in the past. The providing unit can also analyze the user's past advice results to improve the accuracy of the advice. Furthermore, the providing unit can improve the advice method based on the user's feedback. In this way, the accuracy of the advice can be improved by referring to the user's past advice results.

[0084] The providing unit can customize advice based on the user's family structure and lifestyle rhythm when providing the advice. Methods for collecting the family structure and lifestyle rhythm include, but are not limited to, information about family members and daily schedules, for example. The providing unit can, for example, suggest an optimal child-rearing method in accordance with the user's family structure. The providing unit can also suggest an optimal lifestyle in accordance with the user's lifestyle rhythm. Furthermore, the providing unit can customize the advice taking into account the lifestyle rhythm of the user's entire family. This allows the advice to be customized according to the user's family structure and lifestyle rhythm, thereby providing more appropriate advice.

[0085] The providing unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. Methods for determining the priority of advice include, but are not limited to, prioritizing based on an emotional state, for example. For example, when the user is relaxed, the providing unit can prioritize providing detailed advice. Furthermore, when the user is stressed, the providing unit can prioritize providing concise advice. Furthermore, when the user is excited, the providing unit can prioritize providing interactive advice. In this way, by determining the priority of advice according to the user's emotions, more important advice can be provided preferentially.

[0086] The providing unit can provide optimal advice by taking into account the user's geographical location information when providing the advice. Methods for collecting geographical location information include, but are not limited to, GPS data and location information services. For example, when the user is at home, the providing unit can provide advice that can be practiced at home. Furthermore, when the user is out, the providing unit can also provide advice that can be practiced while away from home. Furthermore, the providing unit can suggest optimal advice based on the user's geographical location information. In this way, optimal advice can be provided by taking into account the user's geographical location information.

[0087] The providing unit can analyze the user's social media activity and provide relevant advice at the time of providing the advice. Methods for analyzing social media activity include, but are not limited to, analyzing the content of posts and followers, for example. The providing unit can provide relevant advice based on, for example, information shared by the user on social media. The providing unit can also analyze the user's social media activity patterns and suggest optimal advice. Furthermore, the providing unit can provide relevant advice by referring to the activities of the user's friends on social media. In this way, relevant advice can be efficiently provided by analyzing the user's social media activity.

[0088] The providing unit can improve the advice method by reflecting the user's past feedback when providing the advice. Methods for collecting past feedback include, but are not limited to, feedback data and behavior logs. The providing unit can improve the content of the advice, for example, based on the user's past feedback. The providing unit can also analyze the user's feedback and suggest an optimal advice method. Furthermore, the providing unit can also continuously improve the advice method by referring to the user's past feedback. In this way, the advice method can be continuously improved by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned generation unit, collection unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information about the user's living environment and preferences using the camera 42 and microphone 38B of the smart device 14, and analyzes the information by the control unit 46A or the specific processing unit 290 of the data processing device 12. For example, the provision unit provides advice to the user through the display 40A and speaker 40B of the smart device 14, and is realized by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned generating unit, collecting unit, and providing unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the generating unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the collecting unit collects information about the user's living environment and preferences using the camera 42 and microphone 238 of the smart glasses 214, and the information is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. For example, the providing unit provides advice to the user through the speaker 240 of the smart glasses 214 and is realized by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned generation unit, collection unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information about the user's living environment and preferences using the camera 42 or microphone 238 of the headset type terminal 314, and analyzes the information by the control unit 46A or the specific processing unit 290 of the data processing device 12. For example, the provision unit provides advice to the user through the display 343 or speaker 240 of the headset type terminal 314, and is realized by the control unit 46A or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned generation unit, collection unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the generation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the collection unit collects information about the user's living environment and preferences using the camera 42 or microphone 238 of the robot 414, and the information is analyzed by the control unit 46A or the specific processing unit 290 of the data processing device 12. For example, the provision unit provides advice to the user through the speaker 240 or display device of the robot 414, and is realized by the control unit 46A or the specific processing unit 290 of the data processing device 12.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] When generating a baby's voice, the generation unit can refer to the user's past voice data to generate a more personalized voice. For example, the generation unit can analyze the user's previously recorded voice or the voices of family members and reflect the characteristics of the voice in the baby's voice. The generation unit can also adjust the tone and rhythm of the baby's voice based on the user's past voice data. Furthermore, the generation unit can also use the user's past voice data to impart a specific emotion to the baby's voice. This makes it possible to generate a baby's voice that is more familiar to the user.

[0091] When collecting information about a user's living environment and preferences, the collection unit can analyze the user's social media activities and collect related data. For example, the living environment and preferences can be estimated based on information shared by the user on social media. The collection unit can also analyze the user's social media activity patterns and suggest an optimal data collection method. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, more detailed information about the living environment and preferences can be collected by analyzing the user's social media activities.

[0092] The providing unit can estimate the user's emotions and adjust the way in which advice is expressed based on the estimated user's emotions. For example, if the user is relaxed, advice can be provided in a calm and gentle way. If the user is feeling stressed, advice can be provided in a concise and easy-to-understand way. Furthermore, if the user is excited, advice can be provided in a bright and cheerful way. In this way, by adjusting the way in which advice is expressed according to the user's emotions, more effective advice can be provided.

[0093] When generating a baby voice, the generation unit can generate a voice corresponding to different languages ​​or dialects. For example, a baby voice can be generated to match the user's native language. A baby voice corresponding to the user's local dialect can also be generated. Furthermore, the generation unit can generate baby voices corresponding to multiple languages ​​and allow the user to select one. This makes it possible to meet the diverse needs of users by generating voices corresponding to different languages ​​or dialects.

[0094] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, if the user is relaxed, detailed data can be collected with priority. Also, if the user is feeling stressed, simple data can be collected with priority. Furthermore, if the user is excited, interactive data can be collected with priority. In this way, by determining the priority of data to be collected according to the user's emotions, more important data can be collected with priority.

[0095] The providing unit can improve the method of giving advice by reflecting the user's past feedback. For example, the content of the advice can be improved based on the user's past feedback. The providing unit can also analyze the user's feedback and propose an optimal method of giving advice. Furthermore, the providing unit can also continuously improve the method of giving advice by referring to the user's past feedback. In this way, the method of giving advice can be continuously improved by reflecting the user's past feedback.

[0096] When generating a baby's voice, the generation unit can incorporate background sounds and environmental sounds to provide a more realistic audio experience. For example, a baby's voice can be generated by incorporating natural sounds (birds chirping, wind, etc.) into the background. A baby's voice can also be generated by incorporating household sounds (television, home appliances, etc.) into the background. Furthermore, the generation unit can generate a baby's voice by incorporating park or city sounds into the background. In this way, by incorporating background sounds and environmental sounds, a more realistic audio experience can be provided.

[0097] The collection unit can estimate the user's emotions and adjust the collection method of the living environment and preferences based on the estimated user emotions. For example, if the user is relaxed, a detailed questionnaire can be provided to collect the living environment and preferences. If the user is stressed, the living environment and preferences can be collected in the form of simple questions. Furthermore, if the user is excited, the living environment and preferences can be collected in an interactive manner. This allows more appropriate data to be collected by adjusting the collection method according to the user's emotions.

[0098] The providing unit can provide optimal advice by taking into account the user's geographical location information. For example, if the user is at home, advice that can be practiced at home can be provided. Also, if the user is out, advice that can be practiced while away from home can be provided. Furthermore, the providing unit can also suggest optimal advice based on the user's geographical location information. In this way, optimal advice can be provided by taking into account the user's geographical location information.

[0099] The generation unit can estimate the user's emotions and adjust the frequency at which the baby voice is generated based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency at which the baby voice is generated can be reduced. Also, if the user is relaxed, the frequency at which the baby voice is generated can be increased. Furthermore, if the user is excited, the frequency at which the baby voice is generated can be adjusted to a medium level. In this way, by adjusting the frequency at which the baby voice is generated according to the user's emotions, the voice can be provided at a more appropriate time.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The generator analyzes the ultrasound image and generates the baby's voice. The generator uses voice synthesis technology to generate the baby's voice, and can customize the voice generation method based on the baby's health condition and developmental stage. It can also incorporate the characteristics of the parent's voice to create a more familiar voice. Step 2: The collection unit collects information about the user's living environment and preferences. The collection unit uses questionnaires and sensors to collect information about the user's living environment and preferences, and can estimate the user's emotions and adjust the collection method. It can also select the optimal collection method by referring to past data. Step 3: The providing unit analyzes the information collected by the collecting unit and provides specific advice. The providing unit can suggest childcare methods that are tailored to the baby's development and make suggestions to improve the lifestyle of the entire family. It can also estimate the user's emotions and adjust the way the advice is presented.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 AI 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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 AI 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 AI 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 generator that analyzes ultrasound images and generates a baby's voice; a collection unit that collects information about the user's living environment and preferences; a providing unit that analyzes the information collected by the collecting unit and provides specific advice content. A system characterized by:

2. The generation unit Generate a baby's voice using speech synthesis technology 2. The system of claim 1.

3. The collecting unit Collect information about the user's living environment and preferences using questionnaires or sensors 2. The system of claim 1.

4. The providing unit Propose childcare methods that suit your baby's development 2. The system of claim 1.

5. The providing unit Providing recommendations to improve the lifestyle of the whole family 2. The system of claim 1.

6. The providing unit Offer meal or play suggestions 2. The system of claim 1.

7. The generation unit Estimate the user's emotions and adjust the tone and pitch of the baby's voice based on the estimated user emotions.

2. The system of claim 1.

8. The generation unit Customize voice generation based on your baby's health and developmental stage when analyzing ultrasound images 2. The system of claim 1.

9. The generation unit When generating a baby's voice, it incorporates the characteristics of the parent's voice to create a friendly voice.

2. The system of claim 1.

10. The generation unit Generate baby voices for different languages ​​and dialects 2. The system of claim 1.

Images