System

Abstract

An object of the system according to the embodiment is to analyze an unclear pronunciation and accurately estimate and output a user's intention.SOLUTION: A system according to an embodiment includes a background information collection unit, a pronunciation analysis unit, an intention estimation unit, and an output unit. The background information collection unit collects background information of a user. The pronunciation analysis unit analyzes the unclear pronunciation based on the background information collected by the background information collection unit. The intention estimation section estimates an intention of the user based on the unclear pronunciation analyzed by the pronunciation analysis section. The output section outputs the user's intention estimated by the intention estimation section.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem that it is difficult to accurately understand the intention of hospitalized patients when they speak unclearly.

[0005] The system according to the embodiment aims to analyze unclear pronunciation and accurately predict and output the user's intention. [Means for solving the problem]

[0006] A system according to an embodiment includes a background information collection unit, a pronunciation analysis unit, an intention estimation unit, and an output unit. The background information collection unit collects background information about a user. The pronunciation analysis unit analyzes unclear pronunciation based on the background information collected by the background information collection unit. The intention estimation unit estimates the user's intention based on the unclear pronunciation analyzed by the pronunciation analysis unit. The output unit outputs the user's intention estimated by the intention estimation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze unclear pronunciation and accurately guess and output the user's intention. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The telepathic AI system according to an embodiment of the present invention is a system that complements unclear pronunciation and supports communication when hospitalized patients have difficulty articulating clearly due to pain or the effects of medical equipment. This allows the telepathic AI system to accurately guess the patient's intentions and convey them to medical staff and family members.

[0029] A telepathic AI system according to an embodiment includes a background information collection unit, a pronunciation analysis unit, an intention estimation unit, and an output unit. The background information collection unit collects background information about a user. For example, the background information collection unit collects information such as the user's age, health condition, past medical history, and current treatment. The background information collection unit can also collect information about the user's lifestyle and daily behavior patterns. For example, the background information collection unit collects information such as meal times, sleep rhythms, and exercise frequency. The background information collection unit can also monitor changes in the user's psychological state and emotions. For example, the background information collection unit analyzes stress levels and mood fluctuations. The pronunciation analysis unit analyzes unclear pronunciation based on the background information collected by the background information collection unit. For example, the pronunciation analysis unit uses speech recognition technology to analyze unclear pronunciation and estimate the user's intention. The pronunciation analysis unit can also learn the user's pronunciation patterns and build an individually optimized speech recognition model. For example, the pronunciation analysis unit can learn the pronunciation characteristics of a specific user to improve recognition accuracy. The intention estimation unit estimates the user's intention based on unclear pronunciation analyzed by the pronunciation analysis unit. For example, the intention estimation unit estimates the user's intention based on the user's background information and the pronunciation analysis results. The intention estimation unit can also analyze the user's emotional state using an emotion estimation function and estimate the user's intention based on the emotion. For example, if the user makes a pronunciation while feeling anxious, the intention is estimated. The output unit outputs the user's intention estimated by the intention estimation unit. For example, the output unit conveys the user's intention to medical staff or family members in text or audio. The output unit can also display the user's intention in real time. This allows the telepathy AI system according to the embodiment to effectively support communication even when hospitalized patients have difficulty speaking clearly due to pain or the effects of medical equipment. For example, if a patient requests water or complains of pain, accurately conveying the patient's intention allows medical staff to respond promptly and appropriately. Furthermore, communication with family members is facilitated, reducing the patient's mental burden.

[0030] The background information collection unit collects the user's lifestyle habits or daily behavior patterns, and the intention estimation unit can estimate the user's intentions based on the collected information. The background information collection unit, for example, collects the user's daily behavior patterns and analyzes meal times and sleep rhythms. This allows estimation of the user's intentions at specific time periods. The background information collection unit also collects the user's lifestyle habits and analyzes the frequency of exercise and the contents of meals. This allows estimation of the user's intentions in specific situations. Furthermore, the background information collection unit collects the user's daily behavior patterns and analyzes the amount of time spent watching television or reading. This allows estimation of the user's intentions during specific activities. This allows more accurate intention estimation by estimating the user's intentions based on the user's lifestyle habits and daily behavior patterns.

[0031] The background information collection unit collects feedback from the user's family or friends, and the intention estimation unit can estimate the user's intention using this as background information. The background information collection unit, for example, collects feedback from the user's family and friends and analyzes the user's usual behavior and preferences. This allows the intention estimation unit to estimate what the user intends in a particular situation. The background information collection unit also collects feedback from the user's family and friends and analyzes the user's health condition and past medical history. This allows the intention estimation unit to estimate what the user intends in a particular situation. Furthermore, the background information collection unit collects feedback from the user's family and friends and analyzes changes in the user's psychological state and emotions. This allows the intention estimation unit to estimate what the user intends in a particular situation. As a result, by utilizing feedback from family and friends as background information, more accurate intention estimation is possible.

[0032] The background information collection unit analyzes the user's past communication history, and the intention estimation unit can estimate the user's intention based on the analysis. The background information collection unit, for example, analyzes the user's past communication history to identify frequently used words and phrases. This allows estimation of what the user intends in a particular situation. The background information collection unit also analyzes the user's past communication history to identify statements related to a particular topic or theme. This allows estimation of what the user intends in a particular situation. Furthermore, the background information collection unit analyzes the user's past communication history to identify statements made in a particular emotional state. This allows estimation of what the user intends in a particular situation. This allows more accurate intention estimation by estimating intentions based on the past communication history.

[0033] The pronunciation analysis unit can learn the user's pronunciation pattern and build an individually optimized speech recognition model. The pronunciation analysis unit, for example, collects the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns the pronunciation characteristics of a specific user and improves recognition accuracy. The pronunciation analysis unit also analyzes the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns specific phonemes and pronunciation habits and improves recognition accuracy. Furthermore, the pronunciation analysis unit learns the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns pronunciation in specific situations and environments and improves recognition accuracy. In this way, by learning the user's pronunciation pattern and building an individually optimized speech recognition model, recognition accuracy is improved.

[0034] The pronunciation analysis unit can analyze the user's mouth movements or facial expressions and complement the pronunciation based on the analysis. The pronunciation analysis unit, for example, analyzes the user's mouth movements and builds a system that complements the pronunciation. For example, lip reading technology is used to infer the intention behind the pronunciation. The pronunciation analysis unit also analyzes the user's facial expressions and builds a system that complements the pronunciation. For example, facial expression recognition technology is used to infer the intention behind the pronunciation. The pronunciation analysis unit also analyzes the user's mouth movements and facial expressions and builds a system that complements the pronunciation. For example, a camera is used to perform analysis in real time and infer the intention behind the pronunciation. This improves recognition accuracy by analyzing the user's mouth movements and facial expressions and complementing the pronunciation based on the analysis.

[0035] The pronunciation analysis unit can analyze the user's handwritten characters or gestures and use them as supplementary information for pronunciation. The pronunciation analysis unit, for example, analyzes the user's handwritten characters and builds a system that uses them as supplementary information for pronunciation. For example, handwritten character recognition technology is used to infer the intention of the pronunciation. The pronunciation analysis unit also analyzes the user's gestures and builds a system that uses them as supplementary information for pronunciation. For example, gesture recognition technology is used to infer the intention of the pronunciation. The pronunciation analysis unit also analyzes the user's handwritten characters and gestures and builds a system that uses them as supplementary information for pronunciation. For example, a camera is used to perform analysis in real time and infer the intention of the pronunciation. In this way, by analyzing the user's handwritten characters and gestures and using them as supplementary information for pronunciation, recognition accuracy is improved.

[0036] The pronunciation analysis unit can translate the user's pronunciation in real time to support communication with medical staff and family members who speak different languages. The pronunciation analysis unit, for example, translates the user's pronunciation in real time to build a system that supports communication with medical staff and family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. The pronunciation analysis unit also translates the user's pronunciation in real time to build a system that supports communication with family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. The pronunciation analysis unit also translates the user's pronunciation in real time to build a system that supports communication with medical staff and family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. This enables communication across language barriers by translating the user's pronunciation in real time to support communication with medical staff and family members who speak different languages.

[0037] The intention estimation unit can incorporate feedback from medical staff and family members. For example, the intention estimation unit collects feedback from medical staff and family members and reflects it in estimating the user's intention. For example, feedback on the user's pronunciation and actions is stored in a database. The intention estimation unit also analyzes the feedback from medical staff and family members and identifies common patterns and trends. This improves the accuracy of estimating the user's intention. Furthermore, the intention estimation unit collects feedback from medical staff and family members in real time and reflects it in estimating the user's intention. This enables quick and accurate intention estimation. As a result, incorporating feedback from medical staff and family members improves the accuracy of intention estimation.

[0038] The intention estimation unit can take different scenarios into consideration when inferring the user's intention. For example, the intention estimation unit takes into consideration a scenario during a meal when inferring the user's intention. For example, the intention is estimated based on words spoken and actions taken during a meal. Furthermore, the intention estimation unit takes into consideration a scenario during a break when inferring the user's intention. For example, the intention is estimated based on words spoken and actions taken during a break. Furthermore, the intention estimation unit takes into consideration different scenarios (for example, during a medical examination, during rehabilitation) when inferring the user's intention. In this way, intention estimation according to the scenario is performed. In this way, by taking different scenarios into consideration, intention estimation according to the scenario is possible.

[0039] The intention estimation unit can utilize different devices when inferring the user's intention. For example, the intention estimation unit utilizes a smartphone when inferring the user's intention. For example, the intention is estimated using sensors or an app on the smartphone. Furthermore, the intention estimation unit utilizes a tablet when inferring the user's intention. For example, the intention is estimated using a camera or a microphone on the tablet. Furthermore, the intention estimation unit utilizes different devices (for example, a smart watch or a smart speaker) when inferring the user's intention. This allows for device-specific intention estimation. As a result, by utilizing different devices, it becomes possible to infer intentions according to the device.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The telepathic AI system may further include a motion analysis unit that analyzes the user's physical movements. The motion analysis unit may analyze, for example, the user's hand movements and body posture to infer the user's intentions. For example, if the user raises their hand, the motion analysis unit infers what that movement means. The motion analysis unit may also analyze the user's walking pattern to infer the user's intentions. For example, if the user makes a specific movement while walking, the motion analysis unit infers the user's intentions. Furthermore, the motion analysis unit may analyze the user's facial direction and line of sight to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's physical movements.

[0042] The telepathic AI system may further include an environmental information collection unit that collects environmental information about the user. The environmental information collection unit collects, for example, information about the sounds, temperature, and lighting conditions around the user to infer the user's intentions. For example, if the surroundings are noisy, it infers that the user is seeking a quiet environment. The environmental information collection unit also analyzes the movements of people around the user to infer the user's intentions. For example, if surrounding people are performing a certain behavior, it infers how the user feels about that behavior. Furthermore, the environmental information collection unit analyzes the arrangement of objects around the user to infer the user's intentions. This enables more accurate intention inference by inferring intentions based on the user's environmental information.

[0043] The telepathic AI system may further include a behavioral history analysis unit that analyzes the user's past behavioral history. The behavioral history analysis unit, for example, analyzes the user's past behavioral patterns and infers the user's intentions. For example, if the user has behaved in a specific way at a specific time in the past, it infers what that behavior means. The behavioral history analysis unit also analyzes the user's past choices and infers the user's intentions. For example, if the user has made a specific choice in the past, it infers what that choice means. Furthermore, the behavioral history analysis unit analyzes the user's past emotional states and infers the user's intentions. This enables more accurate intention inference by inferring intentions based on the user's past behavioral history.

[0044] The telepathic AI system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes biometric data such as the user's heart rate, blood pressure, and body temperature to infer the user's intentions. For example, if the user's heart rate is elevated, the health data analysis unit infers the user's intentions. The health data analysis unit also analyzes the user's sleep data to infer the user's intentions. For example, if the user is not getting enough sleep, the health data analysis unit infers the user's intentions. The health data analysis unit also analyzes the user's exercise data to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's health data.

[0045] The telepathic AI system can further include a preference data analysis unit that analyzes the user's preference data. The preference data analysis unit, for example, analyzes the user's food preferences and music preferences to infer the user's intentions. For example, if the user likes a particular food, the intention is inferred. The preference data analysis unit also analyzes the user's movie and book preferences to infer the user's intentions. For example, if the user likes a particular genre of movie, the intention is inferred. The preference data analysis unit also analyzes the user's travel preferences to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's preference data.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The background information collection unit collects background information about the user, such as their age, health condition, past medical history, current treatment, lifestyle habits, daily behavior patterns, psychological state, and emotional changes. Specifically, it analyzes their meal times, sleep rhythm, exercise frequency, stress level, and mood fluctuations. Step 2: The pronunciation analyzer analyzes the unclear pronunciation based on the background information collected by the background information collector. For example, it uses speech recognition technology to analyze the unclear pronunciation and infer what the user intended. It also learns the user's pronunciation patterns and builds an individually optimized speech recognition model. Step 3: The intention guessing unit guesses the user's intention based on the unclear pronunciation analyzed by the pronunciation analysis unit. For example, it guesses what the user intends based on the user's background information and the pronunciation analysis results. It also uses the emotion guessing function to analyze the user's emotional state and guess the intention based on that emotion. Step 4: The output unit outputs the user's intention inferred by the intention inference unit. For example, the output unit conveys the user's intention to medical staff or family members in text or voice. The output unit can also display the user's intention in real time.

[0048] (Example 2) The telepathic AI system according to an embodiment of the present invention is a system that complements unclear pronunciation and supports communication when hospitalized patients have difficulty articulating clearly due to pain or the effects of medical equipment. This allows the telepathic AI system to accurately guess the patient's intentions and convey them to medical staff and family members.

[0049] A telepathic AI system according to an embodiment includes a background information collection unit, a pronunciation analysis unit, an intention estimation unit, and an output unit. The background information collection unit collects background information about a user. For example, the background information collection unit collects information such as the user's age, health condition, past medical history, and current treatment. The background information collection unit can also collect information about the user's lifestyle and daily behavior patterns. For example, the background information collection unit collects information such as meal times, sleep rhythms, and exercise frequency. The background information collection unit can also monitor changes in the user's psychological state and emotions. For example, the background information collection unit analyzes stress levels and mood fluctuations. The pronunciation analysis unit analyzes unclear pronunciation based on the background information collected by the background information collection unit. For example, the pronunciation analysis unit uses speech recognition technology to analyze unclear pronunciation and estimate the user's intention. The pronunciation analysis unit can also learn the user's pronunciation patterns and build an individually optimized speech recognition model. For example, the pronunciation analysis unit can learn the pronunciation characteristics of a specific user to improve recognition accuracy. The intention estimation unit estimates the user's intention based on unclear pronunciation analyzed by the pronunciation analysis unit. For example, the intention estimation unit estimates the user's intention based on the user's background information and the pronunciation analysis results. The intention estimation unit can also analyze the user's emotional state using an emotion estimation function and estimate the user's intention based on the emotion. For example, if the user makes a pronunciation while feeling anxious, the intention is estimated. The output unit outputs the user's intention estimated by the intention estimation unit. For example, the output unit conveys the user's intention to medical staff or family members in text or audio. The output unit can also display the user's intention in real time. This allows the telepathy AI system according to the embodiment to effectively support communication even when hospitalized patients have difficulty speaking clearly due to pain or the effects of medical equipment. For example, if a patient requests water or complains of pain, accurately conveying the patient's intention allows medical staff to respond promptly and appropriately. Furthermore, communication with family members is facilitated, reducing the patient's mental burden.

[0050] The background information collection unit collects the user's lifestyle habits or daily behavior patterns, and the intention estimation unit can estimate the user's intentions based on the collected information. The background information collection unit, for example, collects the user's daily behavior patterns and analyzes meal times and sleep rhythms. This allows estimation of the user's intentions at specific time periods. The background information collection unit also collects the user's lifestyle habits and analyzes the frequency of exercise and the contents of meals. This allows estimation of the user's intentions in specific situations. Furthermore, the background information collection unit collects the user's daily behavior patterns and analyzes the amount of time spent watching television or reading. This allows estimation of the user's intentions during specific activities. This allows more accurate intention estimation by estimating the user's intentions based on the user's lifestyle habits and daily behavior patterns.

[0051] The background information collection unit monitors changes in the user's psychological state or emotions, and the intention estimation unit can estimate the user's intention based on the monitoring. The background information collection unit, for example, monitors the user's psychological state and analyzes stress levels and mood fluctuations. This allows estimation of what the user intends to do in a particular psychological state. The background information collection unit also monitors changes in the user's emotions and analyzes emotions such as joy and sadness. This allows estimation of what the user intends to do in a particular emotional state. Furthermore, the background information collection unit monitors the user's psychological state and analyzes emotions such as anxiety and relief. This allows estimation of what the user intends to do in a particular psychological state. This allows more accurate intention estimation by estimating intentions based on changes in the user's psychological state and emotions.

[0052] The background information collection unit uses the emotion estimation function to analyze the user's emotional state in real time, and the intention estimation unit can infer the user's intention based on the emotion. The background information collection unit, for example, uses the emotion estimation function to analyze the user's facial expressions and voice in real time to estimate the emotional state. This allows the user to infer what the user intends in a particular emotional state. The background information collection unit also uses the emotion estimation function to analyze the user's biometric data (heart rate and galvanic skin response) in real time to estimate the emotional state. This allows the user to infer what the user intends in a particular emotional state. The background information collection unit also uses the emotion estimation function to analyze the user's voice tone and word choice in real time to infer the emotional state. This allows the user to infer what the user intends in a particular emotional state. This allows the emotion estimation function to analyze the emotional state in real time and infer the intention, enabling more accurate intention estimation.

[0053] The background information collection unit collects feedback from the user's family or friends, and the intention estimation unit can estimate the user's intention using this as background information. The background information collection unit, for example, collects feedback from the user's family and friends and analyzes the user's usual behavior and preferences. This allows the intention estimation unit to estimate what the user intends in a particular situation. The background information collection unit also collects feedback from the user's family and friends and analyzes the user's health condition and past medical history. This allows the intention estimation unit to estimate what the user intends in a particular situation. Furthermore, the background information collection unit collects feedback from the user's family and friends and analyzes changes in the user's psychological state and emotions. This allows the intention estimation unit to estimate what the user intends in a particular situation. As a result, by utilizing feedback from family and friends as background information, more accurate intention estimation is possible.

[0054] The background information collection unit analyzes the user's past communication history, and the intention estimation unit can estimate the user's intention based on the analysis. The background information collection unit, for example, analyzes the user's past communication history to identify frequently used words and phrases. This allows estimation of what the user intends in a particular situation. The background information collection unit also analyzes the user's past communication history to identify statements related to a particular topic or theme. This allows estimation of what the user intends in a particular situation. Furthermore, the background information collection unit analyzes the user's past communication history to identify statements made in a particular emotional state. This allows estimation of what the user intends in a particular situation. This allows more accurate intention estimation by estimating intentions based on the past communication history.

[0055] The background information collection unit uses the emotion estimation function to analyze the user's emotional state in real time and share the results with family and medical staff, allowing the intention estimation unit to infer the user's intention. The background information collection unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and share the results with family and medical staff. This allows the user's intention to be accurately conveyed. The background information collection unit also uses the emotion estimation function to analyze the user's emotional state in real time and notify the results to family and medical staff. This encourages a prompt response. Furthermore, the background information collection unit uses the emotion estimation function to analyze the user's emotional state in real time and display the results to family and medical staff. This makes it easier to understand the user's intention. This allows the emotion estimation function to analyze the emotional state in real time and share the results with family and medical staff, enabling more accurate intention estimation and prompt response.

[0056] The pronunciation analysis unit can learn the user's pronunciation pattern and build an individually optimized speech recognition model. The pronunciation analysis unit, for example, collects the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns the pronunciation characteristics of a specific user and improves recognition accuracy. The pronunciation analysis unit also analyzes the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns specific phonemes and pronunciation habits and improves recognition accuracy. Furthermore, the pronunciation analysis unit learns the user's pronunciation pattern and builds an individually optimized speech recognition model. For example, it learns pronunciation in specific situations and environments and improves recognition accuracy. In this way, by learning the user's pronunciation pattern and building an individually optimized speech recognition model, recognition accuracy is improved.

[0057] The pronunciation analysis unit can analyze the user's mouth movements or facial expressions and complement the pronunciation based on the analysis. The pronunciation analysis unit, for example, analyzes the user's mouth movements and builds a system that complements the pronunciation. For example, lip reading technology is used to infer the intention behind the pronunciation. The pronunciation analysis unit also analyzes the user's facial expressions and builds a system that complements the pronunciation. For example, facial expression recognition technology is used to infer the intention behind the pronunciation. The pronunciation analysis unit also analyzes the user's mouth movements and facial expressions and builds a system that complements the pronunciation. For example, a camera is used to perform analysis in real time and infer the intention behind the pronunciation. This improves recognition accuracy by analyzing the user's mouth movements and facial expressions and complementing the pronunciation based on the analysis.

[0058] The pronunciation analysis unit can use the emotion estimation function to analyze the emotional state of the user and complement the intention of the pronunciation based on the emotion. The pronunciation analysis unit, for example, uses the emotion estimation function to analyze the emotional state of the user and complement the intention of the pronunciation based on the emotion. For example, if the user makes a pronunciation in an anxious state, the intention is inferred. The pronunciation analysis unit also uses the emotion estimation function to analyze the emotional state of the user and complement the intention of the pronunciation based on the emotion. For example, if the user makes a pronunciation in an excited state, the intention is inferred. The pronunciation analysis unit also uses the emotion estimation function to analyze the emotional state of the user and complement the intention of the pronunciation based on the emotion. For example, if the user makes a pronunciation in a relaxed state, the intention is inferred. In this way, by analyzing the emotional state of the user using the emotion estimation function and complementing the intention of the pronunciation based on the emotion, recognition accuracy is improved.

[0059] The pronunciation analysis unit can analyze the user's handwritten characters or gestures and use them as supplementary information for pronunciation. The pronunciation analysis unit, for example, analyzes the user's handwritten characters and builds a system that uses them as supplementary information for pronunciation. For example, handwritten character recognition technology is used to infer the intention of the pronunciation. The pronunciation analysis unit also analyzes the user's gestures and builds a system that uses them as supplementary information for pronunciation. For example, gesture recognition technology is used to infer the intention of the pronunciation. The pronunciation analysis unit also analyzes the user's handwritten characters and gestures and builds a system that uses them as supplementary information for pronunciation. For example, a camera is used to perform analysis in real time and infer the intention of the pronunciation. In this way, by analyzing the user's handwritten characters and gestures and using them as supplementary information for pronunciation, recognition accuracy is improved.

[0060] The pronunciation analysis unit can translate the user's pronunciation in real time to support communication with medical staff and family members who speak different languages. The pronunciation analysis unit, for example, translates the user's pronunciation in real time to build a system that supports communication with medical staff and family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. The pronunciation analysis unit also translates the user's pronunciation in real time to build a system that supports communication with family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. The pronunciation analysis unit also translates the user's pronunciation in real time to build a system that supports communication with medical staff and family members who speak different languages. For example, it translates the intention of the pronunciation using speech translation technology. This enables communication across language barriers by translating the user's pronunciation in real time to support communication with medical staff and family members who speak different languages.

[0061] The intention estimation unit can incorporate feedback from medical staff and family members. For example, the intention estimation unit collects feedback from medical staff and family members and reflects it in estimating the user's intention. For example, feedback on the user's pronunciation and actions is stored in a database. The intention estimation unit also analyzes the feedback from medical staff and family members and identifies common patterns and trends. This improves the accuracy of estimating the user's intention. Furthermore, the intention estimation unit collects feedback from medical staff and family members in real time and reflects it in estimating the user's intention. This enables quick and accurate intention estimation. As a result, incorporating feedback from medical staff and family members improves the accuracy of intention estimation.

[0062] The intention estimation unit can use the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, the intention estimation unit uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in an anxious state, the intention is inferred. The intention estimation unit also uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in an excited state, the intention is inferred. Furthermore, the intention estimation unit uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in a relaxed state, the intention is inferred. In this way, by analyzing the emotional state of the user using the emotion estimation function and inferring the intention based on the emotion, the accuracy of intention estimation is improved.

[0063] The intention estimation unit can take different scenarios into consideration when inferring the user's intention. For example, the intention estimation unit takes into consideration a scenario during a meal when inferring the user's intention. For example, the intention is estimated based on words spoken and actions taken during a meal. Furthermore, the intention estimation unit takes into consideration a scenario during a break when inferring the user's intention. For example, the intention is estimated based on words spoken and actions taken during a break. Furthermore, the intention estimation unit takes into consideration different scenarios (for example, during a medical examination, during rehabilitation) when inferring the user's intention. In this way, intention estimation according to the scenario is performed. In this way, by taking different scenarios into consideration, intention estimation according to the scenario is possible.

[0064] The intention estimation unit can utilize different devices when inferring the user's intention. For example, the intention estimation unit utilizes a smartphone when inferring the user's intention. For example, the intention is estimated using sensors or an app on the smartphone. Furthermore, the intention estimation unit utilizes a tablet when inferring the user's intention. For example, the intention is estimated using a camera or a microphone on the tablet. Furthermore, the intention estimation unit utilizes different devices (for example, a smart watch or a smart speaker) when inferring the user's intention. This allows for device-specific intention estimation. As a result, by utilizing different devices, it becomes possible to infer intentions according to the device.

[0065] The intention estimation unit can use the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, the intention estimation unit uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in an anxious state, the intention is inferred. The intention estimation unit also uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in an excited state, the intention is inferred. Furthermore, the intention estimation unit uses the emotion estimation function to analyze the emotional state of the user and infer the intention based on the emotion. For example, if the user makes a pronunciation in a relaxed state, the intention is inferred. In this way, by analyzing the emotional state of the user using the emotion estimation function and inferring the intention based on the emotion, the accuracy of intention estimation is improved.

[0066] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0067] The telepathic AI system may further include a motion analysis unit that analyzes the user's physical movements. The motion analysis unit may analyze, for example, the user's hand movements and body posture to infer the user's intentions. For example, if the user raises their hand, the motion analysis unit infers what that movement means. The motion analysis unit may also analyze the user's walking pattern to infer the user's intentions. For example, if the user makes a specific movement while walking, the motion analysis unit infers the user's intentions. Furthermore, the motion analysis unit may analyze the user's facial direction and line of sight to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's physical movements.

[0068] The telepathic AI system may further include an environmental information collection unit that collects environmental information about the user. The environmental information collection unit collects, for example, information about the sounds, temperature, and lighting conditions around the user to infer the user's intentions. For example, if the surroundings are noisy, it infers that the user is seeking a quiet environment. The environmental information collection unit also analyzes the movements of people around the user to infer the user's intentions. For example, if surrounding people are performing a certain behavior, it infers how the user feels about that behavior. Furthermore, the environmental information collection unit analyzes the arrangement of objects around the user to infer the user's intentions. This enables more accurate intention inference by inferring intentions based on the user's environmental information.

[0069] The telepathic AI system may further include a behavioral history analysis unit that analyzes the user's past behavioral history. The behavioral history analysis unit, for example, analyzes the user's past behavioral patterns and infers the user's intentions. For example, if the user has behaved in a specific way at a specific time in the past, it infers what that behavior means. The behavioral history analysis unit also analyzes the user's past choices and infers the user's intentions. For example, if the user has made a specific choice in the past, it infers what that choice means. Furthermore, the behavioral history analysis unit analyzes the user's past emotional states and infers the user's intentions. This enables more accurate intention inference by inferring intentions based on the user's past behavioral history.

[0070] The telepathic AI system may further include a health data analysis unit that analyzes the user's health data. The health data analysis unit analyzes biometric data such as the user's heart rate, blood pressure, and body temperature to infer the user's intentions. For example, if the user's heart rate is elevated, the health data analysis unit infers the user's intentions. The health data analysis unit also analyzes the user's sleep data to infer the user's intentions. For example, if the user is not getting enough sleep, the health data analysis unit infers the user's intentions. The health data analysis unit also analyzes the user's exercise data to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's health data.

[0071] The telepathic AI system can further include a preference data analysis unit that analyzes the user's preference data. The preference data analysis unit, for example, analyzes the user's food preferences and music preferences to infer the user's intentions. For example, if the user likes a particular food, the intention is inferred. The preference data analysis unit also analyzes the user's movie and book preferences to infer the user's intentions. For example, if the user likes a particular genre of movie, the intention is inferred. The preference data analysis unit also analyzes the user's travel preferences to infer the user's intentions. This allows for more accurate intention inference by inferring intentions based on the user's preference data.

[0072] The telepathic AI system may further include an emotion analysis unit that analyzes the user's emotional state. The emotion analysis unit may analyze, for example, the user's facial expressions and voice to estimate the user's emotional state. For example, if the user is smiling, the emotion may be estimated. The emotion analysis unit may also analyze the user's biometric data (heart rate and galvanic skin response) to estimate the user's emotional state. For example, if the user's heart rate is elevated, the emotion may be estimated. The emotion analysis unit may also analyze the user's tone of voice and choice of words to estimate the user's emotional state. This allows for more accurate intention estimation by inferring intent based on the user's emotional state.

[0073] The telepathic AI system may further include a notification unit that analyzes the user's emotional state in real time and notifies medical staff and family members of the results. The notification unit, for example, analyzes the user's emotional state in real time and notifies medical staff and family members of the results. For example, if the user is in an anxious state, the notification unit notifies the medical staff of that information. The notification unit also analyzes the user's emotional state in real time and notifies the family members of the results. For example, if the user is expressing joy, the notification unit notifies the family members of that information. The notification unit also analyzes the user's emotional state in real time and displays the results. This enables a prompt response based on the user's emotional state.

[0074] The telepathic AI system can further include an advice unit that analyzes the user's emotional state and provides the user with appropriate advice based on the results. The advice unit, for example, analyzes the user's emotional state and suggests ways to relax to the user based on the results. For example, if the user is feeling stressed, it suggests breathing techniques to help the user relax. The advice unit also analyzes the user's emotional state and suggests appropriate actions to the user based on the results. For example, if the user is feeling anxious, it suggests taking a walk. The advice unit also analyzes the user's emotional state and suggests appropriate ways to deal with the situation based on the results. In this way, by providing appropriate advice based on the user's emotional state, it is possible to reduce the user's mental burden.

[0075] The telepathic AI system may further include an intention estimation unit that analyzes the user's emotional state and estimates the user's intention based on the results. The intention estimation unit, for example, analyzes the user's emotional state and estimates the user's intention based on the results. For example, if the user makes a sound while in an anxious state, the intention is estimated. The intention estimation unit also analyzes the user's emotional state and estimates the user's intention based on the results. For example, if the user makes a sound while in an excited state, the intention is estimated. The intention estimation unit also analyzes the user's emotional state and estimates the user's intention based on the results. This enables more accurate intention estimation by estimating the intention based on the user's emotional state.

[0076] The telepathic AI system may further include an intention estimation unit that analyzes the user's emotional state and estimates the user's intention based on the results. The intention estimation unit, for example, analyzes the user's emotional state and estimates the user's intention based on the results. For example, if the user makes a sound while in an anxious state, the intention is estimated. The intention estimation unit also analyzes the user's emotional state and estimates the user's intention based on the results. For example, if the user makes a sound while in an excited state, the intention is estimated. The intention estimation unit also analyzes the user's emotional state and estimates the user's intention based on the results. This enables more accurate intention estimation by estimating the intention based on the user's emotional state.

[0077] The processing flow of the second embodiment will be briefly explained below.

[0078] Step 1: The background information collection unit collects background information about the user, such as their age, health condition, past medical history, current treatment, lifestyle habits, daily behavior patterns, psychological state, and emotional changes. Specifically, it analyzes their meal times, sleep rhythm, exercise frequency, stress level, and mood fluctuations. Step 2: The pronunciation analyzer analyzes the unclear pronunciation based on the background information collected by the background information collector. For example, it uses speech recognition technology to analyze the unclear pronunciation and infer what the user intended. It also learns the user's pronunciation patterns and builds an individually optimized speech recognition model. Step 3: The intention guessing unit guesses the user's intention based on the unclear pronunciation analyzed by the pronunciation analysis unit. For example, it guesses what the user intends based on the user's background information and the pronunciation analysis results. It also uses the emotion guessing function to analyze the user's emotional state and guess the intention based on that emotion. Step 4: The output unit outputs the user's intention inferred by the intention inference unit. For example, the output unit conveys the user's intention to medical staff or family members in text or voice. The output unit can also display the user's intention in real time.

[0079] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0080] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0081] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0082] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0083] 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.

[0084] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0085] 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.

[0086] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0087] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0088] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0089] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0090] 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.

[0091] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0092] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0093] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0097] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0098] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] 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.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] 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.

[0105] 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.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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 specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0113] 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.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] 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.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0129] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0130] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0131] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0132] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0133] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0134] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0135] 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.

[0136] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0137] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0138] 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.

[0139] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0140] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0141] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0142] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0143] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0144] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0145] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0146] 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 background information collection unit that collects background information of a user; a pronunciation analysis unit that analyzes unclear pronunciation based on the background information collected by the background information collection unit; an intention guessing unit that guesses a user's intention based on the unclear pronunciation analyzed by the pronunciation analyzing unit; an output unit that outputs the user's intention estimated by the intention estimation unit. A system characterized by:

2. The background information collection unit The user's lifestyle habits or daily behavior patterns are collected, and the intention inference unit infers the user's intention based on the collected information. The system of claim 1 .

3. The background information collection unit The intention inference unit infers the user's intention based on monitoring changes in the user's mental state or emotions. The system of claim 1 .

4. The background information collection unit The user's emotional state is analyzed in real time, and the intention inference unit infers the user's intention based on the emotional state. The system of claim 1 .

5. The background information collection unit Feedback from the user's family or friends is collected, and the intention inference unit infers the user's intention using this as background information. The system of claim 1 .

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