System

Abstract

To provide a system according to an embodiment which understands feelings or emotions on the basis of vital information and generates proper sentences or languages to produce a speech.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a speech unit. The collection unit collects vital information. The analysis unit analyzes information collected by the collection unit and understands emotions or feelings. The generation unit generates languages or sentences on the basis of the result of analysis obtained by the analysis unit. The speech unit produces speeches from the languages or sentences generated by the generation 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem of not being able to adequately understand emotions and thoughts based on vital signs and generate and speak appropriate words or sentences.

[0005] The system according to the embodiment aims to understand emotions and thoughts based on vital information, and generate and speak appropriate words and sentences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an utterance unit. The collection unit collects vital information. The analysis unit analyzes the information collected by the collection unit to understand emotions or thoughts. The generation unit generates words or sentences based on the analysis results obtained by the analysis unit. The utterance unit utters the words or sentences generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can understand emotions and thoughts based on vital signs, and generate and speak appropriate words and sentences. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The communication system according to the embodiment of the present invention is a system that collects vital information, understands the person's feelings and thoughts, generates words and sentences using a generation AI, and has the person speak using a voice generation AI. This system analyzes the collected vital information and generates words and sentences using a generation AI. Furthermore, communication can be achieved by having the person speak using a voice generation AI. The expected users are ALS patients, intubated people who cannot speak, and people with different native languages, and it is expected that communication between them will expand. For example, an ALS patient wears an EEG sensor and has his / her feelings and thoughts analyzed. The generation AI generates words such as "I want to drink water," and the voice generation AI speaks the words. This allows the patient to communicate his / her intentions to others. In addition, when people with different native languages ​​communicate with each other, the generation AI generates appropriate words and sentences and the voice generation AI speaks them, making it possible to communicate beyond language barriers. This allows the communication system to convey the user's feelings and thoughts to others.

[0029] The communication system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an utterance unit. The collection unit collects vital information. The vital information includes, for example, heart rate, brain waves, body temperature, and the like, but is not limited to such examples. The collection unit collects the vital information using, for example, an brain wave sensor or a heartbeat sensor. The brain wave sensor may be, for example, an EEG sensor or an fNIRS sensor. The heartbeat sensor may be, for example, a photoelectric heartbeat sensor or an electrical heartbeat sensor. The analysis unit uses a generation AI to analyze the vital information collected by the collection unit and understand emotions and thoughts. The generation AI may be, for example, a deep learning model or a natural language processing model. The generation unit uses the generation AI to generate words or sentences based on the analysis results obtained by the analysis unit. The generation AI may be, for example, a text generation AI (for example, LLM) or a multimodal generation AI. The utterance unit uses a voice generation AI to utter the words or sentences generated by the generation unit. The voice generation AI uses, for example, a voice synthesis model, a text-to-speech conversion technology, etc. This allows the communication system according to the embodiment to convey the user's feelings and thoughts to others.

[0030] The collecting unit collects vital information. Examples of vital information include, but are not limited to, heart rate, brain waves, and body temperature. The collecting unit collects vital information using, for example, an brain wave sensor or a heart rate sensor. Examples of the brain wave sensor include an EEG sensor and an fNIRS sensor. The EEG sensor measures electrical activity of the brain by being attached to the scalp, and acquires brain wave data in real time. The fNIRS sensor measures changes in blood flow in the brain using near-infrared rays, and grasps the activity state of the brain. Examples of the heart rate sensor include a photoelectric heart rate sensor and an electric heart rate sensor. The photoelectric heart rate sensor detects the heart rate by irradiating light to the skin and measuring the reflected light. The electric heart rate sensor detects the heart rate by attaching electrodes to the skin and measuring the electrical activity of the heart. These sensors are incorporated into wearable devices and medical equipment, and can continuously monitor the vital information of the user. The collected vital information is transmitted to a central database using wireless communication technology, and is provided to the analysis unit in real time. This allows the collection unit to accurately grasp the user's health condition and emotional state and provide the data necessary for the next analysis step. Furthermore, the collection unit can integrate data from multiple sensors and perform noise removal and outlier filtering to improve the accuracy and reliability of the data. This allows the collection unit to provide more accurate vital information and improve the performance of the entire system.

[0031] The analysis unit uses the generation AI to analyze the vital information collected by the collection unit and understand the emotions and thoughts. For example, a deep learning model or a natural language processing model is used as the generation AI. Specifically, the deep learning model classifies the user's emotional state using the collected brainwave data and heart rate data as input. For example, the brainwave data is analyzed for alpha wave and beta wave patterns to identify the relaxed state and the focused state. The heart rate data is analyzed for heart rate variability to evaluate the stress level and excited state. Based on these analysis results, the natural language processing model expresses the user's emotions and thoughts in text format. For example, it outputs a specific emotional state such as "the user is currently relaxed." Furthermore, the analysis unit can also analyze changes and trends in emotions by utilizing past data and user history information. This makes it possible to grasp the user's long-term emotional patterns and provide more accurate analysis results. The analysis unit can also use an anomaly detection algorithm to detect unusual emotional states and abnormal vital data and issue an early warning. This enables the analysis unit to handle not only real-time emotion analysis, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0032] The generation unit uses the generation AI to generate words and sentences based on the analysis results obtained by the analysis unit. For example, a text generation AI (e.g., LLM) or a multimodal generation AI is used as the generation AI. The text generation AI generates natural words and sentences using the emotional state and thoughts provided by the analysis unit as input. For example, it generates a specific sentence such as "The user is currently relaxed and feeling calm." The multimodal generation AI can integrate not only text but also other modalities such as images and voice to generate richer expressions. For example, it can generate images and voice messages according to the user's emotional state to convey emotions through sight and hearing. The generation unit outputs the generated words and sentences in an appropriate format and provides them to the speech unit. In addition, the generation unit can collect user feedback and continuously improve the model of the generation AI. For example, the accuracy of the generation AI can be improved by the user evaluating the generated sentences as "accurate" or "inaccurate." The generation unit can also generate customized sentences according to specific situations and contexts. This enables the generation unit to accurately and naturally express the user's feelings and thoughts, and provide information for communicating them to others.

[0033] The speech unit uses the speech generation AI to recite the words and sentences generated by the generation unit. For example, speech synthesis models and text-to-speech technologies are used for the speech generation AI. The speech synthesis model uses the text provided by the generation unit as input to generate natural speech. For example, it generates speech with a tone and intonation according to the user's emotional state, and can convey emotions more effectively. When converting text into speech, the text-to-speech technology adjusts the pronunciation and accent to generate speech that is easy to hear. The speech unit plays the generated speech through a speaker or headset to convey the user's emotions and thoughts to others. In addition, the speech unit can collect user feedback and continuously improve the model of the speech generation AI. For example, the user can evaluate the generated speech as "easy to hear" or "difficult to hear," thereby improving the accuracy of the speech generation AI. The speech unit can also generate customized speech according to a specific situation or context. This allows the speech unit to accurately and naturally express the user's emotions and thoughts in speech and provide information to convey them to others.

[0034] The collecting unit may include an electroencephalogram sensor or a heartbeat sensor. The electroencephalogram sensor may include, for example, an EEG sensor or an fNIRS sensor. The EEG sensor is a sensor that electrically measures an electroencephalogram, and the fNIRS sensor is a sensor that uses near-infrared rays to measure cerebral blood flow. The heartbeat sensor may include, for example, a photoelectric heartbeat sensor or an electrical heartbeat sensor. The photoelectric heartbeat sensor is a sensor that uses light to measure blood flow, and the electrical heartbeat sensor is a sensor that electrically measures heartbeat. This allows the collecting unit to collect more accurate vital information by using the electroencephalogram sensor or the heartbeat sensor. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI.

[0035] The analysis unit can analyze emotions and thoughts using generative AI. Generative AI includes, for example, deep learning models and natural language processing models. Deep learning models are models that learn large amounts of data and have advanced analytical capabilities. Natural language processing models are models that analyze text data and understand meaning. As a result, the analysis unit can improve the accuracy of analyzing emotions and thoughts by using generative AI. Some or all of the above-mentioned processing in the analysis unit is performed using generative AI.

[0036] The generation unit can generate words and sentences using a generation AI. Examples of generation AI include text generation AI (e.g., LLM) and multimodal generation AI. Text generation AI is a model that learns large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI is a model that can handle multiple modalities such as images and audio, as well as text. As a result, the generation unit can use the generation AI to improve the accuracy of generating words and sentences. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0037] The speech unit can use a voice generation AI to speak words or sentences. Voice generation AI includes, for example, a voice synthesis model and text-to-speech conversion technology. A voice synthesis model is a model that converts text data into voice, and a text-to-speech conversion technology is a technology that converts text data into voice. As a result, the speech unit can improve the accuracy of speech by using the voice generation AI. Some or all of the above-mentioned processing in the speech unit is performed using the voice generation AI.

[0038] The collecting unit can collect brain waves using an EEG sensor. The EEG sensor includes, for example, an EEG sensor and an fNIRS sensor. The EEG sensor is a sensor that electrically measures brain waves, and the fNIRS sensor is a sensor that measures cerebral blood flow using near-infrared rays. This enables the collecting unit to collect brain waves by using the EEG sensor. A part or all of the above-mentioned processing in the collecting unit may be performed using AI, or may be performed without using AI.

[0039] The collection unit can collect heartbeats using a heartbeat sensor. Heartbeat sensors include, for example, photoelectric heartbeat sensors and electrical heartbeat sensors. A photoelectric heartbeat sensor is a sensor that measures blood flow using light, and an electrical heartbeat sensor is a sensor that measures heartbeats electrically. This allows the collection unit to collect heartbeats by using the heartbeat sensor. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0040] The collection unit can analyze the user's past vital information and select an appropriate collection method. The collection unit, for example, selects the most stable collection timing from the user's past vital information. The collection unit can also concentrate collection in a specific time period based on the user's past vital information. Furthermore, the collection unit can analyze the user's past vital information and determine the optimal sensor placement. In this way, the optimal collection method can be selected by analyzing the past vital information. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0041] The collection unit can filter the vital information based on the user's current health condition and activity level when collecting the vital information. For example, when the user is exercising, the collection unit preferentially collects vital information related to the exercise. In addition, when the user is resting, the collection unit can also collect vital information related to the relaxed state. Furthermore, when the user is sick, the collection unit can also collect detailed vital information related to the health condition. This makes it possible to collect more relevant vital information by filtering based on the health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0042] When collecting vital information, the collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the user. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation and respiratory rate. In addition, when the user is in an urban area, the collection unit can also prioritize collecting stress-related vital information. Furthermore, when the user is in a natural environment, the collection unit can also prioritize collecting relaxation-related vital information. In this way, highly relevant vital information can be collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0043] The collection unit can analyze the user's social media activity and collect related information when collecting vital information. For example, when the user feels stressed on social media, the collection unit preferentially collects stress-related vital information. In addition, when the user is relaxing on social media, the collection unit can also preferentially collect relaxation-related vital information. Furthermore, when the user is excited on social media, the collection unit can also preferentially collect excitement-related vital information. In this way, by analyzing social media activity, related vital information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital information during analysis. For example, the analysis unit performs a detailed analysis on vital information with high importance. The analysis unit can also perform a simplified analysis on vital information with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on vital information with medium importance. This allows for efficient analysis by adjusting the level of detail based on the importance. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0045] The analysis unit can apply different analysis algorithms depending on the category of vital information during analysis. For example, the analysis unit applies a heart rate analysis algorithm to vital information related to heart rate. The analysis unit can also apply an electroencephalogram analysis algorithm to vital information related to electroencephalograms. The analysis unit can also apply a respiration analysis algorithm to vital information related to respiration rate. Thereby, by applying an analysis algorithm according to the category, the accuracy of analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0046] During analysis, the analysis unit can determine the priority of analysis based on the time when the vital information was collected. For example, the analysis unit preferentially analyzes the latest vital information. The analysis unit can also analyze the current vital information while referring to past vital information. Furthermore, the analysis unit can also preferentially analyze vital information collected in a specific time period. This allows the latest information to be preferentially analyzed by determining the priority based on the time of collection. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the vital information during analysis. For example, the analysis unit prioritizes analysis of highly relevant vital information. The analysis unit can also analyze less relevant vital information later. Furthermore, the analysis unit can analyze medium relevance vital information in an appropriate order. This allows for efficient analysis by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0048] The generation unit can adjust the level of detail of the generation based on the intensity of the emotion when generating words or sentences. For example, when the intensity of the emotion is high, the generation unit generates detailed words or sentences. Also, when the intensity of the emotion is low, the generation unit can generate concise words or sentences. Furthermore, when the intensity of the emotion is medium, the generation unit can generate words or sentences with a moderate level of detail. In this way, by adjusting the level of detail based on the intensity of the emotion, words or sentences with an appropriate level of detail can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0049] The generation unit can apply different generation algorithms depending on the type of emotion when generating words or sentences. For example, the generation unit applies a generation algorithm of a positive expression to an emotion of joy. The generation unit can also apply a generation algorithm of an expression that soothes an emotion to an emotion of sadness. Furthermore, the generation unit can also apply a generation algorithm of an expression that maintains calm to an emotion of anger. In this way, by applying a generation algorithm according to the type of emotion, it is possible to generate words or sentences with appropriate expressions. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0050] The generation unit can determine the priority of generation based on the time of occurrence of emotion when generating words and sentences. For example, the generation unit preferentially generates words and sentences based on the latest emotion. The generation unit can also generate words and sentences based on current emotion while referring to past emotions. Furthermore, the generation unit can also generate words and sentences based on emotions that occurred in a specific time period. In this way, by determining the priority based on the time of occurrence, words and sentences based on the latest emotion can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0051] The generation unit can adjust the order of generation based on the relevance of emotions when generating words and sentences. For example, the generation unit generates words and sentences preferentially based on highly relevant emotions. The generation unit can also generate words and sentences later based on less relevant emotions. Furthermore, the generation unit can generate words and sentences in an appropriate order based on moderately relevant emotions. This allows for efficient generation by adjusting the order based on relevance. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0052] The speech unit can adjust the level of detail of the speech based on the importance of the generated words and sentences when speaking. For example, the speech unit speaks in detail for words and sentences with high importance. The speech unit can also speak briefly for words and sentences with low importance. Furthermore, the speech unit can also speak with an appropriate level of detail for words and sentences with medium importance. This allows for efficient speech by adjusting the level of detail based on the importance. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0053] The speech unit can apply different speech algorithms depending on the category of the generated words or sentences when speaking. For example, the speech unit applies a questioning speech algorithm to question-related words or sentences. The speech unit can also apply a clear and directive speech algorithm to instruction-related words or sentences. Furthermore, the speech unit can apply a speech algorithm that expresses gratitude to words or sentences related to gratitude. This makes it possible to produce appropriate speech by applying a speech algorithm according to the category. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0054] The speech unit can determine the priority of speech based on the time of occurrence of the generated words and sentences when speaking. The speech unit, for example, preferentially speaks the latest words and sentences. The speech unit can also speak current words and sentences while referring to past words and sentences. Furthermore, the speech unit can also preferentially speak words and sentences generated in a specific time period. In this way, by determining the priority based on the time of occurrence, the latest words and sentences can be preferentially spoken. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0055] The speech unit can adjust the order of speech based on the relevance of the generated words and sentences when speaking. For example, the speech unit preferentially speaks highly relevant words and sentences. The speech unit can also speak less relevant words and sentences later. Furthermore, the speech unit can also speak words and sentences with a moderate degree of relevance in an appropriate order. This allows for efficient speech by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The communication system may further include a history analysis unit that analyzes the user's past communication history and generates appropriate words and sentences. For example, the system may learn phrases and expressions that the user has frequently used in the past and generate natural conversations based on the learned phrases and expressions. The history analysis unit may also provide appropriate expressions at appropriate times, taking into account the user's past emotional states. Furthermore, the history analysis unit may analyze the user's past communication patterns and prepare predicted questions and responses in advance. This allows for more natural and effective communication by utilizing the user's past communication history.

[0058] The generation unit can further generate words and sentences taking into account the user's preferences and interests. For example, if the user is interested in a particular topic, the generation unit generates words and sentences related to that topic. The generation unit can also learn the user's preferred expressions and style based on the user's past comments and actions, and generate words and sentences that reflect them. Furthermore, the generation unit can generate appropriate words and sentences taking into account the user's current situation and context. This enables communication that reflects the user's preferences and interests.

[0059] The speech unit can further learn the characteristics of the user's voice and generate an individualized voice. For example, the speech unit can learn the pitch, tone, and rhythm of the user's voice and generate a natural voice based on the learned voice. The speech unit can also provide a voice that is close to the user's own voice by generating a voice that reflects the characteristics of the user's voice. Furthermore, the speech unit can generate voices according to different emotional states based on the characteristics of the user's voice. This makes it possible to generate an individualized voice that reflects the characteristics of the user's voice.

[0060] The generation unit can further generate words and sentences taking into account the cultural background of the user. For example, if the user belongs to a particular cultural sphere, it generates expressions and words appropriate to that culture. The generation unit can also generate appropriate words and sentences taking into account the religion and customs of the user. Furthermore, the generation unit can learn expressions and slang specific to the user's region and generate words and sentences that reflect them. This enables communication that takes into account the cultural background.

[0061] The speech unit can further generate voice taking into account the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the speech unit can generate voice that emphasizes low-pitched sounds. Also, if the user has difficulty hearing a specific frequency band, the speech unit can generate voice that emphasizes that frequency band. Furthermore, the speech unit can adjust the speed and rhythm of the voice according to the hearing characteristics of the user. This makes it possible to generate voice that takes into account the hearing characteristics.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collecting unit collects vital information. The vital information includes heart rate, brain waves, body temperature, and the like. The collecting unit collects the vital information using a brain wave sensor and a heart rate sensor. As the brain wave sensor, an EEG sensor or an fNIRS sensor is used, and as the heart rate sensor, a photoelectric heart rate sensor or an electrical heart rate sensor is used. Step 2: The analysis unit uses the generation AI to analyze the vital signs collected by the collection unit and understand emotions and thoughts. A deep learning model or a natural language processing model is used as the generation AI. Step 3: The generator uses a generation AI to generate words and sentences based on the analysis results obtained by the analyzer. As the generation AI, a text generation AI (e.g., LLM) or a multimodal generation AI is used. Step 4: The speech generation unit uses a speech generation AI to speak the words or sentences generated by the generation unit. A speech synthesis model or text-to-speech technology is used as the speech generation AI.

[0064] (Example 2) The communication system according to the embodiment of the present invention is a system that collects vital information, understands the person's feelings and thoughts, generates words and sentences using a generation AI, and has the person speak using a voice generation AI. This system analyzes the collected vital information and generates words and sentences using a generation AI. Furthermore, communication can be achieved by having the person speak using a voice generation AI. The expected users are ALS patients, intubated people who cannot speak, and people with different native languages, and it is expected that communication between them will expand. For example, an ALS patient wears an EEG sensor and has his / her feelings and thoughts analyzed. The generation AI generates words such as "I want to drink water," and the voice generation AI speaks the words. This allows the patient to communicate his / her intentions to others. In addition, when people with different native languages ​​communicate with each other, the generation AI generates appropriate words and sentences and the voice generation AI speaks them, making it possible to communicate beyond language barriers. This allows the communication system to convey the user's feelings and thoughts to others.

[0065] The communication system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and an utterance unit. The collection unit collects vital information. The vital information includes, for example, heart rate, brain waves, body temperature, and the like, but is not limited to such examples. The collection unit collects the vital information using, for example, an brain wave sensor or a heartbeat sensor. The brain wave sensor may be, for example, an EEG sensor or an fNIRS sensor. The heartbeat sensor may be, for example, a photoelectric heartbeat sensor or an electrical heartbeat sensor. The analysis unit uses a generation AI to analyze the vital information collected by the collection unit and understand emotions and thoughts. The generation AI may be, for example, a deep learning model or a natural language processing model. The generation unit uses the generation AI to generate words or sentences based on the analysis results obtained by the analysis unit. The generation AI may be, for example, a text generation AI (for example, LLM) or a multimodal generation AI. The utterance unit uses a voice generation AI to utter the words or sentences generated by the generation unit. The voice generation AI uses, for example, a voice synthesis model, a text-to-speech conversion technology, etc. This allows the communication system according to the embodiment to convey the user's feelings and thoughts to others.

[0066] The collecting unit collects vital information. Examples of vital information include, but are not limited to, heart rate, brain waves, and body temperature. The collecting unit collects vital information using, for example, an brain wave sensor or a heart rate sensor. Examples of the brain wave sensor include an EEG sensor and an fNIRS sensor. The EEG sensor measures electrical activity of the brain by being attached to the scalp, and acquires brain wave data in real time. The fNIRS sensor measures changes in blood flow in the brain using near-infrared rays, and grasps the activity state of the brain. Examples of the heart rate sensor include a photoelectric heart rate sensor and an electric heart rate sensor. The photoelectric heart rate sensor detects the heart rate by irradiating light to the skin and measuring the reflected light. The electric heart rate sensor detects the heart rate by attaching electrodes to the skin and measuring the electrical activity of the heart. These sensors are incorporated into wearable devices and medical equipment, and can continuously monitor the vital information of the user. The collected vital information is transmitted to a central database using wireless communication technology, and is provided to the analysis unit in real time. This allows the collection unit to accurately grasp the user's health condition and emotional state and provide the data necessary for the next analysis step. Furthermore, the collection unit can integrate data from multiple sensors and perform noise removal and outlier filtering to improve the accuracy and reliability of the data. This allows the collection unit to provide more accurate vital information and improve the performance of the entire system.

[0067] The analysis unit uses the generation AI to analyze the vital information collected by the collection unit and understand the emotions and thoughts. For example, a deep learning model or a natural language processing model is used as the generation AI. Specifically, the deep learning model classifies the user's emotional state using the collected brainwave data and heart rate data as input. For example, the brainwave data is analyzed for alpha wave and beta wave patterns to identify the relaxed state and the focused state. The heart rate data is analyzed for heart rate variability to evaluate the stress level and excited state. Based on these analysis results, the natural language processing model expresses the user's emotions and thoughts in text format. For example, it outputs a specific emotional state such as "the user is currently relaxed." Furthermore, the analysis unit can also analyze changes and trends in emotions by utilizing past data and user history information. This makes it possible to grasp the user's long-term emotional patterns and provide more accurate analysis results. The analysis unit can also use an anomaly detection algorithm to detect unusual emotional states and abnormal vital data and issue an early warning. This enables the analysis unit to handle not only real-time emotion analysis, but also long-term emotion management and anomaly detection, improving the reliability and safety of the entire system.

[0068] The generation unit uses the generation AI to generate words and sentences based on the analysis results obtained by the analysis unit. For example, a text generation AI (e.g., LLM) or a multimodal generation AI is used as the generation AI. The text generation AI generates natural words and sentences using the emotional state and thoughts provided by the analysis unit as input. For example, it generates a specific sentence such as "The user is currently relaxed and feeling calm." The multimodal generation AI can integrate not only text but also other modalities such as images and voice to generate richer expressions. For example, it can generate images and voice messages according to the user's emotional state to convey emotions through sight and hearing. The generation unit outputs the generated words and sentences in an appropriate format and provides them to the speech unit. In addition, the generation unit can collect user feedback and continuously improve the model of the generation AI. For example, the accuracy of the generation AI can be improved by the user evaluating the generated sentences as "accurate" or "inaccurate." The generation unit can also generate customized sentences according to specific situations and contexts. This enables the generation unit to accurately and naturally express the user's feelings and thoughts, and provide information for communicating them to others.

[0069] The speech unit uses the speech generation AI to recite the words and sentences generated by the generation unit. For example, speech synthesis models and text-to-speech technologies are used for the speech generation AI. The speech synthesis model uses the text provided by the generation unit as input to generate natural speech. For example, it generates speech with a tone and intonation according to the user's emotional state, and can convey emotions more effectively. When converting text into speech, the text-to-speech technology adjusts the pronunciation and accent to generate speech that is easy to hear. The speech unit plays the generated speech through a speaker or headset to convey the user's emotions and thoughts to others. In addition, the speech unit can collect user feedback and continuously improve the model of the speech generation AI. For example, the user can evaluate the generated speech as "easy to hear" or "difficult to hear," thereby improving the accuracy of the speech generation AI. The speech unit can also generate customized speech according to a specific situation or context. This allows the speech unit to accurately and naturally express the user's emotions and thoughts in speech and provide information to convey them to others.

[0070] The collecting unit may include an electroencephalogram sensor or a heartbeat sensor. The electroencephalogram sensor may include, for example, an EEG sensor or an fNIRS sensor. The EEG sensor is a sensor that electrically measures an electroencephalogram, and the fNIRS sensor is a sensor that uses near-infrared rays to measure cerebral blood flow. The heartbeat sensor may include, for example, a photoelectric heartbeat sensor or an electrical heartbeat sensor. The photoelectric heartbeat sensor is a sensor that uses light to measure blood flow, and the electrical heartbeat sensor is a sensor that electrically measures heartbeat. This allows the collecting unit to collect more accurate vital information by using the electroencephalogram sensor or the heartbeat sensor. A part or all of the above-mentioned processing in the collecting unit may be performed, for example, using AI, or may be performed without using AI.

[0071] The analysis unit can analyze emotions and thoughts using generative AI. Generative AI includes, for example, deep learning models and natural language processing models. Deep learning models are models that learn large amounts of data and have advanced analytical capabilities. Natural language processing models are models that analyze text data and understand meaning. As a result, the analysis unit can improve the accuracy of analyzing emotions and thoughts by using generative AI. Some or all of the above-mentioned processing in the analysis unit is performed using generative AI.

[0072] The generation unit can generate words and sentences using a generation AI. Examples of generation AI include text generation AI (e.g., LLM) and multimodal generation AI. Text generation AI is a model that learns large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI is a model that can handle multiple modalities such as images and audio, as well as text. As a result, the generation unit can use the generation AI to improve the accuracy of generating words and sentences. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0073] The speech unit can use a voice generation AI to speak words or sentences. Voice generation AI includes, for example, a voice synthesis model and text-to-speech conversion technology. A voice synthesis model is a model that converts text data into voice, and a text-to-speech conversion technology is a technology that converts text data into voice. As a result, the speech unit can improve the accuracy of speech by using the voice generation AI. Some or all of the above-mentioned processing in the speech unit is performed using the voice generation AI.

[0074] The collecting unit can collect brain waves using an EEG sensor. The EEG sensor includes, for example, an EEG sensor and an fNIRS sensor. The EEG sensor is a sensor that electrically measures brain waves, and the fNIRS sensor is a sensor that measures cerebral blood flow using near-infrared rays. This enables the collecting unit to collect brain waves by using the EEG sensor. A part or all of the above-mentioned processing in the collecting unit may be performed using AI, or may be performed without using AI.

[0075] The collection unit can collect heartbeats using a heartbeat sensor. Heartbeat sensors include, for example, photoelectric heartbeat sensors and electrical heartbeat sensors. A photoelectric heartbeat sensor is a sensor that measures blood flow using light, and an electrical heartbeat sensor is a sensor that measures heartbeats electrically. This allows the collection unit to collect heartbeats by using the heartbeat sensor. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0076] The collection unit can estimate the user's emotion and adjust the timing of collecting vital information based on the estimated user's emotion. For example, when the user is relaxed, the collection unit sets the collection frequency low and collects the minimum necessary data. Also, when the user is stressed, the collection unit can set the collection frequency high and collect detailed data. Furthermore, when the user is excited, the collection unit can also bring the collection timing closer to real time and collect real-time data. This allows more appropriate vital information to be collected by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using an AI, or may be performed without using an AI.

[0077] The collection unit can analyze the user's past vital information and select an appropriate collection method. The collection unit, for example, selects the most stable collection timing from the user's past vital information. The collection unit can also concentrate collection in a specific time period based on the user's past vital information. Furthermore, the collection unit can analyze the user's past vital information and determine the optimal sensor placement. In this way, the optimal collection method can be selected by analyzing the past vital information. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0078] The collection unit can filter the vital information based on the user's current health condition and activity level when collecting the vital information. For example, when the user is exercising, the collection unit preferentially collects vital information related to the exercise. In addition, when the user is resting, the collection unit can also collect vital information related to the relaxed state. Furthermore, when the user is sick, the collection unit can also collect detailed vital information related to the health condition. This makes it possible to collect more relevant vital information by filtering based on the health condition and activity level. Some or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0079] The collection unit can estimate the user's emotion and determine the priority of vital information to be collected based on the estimated user's emotion. For example, when the user is nervous, the collection unit preferentially collects stress-related vital information such as heart rate and blood pressure. In addition, when the user is relaxed, the collection unit can also preferentially collect relaxation-related vital information such as brain waves and respiratory rate. Furthermore, when the user is excited, the collection unit can also preferentially collect excitement-related vital information such as brain waves and heart rate. In this way, important vital information can be preferentially collected by determining the priority based on emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0080] When collecting vital information, the collection unit can prioritize collecting highly relevant information in consideration of the geographical location information of the user. For example, when the user is at high altitude, the collection unit prioritizes collecting oxygen saturation and respiratory rate. In addition, when the user is in an urban area, the collection unit can also prioritize collecting stress-related vital information. Furthermore, when the user is in a natural environment, the collection unit can also prioritize collecting relaxation-related vital information. In this way, highly relevant vital information can be collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0081] The collection unit can analyze the user's social media activity and collect related information when collecting vital information. For example, when the user feels stressed on social media, the collection unit preferentially collects stress-related vital information. In addition, when the user is relaxing on social media, the collection unit can also preferentially collect relaxation-related vital information. Furthermore, when the user is excited on social media, the collection unit can also preferentially collect excitement-related vital information. In this way, by analyzing social media activity, related vital information can be collected. A part or all of the above-mentioned processing in the collection unit may be performed using AI, or may be performed without using AI.

[0082] The analysis unit can estimate the user's emotion and adjust the expression method of the analysis based on the estimated user's emotion. For example, when the user is nervous, the analysis unit provides a simple and highly visible analysis result. In addition, the analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is excited, the analysis unit can also provide a visually stimulating analysis result. In this way, by adjusting the expression method of the analysis based on the emotion, it is possible to provide an analysis result that is easy for the user to understand. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the vital information during analysis. For example, the analysis unit performs a detailed analysis on vital information with high importance. The analysis unit can also perform a simplified analysis on vital information with low importance. Furthermore, the analysis unit can also perform an analysis with an appropriate level of detail on vital information with medium importance. This allows for efficient analysis by adjusting the level of detail based on the importance. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0084] The analysis unit can apply different analysis algorithms depending on the category of vital information during analysis. For example, the analysis unit applies a heart rate analysis algorithm to vital information related to heart rate. The analysis unit can also apply an electroencephalogram analysis algorithm to vital information related to electroencephalograms. The analysis unit can also apply a respiration analysis algorithm to vital information related to respiration rate. Thereby, by applying an analysis algorithm according to the category, the accuracy of analysis is improved. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0085] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, when the user is excited, the analysis unit can provide a visually stimulating analysis result. In this way, by adjusting the length of the analysis based on the emotion, an analysis result of an appropriate length for the user can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using an AI, or may be performed without using an AI.

[0086] During analysis, the analysis unit can determine the priority of analysis based on the time when the vital information was collected. For example, the analysis unit preferentially analyzes the latest vital information. The analysis unit can also analyze the current vital information while referring to past vital information. Furthermore, the analysis unit can also preferentially analyze vital information collected in a specific time period. This allows the latest information to be preferentially analyzed by determining the priority based on the time of collection. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the vital information during analysis. For example, the analysis unit prioritizes analysis of highly relevant vital information. The analysis unit can also analyze less relevant vital information later. Furthermore, the analysis unit can analyze medium relevance vital information in an appropriate order. This allows for efficient analysis by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the analysis unit may be performed using AI, or may be performed without using AI.

[0088] The generation unit can estimate the user's emotion and adjust the expression method of the words and sentences to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates words and sentences with gentle expressions. In addition, when the user is nervous, the generation unit can also generate words and sentences with concise and clear expressions. Furthermore, when the user is excited, the generation unit can also generate words and sentences with expressions that emphasize emotions. In this way, by adjusting the expression method based on emotions, words and sentences with appropriate expressions for the user can be generated. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0089] The generation unit can adjust the level of detail of the generation based on the intensity of the emotion when generating words or sentences. For example, when the intensity of the emotion is high, the generation unit generates detailed words or sentences. Also, when the intensity of the emotion is low, the generation unit can generate concise words or sentences. Furthermore, when the intensity of the emotion is medium, the generation unit can generate words or sentences with a moderate level of detail. In this way, by adjusting the level of detail based on the intensity of the emotion, words or sentences with an appropriate level of detail can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0090] The generation unit can apply different generation algorithms depending on the type of emotion when generating words or sentences. For example, the generation unit applies a generation algorithm of a positive expression to an emotion of joy. The generation unit can also apply a generation algorithm of an expression that soothes an emotion to an emotion of sadness. Furthermore, the generation unit can also apply a generation algorithm of an expression that maintains calm to an emotion of anger. In this way, by applying a generation algorithm according to the type of emotion, it is possible to generate words or sentences with appropriate expressions. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0091] The generation unit can estimate the user's emotion and adjust the length of the words and sentences to be generated based on the estimated user's emotion. For example, when the user is in a hurry, the generation unit generates short words and sentences that are to the point. In addition, when the user is relaxed, the generation unit can also generate longer words and sentences that include detailed explanations. Furthermore, when the user is excited, the generation unit can also generate words and sentences of a length that emphasizes the emotion. In this way, by adjusting the length based on the emotion, words and sentences of an appropriate length for the user can be generated. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit is performed using the generation AI.

[0092] The generation unit can determine the priority of generation based on the time of occurrence of emotion when generating words and sentences. For example, the generation unit preferentially generates words and sentences based on the latest emotion. The generation unit can also generate words and sentences based on current emotion while referring to past emotions. Furthermore, the generation unit can also generate words and sentences based on emotions that occurred in a specific time period. In this way, by determining the priority based on the time of occurrence, words and sentences based on the latest emotion can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0093] The generation unit can adjust the order of generation based on the relevance of emotions when generating words and sentences. For example, the generation unit generates words and sentences preferentially based on highly relevant emotions. The generation unit can also generate words and sentences later based on less relevant emotions. Furthermore, the generation unit can generate words and sentences in an appropriate order based on moderately relevant emotions. This allows for efficient generation by adjusting the order based on relevance. A part or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI.

[0094] The speech unit can estimate the user's emotion and adjust the manner of expression of speech based on the estimated emotion of the user. For example, when the user is relaxed, the speech unit speaks in a calm voice. Also, when the user is nervous, the speech unit can speak in a clear and calm voice. Furthermore, when the user is excited, the speech unit can speak in a voice that emphasizes the emotion. In this way, by adjusting the expression method based on the emotion, it is possible to perform speech of an appropriate expression for the user. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the speech unit may be performed using an AI, or may be performed without using an AI.

[0095] The speech unit can adjust the level of detail of the speech based on the importance of the generated words and sentences when speaking. For example, the speech unit speaks in detail for words and sentences with high importance. The speech unit can also speak briefly for words and sentences with low importance. Furthermore, the speech unit can also speak with an appropriate level of detail for words and sentences with medium importance. This allows for efficient speech by adjusting the level of detail based on the importance. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0096] The speech unit can apply different speech algorithms depending on the category of the generated words or sentences when speaking. For example, the speech unit applies a questioning speech algorithm to question-related words or sentences. The speech unit can also apply a clear and directive speech algorithm to instruction-related words or sentences. Furthermore, the speech unit can apply a speech algorithm that expresses gratitude to words or sentences related to gratitude. This makes it possible to produce appropriate speech by applying a speech algorithm according to the category. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0097] The speech unit can estimate the user's emotion and adjust the length of the speech based on the estimated user's emotion. For example, when the user is in a hurry, the speech unit performs short speech that focuses on the main points. Also, when the user is relaxed, the speech unit can perform longer speech that includes detailed explanations. Furthermore, when the user is excited, the speech unit can perform speech of a length that emphasizes the emotion. In this way, by adjusting the length based on the emotion, speech of an appropriate length for the user can be performed. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the speech unit may be performed using an AI, or may be performed without using an AI.

[0098] The speech unit can determine the priority of speech based on the time of occurrence of the generated words and sentences when speaking. The speech unit, for example, preferentially speaks the latest words and sentences. The speech unit can also speak current words and sentences while referring to past words and sentences. Furthermore, the speech unit can also preferentially speak words and sentences generated in a specific time period. In this way, by determining the priority based on the time of occurrence, the latest words and sentences can be preferentially spoken. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0099] The speech unit can adjust the order of speech based on the relevance of the generated words and sentences when speaking. For example, the speech unit preferentially speaks highly relevant words and sentences. The speech unit can also speak less relevant words and sentences later. Furthermore, the speech unit can also speak words and sentences with a moderate degree of relevance in an appropriate order. This allows for efficient speech by adjusting the order based on the relevance. A part or all of the above-mentioned processing in the speech unit may be performed using AI, or may be performed without using AI.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The communication system may further include a history analysis unit that analyzes the user's past communication history and generates appropriate words and sentences. For example, the system may learn phrases and expressions that the user has frequently used in the past and generate natural conversations based on the learned phrases and expressions. The history analysis unit may also provide appropriate expressions at appropriate times, taking into account the user's past emotional states. Furthermore, the history analysis unit may analyze the user's past communication patterns and prepare predicted questions and responses in advance. This allows for more natural and effective communication by utilizing the user's past communication history.

[0102] The collection unit can further collect the user's environmental sounds and analyze the sounds with the analysis unit. For example, when the user is in a noisy environment, the collection unit collects the environmental sounds and performs noise filtering with the analysis unit. Also, when the user is in a quiet environment, the collection unit can collect the environmental sounds and estimate the user's relaxed state with the analysis unit. Furthermore, when the user is listening to a specific sound (e.g., music or natural sounds), the collection unit can collect the sound and estimate the user's emotional state with the analysis unit. This allows for more accurate analysis of vital information by taking the environmental sounds into account.

[0103] The analysis unit may further include an expression analysis unit that analyzes the user's expression. For example, a camera is used to capture the user's face, and the expression is analyzed by the expression analysis unit. The expression analysis unit may also detect subtle changes in the user's expression and estimate the user's emotional state. The expression analysis unit may also analyze the user's eye and mouth movements to estimate the user's emotional state in more detail. This allows the user's emotional state to be grasped more accurately by performing the expression analysis.

[0104] The generation unit can further generate words and sentences taking into account the user's preferences and interests. For example, if the user is interested in a particular topic, the generation unit generates words and sentences related to that topic. The generation unit can also learn the user's preferred expressions and style based on the user's past comments and actions, and generate words and sentences that reflect them. Furthermore, the generation unit can generate appropriate words and sentences taking into account the user's current situation and context. This enables communication that reflects the user's preferences and interests.

[0105] The speech unit can further learn the characteristics of the user's voice and generate an individualized voice. For example, the speech unit can learn the pitch, tone, and rhythm of the user's voice and generate a natural voice based on the learned voice. The speech unit can also provide a voice that is close to the user's own voice by generating a voice that reflects the characteristics of the user's voice. Furthermore, the speech unit can generate voices according to different emotional states based on the characteristics of the user's voice. This makes it possible to generate an individualized voice that reflects the characteristics of the user's voice.

[0106] The collection unit can further detect the user's physical movements and collect vital information based on the movements. For example, when the user is exercising, the movements are detected and vital information related to the exercise is collected. The collection unit can also detect the user's movements when the user is relaxing and collect vital information related to the relaxed state. Furthermore, when the user is performing a specific movement (e.g., raising a hand, walking), the collection unit can detect the movement and collect appropriate vital information. This allows for more accurate collection of vital information by taking into account the physical movements.

[0107] The analysis unit can further analyze the user's voice and estimate the emotional state from the voice. For example, the tone, rhythm, and speed of the user's voice are analyzed to estimate the emotional state. The analysis unit can also analyze the strength and intonation of the user's voice to estimate the emotional state. Furthermore, the analysis unit can analyze changes in the user's voice in real time and estimate the emotional state in real time. In this way, the user's emotional state can be grasped more accurately by performing voice analysis.

[0108] The generation unit can further generate words and sentences taking into account the cultural background of the user. For example, if the user belongs to a particular cultural sphere, it generates expressions and words appropriate to that culture. The generation unit can also generate appropriate words and sentences taking into account the religion and customs of the user. Furthermore, the generation unit can learn expressions and slang specific to the user's region and generate words and sentences that reflect them. This enables communication that takes into account the cultural background.

[0109] The speech unit can further generate voice taking into account the hearing characteristics of the user. For example, if the user has difficulty hearing high-pitched sounds, the speech unit can generate voice that emphasizes low-pitched sounds. Also, if the user has difficulty hearing a specific frequency band, the speech unit can generate voice that emphasizes that frequency band. Furthermore, the speech unit can adjust the speed and rhythm of the voice according to the hearing characteristics of the user. This makes it possible to generate voice that takes into account the hearing characteristics.

[0110] The collecting unit can further measure the skin conductance of the user and analyze the data in the analyzing unit. For example, when the user is tense, the collecting unit detects an increase in skin conductance and analyzes the data in the analyzing unit. The collecting unit can also detect a decrease in skin conductance when the user is relaxed and analyze the data in the analyzing unit. Furthermore, when the user is excited, the collecting unit can detect a change in skin conductance in real time and analyze it in real time in the analyzing unit. This allows the user's emotional state to be grasped more accurately by measuring the skin conductance.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collecting unit collects vital information. The vital information includes heart rate, brain waves, body temperature, and the like. The collecting unit collects the vital information using a brain wave sensor and a heart rate sensor. As the brain wave sensor, an EEG sensor or an fNIRS sensor is used, and as the heart rate sensor, a photoelectric heart rate sensor or an electrical heart rate sensor is used. Step 2: The analysis unit uses the generation AI to analyze the vital signs collected by the collection unit and understand emotions and thoughts. A deep learning model or a natural language processing model is used as the generation AI. Step 3: The generator uses a generation AI to generate words and sentences based on the analysis results obtained by the analyzer. As the generation AI, a text generation AI (e.g., LLM) or a multimodal generation AI is used. Step 4: The speech generation unit uses a speech generation AI to speak the words or sentences generated by the generation unit. A speech synthesis model or text-to-speech technology is used as the speech generation AI.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0114] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and speech unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects vital information using an electroencephalogram sensor and a heartbeat sensor of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected vital information to understand emotions and thoughts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates words and sentences based on the analysis results. The speech unit speaks words and sentences generated, for example, by using the voice generation AI of the smart device 14. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0120] 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.

[0121] 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 the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] 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 performed in a secure state.

[0124] Fig. 4 shows an example of 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.

[0125] 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.

[0126] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0130] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and speech unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects vital information using an electroencephalogram sensor or a heartbeat sensor of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected vital information to understand emotions and thoughts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates words and sentences based on the analysis results. The speech unit speaks words and sentences generated, for example, by the voice generation AI of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0136] 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.

[0137] 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 the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] 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 performed in a secure state.

[0140] 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.

[0141] 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.

[0142] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific 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.

[0144] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] 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 voice indicating a user input for 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.

[0146] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and speech unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects vital information using an electroencephalogram sensor and a heartbeat sensor of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected vital information to understand emotions and thoughts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates words and sentences based on the analysis results. The speech unit speaks words and sentences generated, for example, by the voice generation AI of the headset type terminal 314. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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.

[0152] 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.

[0153] 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 the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] 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 performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0157] 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.

[0158] 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.

[0159] 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0161] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] 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 a voice indicating a user input for 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.

[0163] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and speech unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects vital information using an electroencephalogram sensor and a heartbeat sensor of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected vital information to understand emotions and thoughts. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates words and sentences based on the analysis results. The speech unit speaks words and sentences generated, for example, by the voice generation AI of the robot 414. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0173] 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.

[0174] 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 Universal Serial Bus (USB) 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.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] 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.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0179] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit for collecting vital information; an analysis unit that analyzes the information collected by the collection unit and understands emotions or thoughts; a generation unit that generates words or sentences based on the analysis results obtained by the analysis unit; and a speech unit that speaks the words or sentences generated by the generation unit. A system characterized by: (Appendix 2) The collecting unit includes: Includes brainwave or heart rate sensor 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing emotions and thoughts using generative AI 2. The system of claim 1, (Appendix 4) The generation unit is Generate words and sentences using generative AI 2. The system of claim 1, (Appendix 5) The speech unit is Use speech generation AI to speak words and sentences 2. The system of claim 1, (Appendix 6) The collecting unit includes: Collecting brainwaves using an EEG sensor 2. The system of claim 1, (Appendix 7) The collecting unit includes: Collect heart rate using a heart rate sensor 2. The system of claim 1, (Appendix 8) The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting vital information based on the estimated user's emotion. 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the user's past vital information and select the appropriate collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting vital information, it is filtered based on the user's current health status and activity level. 2. The system of claim 1, (Appendix 11) The collecting unit includes: To estimate a user's emotion and determine a priority of vital information to be collected based on the estimated user's emotion. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting vital information, the system takes into account the user's geographic location information and prioritizes the collection of information that is more relevant. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting vital information, we analyze the user's social media activity and collect related information. 2. The system of claim 1, (Appendix 14) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, adjust the level of detail of the analysis based on the importance of vital information 2. The system of claim 1, (Appendix 16) The analysis unit is During analysis, different analysis algorithms are applied depending on the category of vital information. 2. The system of claim 1, (Appendix 17) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, prioritize analysis based on when vital information was collected 2. The system of claim 1, (Appendix 19) The analysis unit is During analysis, adjust the order of analysis based on the relevance of vital information 2. The system of claim 1, (Appendix 20) The generation unit is Estimate the user's emotions and adjust the way words and sentences are generated based on the estimated user emotions 2. The system of claim 1, (Appendix 21) The generation unit is When generating words and sentences, adjust the level of detail in the generation based on the strength of the emotion 2. The system of claim 1, (Appendix 22) The generation unit is When generating words and sentences, different generation algorithms are applied depending on the type of emotion. 2. The system of claim 1, (Appendix 23) The generation unit is Estimate the user's emotions and adjust the length of the words and sentences to be generated based on the estimated user emotions. 2. The system of claim 1, (Appendix 24) The generation unit is When generating words and sentences, prioritize generation based on the timing of emotion occurrence 2. The system of claim 1, (Appendix 25) The generation unit is When generating words and sentences, adjust the order of generation based on emotional relevance 2. The system of claim 1, (Appendix 26) The speech unit is Estimate the user's emotions and adjust the way the user expresses their speech based on the estimated emotions. 2. The system of claim 1, (Appendix 27) The speech unit is During speech, adjust the level of detail in speech based on the importance of generated words and sentences 2. The system of claim 1, (Appendix 28) The speech unit is During speech generation, different speech generation algorithms are applied depending on the category of the generated word or sentence. 2. The system of claim 1, (Appendix 29) The speech unit is Estimating a user's emotion and adjusting the length of the utterance based on the estimated user's emotion 2. The system of claim 1, (Appendix 30) The speech unit is During speech generation, prioritize speech based on the timing of generated words and sentences 2. The system of claim 1, (Appendix 31) The speech unit is During speech, the order of speech is adjusted based on the relevance of generated words and sentences 2. The system of claim 1, [Explanation of symbols]

[0185] 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 for collecting vital information; an analysis unit that analyzes the information collected by the collection unit and understands emotions or thoughts; a generation unit that generates words or sentences based on the analysis results obtained by the analysis unit; a speech unit that speaks the words or sentences generated by the generation unit; A system characterized by:

2. The analysis unit is Analyzing emotions and thoughts using generative AI 2. The system of claim 1 .

3. The generation unit is Generate words and sentences using generative AI 2. The system of claim 1 .

4. The speech unit is Use generative AI to speak words and sentences 2. The system of claim 1 .

5. The collecting unit includes: Collecting brainwaves using an EEG sensor 2. The system of claim 1 .

6. The collecting unit includes: Collect heart rate using a heart rate sensor 2. The system of claim 1 .

7. The collecting unit includes: To estimate a user's emotion and adjust a timing of collecting vital information based on the estimated user's emotion.

2. The system of claim 1 .

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