System

Abstract

An object of a system according to an embodiment is to predict a baby language and a senior language and facilitate communication.SOLUTION: A system includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and learns a pattern of baby language or geriatric language. The prediction unit predicts a baby language or a senior language based on the pattern learned by the analysis unit. The providing unit provides a result predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of being unable to understand baby talk or elderly people's language, making it difficult to communicate smoothly.

[0005] The system according to the embodiment aims to predict baby talk and elderly talk and facilitate smooth communication. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects speech data. The analysis unit analyzes the speech data collected by the collection unit and learns patterns of baby talk or elderly talk. The prediction unit predicts baby talk or elderly talk based on the patterns learned by the analysis unit. The provision unit provides the results predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict baby talk and elderly talk, facilitating communication. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A speech translation system according to an embodiment of the present invention supports people who have difficulty understanding baby talk and elderly speech. By linking a smart speaker with Bluetooth® earphones, this system extracts, records, and accumulates conversational data on baby talk and elderly speech from everyday conversations. Based on the accumulated data, the system predicts baby talk and elderly speech and provides a response. For example, the speech translation system collects everyday conversations. For example, when a baby says "manma" (eating, eating, eating), the system records the speech data. Next, the speech translation system uses AI to analyze the collected speech data and learns patterns of baby talk and elderly speech. For example, it learns that "manma" often means "rice." Next, the speech translation system predicts baby talk and elderly speech based on the accumulated data and provides an appropriate response. For example, when a baby says "manma" (eating, eating, eating), the AI ​​predicts and responds, "Does he want rice?" This prediction can be used not only in the home but also in facilities such as daycare centers, nursing homes, and nursing homes. Furthermore, by linking speech data collected from each home and facility, the speech translation system can achieve even more accurate predictions. For example, by integrating data collected at daycare centers with data collected at home, it is possible to more accurately grasp patterns of baby talk and elderly speech. This allows speech translation systems to communicate more smoothly with babies and grandparents, reducing the burden on workers. For example, it will be easier for childcare workers and caregivers to understand the speech of babies and elderly people, allowing them to respond appropriately.

[0029] A speech translation system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects speech data. The speech data includes, for example, an audio file format, a sampling rate, a bit rate, and the like, but is not limited to these examples. The collection unit collects everyday conversations using, for example, a smart speaker. The collection unit can also collect speech data using Bluetooth earphones. For example, the collection unit collects conversations at home or at work. The analysis unit analyzes the speech data collected by the collection unit and learns patterns of baby talk and elderly talk. The analysis unit analyzes the speech data using, for example, AI and learns specific phonemes, vocabulary, and pronunciation patterns. For example, the analysis unit learns that the baby talk "manma" means "rice." The prediction unit predicts baby talk and elderly talk based on the patterns learned by the analysis unit. For example, the prediction unit predicts baby talk and elderly talk based on accumulated data using AI. For example, if a baby says "manma," the prediction unit predicts, "Does he want rice?" The providing unit provides the result predicted by the predicting unit. The providing unit provides the prediction result using, for example, AI. For example, the providing unit provides the prediction result by voice through a smart speaker. The providing unit can also provide the prediction result by voice through Bluetooth earphones. This enables the speech translation system according to the embodiment to support the understanding of baby talk and elderly people's language. For example, it becomes easier for childcare workers and caregivers to understand the language of babies and elderly people, enabling them to respond appropriately.

[0030] The collection unit can collect everyday conversations using a smart speaker or Bluetooth earphones. The collection unit collects everyday conversations using, for example, a smart speaker. For example, the collection unit collects conversations at home or at work. The collection unit can also collect voice data using Bluetooth earphones. For example, the collection unit collects conversations in public places. In this way, voice data of everyday conversations can be efficiently collected by using a smart speaker and Bluetooth earphones. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data collected by the smart speaker to a generation AI and have the generation AI analyze the voice data.

[0031] The speech translation system shares voice data from each home or facility and performs data linkage via a cloud server. The cloud server includes, for example, data storage, data processing capabilities, and security functions. The speech translation system shares voice data from each home or facility via the cloud server. For example, the speech translation system uploads voice data collected at home to the cloud server and links it with voice data collected at daycare centers or nursing homes. Furthermore, by linking data via the cloud server, the speech translation system can make more accurate predictions. For example, the speech translation system integrates data collected at daycare centers with data collected at home to more accurately grasp patterns of baby talk and elderly speech. This allows for more accurate predictions by sharing data via the cloud server. Some or all of the above-described processing on the cloud server may be performed using, or without, AI. For example, the cloud server may input collected voice data into a generation AI and have the generation AI analyze the data.

[0032] When collecting voice data, the collection unit can analyze the user's past conversation history and select an appropriate collection method. For example, the collection unit can prioritize collection of words and phrases that the user has frequently used in the past. The collection unit can also concentrate collection on a specific time period based on the user's past conversation history. The collection unit can also analyze the user's past conversation history and customize the collection method for a specific situation. In this way, the analysis of the past conversation history can select the optimal collection method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past conversation history into a generation AI and have the generation AI select a collection method.

[0033] When collecting voice data, the collection unit can perform filtering based on the user's current environment and situation. For example, when the user is in a quiet environment, the collection unit collects detailed voice data. Furthermore, when the user is in a noisy environment, the collection unit can also collect voice data after removing noise. Furthermore, when the user is in a specific location, the collection unit can collect voice data appropriate for that location. In this way, by performing filtering according to the environment and situation, noise can be removed and detailed voice data can be collected. Some or all of the above-mentioned processing in 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 user's environmental data to a generation AI and have the generation AI perform filtering.

[0034] When collecting voice data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. Furthermore, if the user is using text input, the collection unit can also collect text data and integrate it with voice data. Furthermore, if the user is using gesture input, the collection unit can also collect gesture data and combine it with voice data. This allows voice data to be collected efficiently by selecting the optimal collection means depending on the input method. Some or all of the above-described processing in 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 user's input data to a generation AI and have the generation AI select a collection means.

[0035] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the audio data. For example, the analysis unit performs a detailed analysis on important audio data. The analysis unit can also perform a concise analysis on general audio data. The analysis unit can also perform a detailed analysis on audio data that is highly important in a specific situation. This allows for efficient analysis by adjusting the level of detail of the analysis according to 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 may be performed without using AI. For example, the analysis unit can input the importance of the audio data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0036] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the voice data. For example, the analysis unit applies a specific algorithm to baby talk. The analysis unit can also apply a different algorithm to elderly talk. The analysis unit can also apply a standard algorithm to general everyday conversation. In this way, by applying different analysis algorithms depending on the category of voice data, the analysis accuracy is improved. 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 category of voice data to the generation AI and have the generation AI apply the analysis algorithm.

[0037] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis method. This improves the analysis accuracy by referring to the past analysis results. 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 user's past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0038] The prediction unit can improve the accuracy of prediction by taking into account the relevance of the audio data when making predictions. The prediction unit can, for example, analyze the interrelationships of the audio data to improve the accuracy of the prediction. The prediction unit can also make predictions by taking into account specific combinations of audio data. The prediction unit can also learn the interrelationships of the audio data and optimize the prediction algorithm. This improves the accuracy of predictions by taking the interrelationships of the audio data into account. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the interrelationships of the audio data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0039] When making a prediction, the prediction unit can take into account characteristic information of the person who submitted the voice data. The prediction unit can make a prediction by taking into account, for example, the age and gender of the person who submitted the voice data. The prediction unit can also make a prediction by taking into account the person's past behavioral patterns. The prediction unit can also adjust the prediction algorithm based on the attribute information of the person who submitted the voice data. By taking into account the attribute information of the person who submitted the voice data, more accurate predictions are possible. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the attribute information of the person who submitted the voice data into the generation AI and cause the generation AI to adjust the prediction.

[0040] The prediction unit can weight the prediction based on the frequency of voice data transmission during prediction. For example, the prediction unit can weight the prediction by prioritizing voice data submitted frequently. The prediction unit can also weight the prediction by disregarding voice data submitted infrequently. The prediction unit can also adjust the prediction algorithm based on the submission frequency. By weighting the prediction based on the submission frequency, the accuracy of the prediction is improved. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the submission frequency data to a generation AI and have the generation AI perform prediction weighting.

[0041] The providing unit can adjust the level of detail of the provided information based on the priority of the prediction result when providing the information. For example, the providing unit can provide detailed information for important prediction results. The providing unit can also provide concise information for general prediction results. The providing unit can also provide detailed information for prediction results that are highly important in a specific situation. In this way, by adjusting the level of detail of the provided information according to the importance of the prediction result, information can be provided efficiently. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0042] The providing unit can apply different providing algorithms depending on the classification of the prediction result when providing the prediction result. For example, the providing unit applies a specific algorithm to baby talk. The providing unit can also apply a different algorithm to elderly talk. The providing unit can also apply a standard algorithm to general everyday conversation. In this way, by applying different providing algorithms depending on the category of the prediction result, the providing accuracy is improved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the prediction result to the generation AI and cause the generation AI to apply the providing algorithm.

[0043] The providing unit can improve the accuracy of provision by referring to the user's previous provision results when providing the information. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also learn specific patterns from the user's past provision results and improve the provision accuracy. The providing unit can also analyze the user's past provision results and optimize the provision method. This improves the provision accuracy by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into the generation AI and cause the generation AI to improve the provision accuracy.

[0044] When data is linked via a cloud server, an appropriate linking method can be selected by referring to the past data linking history of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the optimal linking method is selected by referring to the past data linking history of each household or facility. For example, the cloud server selects the optimal linking method based on the past data linking history of each household or facility. The cloud server can also learn specific patterns from the past data linking history and select the optimal linking method. The cloud server can also analyze the past data linking history of each household or facility and optimize the linking method. In this way, the optimal linking method can be selected by referring to the past data linking history. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input the past data linking history into a generation AI and have the generation AI select the linking method.

[0045] When data is linked via a cloud server, the means for data linkage can be customized based on the current status of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the means for data linkage is customized based on the current status of each household or facility. For example, the cloud server customizes the means for data linkage taking into account the current status of each household or facility. The cloud server can also select the optimal means for data linkage based on the current status. The cloud server can also analyze the current status of each household or facility and optimize the means for data linkage. This enables more appropriate data linkage by customizing the means for data linkage based on the current status. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input current status data into a generation AI and have the generation AI customize the means for data linkage.

[0046] When data is linked via the cloud server, the method of data linkage can be improved by reflecting feedback from each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the method of data linkage can be improved by reflecting feedback from each household or facility. For example, the cloud server improves the method of data linkage based on feedback from each household or facility. The cloud server can also learn specific patterns from the feedback and improve the method of data linkage. The cloud server can also analyze feedback from each household or facility and optimize the method of data linkage. In this way, the method of data linkage can be improved by reflecting feedback. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input feedback data into a generation AI and have the generation AI improve the method of data linkage.

[0047] When data is linked via a cloud server, an appropriate data linkage method can be selected taking into account the geographical location information of each home or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the optimal data linkage method is selected taking into account the geographical location information of each home or facility. For example, the cloud server selects the optimal data linkage method based on the geographical location information of each home or facility. The cloud server can also customize the data linkage method taking into account the geographical location information. The cloud server can also analyze the geographical location information of each home or facility and optimize the data linkage method. In this way, the optimal data linkage method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input geographical location information into a generation AI and have the generation AI select a data linkage method.

[0048] When data is linked via the cloud server, the SNS activity of each household or facility can be analyzed to propose a data linking method. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the SNS activity of each household or facility can be analyzed to propose a data linking method. For example, the cloud server can propose an optimal data linking method based on the SNS activity of each household or facility. The cloud server can also analyze the SNS activity and customize the data linking method. The cloud server can also optimize the data linking method based on the SNS activity of each household or facility. In this way, the SNS activity can be analyzed to propose an optimal data linking method. Some or all of the above-mentioned processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input SNS activity data into a generation AI and have the generation AI execute a data linking method proposal.

[0049] When data is linked via a cloud server, the method of data linkage can be customized by reflecting the past opinions of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the method of data linkage can be customized by reflecting the past opinions of each household or facility. For example, the cloud server customizes the data linkage method based on the past opinions of each household or facility. The cloud server can also learn specific patterns from the past opinions and customize the data linkage method. The cloud server can also analyze the past opinions of each household or facility and optimize the data linkage method. In this way, the method of data linkage can be customized by reflecting the past opinions. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input past opinion data into a generation AI and have the generation AI customize the data linkage method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit can analyze the user's activity pattern and determine the optimal collection timing. For example, if the user is active in the morning, voice data can be collected preferentially during that time period. Also, if the user is relaxing at night, detailed voice data can be collected during that time period. Furthermore, if the user tends to perform certain activities on certain days of the week, the collection timing can be adjusted to match those days. This allows for more effective collection of voice data by optimizing the collection timing based on the user's activity pattern.

[0052] The analysis unit can analyze background sounds in the audio data and identify specific environmental sounds. For example, the analysis unit can identify car sounds and bird songs in the audio data and identify the audio data collection environment based on the sounds. The analysis unit can also identify television sounds and music in the audio data and infer the user's activities based on the sounds. Furthermore, the analysis unit can identify multiple voices in the audio data and identify the participants in the conversation. This allows for more detailed environmental information to be obtained by analyzing the background sounds in the audio data.

[0053] The cloud server can analyze voice data from each home or facility in real time to support emergency response. For example, the cloud server can detect an emergency in the voice data and immediately issue an alert. The cloud server can also detect abnormal patterns in the voice data and notify relevant parties. Furthermore, the cloud server can detect specific keywords in the voice data and suggest appropriate responses. This allows for rapid emergency response through real-time analysis.

[0054] The analysis unit can automatically identify the language of the audio data and apply an appropriate analysis algorithm. For example, the analysis unit can apply an analysis algorithm specifically for English to English audio data. The analysis unit can also apply an analysis algorithm specifically for Japanese to Japanese audio data. Furthermore, the analysis unit can also apply analysis algorithms corresponding to each language to audio data containing a mixture of multiple languages. This improves analysis accuracy by performing an appropriate analysis depending on the language of the audio data.

[0055] The providing unit can analyze the user's past behavioral history and determine the optimal timing for providing information. For example, if the user has previously preferred to receive information during a specific time period, the information can be provided during that time period. Also, if the user has preferred to receive information on a specific day of the week, the information can be provided according to that day of the week. Furthermore, if the user has preferred to receive information in a specific situation, the information can be provided according to that situation. This makes it possible to provide information more effectively by determining the optimal timing for providing information based on the user's past behavioral history.

[0056] The cloud server can analyze the voice data from each home or facility and link data taking into account the characteristics of each region. For example, the cloud server can learn the dialects and expressions commonly used in a particular region and link data based on that. The cloud server can also adjust the data linking method to take into account the culture and customs of each region. Furthermore, by linking data that reflects the characteristics of each region, the cloud server can make more accurate predictions and analyses. This allows for more appropriate data linking by linking data that takes into account the characteristics of each region.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects voice data. The voice data includes, but is not limited to, for example, an audio file format, a sampling rate, and a bit rate. For example, the collection unit collects everyday conversations using a smart speaker. The collection unit can also collect voice data using Bluetooth earphones. For example, the collection unit collects conversations at home or at work. Step 2: The analysis unit analyzes the speech data collected by the collection unit and learns patterns of baby talk and elderly talk. For example, the analysis unit uses AI to analyze the speech data and learns specific phonemes, vocabulary, and pronunciation patterns. For example, the analysis unit learns that the baby talk "manma" means "rice." Step 3: The prediction unit predicts baby talk or elderly slang based on the patterns learned by the analysis unit. The prediction unit predicts baby talk or elderly slang based on data accumulated using AI, for example. For example, if a baby says "manma" (eaten whole), the prediction unit predicts "Does he want food?" Step 4: The providing unit provides the result predicted by the predicting unit. The providing unit provides the prediction result using, for example, AI. For example, the providing unit provides the prediction result by voice through a smart speaker. The providing unit can also provide the prediction result by voice through Bluetooth earphones.

[0059] (Example 2) A speech translation system according to an embodiment of the present invention supports people who struggle to understand baby talk and elderly speech. By linking a smart speaker and Bluetooth earphones, this system extracts, records, and accumulates conversational data on baby talk and elderly speech from everyday conversations. Based on the accumulated data, the system predicts baby talk and elderly speech and provides a response. For example, the speech translation system collects everyday conversations. For example, when a baby says "manma" (eating well), it records the speech data. Next, the speech translation system uses AI to analyze the collected speech data and learns patterns of baby talk and elderly speech. For example, it learns that "manma" often means "rice." Next, the speech translation system predicts baby talk and elderly speech based on the accumulated data and provides an appropriate response. For example, when a baby says "manma" (eating well), the AI ​​predicts and responds, "Does he want rice?" This prediction can be used not only in the home but also in facilities such as daycare centers, nursing homes, and nursing homes. Furthermore, by linking speech data collected from each home and facility, the speech translation system can achieve even more accurate predictions. For example, by integrating data collected at daycare centers with data collected at home, it is possible to more accurately grasp patterns of baby talk and elderly speech. This allows speech translation systems to communicate more smoothly with babies and grandparents, reducing the burden on workers. For example, it will be easier for childcare workers and caregivers to understand the speech of babies and elderly people, allowing them to respond appropriately.

[0060] A speech translation system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, and a provision unit. The collection unit collects speech data. The speech data includes, for example, an audio file format, a sampling rate, a bit rate, and the like, but is not limited to these examples. The collection unit collects everyday conversations using, for example, a smart speaker. The collection unit can also collect speech data using Bluetooth earphones. For example, the collection unit collects conversations at home or at work. The analysis unit analyzes the speech data collected by the collection unit and learns patterns of baby talk and elderly talk. The analysis unit analyzes the speech data using, for example, AI and learns specific phonemes, vocabulary, and pronunciation patterns. For example, the analysis unit learns that the baby talk "manma" means "rice." The prediction unit predicts baby talk and elderly talk based on the patterns learned by the analysis unit. For example, the prediction unit predicts baby talk and elderly talk based on accumulated data using AI. For example, if a baby says "manma," the prediction unit predicts, "Does he want rice?" The providing unit provides the result predicted by the predicting unit. The providing unit provides the prediction result using, for example, AI. For example, the providing unit provides the prediction result by voice through a smart speaker. The providing unit can also provide the prediction result by voice through Bluetooth earphones. This enables the speech translation system according to the embodiment to support the understanding of baby talk and elderly people's language. For example, it becomes easier for childcare workers and caregivers to understand the language of babies and elderly people, enabling them to respond appropriately.

[0061] The collection unit can collect everyday conversations using a smart speaker or Bluetooth earphones. The collection unit collects everyday conversations using, for example, a smart speaker. For example, the collection unit collects conversations at home or at work. The collection unit can also collect voice data using Bluetooth earphones. For example, the collection unit collects conversations in public places. In this way, voice data of everyday conversations can be efficiently collected by using a smart speaker and Bluetooth earphones. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input voice data collected by the smart speaker to a generation AI and have the generation AI analyze the voice data.

[0062] The speech translation system shares voice data from each home or facility and performs data linkage via a cloud server. The cloud server includes, for example, data storage, data processing capabilities, and security functions. The speech translation system shares voice data from each home or facility via the cloud server. For example, the speech translation system uploads voice data collected at home to the cloud server and links it with voice data collected at daycare centers or nursing homes. Furthermore, by linking data via the cloud server, the speech translation system can make more accurate predictions. For example, the speech translation system integrates data collected at daycare centers with data collected at home to more accurately grasp patterns of baby talk and elderly speech. This allows for more accurate predictions by sharing data via the cloud server. Some or all of the above-described processing on the cloud server may be performed using, or without, AI. For example, the cloud server may input collected voice data into a generation AI and have the generation AI analyze the data.

[0063] The collection unit can estimate the user's emotions and adjust the timing of voice data collection based on the estimated user emotions. For example, when the user is relaxed, the collection unit collects voice data frequently and accumulates detailed data. Furthermore, when the user is feeling stressed, the collection unit can reduce the frequency of voice data collection to reduce the user's burden. Furthermore, when the user is excited, the collection unit can temporarily stop collecting voice data and resume collection after the user's emotions have calmed down. This allows for more appropriate voice data to be collected by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0064] When collecting voice data, the collection unit can analyze the user's past conversation history and select an appropriate collection method. For example, the collection unit can prioritize collection of words and phrases that the user has frequently used in the past. The collection unit can also concentrate collection on a specific time period based on the user's past conversation history. The collection unit can also analyze the user's past conversation history and customize the collection method for a specific situation. In this way, the analysis of the past conversation history can select the optimal collection method. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past conversation history into a generation AI and have the generation AI select a collection method.

[0065] When collecting voice data, the collection unit can perform filtering based on the user's current environment and situation. For example, when the user is in a quiet environment, the collection unit collects detailed voice data. Furthermore, when the user is in a noisy environment, the collection unit can also collect voice data after removing noise. Furthermore, when the user is in a specific location, the collection unit can collect voice data appropriate for that location. In this way, by performing filtering according to the environment and situation, noise can be removed and detailed voice data can be collected. Some or all of the above-mentioned processing in 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 user's environmental data to a generation AI and have the generation AI perform filtering.

[0066] When collecting voice data, the collection unit can select an appropriate collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collection of voice data. Furthermore, if the user is using text input, the collection unit can also collect text data and integrate it with voice data. Furthermore, if the user is using gesture input, the collection unit can also collect gesture data and combine it with voice data. This allows voice data to be collected efficiently by selecting the optimal collection means depending on the input method. Some or all of the above-described processing in 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 user's input data to a generation AI and have the generation AI select a collection means.

[0067] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is stressed. The analysis unit can also provide visually easy-to-understand analysis results when the user is excited. This allows for more appropriate analysis results to be provided by adjusting the way the analysis is presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the way the analysis is presented.

[0068] During analysis, the analysis unit can adjust the level of detail of the analysis based on the priority of the audio data. For example, the analysis unit performs a detailed analysis on important audio data. The analysis unit can also perform a concise analysis on general audio data. The analysis unit can also perform a detailed analysis on audio data that is highly important in a specific situation. This allows for efficient analysis by adjusting the level of detail of the analysis according to 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 may be performed without using AI. For example, the analysis unit can input the importance of the audio data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0069] During analysis, the analysis unit can apply different analysis algorithms depending on the classification of the voice data. For example, the analysis unit applies a specific algorithm to baby talk. The analysis unit can also apply a different algorithm to elderly talk. The analysis unit can also apply a standard algorithm to general everyday conversation. In this way, by applying different analysis algorithms depending on the category of voice data, the analysis accuracy is improved. 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 category of voice data to the generation AI and have the generation AI apply the analysis algorithm.

[0070] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's previous analysis results. The analysis unit, for example, adjusts the analysis algorithm based on the user's past analysis results. The analysis unit can also learn specific patterns from the user's past analysis results and improve the analysis accuracy. The analysis unit can also analyze the user's past analysis results and optimize the analysis method. This improves the analysis accuracy by referring to the past analysis results. 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 user's past analysis results into the generation AI and have the generation AI improve the analysis accuracy.

[0071] The prediction unit can estimate the user's emotions and adjust the prediction criteria based on the estimated user emotions. For example, the prediction unit can make a detailed prediction when the user is relaxed. The prediction unit can also make a concise prediction when the user is stressed. The prediction unit can also make a visually easy-to-understand prediction when the user is excited. By adjusting the prediction criteria according to the user's emotions, more appropriate prediction results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the prediction unit can be performed using, for example, an AI, or without an AI. For example, the prediction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the prediction criteria.

[0072] The prediction unit can improve the accuracy of prediction by taking into account the relevance of the audio data when making predictions. The prediction unit can, for example, analyze the interrelationships of the audio data to improve the accuracy of the prediction. The prediction unit can also make predictions by taking into account specific combinations of audio data. The prediction unit can also learn the interrelationships of the audio data and optimize the prediction algorithm. This improves the accuracy of predictions by taking the interrelationships of the audio data into account. Some or all of the above-mentioned processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the interrelationships of the audio data into the generation AI and cause the generation AI to improve the accuracy of the prediction.

[0073] When making a prediction, the prediction unit can take into account characteristic information of the person who submitted the voice data. The prediction unit can make a prediction by taking into account, for example, the age and gender of the person who submitted the voice data. The prediction unit can also make a prediction by taking into account the person's past behavioral patterns. The prediction unit can also adjust the prediction algorithm based on the attribute information of the person who submitted the voice data. By taking into account the attribute information of the person who submitted the voice data, more accurate predictions are possible. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the attribute information of the person who submitted the voice data into the generation AI and cause the generation AI to adjust the prediction.

[0074] The prediction unit can weight the prediction based on the frequency of voice data transmission during prediction. For example, the prediction unit can weight the prediction by prioritizing voice data submitted frequently. The prediction unit can also weight the prediction by disregarding voice data submitted infrequently. The prediction unit can also adjust the prediction algorithm based on the submission frequency. By weighting the prediction based on the submission frequency, the accuracy of the prediction is improved. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit can input the submission frequency data to a generation AI and have the generation AI perform prediction weighting.

[0075] The providing unit can estimate the user's emotions and adjust the way information is presented based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide detailed information. When the user is stressed, the providing unit can also provide concise information. When the user is excited, the providing unit can also provide visually easy-to-understand information. This allows for adjusting the way information is presented according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way information is presented.

[0076] The providing unit can adjust the level of detail of the provided information based on the priority of the prediction result when providing the information. For example, the providing unit can provide detailed information for important prediction results. The providing unit can also provide concise information for general prediction results. The providing unit can also provide detailed information for prediction results that are highly important in a specific situation. In this way, by adjusting the level of detail of the provided information according to the importance of the prediction result, information can be provided efficiently. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the importance of the prediction result to the generating AI and cause the generating AI to adjust the level of detail of the provided information.

[0077] The providing unit can apply different providing algorithms depending on the classification of the prediction result when providing the prediction result. For example, the providing unit applies a specific algorithm to baby talk. The providing unit can also apply a different algorithm to elderly talk. The providing unit can also apply a standard algorithm to general everyday conversation. In this way, by applying different providing algorithms depending on the category of the prediction result, the providing accuracy is improved. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the category of the prediction result to the generation AI and cause the generation AI to apply the providing algorithm.

[0078] The providing unit can improve the accuracy of provision by referring to the user's previous provision results when providing the information. The providing unit, for example, adjusts the provision algorithm based on the user's past provision results. The providing unit can also learn specific patterns from the user's past provision results and improve the provision accuracy. The providing unit can also analyze the user's past provision results and optimize the provision method. This improves the provision accuracy by referring to the past provision results. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's past provision results into the generation AI and cause the generation AI to improve the provision accuracy.

[0079] When data is linked via a cloud server, the user's emotions can be estimated and the data linking method can be adjusted based on the estimated user's emotions. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the user's emotions can be estimated and the data linking method can be adjusted based on the estimated user's emotions. For example, if the user is relaxed, detailed data linking can be performed. If the user is stressed, simple data linking can be performed. If the user is excited, visually easy-to-understand data linking can be performed. This enables more appropriate data linking by adjusting the data linking method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the cloud server can be performed using AI, for example, or without AI. For example, the cloud server can input user's emotion data into the generation AI and have the generation AI adjust the data linking method.

[0080] When data is linked via a cloud server, an appropriate linking method can be selected by referring to the past data linking history of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the optimal linking method is selected by referring to the past data linking history of each household or facility. For example, the cloud server selects the optimal linking method based on the past data linking history of each household or facility. The cloud server can also learn specific patterns from the past data linking history and select the optimal linking method. The cloud server can also analyze the past data linking history of each household or facility and optimize the linking method. In this way, the optimal linking method can be selected by referring to the past data linking history. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input the past data linking history into a generation AI and have the generation AI select the linking method.

[0081] When data is linked via a cloud server, the means for data linkage can be customized based on the current status of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the means for data linkage is customized based on the current status of each household or facility. For example, the cloud server customizes the means for data linkage taking into account the current status of each household or facility. The cloud server can also select the optimal means for data linkage based on the current status. The cloud server can also analyze the current status of each household or facility and optimize the means for data linkage. This enables more appropriate data linkage by customizing the means for data linkage based on the current status. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input current status data into a generation AI and have the generation AI customize the means for data linkage.

[0082] When data is linked via the cloud server, the method of data linkage can be improved by reflecting feedback from each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the method of data linkage can be improved by reflecting feedback from each household or facility. For example, the cloud server improves the method of data linkage based on feedback from each household or facility. The cloud server can also learn specific patterns from the feedback and improve the method of data linkage. The cloud server can also analyze feedback from each household or facility and optimize the method of data linkage. In this way, the method of data linkage can be improved by reflecting feedback. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input feedback data into a generation AI and have the generation AI improve the method of data linkage.

[0083] When data is linked via a cloud server, a user's emotions can be estimated and the priority of data linkage can be determined based on the estimated user's emotions. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, a user's emotions can be estimated and the priority of data linkage can be determined based on the estimated user's emotions. For example, if a user is relaxed, detailed data linkage can be prioritized. Also, if a user is stressed, concise data linkage can be prioritized. Also, if a user is excited, visually easy-to-understand data linkage can be prioritized. This enables more appropriate data linkage by determining the priority of data linkage according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the cloud server can be performed using, for example, AI, or without AI. For example, the cloud server can input user emotional data into the generation AI and have the generation AI determine the priority of data linkage.

[0084] When data is linked via a cloud server, an appropriate data linkage method can be selected taking into account the geographical location information of each home or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the optimal data linkage method is selected taking into account the geographical location information of each home or facility. For example, the cloud server selects the optimal data linkage method based on the geographical location information of each home or facility. The cloud server can also customize the data linkage method taking into account the geographical location information. The cloud server can also analyze the geographical location information of each home or facility and optimize the data linkage method. In this way, the optimal data linkage method can be selected by taking into account the geographical location information. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input geographical location information into a generation AI and have the generation AI select a data linkage method.

[0085] When data is linked via the cloud server, the SNS activity of each household or facility can be analyzed to propose a data linking method. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the SNS activity of each household or facility can be analyzed to propose a data linking method. For example, the cloud server can propose an optimal data linking method based on the SNS activity of each household or facility. The cloud server can also analyze the SNS activity and customize the data linking method. The cloud server can also optimize the data linking method based on the SNS activity of each household or facility. In this way, the SNS activity can be analyzed to propose an optimal data linking method. Some or all of the above-mentioned processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input SNS activity data into a generation AI and have the generation AI execute a data linking method proposal.

[0086] When data is linked via a cloud server, the method of data linkage can be customized by reflecting the past opinions of each household or facility. The cloud server includes, for example, data storage, data processing capabilities, and security functions. When data is linked via the cloud server, the method of data linkage can be customized by reflecting the past opinions of each household or facility. For example, the cloud server customizes the data linkage method based on the past opinions of each household or facility. The cloud server can also learn specific patterns from the past opinions and customize the data linkage method. The cloud server can also analyze the past opinions of each household or facility and optimize the data linkage method. In this way, the method of data linkage can be customized by reflecting the past opinions. Some or all of the above-described processing in the cloud server may be performed using, for example, AI, or may be performed without using AI. For example, the cloud server can input past opinion data into a generation AI and have the generation AI customize the data linkage method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and provision 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 can collect voice data using the camera 42 or microphone 38B of the smart device 14. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and learns patterns of baby talk or elderly talk. For example, the prediction unit can be realized by the specific processing unit 290 of the data processing device 12, and predicts baby talk or elderly talk based on the patterns learned by the analysis unit. For example, the provision unit can provide the prediction result by voice using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, prediction unit, and provision 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 can collect voice data using the camera 42 or microphone 238 of the smart glasses 214. For example, the analysis unit can be realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and learns patterns of baby talk or elderly talk. For example, the prediction unit can be realized by the specific processing unit 290 of the data processing device 12, and predicts baby talk or elderly talk based on the patterns learned by the analysis unit. For example, the provision unit can provide the prediction result by voice using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect voice data using the camera 42 or microphone 238 of the headset type terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and learns patterns of baby talk and elderly talk. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts baby talk and elderly talk based on the patterns learned by the analysis unit. For example, the provision unit can provide the prediction result by voice using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, prediction unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect voice data using the camera 42 or microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected voice data and learns patterns of baby talk or elderly talk. For example, the prediction unit is realized by the specific processing unit 290 of the data processing device 12, and predicts baby talk or elderly talk based on the patterns learned by the analysis unit. For example, the provision unit can provide the prediction result by voice using the control unit 46A of the robot 414.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] The collection unit can analyze the user's activity pattern and determine the optimal collection timing. For example, if the user is active in the morning, voice data can be collected preferentially during that time period. Also, if the user is relaxing at night, detailed voice data can be collected during that time period. Furthermore, if the user tends to perform certain activities on certain days of the week, the collection timing can be adjusted to match those days. This allows for more effective collection of voice data by optimizing the collection timing based on the user's activity pattern.

[0089] The analysis unit can analyze background sounds in the audio data and identify specific environmental sounds. For example, the analysis unit can identify car sounds and bird songs in the audio data and identify the audio data collection environment based on the sounds. The analysis unit can also identify television sounds and music in the audio data and infer the user's activities based on the sounds. Furthermore, the analysis unit can identify multiple voices in the audio data and identify the participants in the conversation. This allows for more detailed environmental information to be obtained by analyzing the background sounds in the audio data.

[0090] The prediction unit can estimate the user's emotion and adjust the reliability of the prediction result based on the estimated emotion. For example, if the user is relaxed, the reliability of the prediction result can be set high. Also, if the user is stressed, the reliability of the prediction result can be set low. Furthermore, if the user is excited, the reliability of the prediction result can be set to medium. In this way, by adjusting the reliability of the prediction result according to the user's emotion, more appropriate prediction results can be provided.

[0091] The providing unit can estimate the user's emotion and select the format of information to be provided based on the estimated emotion. For example, if the user is relaxed, detailed text information can be provided. If the user is stressed, concise audio information can be provided. Furthermore, if the user is excited, easy-to-understand graphical information can be provided. This makes it possible to provide information more effectively by selecting the format of information according to the user's emotion.

[0092] The cloud server can analyze voice data from each home or facility in real time to support emergency response. For example, the cloud server can detect an emergency in the voice data and immediately issue an alert. The cloud server can also detect abnormal patterns in the voice data and notify relevant parties. Furthermore, the cloud server can detect specific keywords in the voice data and suggest appropriate responses. This allows for rapid emergency response through real-time analysis.

[0093] The collection unit can estimate the user's emotions and select the type of voice data to collect based on the estimated emotions. For example, if the user is relaxed, voice data of everyday conversations can be collected preferentially. If the user is stressed, voice data with many emotional expressions can be collected. Furthermore, if the user is excited, voice data related to a specific topic can be collected. This allows for more appropriate data collection by selecting the type of voice data to collect depending on the user's emotions.

[0094] The analysis unit can automatically identify the language of the audio data and apply an appropriate analysis algorithm. For example, the analysis unit can apply an analysis algorithm specifically for English to English audio data. The analysis unit can also apply an analysis algorithm specifically for Japanese to Japanese audio data. Furthermore, the analysis unit can also apply analysis algorithms corresponding to each language to audio data containing a mixture of multiple languages. This improves analysis accuracy by performing an appropriate analysis depending on the language of the audio data.

[0095] The prediction unit can estimate the user's emotions and adjust the presentation method of the prediction results based on the estimated emotions. For example, if the user is relaxed, detailed prediction results can be presented in text. If the user is stressed, concise prediction results can be presented in audio. Furthermore, if the user is excited, easy-to-understand graphical prediction results can be presented. This makes it possible to provide information more effectively by adjusting the presentation method of the prediction results according to the user's emotions.

[0096] The providing unit can analyze the user's past behavioral history and determine the optimal timing for providing information. For example, if the user has previously preferred to receive information during a specific time period, the information can be provided during that time period. Also, if the user has preferred to receive information on a specific day of the week, the information can be provided according to that day of the week. Furthermore, if the user has preferred to receive information in a specific situation, the information can be provided according to that situation. This makes it possible to provide information more effectively by determining the optimal timing for providing information based on the user's past behavioral history.

[0097] The cloud server can analyze the voice data from each home or facility and link data taking into account the characteristics of each region. For example, the cloud server can learn the dialects and expressions commonly used in a particular region and link data based on that. The cloud server can also adjust the data linking method to take into account the culture and customs of each region. Furthermore, by linking data that reflects the characteristics of each region, the cloud server can make more accurate predictions and analyses. This allows for more appropriate data linking by linking data that takes into account the characteristics of each region.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The collection unit collects voice data. The voice data includes, but is not limited to, for example, an audio file format, a sampling rate, and a bit rate. For example, the collection unit collects everyday conversations using a smart speaker. The collection unit can also collect voice data using Bluetooth earphones. For example, the collection unit collects conversations at home or at work. Step 2: The analysis unit analyzes the speech data collected by the collection unit and learns patterns of baby talk and elderly talk. For example, the analysis unit uses AI to analyze the speech data and learns specific phonemes, vocabulary, and pronunciation patterns. For example, the analysis unit learns that the baby talk "manma" means "rice." Step 3: The prediction unit predicts baby talk or elderly slang based on the patterns learned by the analysis unit. The prediction unit predicts baby talk or elderly slang based on data accumulated using AI, for example. For example, if a baby says "manma" (eaten whole), the prediction unit predicts "Does he want food?" Step 4: The providing unit provides the result predicted by the predicting unit. The providing unit provides the prediction result using, for example, AI. For example, the providing unit provides the prediction result by voice through a smart speaker. The providing unit can also provide the prediction result by voice through Bluetooth earphones.

[0100] 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.

[0101] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] 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.

[0171] [Explanation of symbols]

[0172] 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 voice data; an analysis unit that analyzes the speech data collected by the collection unit and learns patterns of baby talk or elderly talk; a prediction unit that predicts baby talk or elderly talk based on the patterns learned by the analysis unit; a providing unit that provides the result predicted by the prediction unit. A system characterized by:

2. The collecting unit Use a smart speaker or Bluetooth earphones to collect everyday conversations 2. The system of claim 1.

3. Voice data from each home or facility is shared via a cloud server, and data is linked.

2. The system of claim 1.

4. The collecting unit The user's emotions are estimated, and the timing of collecting voice data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

5. The collecting unit When collecting voice data, analyze the user's past conversation history and select the appropriate collection method.

2. The system of claim 1.

6. The collecting unit Filtering audio data as it is collected based on the user's current environment or situation 2. The system of claim 1.

7. The collecting unit When collecting voice data, select the appropriate collection method depending on the user's input method.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the presentation of analysis based on the estimated user emotions 2. The system of claim 1.

9. The analysis unit During analysis, adjust the level of analysis detail based on the priority of the audio data 2. The system of claim 1.

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