system

Abstract

The system according to the embodiment aims to analyze the voice of an infant and facilitate smooth communication between family members. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an output unit, a display unit, an audio output unit, and a learning unit. The collection unit collects the voice of an infant. The analysis unit analyzes the voice collected by the collection unit. The output unit outputs the results of the analysis by the analysis unit. The display unit displays the results of the analysis by the analysis unit. The audio output unit outputs the results of the analysis by the analysis unit as audio. The learning unit learns new voice collected by the collection unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not been able to accurately analyze the voices of young children and facilitate smooth communication between family members, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the voice of an infant and facilitate smooth communication between family members. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an output unit, a display unit, an audio output unit, and a learning unit. The collection unit collects the voice of the infant. The analysis unit analyzes the voice collected by the collection unit. The output unit outputs the results of the analysis by the analysis unit. The display unit displays the results of the analysis by the analysis unit. The audio output unit outputs the results of the analysis by the analysis unit as audio. The learning unit learns new voice collected by the collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the voice of an infant and facilitate communication between family members. [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) An interpretation system according to an embodiment of the present invention is an interpreter that interprets the words and feelings of young children who are just beginning to learn language, thereby increasing conversations within families. This interpretation system analyzes the words and feelings of young children and conveys them to family members in an easy-to-understand format, thereby promoting communication within families and supporting loving interactions. First, the interpretation system collects the words and sounds uttered by the young child. For example, if the young child says "manma," the audio data is input into the interpretation system. Next, the interpretation system analyzes the collected audio data to identify the young child's intentions and emotions. For example, it analyzes that the word "manma" means "I'm hungry." The analysis results are communicated to family members via the interpretation system's display or audio output. For example, the interpretation system may display "I'm hungry" or say "I'm hungry." This allows family members to more easily understand the young child's feelings and take appropriate measures. By interpreting the young child's words and feelings, this interpretation system increases conversations within families and promotes loving interactions. For example, when a toddler says "Manma" (food), a family member can respond with "Are you hungry?", fostering communication with the toddler. Understanding a toddler's emotions allows families to respond more appropriately, reducing the burden of childcare and parenting. Furthermore, this interpretation system can learn as the toddler grows, providing more accurate interpretations. For example, when a toddler learns a new word, the interpretation system can learn the meaning of that word and reflect it in subsequent interpretations. This allows the interpretation system to always provide interpretations based on the most up-to-date information, supporting communication between family members. In this way, the interpretation system is a device that increases conversations between family members and promotes loving interactions by interpreting a toddler's words and emotions. It is a powerful tool for supporting childcare and parenting and deepening family bonds. The interpretation system can interpret a toddler's words and emotions, promote communication between family members, and support loving interactions.

[0029] The interpretation system according to the embodiment includes a collection unit, an analysis unit, an output unit, a display unit, an audio output unit, and a learning unit. The collection unit collects the voice of an infant. The voice of an infant includes, but is not limited to, crying, laughter, and speech. The collection unit collects, for example, words and speech uttered by the infant using a microphone. The collection unit can also collect the voice of an infant in real time. For example, the collection unit collects the words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear audio data. The analysis unit analyzes the voice collected by the collection unit. The analysis is performed using, for example, speech recognition technology and an emotion analysis algorithm, but is not limited to, for example. For example, the analysis unit converts the words uttered by the infant into text data using speech recognition technology. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit estimates the infant's emotions from the voice data and outputs the result as analysis data. The output unit communicates the analysis result obtained by the analysis unit to the family. The output may be performed, for example, by a method such as text output or audio output, but is not limited to these examples. For example, the output unit transmits the analysis results to the family as a text message. The output unit may also communicate the analysis results to the family as an audio message. The display unit displays the results analyzed by the analysis unit on a display. The display may be performed using, for example, a device such as a smartphone, a tablet, or a dedicated display, but is not limited to these examples. For example, the display unit displays the analysis results on a smartphone screen. The display unit may also display the analysis results on a tablet screen. The audio output unit outputs the results analyzed by the analysis unit as audio. The audio output may be performed, for example, by using voice synthesis technology, but is not limited to these examples. For example, the audio output unit outputs the analysis results as an audio message using voice synthesis technology. The audio output unit may also output the analysis results as audio through a speaker. The learning unit learns new audio collected by the collection unit. The learning may be performed, for example, by using a machine learning algorithm, but is not limited to these examples. For example, the learning unit learns new words and emotions as the child grows and reflects them in subsequent interpretations.As a result, the interpretation system according to the embodiment can interpret the words and emotions of young children, promote communication between family members, and support affectionate interactions.

[0030] The collection unit can collect words or sounds uttered by the infant. The collection unit, for example, collects words or sounds uttered by the infant using a microphone. For example, when the infant says "manma," the collection unit collects the sound data. The collection unit can also collect the infant's sound in real time. For example, the collection unit collects words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear sound data. In this way, by collecting the words and sounds uttered by the infant, data for identifying the infant's intentions and emotions can be provided. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the infant's sound data into a generation AI and cause the generation AI to collect the sound data.

[0031] The analysis unit can analyze the collected voice data and identify the infant's intentions and emotions. The analysis unit, for example, converts the infant's words into text data using voice recognition technology. For example, if the infant says "manma," the analysis unit converts the voice data into text data. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit infers the infant's emotions from the voice data and outputs the result as analysis data. In this way, by analyzing the collected voice data, the infant's intentions and emotions can be identified and communicated to the family. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the infant's voice data into a generation AI and have the generation AI analyze the voice data.

[0032] The output unit can communicate the analysis results to the family. For example, the output unit transmits the analysis results to the family as a text message. For example, if the infant says "Manma," the output unit transmits the analysis result to the family as a text message saying "I'm hungry." The output unit can also communicate the analysis results to the family as a voice message. For example, if the infant says "Manma," the output unit transmits the analysis result to the family as a voice message saying "I'm hungry." By communicating the analysis results to the family, the family can more easily understand the infant's intentions and emotions. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the analysis results to a generation AI and cause the generation AI to generate a text message or a voice message.

[0033] The display unit can display the analyzed results on a display. The display unit, for example, displays the analyzed results on a smartphone screen. For example, when a toddler says "Manma," the display unit displays the analyzed result as "I'm hungry" on the smartphone screen. The display unit can also display the analyzed results on a tablet screen. For example, when a toddler says "Manma," the display unit displays the analyzed result as "I'm hungry" on the tablet screen. By displaying the analyzed results on a display, family members can visually understand the toddler's intentions and emotions. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the analyzed results to a generation AI and have the generation AI execute data for display on the display.

[0034] The audio output unit can output the analysis results as audio. For example, the audio output unit outputs the analysis results as an audio message using speech synthesis technology. For example, when a toddler says "Manma," the audio output unit outputs the analysis result as an audio message such as "I'm hungry." The audio output unit can also output the analysis results as audio through a speaker. For example, when a toddler says "Manma," the audio output unit outputs the analysis result as an audio message such as "I'm hungry" through a speaker. By outputting the analysis results as audio, family members can auditorily understand the toddler's intentions and emotions. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit can input the analysis results to a generation AI and cause the generation AI to generate an audio message.

[0035] The learning unit can learn new words and emotions as the infant grows. The learning unit learns new words and emotions as the infant grows, for example, using a machine learning algorithm. For example, when the infant learns a new word, the learning unit learns the meaning of that word and reflects it in subsequent interpretations. The learning unit can also learn variations in the infant's emotional expressions. For example, when the infant expresses a new emotion, the learning unit learns that emotion and reflects it in subsequent interpretations. In this way, by learning new words and emotions as the infant grows, interpretation can always be performed based on the latest information. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the infant's new words and emotion data into the generation AI and cause the generation AI to perform learning.

[0036] The interpretation system further includes a collection unit that analyzes the child's past voice data and selects an optimal collection method. For example, the collection unit analyzes the speech patterns the child utters during a specific time period from the past voice data and concentrates collection on that time period. For example, the collection unit may determine, based on the past voice data, that the child utters more words in the morning and concentrate collection on that time period. The collection unit may also predict, based on the past voice data, what words the child will utter in a specific situation and optimize the collection method for that situation. For example, the collection unit may determine, based on the past voice data, that the child utters more words during mealtimes and concentrate collection on mealtimes. The collection unit may also analyze the past voice data to identify voice characteristics used when the child expresses a specific emotion and select a collection method based on those characteristics. For example, the collection unit may identify, based on the past voice data, voice characteristics used when the child is angry and select a collection method based on those characteristics. This allows the optimal collection method to be selected by analyzing the past voice data, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past voice data into the generation AI and have the generation AI select the optimal collection method.

[0037] The interpretation system further includes a collection unit that performs filtering based on the infant's current activity status and environment when collecting audio. For example, when the infant is playing, the collection unit filters background sounds and collects only the infant's voice. For example, when the infant is playing, the collection unit removes background sounds using noise canceling technology and collects only the infant's voice. When the infant is eating, the collection unit can also filter the sound of dishes to clearly collect the infant's words. For example, when collecting the infant's words while eating, the collection unit removes the sound of dishes using noise canceling technology. When the infant is out, the collection unit can also filter wind and traffic sounds to clearly collect the infant's voice. For example, when collecting the infant's words while out, the collection unit removes wind and traffic sounds using noise canceling technology. In this way, filtering based on the infant's current activity status and environment removes noise and allows clear audio data to be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the infant's activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0038] The interpretation system further includes a collection unit that, when collecting audio, prioritizes collecting highly relevant audio in consideration of the child's geographical location information. For example, when the child is in a park, the collection unit prioritizes collecting audio related to activities in the park. For example, when the child is playing in a park, the collection unit prioritizes collecting words and audio related to activities in the park. Furthermore, when the child is at home, the collection unit can also prioritize collecting audio related to activities at home. For example, when the child is playing at home, the collection unit prioritizes collecting words and audio related to activities at home. Furthermore, when the child is at a nursery school, the collection unit can also prioritize collecting audio related to activities at the nursery school. For example, when the child is playing at the nursery school, the collection unit prioritizes collecting words and audio related to activities at the nursery school. In this way, highly relevant audio can be preferentially collected by considering the geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the geographical location information of the infant into the generation AI and cause the generation AI to collect highly relevant audio.

[0039] The interpretation system further includes a collection unit that analyzes the infant's social media activity and collects related audio when collecting audio. For example, if the infant talks about a specific topic on social media, the collection unit collects audio related to that topic. For example, if the infant talks about "toys" on social media, the collection unit collects words and audio related to that topic. Furthermore, if the infant expresses a specific emotion on social media, the collection unit can also collect audio related to that emotion. For example, if the infant expresses "fun" on social media, the collection unit collects words and audio related to that emotion. Furthermore, if the infant participates in a specific event on social media, the collection unit can also collect audio related to that event. For example, if the infant participates in a "birthday party" on social media, the collection unit collects words and audio related to the event. This allows for efficient collection of related audio by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the infant's social media activity data into a generation AI and cause the generation AI to collect related audio.

[0040] The interpretation system further includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the audio data during analysis. The analysis unit, for example, analyzes audio data with high importance in detail to identify even the finest nuances. For example, if a toddler says "manma," the analysis unit analyzes the audio data in detail to identify the intention of "I'm hungry." The analysis unit can also analyze audio data with low importance in a simplified manner to identify only basic information. For example, if a toddler says "hmm," the analysis unit analyzes the audio data in a simplified manner to identify the intention of "unsatisfied." The analysis unit can also analyze audio data with medium importance in a moderate manner to identify necessary information. For example, if a toddler says "papa," the analysis unit analyzes the audio data in a moderate manner to identify the intention of "calling for father." This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the importance of the audio data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0041] The interpretation system further includes an analysis unit that applies different analysis algorithms depending on the category of the audio data during analysis. The analysis unit applies, for example, a speech recognition algorithm to speech data containing words. For example, if a toddler says "manma," the analysis unit applies a speech recognition algorithm to the speech data to identify the intention as "I'm hungry." The analysis unit can also apply an emotion analysis algorithm to emotional speech data. For example, if a toddler says "hmm," the analysis unit applies an emotion analysis algorithm to the speech data to identify the emotion as "unhappy." The analysis unit can also apply an environmental sound analysis algorithm to environmental sound data. For example, if a toddler says "papa," the analysis unit applies an environmental sound analysis algorithm to the speech data to identify the intention as "calling for father." This allows for more accurate analysis results by applying different analysis algorithms depending on the category of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input categories of audio data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] The interpretation system further includes an analysis unit that determines analysis priorities based on when the audio data was collected. The analysis unit, for example, prioritizes analysis of the most recent audio data and provides real-time information. For example, if a toddler says "manma" (eat as usual), the analysis unit prioritizes analysis of the audio data and provides the intention of "I'm hungry" in real time. The analysis unit can also prioritize analysis of data related to the current situation while referring to past audio data. For example, if a toddler says "papa" (father), the analysis unit prioritizes analysis of the audio data and provides the intention of "calling for father" in real time. The analysis unit can also prioritize analysis of audio data related to a specific event to grasp the event situation. For example, if a toddler is attending a "birthday party," the analysis unit prioritizes analysis of the audio data and provides the event situation in real time. In this way, real-time information can be provided by determining analysis priorities based on when the audio data was collected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the time when the voice data was collected into the generation AI and have the generation AI determine the analysis priorities.

[0043] The interpretation system further includes an analysis unit that adjusts the order of analysis based on the relevance of the audio data during analysis. The analysis unit, for example, prioritizes analysis of audio data related to the infant's emotions. For example, if the infant says "hmm," the analysis unit prioritizes analysis of the audio data to identify the emotion "unsatisfied." The analysis unit can also prioritize analysis of audio data related to the infant's words. For example, if the infant says "manma," the analysis unit prioritizes analysis of the audio data to identify the intention of "I'm hungry." The analysis unit can also prioritize analysis of audio data related to the infant's environment. For example, if the infant says "papa," the analysis unit prioritizes analysis of the audio data to identify the intention of "calling for my father." This allows important data to be analyzed preferentially by adjusting the order of analysis based on the relevance of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the relevance of the audio data into a generation AI and have the generation AI adjust the order of analysis.

[0044] The interpretation system further includes an output unit that adjusts the level of detail of the output based on the importance of the analysis result. For example, the output unit outputs analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the output unit outputs the analysis result in detail, conveying the intention of "I'm hungry" in detail. The output unit can also output analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the output unit outputs the analysis result in a simplified manner, conveying the intention of "unsatisfied." The output unit can also output analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the output unit outputs the analysis result in a moderate manner, conveying the intention of "calling for father." By adjusting the level of detail of the output based on the importance of the analysis result, information can be conveyed efficiently. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or without AI. For example, the output unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the output.

[0045] The interpretation system further includes an output unit that applies different output methods depending on the category of the analysis result. For example, the output unit applies text output to the analysis result of words. For example, if the infant says "manma," the output unit sends the analysis result to the family as a text message. The output unit can also apply audio output to the analysis result of emotions. For example, if the infant says "hmm," the output unit communicates the analysis result to the family as an audio message. The output unit can also apply visual output to the analysis result of environmental sounds. For example, if the infant says "papa," the output unit communicates the analysis result to the family as a visual message. This enables more appropriate communication by applying different output methods depending on the category of the analysis result. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the category of the analysis result to a generation AI and have the generation AI apply different output methods.

[0046] The interpretation system further includes an output unit that adjusts the output order based on the collection date of the analysis results. The output unit, for example, prioritizes output of the most recent analysis results, providing real-time information. For example, when a toddler says "manma," the output unit prioritizes output of the analysis result and provides the intention of "I'm hungry" in real time. The output unit can also prioritize output of results related to the current situation while referring to past analysis results. For example, when a toddler says "papa," the output unit prioritizes output of the analysis result and provides the intention of "calling for father" in real time. The output unit can also prioritize output of analysis results related to a specific event, allowing understanding of the event situation. For example, when a toddler is attending a "birthday party," the output unit prioritizes output of the analysis result and provides the event situation in real time. In this way, real-time information can be provided by adjusting the output order based on the collection date of the analysis results. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the collection time of the analysis results to the generation AI and cause the generation AI to adjust the order of output.

[0047] The interpretation system further includes an output unit that adjusts the output order based on the relevance of the analysis results. The output unit, for example, prioritizes output of analysis results related to the infant's emotions. For example, if the infant says "hmm," the output unit prioritizes output of the analysis result and identifies the emotion "unsatisfied." The output unit can also prioritize output of analysis results related to the infant's words. For example, if the infant says "manma," the output unit prioritizes output of the analysis result and identifies the intention "I'm hungry." The output unit can also prioritize output of analysis results related to the infant's environment. For example, if the infant says "papa," the output unit prioritizes output of the analysis result and identifies the intention "I'm calling for my father." Adjusting the output order based on the relevance of the analysis results allows important information to be conveyed preferentially. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the relevance of the analysis results to a generation AI and have the generation AI adjust the output order.

[0048] The interpretation system further includes a display unit that adjusts the level of detail of the display based on the importance of the analysis result. For example, the display unit displays analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the display unit displays the analysis result in detail, conveying the intention of "I'm hungry" in detail. The display unit can also display analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the display unit displays the analysis result in a simplified manner, conveying the intention of "unsatisfied." The display unit can also display analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the display unit displays the analysis result in a moderate manner, conveying the intention of "calling for father." This allows information to be conveyed efficiently by adjusting the level of detail of the display based on the importance of the analysis result. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0049] The interpretation system further includes a display unit that applies different display methods depending on the category of the analysis results. The display unit, for example, applies text display to the analysis results of words. For example, if the infant says "manma," the display unit displays the analysis results to the family as a text message. The display unit can also apply graphical display to the analysis results of emotions. For example, if the infant says "hmm," the display unit displays the analysis results to the family as a graphical message. The display unit can also apply visual display to the analysis results of environmental sounds. For example, if the infant says "papa," the display unit displays the analysis results to the family as a visual message. This enables more appropriate information communication by applying different display methods depending on the category of the analysis results. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the category of the analysis results to the generation AI and cause the generation AI to apply different display methods.

[0050] The interpretation system further includes a display unit that adjusts the display order based on the collection date of the analysis results. The display unit, for example, prioritizes displaying the most recent analysis results and provides real-time information. For example, when a toddler says "manma," the display unit prioritizes displaying the analysis result and provides the intention of "I'm hungry" in real time. The display unit can also prioritize displaying results related to the current situation while referring to past analysis results. For example, when a toddler says "papa," the display unit prioritizes displaying the analysis result and provides the intention of "calling for father" in real time. The display unit can also prioritize displaying analysis results related to a specific event to grasp the event status. For example, when a toddler is attending a "birthday party," the display unit prioritizes displaying the analysis result and provides the event status in real time. By adjusting the display order based on the collection date of the analysis results, real-time information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input the collection time of the analysis results to the generation AI and cause the generation AI to adjust the display order.

[0051] The interpretation system further includes a display unit that adjusts the display order based on the relevance of the analysis results. The display unit, for example, prioritizes displaying analysis results related to the infant's emotions. For example, if the infant says "hmm," the display unit prioritizes displaying the analysis result and identifies the emotion as "unsatisfied." The display unit can also prioritize displaying analysis results related to the infant's words. For example, if the infant says "manma," the display unit prioritizes displaying the analysis result and identifies the intention as "I'm hungry." The display unit can also prioritize displaying analysis results related to the infant's environment. For example, if the infant says "papa," the display unit prioritizes displaying the analysis result and identifies the intention as "I'm calling for my father." Adjusting the display order based on the relevance of the analysis results allows important information to be conveyed preferentially. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the relevance of the analysis results to a generation AI and have the generation AI adjust the display order.

[0052] The interpretation system further includes an audio output unit that adjusts the level of detail of the audio output based on the importance of the analysis result when outputting audio. The audio output unit, for example, outputs analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the audio output unit outputs the analysis result in detail, conveying the intention of "I'm hungry" in detail. The audio output unit can also output analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the audio output unit outputs the analysis result in a simplified manner, simply conveying the intention of "I'm dissatisfied." The audio output unit can also output analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the audio output unit outputs the analysis result in a moderate manner, appropriately conveying the intention of "I'm calling my father." In this way, information can be efficiently conveyed by adjusting the level of detail of the audio output based on the importance of the analysis results. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the audio output.

[0053] The interpretation system further includes an audio output unit that applies different audio output methods depending on the category of the analysis result when outputting audio. The audio output unit applies text-to-speech to the analysis result of words. For example, if the infant says "Manma," the audio output unit communicates the analysis result to the family as text-to-speech. The audio output unit can also apply audio output including emotional expressions to the analysis result of emotions. For example, if the infant says "Hmm," the audio output unit communicates the analysis result to the family as audio output including emotional expressions. The audio output unit can also apply visual audio output to the analysis result of environmental sounds. For example, if the infant says "Papa," the audio output unit communicates the analysis result to the family as visual audio output. This enables more appropriate information communication by applying different audio output methods depending on the category of the analysis result. Some or all of the above-described processing in the audio output unit may be performed using, or without, AI. For example, the audio output unit can input the category of the analysis result to a generation AI and cause the generation AI to apply different audio output methods.

[0054] The interpretation system further includes an audio output unit that adjusts the order of audio output based on the collection date of the analysis results. The audio output unit, for example, prioritizes the most recent analysis result and provides real-time information. For example, when a child says "manma," the audio output unit prioritizes the analysis result and provides the intention of "I'm hungry" in real time. The audio output unit can also prioritize audio output of results related to the current situation while referring to past analysis results. For example, when a child says "papa," the audio output unit prioritizes the analysis result and provides the intention of "calling for father" in real time. The audio output unit can also prioritize audio output of analysis results related to a specific event and grasp the event status. For example, when a child is attending a "birthday party," the audio output unit prioritizes the analysis result and provides the event status in real time. In this way, real-time information can be provided by adjusting the order of audio output based on the collection date of the analysis results. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or without AI. For example, the audio output unit can input the time when the analysis results were collected to the generation AI and cause the generation AI to adjust the order of audio output.

[0055] The interpretation system further includes an audio output unit that adjusts the order of audio output based on the relevance of the analysis results during audio output. The audio output unit, for example, prioritizes audio output of analysis results related to the infant's emotions. For example, if the infant says "hmm," the audio output unit prioritizes audio output of the analysis results and identifies the emotion as "unsatisfied." The audio output unit can also prioritize audio output of analysis results related to the infant's words. For example, if the infant says "manma," the audio output unit prioritizes audio output of the analysis results and identifies the intention as "I'm hungry." The audio output unit can also prioritize audio output of analysis results related to the infant's environment. For example, if the infant says "papa," the audio output unit prioritizes audio output of the analysis results and identifies the intention as "I'm calling my father." By adjusting the order of audio output based on the relevance of the analysis results, important information can be prioritized. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or without AI. For example, the audio output unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of audio output.

[0056] Furthermore, the interpretation system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on the past learning data to improve accuracy. For example, the learning unit selects an optimal learning algorithm based on the past learning data to improve accuracy. The learning unit can also analyze the past learning data, extract specific patterns, and reflect them in the learning algorithm. For example, the learning unit analyzes the past learning data, extracts specific patterns, and reflects them in the learning algorithm. The learning unit can also refer to the past learning data and adjust and optimize parameters of the learning algorithm. For example, the learning unit refers to the past learning data and adjusts and optimizes parameters of the learning algorithm. In this way, by referring to the past learning data, the learning algorithm can be optimized and accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0057] The interpretation system further includes a learning unit that, during learning, weights the learning data based on the time when the voice data was collected. The learning unit, for example, emphasizes the most recent voice data and reflects this in the learning algorithm. For example, the learning unit emphasizes voice data recently uttered by an infant and reflects this in the learning algorithm. The learning unit can also learn by emphasizing data related to the current situation while referring to past voice data. For example, the learning unit can learn by emphasizing data related to the current situation while referring to voice data uttered by an infant in the past. The learning unit can also emphasize voice data related to a specific event and reflect this in the learning algorithm. For example, the learning unit emphasizes voice data uttered by an infant related to a specific event and reflects this in the learning algorithm. This weighting of the learning data based on the time when the voice data was collected enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input the time when the voice data was collected into the generation AI and cause the generation AI to weight the learning data.

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

[0059] The interpretation system may further include a biometric information collection unit that collects biometric information such as the infant's body temperature and heart rate. For example, if the infant's body temperature rises, the collection unit may collect this as data suggesting possible poor health. Also, if the heart rate rises rapidly, this may be collected as data indicating excitement or stress. By collecting the infant's biometric information, more detailed changes in health status and emotions can be identified, and appropriate responses can be supported.

[0060] The interpretation system may further include a reaction collection unit that collects family members' reactions to the words and sounds uttered by the infant. For example, the collection unit may record how the family members respond to the words of the infant. The collection unit may also analyze the tone of voice and facial expressions of the family members to collect emotional reactions to the words of the infant. By collecting the reactions of the family members, the system can learn how the family members respond to the words and emotions of the infant and support more effective communication.

[0061] The interpretation system can further include a translation unit that translates the words and sounds uttered by the infant into another language. For example, if an infant says "Manma," the analysis unit translates the voice data into another language and displays "I'm hungry." The translation unit can also express the infant's emotions in another language. For example, if an infant says "Hmm," the voice data is translated as "I'm not happy." This makes it easier for family members who speak different languages ​​to understand the infant's intentions and emotions.

[0062] The interpretation system may further include a recording / playback unit that records the words and sounds uttered by the infant and plays them back later. For example, if an infant says "manma," the collection unit can record the audio data and play it back later. The recording / playback unit can also record the infant's growth process so that the family can look back on it later. This allows the infant's growth to be recorded and the family to share memories.

[0063] The interpretation system can further include a response generation unit that generates appropriate responses to the words and sounds uttered by the infant. For example, if the infant says "Manma," the analysis unit analyzes the voice data and replies, "Are you hungry?" The response generation unit can also generate responses according to the infant's emotions. For example, if the infant says "Hmm," the response unit replies, "What's wrong?" This can promote dialogue with the infant and support communication.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects the voice of the infant. The voice of the infant includes, but is not limited to, crying, laughing, and speaking. For example, the collection unit collects the words and voices uttered by the infant using a microphone. The collection unit can also collect the voice of the infant in real time. For example, the collection unit collects the words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear voice data. Step 2: The analysis unit analyzes the voice collected by the collection unit. The analysis is performed using, for example, voice recognition technology or an emotion analysis algorithm, but is not limited to these examples. For example, the analysis unit converts the words spoken by the infant into text data using voice recognition technology. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit estimates the infant's emotions from the voice data and outputs the result as analysis data. Step 3: The output unit notifies the family of the results of the analysis performed by the analysis unit. The output may be, for example, text output or audio output, but is not limited to these examples. For example, the output unit may send the analysis results to the family as a text message. Alternatively, the output unit may also notify the family of the analysis results as an audio message. Step 4: The display unit displays the results of the analysis performed by the analysis unit on a display. The display may be performed using a device such as a smartphone, a tablet, or a dedicated display, but is not limited to these examples. For example, the display unit may display the analysis results on a smartphone screen. The display unit may also display the analysis results on a tablet screen. Step 5: The audio output unit outputs the analysis result obtained by the analysis unit as audio. The audio output is performed using, for example, voice synthesis technology, but is not limited to this example. For example, the audio output unit outputs the analysis result as a voice message using voice synthesis technology. The audio output unit can also output the analysis result as audio through a speaker. Step 6: The learning unit learns the new speech collected by the collection unit. The learning is performed using, for example, but not limited to, a machine learning algorithm. For example, the learning unit learns new words and emotions as the child grows and reflects them in subsequent interpretations.

[0066] (Example 2) An interpretation system according to an embodiment of the present invention is an interpreter that interprets the words and feelings of young children who are just beginning to learn language, thereby increasing conversations within families. This interpretation system analyzes the words and feelings of young children and conveys them to family members in an easy-to-understand format, thereby promoting communication within families and supporting loving interactions. First, the interpretation system collects the words and sounds uttered by the young child. For example, if the young child says "manma," the audio data is input into the interpretation system. Next, the interpretation system analyzes the collected audio data to identify the young child's intentions and emotions. For example, it analyzes that the word "manma" means "I'm hungry." The analysis results are communicated to family members via the interpretation system's display or audio output. For example, the interpretation system may display "I'm hungry" or say "I'm hungry." This allows family members to more easily understand the young child's feelings and take appropriate measures. By interpreting the young child's words and feelings, this interpretation system increases conversations within families and promotes loving interactions. For example, when a toddler says "Manma" (food), a family member can respond with "Are you hungry?", fostering communication with the toddler. Understanding a toddler's emotions allows families to respond more appropriately, reducing the burden of childcare and parenting. Furthermore, this interpretation system can learn as the toddler grows, providing more accurate interpretations. For example, when a toddler learns a new word, the interpretation system can learn the meaning of that word and reflect it in subsequent interpretations. This allows the interpretation system to always provide interpretations based on the most up-to-date information, supporting communication between family members. In this way, the interpretation system is a device that increases conversations between family members and promotes loving interactions by interpreting a toddler's words and emotions. It is a powerful tool for supporting childcare and parenting and deepening family bonds. The interpretation system can interpret a toddler's words and emotions, promote communication between family members, and support loving interactions.

[0067] The interpretation system according to the embodiment includes a collection unit, an analysis unit, an output unit, a display unit, an audio output unit, and a learning unit. The collection unit collects the voice of an infant. The voice of an infant includes, but is not limited to, crying, laughter, and speech. The collection unit collects, for example, words and speech uttered by the infant using a microphone. The collection unit can also collect the voice of an infant in real time. For example, the collection unit collects the words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear audio data. The analysis unit analyzes the voice collected by the collection unit. The analysis is performed using, for example, speech recognition technology and an emotion analysis algorithm, but is not limited to, for example. For example, the analysis unit converts the words uttered by the infant into text data using speech recognition technology. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit estimates the infant's emotions from the voice data and outputs the result as analysis data. The output unit communicates the analysis result obtained by the analysis unit to the family. The output may be performed, for example, by a method such as text output or audio output, but is not limited to these examples. For example, the output unit transmits the analysis results to the family as a text message. The output unit may also communicate the analysis results to the family as an audio message. The display unit displays the results analyzed by the analysis unit on a display. The display may be performed using, for example, a device such as a smartphone, a tablet, or a dedicated display, but is not limited to these examples. For example, the display unit displays the analysis results on a smartphone screen. The display unit may also display the analysis results on a tablet screen. The audio output unit outputs the results analyzed by the analysis unit as audio. The audio output may be performed, for example, by using voice synthesis technology, but is not limited to these examples. For example, the audio output unit outputs the analysis results as an audio message using voice synthesis technology. The audio output unit may also output the analysis results as audio through a speaker. The learning unit learns new audio collected by the collection unit. The learning may be performed, for example, by using a machine learning algorithm, but is not limited to these examples. For example, the learning unit learns new words and emotions as the child grows and reflects them in subsequent interpretations.As a result, the interpretation system according to the embodiment can interpret the words and emotions of young children, promote communication between family members, and support affectionate interactions.

[0068] The collection unit can collect words or sounds uttered by the infant. The collection unit, for example, collects words or sounds uttered by the infant using a microphone. For example, when the infant says "manma," the collection unit collects the sound data. The collection unit can also collect the infant's sound in real time. For example, the collection unit collects words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear sound data. In this way, by collecting the words and sounds uttered by the infant, data for identifying the infant's intentions and emotions can be provided. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the infant's sound data into a generation AI and cause the generation AI to collect the sound data.

[0069] The analysis unit can analyze the collected voice data and identify the infant's intentions and emotions. The analysis unit, for example, converts the infant's words into text data using voice recognition technology. For example, if the infant says "manma," the analysis unit converts the voice data into text data. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit infers the infant's emotions from the voice data and outputs the result as analysis data. In this way, by analyzing the collected voice data, the infant's intentions and emotions can be identified and communicated to the family. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the infant's voice data into a generation AI and have the generation AI analyze the voice data.

[0070] The output unit can communicate the analysis results to the family. For example, the output unit transmits the analysis results to the family as a text message. For example, if the infant says "Manma," the output unit transmits the analysis result to the family as a text message saying "I'm hungry." The output unit can also communicate the analysis results to the family as a voice message. For example, if the infant says "Manma," the output unit transmits the analysis result to the family as a voice message saying "I'm hungry." By communicating the analysis results to the family, the family can more easily understand the infant's intentions and emotions. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the analysis results to a generation AI and cause the generation AI to generate a text message or a voice message.

[0071] The display unit can display the analyzed results on a display. The display unit, for example, displays the analyzed results on a smartphone screen. For example, when a toddler says "Manma," the display unit displays the analyzed result as "I'm hungry" on the smartphone screen. The display unit can also display the analyzed results on a tablet screen. For example, when a toddler says "Manma," the display unit displays the analyzed result as "I'm hungry" on the tablet screen. By displaying the analyzed results on a display, family members can visually understand the toddler's intentions and emotions. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the analyzed results to a generation AI and have the generation AI execute data for display on the display.

[0072] The audio output unit can output the analysis results as audio. For example, the audio output unit outputs the analysis results as an audio message using speech synthesis technology. For example, when a toddler says "Manma," the audio output unit outputs the analysis result as an audio message such as "I'm hungry." The audio output unit can also output the analysis results as audio through a speaker. For example, when a toddler says "Manma," the audio output unit outputs the analysis result as an audio message such as "I'm hungry" through a speaker. By outputting the analysis results as audio, family members can auditorily understand the toddler's intentions and emotions. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit can input the analysis results to a generation AI and cause the generation AI to generate an audio message.

[0073] The learning unit can learn new words and emotions as the infant grows. The learning unit learns new words and emotions as the infant grows, for example, using a machine learning algorithm. For example, when the infant learns a new word, the learning unit learns the meaning of that word and reflects it in subsequent interpretations. The learning unit can also learn variations in the infant's emotional expressions. For example, when the infant expresses a new emotion, the learning unit learns that emotion and reflects it in subsequent interpretations. In this way, by learning new words and emotions as the infant grows, interpretation can always be performed based on the latest information. Some or all of the above-mentioned processing in the learning unit may be performed using, for example, AI, or may be performed without AI. For example, the learning unit can input the infant's new words and emotion data into the generation AI and cause the generation AI to perform learning.

[0074] The interpretation system further includes a collection unit that estimates the infant's emotions and adjusts the timing of audio collection based on the estimated infant's emotions. For example, when the infant is excited, the collection unit increases the frequency of audio collection to acquire detailed data. For example, when the infant is excited, the collection unit collects audio data every second. Furthermore, when the infant is calm, the collection unit can reduce the frequency of audio collection to acquire the minimum necessary data. For example, when the infant is calm, the collection unit collects audio data every 10 seconds. Furthermore, when the infant is crying, the collection unit can analyze the crying pattern and prioritize collecting audio related to specific emotions. For example, when the infant is crying, the collection unit analyzes the crying pattern and prioritizes collecting audio related to emotions such as anger and sadness. By adjusting the timing of audio collection based on the infant's emotions, more appropriate data can be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input emotional data of an infant into the generation AI and cause the generation AI to adjust the timing of voice collection.

[0075] The interpretation system further includes a collection unit that analyzes the child's past voice data and selects an optimal collection method. For example, the collection unit analyzes the speech patterns the child utters during a specific time period from the past voice data and concentrates collection on that time period. For example, the collection unit may determine, based on the past voice data, that the child utters more words in the morning and concentrate collection on that time period. The collection unit may also predict, based on the past voice data, what words the child will utter in a specific situation and optimize the collection method for that situation. For example, the collection unit may determine, based on the past voice data, that the child utters more words during mealtimes and concentrate collection on mealtimes. The collection unit may also analyze the past voice data to identify voice characteristics used when the child expresses a specific emotion and select a collection method based on those characteristics. For example, the collection unit may identify, based on the past voice data, voice characteristics used when the child is angry and select a collection method based on those characteristics. This allows the optimal collection method to be selected by analyzing the past voice data, enabling efficient data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input past voice data into the generation AI and have the generation AI select the optimal collection method.

[0076] The interpretation system further includes a collection unit that performs filtering based on the infant's current activity status and environment when collecting audio. For example, when the infant is playing, the collection unit filters background sounds and collects only the infant's voice. For example, when the infant is playing, the collection unit removes background sounds using noise canceling technology and collects only the infant's voice. When the infant is eating, the collection unit can also filter the sound of dishes to clearly collect the infant's words. For example, when collecting the infant's words while eating, the collection unit removes the sound of dishes using noise canceling technology. When the infant is out, the collection unit can also filter wind and traffic sounds to clearly collect the infant's voice. For example, when collecting the infant's words while out, the collection unit removes wind and traffic sounds using noise canceling technology. In this way, filtering based on the infant's current activity status and environment removes noise and allows clear audio data to be collected. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the infant's activity status and environmental data into the generation AI and have the generation AI perform filtering.

[0077] The interpretation system further includes a collection unit that estimates the infant's emotion and prioritizes the audio to be collected based on the estimated infant's emotion. For example, when the infant is angry, the collection unit prioritizes collecting audio related to that emotion. For example, when the infant is angry, the collection unit prioritizes collecting words and audio related to anger. Furthermore, when the infant is happy, the collection unit can also prioritize collecting audio related to that emotion. For example, when the infant is happy, the collection unit prioritizes collecting words and audio related to joy. Furthermore, when the infant is sad, the collection unit can also prioritize collecting audio related to that emotion. For example, when the infant is sad, the collection unit prioritizes collecting words and audio related to sadness. Thus, by prioritizing the audio to be collected based on the infant's emotion, important data can be collected preferentially. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit may input the infant's emotion data to a generation AI and cause the generation AI to determine the audio priorities.

[0078] The interpretation system further includes a collection unit that, when collecting audio, prioritizes collecting highly relevant audio in consideration of the child's geographical location information. For example, when the child is in a park, the collection unit prioritizes collecting audio related to activities in the park. For example, when the child is playing in a park, the collection unit prioritizes collecting words and audio related to activities in the park. Furthermore, when the child is at home, the collection unit can also prioritize collecting audio related to activities at home. For example, when the child is playing at home, the collection unit prioritizes collecting words and audio related to activities at home. Furthermore, when the child is at a nursery school, the collection unit can also prioritize collecting audio related to activities at the nursery school. For example, when the child is playing at the nursery school, the collection unit prioritizes collecting words and audio related to activities at the nursery school. In this way, highly relevant audio can be preferentially collected by considering the geographical location information. Some or all of the above-described processing in the collection unit may be performed, for example, using AI or without using AI. For example, the collection unit can input the geographical location information of the infant into the generation AI and cause the generation AI to collect highly relevant audio.

[0079] The interpretation system further includes a collection unit that analyzes the infant's social media activity and collects related audio when collecting audio. For example, if the infant talks about a specific topic on social media, the collection unit collects audio related to that topic. For example, if the infant talks about "toys" on social media, the collection unit collects words and audio related to that topic. Furthermore, if the infant expresses a specific emotion on social media, the collection unit can also collect audio related to that emotion. For example, if the infant expresses "fun" on social media, the collection unit collects words and audio related to that emotion. Furthermore, if the infant participates in a specific event on social media, the collection unit can also collect audio related to that event. For example, if the infant participates in a "birthday party" on social media, the collection unit collects words and audio related to the event. This allows for efficient collection of related audio by analyzing social media activity. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit may input the infant's social media activity data into a generation AI and cause the generation AI to collect related audio.

[0080] The interpretation system further includes an analysis unit that estimates the infant's emotions and adjusts the expression method of the analysis based on the estimated infant's emotions. The analysis unit, for example, highlights the analysis results when the infant is angry. For example, the analysis unit displays the analysis results in bold or red when the infant is angry. The analysis unit can also display the analysis results in brighter colors when the infant is happy. For example, the analysis unit displays the analysis results in yellow or green when the infant is happy. The analysis unit can also display the analysis results in a more subdued tone when the infant is sad. For example, the analysis unit displays the analysis results in blue or gray when the infant is sad. This allows for adjusting the expression method of the analysis based on the infant's emotions, thereby providing more appropriate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit may input the infant's emotion data into a generation AI and cause the generation AI to adjust the expression method of the analysis.

[0081] The interpretation system further includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the audio data during analysis. The analysis unit, for example, analyzes audio data with high importance in detail to identify even the finest nuances. For example, if a toddler says "manma," the analysis unit analyzes the audio data in detail to identify the intention of "I'm hungry." The analysis unit can also analyze audio data with low importance in a simplified manner to identify only basic information. For example, if a toddler says "hmm," the analysis unit analyzes the audio data in a simplified manner to identify the intention of "unsatisfied." The analysis unit can also analyze audio data with medium importance in a moderate manner to identify necessary information. For example, if a toddler says "papa," the analysis unit analyzes the audio data in a moderate manner to identify the intention of "calling for father." This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the importance of the audio data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0082] The interpretation system further includes an analysis unit that applies different analysis algorithms depending on the category of the audio data during analysis. The analysis unit applies, for example, a speech recognition algorithm to speech data containing words. For example, if a toddler says "manma," the analysis unit applies a speech recognition algorithm to the speech data to identify the intention as "I'm hungry." The analysis unit can also apply an emotion analysis algorithm to emotional speech data. For example, if a toddler says "hmm," the analysis unit applies an emotion analysis algorithm to the speech data to identify the emotion as "unhappy." The analysis unit can also apply an environmental sound analysis algorithm to environmental sound data. For example, if a toddler says "papa," the analysis unit applies an environmental sound analysis algorithm to the speech data to identify the intention as "calling for father." This allows for more accurate analysis results by applying different analysis algorithms depending on the category of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input categories of audio data to the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] The interpretation system further includes an analysis unit that estimates the infant's emotions and adjusts the length of the analysis based on the estimated infant's emotions. For example, when the infant is excited, the analysis unit performs a detailed analysis and provides a longer result. For example, when the infant is excited, the analysis unit analyzes the audio data in detail and provides a longer analysis result. The analysis unit can also perform a simplified analysis and provide a shorter result when the infant is calm. For example, when the infant is calm, the analysis unit analyzes the audio data in detail and provides a shorter analysis result. The analysis unit can also analyze the crying pattern in detail and provide a longer result when the infant is crying. For example, when the infant is crying, the analysis unit analyzes the crying pattern in detail and provides a longer analysis result. In this way, by adjusting the length of the analysis based on the infant's emotions, an analysis result of an appropriate length can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input emotional data of a child into the generation AI and have the generation AI adjust the length of the analysis.

[0084] The interpretation system further includes an analysis unit that determines analysis priorities based on when the audio data was collected. The analysis unit, for example, prioritizes analysis of the most recent audio data and provides real-time information. For example, if a toddler says "manma" (eat as usual), the analysis unit prioritizes analysis of the audio data and provides the intention of "I'm hungry" in real time. The analysis unit can also prioritize analysis of data related to the current situation while referring to past audio data. For example, if a toddler says "papa" (father), the analysis unit prioritizes analysis of the audio data and provides the intention of "calling for father" in real time. The analysis unit can also prioritize analysis of audio data related to a specific event to grasp the event situation. For example, if a toddler is attending a "birthday party," the analysis unit prioritizes analysis of the audio data and provides the event situation in real time. In this way, real-time information can be provided by determining analysis priorities based on when the audio data was collected. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the time when the voice data was collected into the generation AI and have the generation AI determine the analysis priorities.

[0085] The interpretation system further includes an analysis unit that adjusts the order of analysis based on the relevance of the audio data during analysis. The analysis unit, for example, prioritizes analysis of audio data related to the infant's emotions. For example, if the infant says "hmm," the analysis unit prioritizes analysis of the audio data to identify the emotion "unsatisfied." The analysis unit can also prioritize analysis of audio data related to the infant's words. For example, if the infant says "manma," the analysis unit prioritizes analysis of the audio data to identify the intention of "I'm hungry." The analysis unit can also prioritize analysis of audio data related to the infant's environment. For example, if the infant says "papa," the analysis unit prioritizes analysis of the audio data to identify the intention of "calling for my father." This allows important data to be analyzed preferentially by adjusting the order of analysis based on the relevance of the audio data. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input the relevance of the audio data into a generation AI and have the generation AI adjust the order of analysis.

[0086] The interpretation system further includes an output unit that estimates the infant's emotions and adjusts the output method based on the estimated infant's emotions. For example, if the infant is angry, the output unit emphasizes and outputs the analysis results. For example, if the infant is angry, the output unit outputs the analysis results in bold or red. The output unit can also output the analysis results in a brighter color if the infant is happy. For example, if the infant is happy, the output unit outputs the analysis results in yellow or green. The output unit can also output the analysis results in a more subdued tone if the infant is sad. For example, if the infant is sad, the output unit outputs the analysis results in blue or gray. This allows for adjusting the output method based on the infant's emotions to provide a more appropriate output result. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit may input the infant's emotion data to a generation AI and cause the generation AI to adjust the output method.

[0087] The interpretation system further includes an output unit that adjusts the level of detail of the output based on the importance of the analysis result. For example, the output unit outputs analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the output unit outputs the analysis result in detail, conveying the intention of "I'm hungry" in detail. The output unit can also output analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the output unit outputs the analysis result in a simplified manner, conveying the intention of "unsatisfied." The output unit can also output analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the output unit outputs the analysis result in a moderate manner, conveying the intention of "calling for father." By adjusting the level of detail of the output based on the importance of the analysis result, information can be conveyed efficiently. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or without AI. For example, the output unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the output.

[0088] The interpretation system further includes an output unit that applies different output methods depending on the category of the analysis result. For example, the output unit applies text output to the analysis result of words. For example, if the infant says "manma," the output unit sends the analysis result to the family as a text message. The output unit can also apply audio output to the analysis result of emotions. For example, if the infant says "hmm," the output unit communicates the analysis result to the family as an audio message. The output unit can also apply visual output to the analysis result of environmental sounds. For example, if the infant says "papa," the output unit communicates the analysis result to the family as a visual message. This enables more appropriate communication by applying different output methods depending on the category of the analysis result. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the category of the analysis result to a generation AI and have the generation AI apply different output methods.

[0089] The interpretation system further includes an output unit that estimates the infant's emotion and determines output priorities based on the estimated infant's emotion. For example, when the infant is angry, the output unit prioritizes output of analysis results related to the emotion. For example, when the infant is angry, the output unit prioritizes output of analysis results related to anger. Furthermore, when the infant is happy, the output unit can also prioritize output of analysis results related to the emotion. For example, when the infant is happy, the output unit prioritizes output of analysis results related to joy. Furthermore, when the infant is sad, the output unit can also prioritize output of analysis results related to the emotion. For example, when the infant is sad, the output unit prioritizes output of analysis results related to sadness. Thus, by determining output priorities based on the infant's emotion, important information can be conveyed preferentially. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit may input the infant's emotion data to a generation AI and cause the generation AI to determine the output priorities.

[0090] The interpretation system further includes an output unit that adjusts the output order based on the collection date of the analysis results. The output unit, for example, prioritizes output of the most recent analysis results, providing real-time information. For example, when a toddler says "manma," the output unit prioritizes output of the analysis result and provides the intention of "I'm hungry" in real time. The output unit can also prioritize output of results related to the current situation while referring to past analysis results. For example, when a toddler says "papa," the output unit prioritizes output of the analysis result and provides the intention of "calling for father" in real time. The output unit can also prioritize output of analysis results related to a specific event, allowing understanding of the event situation. For example, when a toddler is attending a "birthday party," the output unit prioritizes output of the analysis result and provides the event situation in real time. In this way, real-time information can be provided by adjusting the output order based on the collection date of the analysis results. Some or all of the above-described processing in the output unit may be performed using, for example, AI, or may be performed without AI. For example, the output unit can input the collection time of the analysis results to the generation AI and cause the generation AI to adjust the order of output.

[0091] The interpretation system further includes an output unit that adjusts the output order based on the relevance of the analysis results. The output unit, for example, prioritizes output of analysis results related to the infant's emotions. For example, if the infant says "hmm," the output unit prioritizes output of the analysis result and identifies the emotion "unsatisfied." The output unit can also prioritize output of analysis results related to the infant's words. For example, if the infant says "manma," the output unit prioritizes output of the analysis result and identifies the intention "I'm hungry." The output unit can also prioritize output of analysis results related to the infant's environment. For example, if the infant says "papa," the output unit prioritizes output of the analysis result and identifies the intention "I'm calling for my father." Adjusting the output order based on the relevance of the analysis results allows important information to be conveyed preferentially. Some or all of the above-described processing in the output unit may be performed using, or without, AI. For example, the output unit can input the relevance of the analysis results to a generation AI and have the generation AI adjust the output order.

[0092] The interpretation system further includes a display unit that estimates the infant's emotions and adjusts the display method based on the estimated infant's emotions. The display unit, for example, highlights the analysis results when the infant is angry. For example, the display unit displays the analysis results in bold or red when the infant is angry. The display unit can also display the analysis results in brighter colors when the infant is happy. For example, the display unit displays the analysis results in yellow or green when the infant is happy. The display unit can also display the analysis results in subdued tones when the infant is sad. For example, the display unit displays the analysis results in blue or gray when the infant is sad. This allows for adjusting the display method based on the infant's emotions to provide more appropriate display results. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may input the infant's emotion data to a generation AI and cause the generation AI to adjust the display method.

[0093] The interpretation system further includes a display unit that adjusts the level of detail of the display based on the importance of the analysis result. For example, the display unit displays analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the display unit displays the analysis result in detail, conveying the intention of "I'm hungry" in detail. The display unit can also display analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the display unit displays the analysis result in a simplified manner, conveying the intention of "unsatisfied." The display unit can also display analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the display unit displays the analysis result in a moderate manner, conveying the intention of "calling for father." This allows information to be conveyed efficiently by adjusting the level of detail of the display based on the importance of the analysis result. Some or all of the above-described processing in the display unit may be performed, for example, using AI or without AI. For example, the display unit can input the importance of the analysis results to the generation AI and cause the generation AI to adjust the level of detail of the display.

[0094] The interpretation system further includes a display unit that applies different display methods depending on the category of the analysis results. The display unit, for example, applies text display to the analysis results of words. For example, if the infant says "manma," the display unit displays the analysis results to the family as a text message. The display unit can also apply graphical display to the analysis results of emotions. For example, if the infant says "hmm," the display unit displays the analysis results to the family as a graphical message. The display unit can also apply visual display to the analysis results of environmental sounds. For example, if the infant says "papa," the display unit displays the analysis results to the family as a visual message. This enables more appropriate information communication by applying different display methods depending on the category of the analysis results. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the category of the analysis results to the generation AI and cause the generation AI to apply different display methods.

[0095] The interpretation system further includes a display unit that estimates the infant's emotions and determines display priorities based on the estimated infant's emotions. For example, when the infant is angry, the display unit prioritizes displaying analysis results related to the infant's emotions. For example, when the infant is angry, the display unit prioritizes displaying analysis results related to the infant's emotions. Furthermore, when the infant is happy, the display unit can also prioritize displaying analysis results related to the infant's emotions. For example, when the infant is happy, the display unit prioritizes displaying analysis results related to joy. Furthermore, when the infant is sad, the display unit can also prioritize displaying analysis results related to the infant's emotions. For example, when the infant is sad, the display unit prioritizes displaying analysis results related to sadness. In this way, by determining display priorities based on the infant's emotions, important information can be conveyed preferentially. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or may be performed without AI. For example, the display unit may input the infant's emotion data to a generation AI and have the generation AI determine the display priorities.

[0096] The interpretation system further includes a display unit that adjusts the display order based on the collection date of the analysis results. The display unit, for example, prioritizes displaying the most recent analysis results and provides real-time information. For example, when a toddler says "manma," the display unit prioritizes displaying the analysis result and provides the intention of "I'm hungry" in real time. The display unit can also prioritize displaying results related to the current situation while referring to past analysis results. For example, when a toddler says "papa," the display unit prioritizes displaying the analysis result and provides the intention of "calling for father" in real time. The display unit can also prioritize displaying analysis results related to a specific event to grasp the event status. For example, when a toddler is attending a "birthday party," the display unit prioritizes displaying the analysis result and provides the event status in real time. By adjusting the display order based on the collection date of the analysis results, real-time information can be provided. Some or all of the above-described processing in the display unit may be performed using, for example, AI, or without AI. For example, the display unit can input the collection time of the analysis results to the generation AI and cause the generation AI to adjust the display order.

[0097] The interpretation system further includes a display unit that adjusts the display order based on the relevance of the analysis results. The display unit, for example, prioritizes displaying analysis results related to the infant's emotions. For example, if the infant says "hmm," the display unit prioritizes displaying the analysis result and identifies the emotion as "unsatisfied." The display unit can also prioritize displaying analysis results related to the infant's words. For example, if the infant says "manma," the display unit prioritizes displaying the analysis result and identifies the intention as "I'm hungry." The display unit can also prioritize displaying analysis results related to the infant's environment. For example, if the infant says "papa," the display unit prioritizes displaying the analysis result and identifies the intention as "I'm calling for my father." Adjusting the display order based on the relevance of the analysis results allows important information to be conveyed preferentially. Some or all of the above-described processing in the display unit may be performed using, or without, AI. For example, the display unit can input the relevance of the analysis results to a generation AI and have the generation AI adjust the display order.

[0098] The interpretation system further includes an audio output unit that estimates the infant's emotions and adjusts the audio output method based on the estimated infant's emotions. The audio output unit, for example, emphasizes the analysis result when the infant is angry. For example, when the infant is angry, the audio output unit outputs the analysis result in an emphasized tone. The audio output unit can also output the analysis result in a brighter tone when the infant is happy. For example, when the infant is happy, the audio output unit outputs the analysis result in a brighter tone. The audio output unit can also output the analysis result in a calmer tone when the infant is sad. For example, when the infant is sad, the audio output unit outputs the analysis result in a calmer tone. This allows for adjusting the audio output method based on the infant's emotions to provide a more appropriate audio output result. Some or all of the above-described processing in the audio output unit may be performed using, or without, AI. For example, the audio output unit may input the infant's emotion data to a generation AI and cause the generation AI to adjust the audio output method.

[0099] The interpretation system further includes an audio output unit that adjusts the level of detail of the audio output based on the importance of the analysis result when outputting audio. The audio output unit, for example, outputs analysis results with high importance in detail, conveying even the finest nuances. For example, when a toddler says "manma," the audio output unit outputs the analysis result in detail, conveying the intention of "I'm hungry" in detail. The audio output unit can also output analysis results with low importance in a simplified manner, conveying only basic information. For example, when a toddler says "hmm," the audio output unit outputs the analysis result in a simplified manner, simply conveying the intention of "I'm dissatisfied." The audio output unit can also output analysis results with medium importance in a moderate manner, conveying necessary information. For example, when a toddler says "papa," the audio output unit outputs the analysis result in a moderate manner, appropriately conveying the intention of "I'm calling my father." In this way, information can be efficiently conveyed by adjusting the level of detail of the audio output based on the importance of the analysis results. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the audio output unit may input the importance of the analysis result to the generation AI and cause the generation AI to adjust the level of detail of the audio output.

[0100] The interpretation system further includes an audio output unit that applies different audio output methods depending on the category of the analysis result when outputting audio. The audio output unit applies text-to-speech to the analysis result of words. For example, if the infant says "Manma," the audio output unit communicates the analysis result to the family as text-to-speech. The audio output unit can also apply audio output including emotional expressions to the analysis result of emotions. For example, if the infant says "Hmm," the audio output unit communicates the analysis result to the family as audio output including emotional expressions. The audio output unit can also apply visual audio output to the analysis result of environmental sounds. For example, if the infant says "Papa," the audio output unit communicates the analysis result to the family as visual audio output. This enables more appropriate information communication by applying different audio output methods depending on the category of the analysis result. Some or all of the above-described processing in the audio output unit may be performed using, or without, AI. For example, the audio output unit can input the category of the analysis result to a generation AI and cause the generation AI to apply different audio output methods.

[0101] The interpretation system further includes an audio output unit that estimates the infant's emotion and determines the priority of audio output based on the estimated infant's emotion. For example, when the infant is angry, the audio output unit prioritizes audio output of analysis results related to the emotion. For example, when the infant is angry, the audio output unit prioritizes audio output of analysis results related to anger. Furthermore, when the infant is happy, the audio output unit can also prioritize audio output of analysis results related to the emotion. For example, when the infant is happy, the audio output unit prioritizes audio output of analysis results related to joy. Furthermore, when the infant is sad, the audio output unit can also prioritize audio output of analysis results related to the emotion. For example, when the infant is sad, the audio output unit prioritizes audio output of analysis results related to sadness. In this way, by determining the priority of audio output based on the infant's emotion, important information can be conveyed preferentially. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or may be performed without using AI. For example, the voice output unit can input emotional data of an infant to the generation AI and have the generation AI determine the priority of voice output.

[0102] The interpretation system further includes an audio output unit that adjusts the order of audio output based on the collection date of the analysis results. The audio output unit, for example, prioritizes the most recent analysis result and provides real-time information. For example, when a child says "manma," the audio output unit prioritizes the analysis result and provides the intention of "I'm hungry" in real time. The audio output unit can also prioritize audio output of results related to the current situation while referring to past analysis results. For example, when a child says "papa," the audio output unit prioritizes the analysis result and provides the intention of "calling for father" in real time. The audio output unit can also prioritize audio output of analysis results related to a specific event and grasp the event status. For example, when a child is attending a "birthday party," the audio output unit prioritizes the analysis result and provides the event status in real time. In this way, real-time information can be provided by adjusting the order of audio output based on the collection date of the analysis results. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or without AI. For example, the audio output unit can input the time when the analysis results were collected to the generation AI and cause the generation AI to adjust the order of audio output.

[0103] The interpretation system further includes an audio output unit that adjusts the order of audio output based on the relevance of the analysis results during audio output. The audio output unit, for example, prioritizes audio output of analysis results related to the infant's emotions. For example, if the infant says "hmm," the audio output unit prioritizes audio output of the analysis results and identifies the emotion as "unsatisfied." The audio output unit can also prioritize audio output of analysis results related to the infant's words. For example, if the infant says "manma," the audio output unit prioritizes audio output of the analysis results and identifies the intention as "I'm hungry." The audio output unit can also prioritize audio output of analysis results related to the infant's environment. For example, if the infant says "papa," the audio output unit prioritizes audio output of the analysis results and identifies the intention as "I'm calling my father." By adjusting the order of audio output based on the relevance of the analysis results, important information can be prioritized. Some or all of the above-described processing in the audio output unit may be performed using, for example, AI, or without AI. For example, the audio output unit can input the relevance of the analysis results to the generation AI and cause the generation AI to adjust the order of audio output.

[0104] The interpretation system further includes a learning unit that estimates the infant's emotions and selects training data based on the estimated infant's emotions. For example, when the infant is angry, the learning unit prioritizes learning audio data related to that emotion. For example, when the infant is angry, the learning unit prioritizes learning audio data related to anger. The learning unit can also prioritize learning audio data related to a happy infant's emotion. For example, when the infant is happy, the learning unit prioritizes learning audio data related to joy. The learning unit can also prioritize learning audio data related to a sad infant's emotion. For example, when the infant is sad, the learning unit prioritizes learning audio data related to sadness. This enables more appropriate training by selecting training data based on the infant's emotions. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input the infant's emotional data into a generation AI and cause the generation AI to select training data.

[0105] Furthermore, the interpretation system includes a learning unit that optimizes a learning algorithm by referring to past learning data during learning. The learning unit, for example, selects an optimal learning algorithm based on the past learning data to improve accuracy. For example, the learning unit selects an optimal learning algorithm based on the past learning data to improve accuracy. The learning unit can also analyze the past learning data, extract specific patterns, and reflect them in the learning algorithm. For example, the learning unit analyzes the past learning data, extracts specific patterns, and reflects them in the learning algorithm. The learning unit can also refer to the past learning data and adjust and optimize parameters of the learning algorithm. For example, the learning unit refers to the past learning data and adjusts and optimizes parameters of the learning algorithm. In this way, by referring to the past learning data, the learning algorithm can be optimized and accuracy can be improved. Some or all of the above-described processing in the learning unit may be performed using, for example, AI, or may be performed without using AI. For example, the learning unit can input past learning data to a generation AI and cause the generation AI to optimize the learning algorithm.

[0106] The interpretation system further includes a learning unit that estimates the infant's emotion and adjusts the frequency of learning based on the estimated infant's emotion. For example, when the infant is angry, the learning unit frequently learns voice data related to that emotion. For example, when the infant is angry, the learning unit frequently learns voice data related to anger. Also, when the infant is happy, the learning unit can frequently learn voice data related to that emotion. For example, when the infant is happy, the learning unit frequently learns voice data related to joy. Also, when the infant is sad, the learning unit can frequently learn voice data related to that emotion. For example, when the infant is sad, the learning unit frequently learns voice data related to sadness. This allows for more appropriate learning by adjusting the frequency of learning based on the infant's emotion. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input the infant's emotion data into the generation AI and cause the generation AI to adjust the frequency of learning.

[0107] The interpretation system further includes a learning unit that, during learning, weights the learning data based on the time when the voice data was collected. The learning unit, for example, emphasizes the most recent voice data and reflects this in the learning algorithm. For example, the learning unit emphasizes voice data recently uttered by an infant and reflects this in the learning algorithm. The learning unit can also learn by emphasizing data related to the current situation while referring to past voice data. For example, the learning unit can learn by emphasizing data related to the current situation while referring to voice data uttered by an infant in the past. The learning unit can also emphasize voice data related to a specific event and reflect this in the learning algorithm. For example, the learning unit emphasizes voice data uttered by an infant related to a specific event and reflects this in the learning algorithm. This weighting of the learning data based on the time when the voice data was collected enables more appropriate learning. Some or all of the above-described processing in the learning unit may be performed using, or without, AI. For example, the learning unit may input the time when the voice data was collected into the generation AI and cause the generation AI to weight the learning data. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, output unit, display unit, audio output unit, and learning unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the infant's voice using the microphone 38B of the smart device 14. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12 to identify the infant's intentions and emotions. The output unit communicates the analysis results to the family via the display 40A and speaker 40B of the smart device 14. The display unit displays the analysis results on the display 40A of the smart device 14. The audio output unit outputs the analysis results by audio using the speaker 40B of the smart device 14. The learning unit learns new voice data using the specific processing unit 290 of the data processing device 12 and reflects the learned data in subsequent interpretations. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, output unit, display unit, audio output unit, and learning unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the infant's voice using the microphone 238 of the smart glasses 214. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12 to identify the infant's intentions and emotions. The output unit communicates the analysis results to the family through the speaker 240 of the smart glasses 214. The display unit displays the analysis results on the display of the smart glasses 214. The audio output unit outputs the analysis results by audio using the speaker 240 of the smart glasses 214. The learning unit learns new voice data using the specific processing unit 290 of the data processing device 12 and reflects it in subsequent interpretations. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, output unit, display unit, audio output unit, and learning unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects the voice of the infant using the microphone 238 of the headset-type terminal 314. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12 to identify the infant's intentions and emotions. The output unit communicates the analysis results to the family via the display 343 and speaker 240 of the headset-type terminal 314. The display unit displays the analysis results on the display 343 of the headset-type terminal 314. The audio output unit outputs the analysis results as audio using the speaker 240 of the headset-type terminal 314. The learning unit learns new voice data using the specific processing unit 290 of the data processing device 12 and reflects it in subsequent interpretations. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, output unit, display unit, audio output unit, and learning unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the voice of the infant using the microphone 238 of the robot 414. The analysis unit analyzes the voice data using the specific processing unit 290 of the data processing device 12 to identify the infant's intentions and emotions. The output unit communicates the analysis results to the family through the speaker 240 of the robot 414. The display unit displays the analysis results on the display of the robot 414. The audio output unit outputs the analysis results by audio using the speaker 240 of the robot 414. The learning unit learns new voice data using the specific processing unit 290 of the data processing device 12 and reflects it in subsequent interpretations.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The interpretation system may further include a biometric information collection unit that collects biometric information such as the infant's body temperature and heart rate. For example, if the infant's body temperature rises, the collection unit may collect this as data suggesting possible poor health. Also, if the heart rate rises rapidly, this may be collected as data indicating excitement or stress. By collecting the infant's biometric information, more detailed changes in health status and emotions can be identified, and appropriate responses can be supported.

[0110] The interpretation system may further include a reaction collection unit that collects family members' reactions to the words and sounds uttered by the infant. For example, the collection unit may record how the family members respond to the words of the infant. The collection unit may also analyze the tone of voice and facial expressions of the family members to collect emotional reactions to the words of the infant. By collecting the reactions of the family members, the system can learn how the family members respond to the words and emotions of the infant and support more effective communication.

[0111] The interpretation system can further include a translation unit that translates the words and sounds uttered by the infant into another language. For example, if an infant says "Manma," the analysis unit translates the voice data into another language and displays "I'm hungry." The translation unit can also express the infant's emotions in another language. For example, if an infant says "Hmm," the voice data is translated as "I'm not happy." This makes it easier for family members who speak different languages ​​to understand the infant's intentions and emotions.

[0112] The interpretation system may further include a recording / playback unit that records the words and sounds uttered by the infant and plays them back later. For example, if an infant says "manma," the collection unit can record the audio data and play it back later. The recording / playback unit can also record the infant's growth process so that the family can look back on it later. This allows the infant's growth to be recorded and the family to share memories.

[0113] The interpretation system can further include a response generation unit that generates appropriate responses to the words and sounds uttered by the infant. For example, if the infant says "Manma," the analysis unit analyzes the voice data and replies, "Are you hungry?" The response generation unit can also generate responses according to the infant's emotions. For example, if the infant says "Hmm," the response unit replies, "What's wrong?" This can promote dialogue with the infant and support communication.

[0114] The interpretation system may further include a music playback unit that estimates the infant's emotions and plays music based on the estimated infant's emotions. For example, if the infant is angry, calm music can be played to soothe the infant's mood. If the infant is happy, cheerful music can be played to further enhance the infant's emotions. In this way, playing music according to the infant's emotions can support the infant's emotional regulation.

[0115] The interpretation system may further include a lighting adjustment unit that estimates the infant's emotions and adjusts the color and brightness of the lighting based on the estimated infant's emotions. For example, if the infant is angry, the lighting may be changed to a calm color to calm the infant's mood. On the other hand, if the infant is happy, the lighting may be changed to a bright color to further enhance the infant's emotions. In this way, the adjustment of the lighting according to the infant's emotions can be supported.

[0116] The interpretation system may further include a scent emitter that estimates the infant's emotions and emits a scent based on the estimated infant's emotions. For example, if the infant is angry, a scent with a relaxing effect may be emitted to soothe the infant's mood. If the infant is happy, a refreshing scent may be emitted to further enhance the infant's emotions. In this way, emitting a scent according to the infant's emotions can support the adjustment of emotions.

[0117] The interpretation system can further include a video playback unit that estimates the infant's emotions and plays videos based on the estimated infant's emotions. For example, if the infant is angry, a video with a relaxing effect can be played to calm the infant's mood. If the infant is happy, a fun video can be played to further enhance the infant's emotions. In this way, by playing videos that correspond to the infant's emotions, it is possible to support the infant's emotional regulation.

[0118] The interpretation system may further include a vibration generator that estimates the infant's emotions and generates vibrations based on the estimated infant's emotions. For example, if the infant is angry, a relaxing vibration may be generated to soothe the infant's mood. If the infant is happy, a joyful vibration may be generated to further enhance the infant's emotions. In this way, by generating vibrations according to the infant's emotions, it is possible to support the infant's emotional regulation.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects the voice of the infant. The voice of the infant includes, but is not limited to, crying, laughing, and speaking. For example, the collection unit collects the words and voices uttered by the infant using a microphone. The collection unit can also collect the voice of the infant in real time. For example, the collection unit collects the words uttered by the infant using a high-sensitivity microphone and removes noise to obtain clear voice data. Step 2: The analysis unit analyzes the voice collected by the collection unit. The analysis is performed using, for example, voice recognition technology or an emotion analysis algorithm, but is not limited to these examples. For example, the analysis unit converts the words spoken by the infant into text data using voice recognition technology. The analysis unit can also identify the infant's emotions using an emotion analysis algorithm. For example, the analysis unit estimates the infant's emotions from the voice data and outputs the result as analysis data. Step 3: The output unit notifies the family of the results of the analysis performed by the analysis unit. The output may be, for example, text output or audio output, but is not limited to these examples. For example, the output unit may send the analysis results to the family as a text message. Alternatively, the output unit may also notify the family of the analysis results as an audio message. Step 4: The display unit displays the results of the analysis performed by the analysis unit on a display. The display may be performed using a device such as a smartphone, a tablet, or a dedicated display, but is not limited to these examples. For example, the display unit may display the analysis results on a smartphone screen. The display unit may also display the analysis results on a tablet screen. Step 5: The audio output unit outputs the analysis result obtained by the analysis unit as audio. The audio output is performed using, for example, voice synthesis technology, but is not limited to this example. For example, the audio output unit outputs the analysis result as a voice message using voice synthesis technology. The audio output unit can also output the analysis result as audio through a speaker. Step 6: The learning unit learns the new speech collected by the collection unit. The learning is performed using, for example, but not limited to, a machine learning algorithm. For example, the learning unit learns new words and emotions as the child grows and reflects them in subsequent interpretations.

[0121] 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.

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

[0123] 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.

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

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

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

[0128] The 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.

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

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

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

[0132] Fig. 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.

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

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

[0135] In the 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.

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

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

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

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 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.

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

[0144] The 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.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (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).

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

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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, in order to avoid confusion and to 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects the voice of the infant; an analysis unit that analyzes the voice collected by the collection unit; an output unit that outputs the results of the analysis performed by the analysis unit; a display unit that displays the results of the analysis by the analysis unit; an audio output unit that outputs the result of the analysis by the analysis unit by audio; a learning unit that learns new voices collected by the collection unit. A system characterized by:

2. The collecting unit Collecting speech or sounds made by the infant 2. The system of claim 1.

3. The analysis unit Analyzing collected voice data to identify the child's intentions and emotions 2. The system of claim 1.

4. The output unit Communicate the analysis results to the family 2. The system of claim 1.

5. The display unit Display the analyzed results on the screen 2. The system of claim 1.

6. The audio output unit Output the analyzed results as voice 2. The system of claim 1.

7. The learning unit Learning new words and emotions as your toddler grows 2. The system of claim 1.

8. The collecting unit Estimating the infant's emotions and adjusting the timing of audio collection based on the estimated infant's emotions 2. The system of claim 1.

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