System

Abstract

To provide a system according to an embodiment which can properly convey intents or emotions of people with language disorders.SOLUTION: The system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects eye-level data and tone data in the daily life of a user. The analysis unit analyzes the eye-level data and the tone data collected by the collection unit. The estimation unit estimates an intent or an emotion of a user on the basis of the data analyzed by the analysis unit. The provision unit outputs information which fits the needs of the user on the basis of the intent or the emotion estimated by the estimation unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, people with speech disorders had difficulty conveying their intentions and feelings appropriately, resulting in a decline in the quality of communication.

[0005] The system according to the embodiment aims to enable people with speech impediments to appropriately communicate their intentions and feelings. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects gaze data and tone of speech data from the user's daily life. The analysis unit analyzes the gaze data and tone of speech data collected by the collection unit. The estimation unit estimates the user's intentions and emotions based on the data analyzed by the analysis unit. The provision unit outputs information that meets the user's needs based on the intentions and emotions estimated by the estimation unit. Effect of the Invention

[0007] The system according to the embodiment enables people with speech impediments to communicate their intentions and feelings appropriately. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The support system according to the embodiment of the present invention is a support system for people who are gradually unable to speak due to a language disorder. This support system collects data on the user's gaze and tone of voice in their daily life, analyzes it with AI, estimates the user's intention and feelings, and outputs appropriate information. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the user's intention from the user's gaze and tone of voice and convey "I want water" to the other person. Also, if the user wants to obtain necessary information, the AI ​​can understand the intention and provide appropriate information. This system allows people with language disorders to communicate more smoothly and improve their quality of life. For example, data on the user's gaze and tone of voice in their daily life is collected. At this time, detailed data such as how the user is looking and what tone of voice is used is collected. For example, when the user is looking at a specific object, the gaze data is collected. Also, what tone of voice the user is using is collected. This makes it possible to grasp the data on the user's gaze and tone of voice in their daily life. Next, the collected data is analyzed by AI. AI analyzes the collected eye gaze data and tone of speech data to estimate the user's intentions and emotions. For example, if a user is looking at a specific object, the eye gaze data is analyzed to estimate that the user wants that object. The user's tone of speech data is also analyzed to estimate what the user wants to say. This allows the system to understand the user's intentions and emotions. Furthermore, appropriate information is output based on the estimated intentions and emotions. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the user's intentions from the user's eye gaze and tone of speech and convey "I want water" to the other person. Also, if the user wants to obtain necessary information, the AI ​​can understand the user's intentions and provide appropriate information. This allows people with speech disorders to communicate more smoothly and improve their quality of life. This allows the assistance system to allow people with speech disorders to communicate smoothly and improve their quality of life.

[0029] The support system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects gaze data and tone of speech data in the user's daily life. The user's daily life includes, for example, at home, at work, and in public places, but is not limited to such examples. The collection unit collects gaze data using, for example, an eye tracking device or camera data. The collection unit can also collect tone of speech data using voice recording or voice analysis. For example, the collection unit collects gaze data when the user is gazing at a specific object. The collection unit also collects the tone of speech in which the user is speaking. This makes it possible to grasp the gaze and tone of speech data in the user's daily life. The analysis unit analyzes the gaze data and tone of speech data collected by the collection unit. The analysis is performed, for example, using data preprocessing and an analysis algorithm, but is not limited to such examples. For example, the analysis unit analyzes the collected gaze data and estimates whether the user wants a specific object. The analysis unit also analyzes the collected tone of speech data and estimates what the user wants to say. This allows the user's intention and emotions to be understood. The estimation unit estimates the user's intention and emotions based on the data analyzed by the analysis unit. The estimation is performed, for example, using an emotion recognition algorithm or an intention estimation model, but is not limited to such examples. For example, when a user wants to say "I want water" but is unable to express it well due to slurred speech, the estimation unit can understand the intention from the user's gaze and tone of voice and convey "I want water" to the other person. The provision unit outputs appropriate information based on the intention and emotions estimated by the estimation unit. The output is performed, for example, using text display or voice output, but is not limited to such examples. For example, even when a user wants to obtain necessary information, the provision unit uses the AI ​​to understand the intention and provide appropriate information. This allows the support system to allow people with language disabilities to communicate more smoothly, improving their quality of life. This allows the support system according to the embodiment to allow people with language disabilities to communicate smoothly, improving their quality of life.

[0030] The collection unit collects gaze data and tone of speech data in the user's daily life. The user's daily life includes, but is not limited to, for example, at home, at work, and in public places. The collection unit collects gaze data using, for example, an eye tracking device or camera data. The eye tracking device tracks the user's eye movements with high accuracy and records in real time which direction the user is looking and which object the user is paying attention to. The camera data plays a role in complementing the gaze direction by analyzing the user's face direction and facial expression. The collection unit can also collect tone of speech data using voice recording and voice analysis. The voice recording device records the user's speech with high quality, and the voice analysis algorithm analyzes the voice data to extract features such as pitch, strength, and rhythm. For example, the collection unit collects gaze data when the user is gazing at a specific object. This makes it possible to understand what the user is interested in and what he or she needs. The collection unit also collects the tone of speech in which the user is speaking. This makes it possible to more accurately understand the user's emotions and intentions. Furthermore, the collection unit manages these data in a centralized manner and allows the analysis unit and estimation unit to access them. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0031] The analysis unit analyzes the gaze data and tone of speech data collected by the collection unit. The analysis is performed, for example, using data preprocessing and an analysis algorithm, but is not limited to such examples. Data preprocessing includes noise removal and data normalization, which improves the accuracy of the analysis. For example, the analysis unit analyzes the collected gaze data and estimates whether the user wants a specific object. The gaze data is analyzed using a method of analyzing the gaze fixation time and gaze movement pattern. This makes it possible to estimate with high accuracy which object the user is interested in and which object the user needs. In addition, the analysis unit analyzes the collected tone of speech data and estimates what the user wants to say. The tone of speech data is analyzed using voice recognition technology and natural language processing technology to analyze the user's speech content and emotions. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the analysis unit can understand the user's intention from the user's gaze and tone of speech and convey "I want water" to the other person. This makes it possible to grasp the user's intention and emotions. In addition, the analysis unit can also use historical data and statistical information to analyze long-term trends and patterns, making it possible to predict changes in user behavior and emotions and provide more appropriate support.

[0032] The estimation unit estimates the user's intention and emotion based on the data analyzed by the analysis unit. The estimation is performed, for example, using an emotion recognition algorithm or an intention estimation model, but is not limited to such examples. The emotion recognition algorithm analyzes the user's gaze data and tone of speech data to estimate the user's emotional state with high accuracy. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the estimation unit can understand the user's intention from the user's gaze and tone of speech and convey "I want water" to the other person. The intention estimation model analyzes the user's behavior and speech content to estimate what the user wants and what the user intends. For example, if the user is gazing at a specific object, the gaze data is analyzed to estimate whether the user wants that object. This allows the estimation unit to estimate the user's intention and emotion with high accuracy and provide information for providing appropriate support. Furthermore, the estimation unit can continuously correct the estimation result based on data updated in real time to respond to the latest situation. This allows the estimation unit to always provide highly accurate estimation based on the latest information and support quick and appropriate response.

[0033] The providing unit outputs appropriate information based on the intention and emotion estimated by the estimating unit. The output is performed, for example, using text display or voice output, but is not limited to such examples. The text display is a means of visually conveying the user's intention and emotion, and is displayed on a display or smartphone screen. The voice output is a means of audibly conveying the user's intention and emotion, and is output through a speaker or earphones. For example, even when the user wants to obtain necessary information, the providing unit uses the AI ​​to understand the intention and provide appropriate information. This allows the user to quickly and accurately obtain the necessary information. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the output. For example, the providing unit records how the user responded to the provided information, and reviews the content and method of the output based on the data. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only text display but also voice output and vibration notification. This allows the providing unit to quickly and reliably provide information to the user, maximizing the effectiveness of the entire support system.

[0034] The collection unit can collect what kind of gaze the user is directing. The collection unit collects the gaze data of the user using, for example, an eye tracking device. For example, when the user is gazing at a specific object, the collection unit collects the gaze data. The collection unit can also collect the gaze data of the user using camera data. For example, when the user is looking in a specific direction, the collection unit collects the gaze data. This allows the collection unit to collect what kind of gaze the user is directing. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the gaze data acquired by the eye tracking device to AI and have the AI ​​perform analysis of the gaze data.

[0035] The collection unit can collect the tone of speech of the user. The collection unit collects the tone of speech data of the user using, for example, voice recording. For example, the collection unit collects the tone of speech of the user. The collection unit can also collect the tone of speech data of the user using voice analysis. For example, the collection unit collects the speech pattern in which the user speaks. In this way, the collection unit can collect the tone of speech of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the tone of speech data acquired by voice recording to AI and have the AI ​​perform analysis of the tone of speech data.

[0036] The analysis unit can analyze the collected gaze data and estimate whether the user wants a specific item. The analysis unit, for example, analyzes the collected gaze data and estimates whether the user wants a specific item. For example, when the user is gazing at a specific object, the analysis unit analyzes the gaze data and estimates that the user wants that object. The analysis unit can also estimate whether the user wants a specific item based on the collected gaze data. For example, when the user is looking in a specific direction, the analysis unit analyzes the gaze data and estimates that the user wants an object in that direction. This allows the analysis unit to analyze the collected gaze data and estimate whether the user wants a specific item. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected gaze data to AI and cause the AI ​​to execute analysis of the gaze data.

[0037] The analysis unit can analyze the collected tone of speech data and estimate the specific content of the user's speech. The analysis unit, for example, analyzes the collected tone of speech data and estimates the specific content of the user's speech. For example, the analysis unit analyzes the tone of speech in which the user speaks and estimates what the user wants to say. The analysis unit can also estimate the specific content of the user's speech based on the collected tone of speech data. For example, the analysis unit analyzes the speech pattern in which the user speaks and estimates what the user wants to say. In this way, the analysis unit can analyze the collected tone of speech data and estimate the specific content of the user's speech. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected tone of speech data to AI and cause AI to execute analysis of the tone of speech data.

[0038] The providing unit can output information required by the user based on the estimated intention and emotion. The providing unit outputs information required by the user based on, for example, the estimated intention and emotion. For example, when the user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the intention from the user's gaze and tone of voice and convey "I want water" to the other person. In addition, when the user wants to obtain necessary information, the providing unit can also understand the intention and provide appropriate information. For example, when the user wants to know the weather forecast, the providing unit understands the intention and provides weather forecast information. In this way, the providing unit can output information required by the user based on the estimated intention and emotion. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the estimated intention and emotion to the AI ​​and cause the AI ​​to execute the output of information.

[0039] The collection unit can analyze the user's past gaze data and tone of speech data and select an efficient collection method. The collection unit can set a collection point for gaze data based on, for example, an object that the user frequently gazed at in the past. The collection unit can also analyze the user's past tone of speech data and collect the data by focusing on specific phrases and words. Furthermore, the collection unit can optimize collection timing from the user's past data and efficiently collect data. As a result, the collection unit can select an optimal collection method and efficiently collect data by analyzing the user's past data. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the user's past gaze data and tone of speech data into AI and cause AI to select an efficient collection method.

[0040] The collection unit can perform filtering based on the user's current activity status and environment when collecting the eye movement data and speech tone data. For example, when the user is in a quiet environment, the collection unit collects detailed speech tone data. In addition, when the user is moving, the collection unit can prioritize collection of eye movement data and respond to the movement. Furthermore, when the user is talking, the collection unit can filter data based on the content of the conversation and collect important information. In this way, the collection unit can efficiently collect important information by filtering data based on the user's current activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's current activity status and environmental data to AI and have the AI ​​perform filtering.

[0041] When collecting the gaze data and the tone of speech data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit preferentially collects gaze data related to the location. In addition, when the user is moving, the collection unit can also preferentially collect tone of speech data related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect data related to the event. Thereby, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to collect highly relevant data.

[0042] The collection unit can analyze the user's social media activity and collect related data when collecting the eye gaze data and tone data. For example, when the user is talking about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user is viewing a specific image on social media, the collection unit can also collect eye gaze data related to the image. Furthermore, when the user is watching a specific video on social media, the collection unit can also collect tone data related to the video. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to collect related data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance to improve accuracy. The analysis unit can also perform a simplified analysis on data with low importance to emphasize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to analyze data efficiently by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause AI to adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an eye tracking algorithm to eye gaze data. The analysis unit can also apply a voice analysis algorithm to tone data. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the category of data. In this way, the analysis unit can improve the accuracy of analysis by applying an optimal analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to select and apply an optimal analysis algorithm.

[0045] The analysis unit can determine the priority of the analysis based on the time when the data was collected during the analysis. For example, the analysis unit prioritizes the analysis of the latest data and provides real-time information. The analysis unit can also refer to past data to grasp long-term trends. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time when the data was collected. As a result, the analysis unit can provide real-time information by determining the priority of the analysis based on the time when the data was collected. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time when the data was collected into the AI ​​and have the AI ​​execute the determination of the priority of the analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit determines the order of analysis, for example, by considering the relevance of eye movement data and tone data. The analysis unit can also prioritize analysis of highly relevant data to improve efficiency. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of data. This allows the analysis unit to analyze data efficiently by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0047] The estimation unit can optimize the estimation algorithm by referring to past estimation data at the time of estimation. The estimation unit can improve the accuracy of the algorithm based on, for example, past estimation data. The estimation unit can also analyze past estimation data and adjust parameters of the algorithm. Furthermore, the estimation unit can also improve the reliability of the estimation result by referring to past estimation data. As a result, the estimation unit can improve the accuracy of the estimation algorithm by referring to the past estimation data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past estimation data to AI and cause AI to execute optimization of the estimation algorithm.

[0048] The estimation unit can improve the accuracy of the estimation by considering the interrelationship of data at the time of estimation. The estimation unit can improve the accuracy of the estimation by considering the interrelationship of eye movement data and tone of voice data, for example. The estimation unit can also analyze the interrelationship of data and optimize the estimation algorithm. Furthermore, the estimation unit can improve the reliability of the estimation result based on the interrelationship of data. Thereby, the estimation unit can improve the accuracy of the estimation by considering the interrelationship of data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the interrelationship of data to AI and cause the AI ​​to execute the improvement of the accuracy of the estimation.

[0049] The estimation unit can perform estimation taking into account the geographical distribution of data when making an estimation. For example, when the user is in a specific location, the estimation unit performs estimation based on data related to the location. In addition, when the user is moving, the estimation unit can also perform estimation based on data related to the destination. Furthermore, when the user is participating in a specific event, the estimation unit can also perform estimation based on data related to the event. In this way, the estimation unit can perform more accurate estimation by taking into account the geographical distribution of data. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the geographical distribution of data to AI and cause the AI ​​to perform the estimation.

[0050] The estimation unit can improve the accuracy of the estimation by referring to related literature of the data at the time of estimation. The estimation unit can improve the accuracy of the estimation algorithm, for example, based on related literature. The estimation unit can also improve the reliability of the estimation result by referring to related literature. Furthermore, the estimation unit can adjust the parameters of the estimation algorithm based on related literature. Thereby, the estimation unit can improve the accuracy of the estimation by referring to related literature. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using AI, or can be performed without using AI. For example, the estimation unit can input related literature to AI and cause AI to execute the improvement of the accuracy of the estimation algorithm.

[0051] The providing unit can provide optimal information by referring to the user's past behavioral history at the time of output. The providing unit provides optimal information based on, for example, information frequently requested by the user in the past. The providing unit can also analyze the user's past behavioral history and provide related information. Furthermore, the providing unit can predict and provide information required in a specific time period from the user's past behavioral history. As a result, the providing unit can provide optimal information by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavioral history into AI and cause AI to provide optimal information.

[0052] The providing unit can customize the method of providing information based on the user's current situation at the time of output. For example, when the user is in a quiet environment, the providing unit provides detailed information. In addition, when the user is moving, the providing unit can also provide simplified information. Furthermore, when the user is talking, the providing unit can also provide information related to the content of the conversation. In this way, the providing unit can provide optimal information for the user by customizing the method of providing information based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI and cause AI to customize the method of providing information.

[0053] The providing unit can provide optimal information by taking into account the geographical location information of the user at the time of output. For example, when the user is in a specific location, the providing unit provides information related to the location. In addition, when the user is moving, the providing unit can also provide information related to the destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. Thereby, the providing unit can provide optimal information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to provide optimal information.

[0054] The providing unit can provide related information by analyzing the social media activity of the user at the time of output. For example, when the user is talking about a specific topic on social media, the providing unit provides information related to the topic. In addition, when the user is viewing a specific image on social media, the providing unit can also provide information related to the image. Furthermore, when the user is watching a specific video on social media, the providing unit can also provide information related to the video. In this way, the providing unit can efficiently provide related information by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to AI and cause the AI ​​to provide related information.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The assistance system can also collect and analyze the user's physical motion data. For example, the collection unit can collect the user's hand movements and walking patterns. The analysis unit can analyze these data and estimate what the user is trying to do. For example, if the user is reaching out, it can be estimated from the movement that the user is trying to pick something up. Also, if the user changes his or her walking pattern, it can be estimated from the change that the user is tired or in a hurry. In this way, the assistance system can more accurately grasp the user's intentions and state by utilizing the user's physical motion data.

[0057] The analysis unit can learn the user's past behavioral patterns and predict future behavior. For example, if a user repeats a certain behavior at a certain time of day, the analysis unit can learn that pattern and predict that the user will behave in a similar manner the next time. This allows the assistance system to anticipate the user's behavior and provide necessary support in advance. For example, if the user has a habit of drinking coffee every morning, the system can prompt the user to prepare coffee at that time of day. Also, if the user has a habit of going to a certain place on a certain day of the week, the system can provide information related to that place in advance.

[0058] The analysis unit can analyze the user's social network data to grasp the user's interests and human relationships. For example, it can analyze the people with whom the user frequently communicates and the topics that the user frequently talks about. This allows the assistance system to provide appropriate information based on the user's interests. For example, if the user frequently communicates with a specific friend, it can provide information related to that friend. Also, if the user is interested in a specific topic, it can provide the latest information related to that topic.

[0059] The providing unit can customize the method of providing information based on the user's current activity status. For example, when the user is in a quiet environment, detailed information can be provided. When the user is moving, concise information can be provided. Furthermore, when the user is talking, information related to the content of the conversation can be provided. In this way, the assistance system can select the optimal information providing method according to the user's current status and improve the user's convenience.

[0060] The providing unit can provide optimal information by referring to the user's past behavioral history. For example, optimal information can be provided based on information that the user frequently requested in the past. The providing unit can also analyze the user's past behavioral history and provide related information. Furthermore, information required during a specific time period can be predicted and provided based on the user's past behavioral history. In this way, the support system can provide optimal information for the user by utilizing the user's past behavioral history.

[0061] The providing unit can provide optimal information taking into consideration the geographical location information of the user. For example, when the user is in a specific location, information related to the location can be provided. Also, when the user is moving, information related to the destination can be provided. Furthermore, when the user is participating in a specific event, information related to the event can be provided. In this way, the assistance system can provide optimal information for the user by utilizing the geographical location information of the user.

[0062] The providing unit can provide related information by analyzing the user's social media activity. For example, if the user is talking about a specific topic on social media, information related to the topic can be provided. Also, if the user is viewing a specific image on social media, information related to the image can be provided. Furthermore, if the user is watching a specific video on social media, information related to the video can be provided. In this way, the assistance system can utilize the user's social media activity to provide the most suitable information for the user.

[0063] The process flow of the first embodiment will be briefly described below.

[0064] Step 1: The collection unit collects gaze data and tone of voice data in the user's daily life. The user's daily life includes at home, at work, and in public places. The collection unit collects gaze data using an eye tracking device or camera data, and collects tone of voice data using voice recording and voice analysis. For example, when a user is looking at a specific object, the collection unit collects the gaze data and collects the tone of voice in which the user is speaking. Step 2: The analysis unit analyzes the gaze data and speech tone data collected by the collection unit. The analysis is performed using data pre-processing and analysis algorithms. For example, the collected gaze data is analyzed to estimate whether the user wants a specific item, and the collected speech tone data is analyzed to estimate what the user wants to say. Step 3: The estimation unit infers the user's intentions and emotions based on the data analyzed by the analysis unit. The estimation is performed using an emotion recognition algorithm and an intention estimation model. For example, if a user wants to say "I want water" but is unable to express themselves clearly due to slurred speech, the estimation unit can infer the user's intention from their gaze and tone of voice and convey "I want water" to the other person. Step 4: The provision unit outputs appropriate information based on the intent and emotion estimated by the estimation unit. The output is done using text display or voice output. For example, when a user wants to obtain necessary information, the AI ​​will understand the intent and provide the appropriate information.

[0065] (Example 2) The support system according to the embodiment of the present invention is a support system for people who are gradually unable to speak due to a language disorder. This support system collects data on the user's gaze and tone of voice in their daily life, analyzes it with AI, estimates the user's intention and feelings, and outputs appropriate information. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the user's intention from the user's gaze and tone of voice and convey "I want water" to the other person. Also, if the user wants to obtain necessary information, the AI ​​can understand the intention and provide appropriate information. This system allows people with language disorders to communicate more smoothly and improve their quality of life. For example, data on the user's gaze and tone of voice in their daily life is collected. At this time, detailed data such as how the user is looking and what tone of voice is used is collected. For example, when the user is looking at a specific object, the gaze data is collected. Also, what tone of voice the user is using is collected. This makes it possible to grasp the data on the user's gaze and tone of voice in their daily life. Next, the collected data is analyzed by AI. AI analyzes the collected eye gaze data and tone of speech data to estimate the user's intentions and emotions. For example, if a user is looking at a specific object, the eye gaze data is analyzed to estimate that the user wants that object. The user's tone of speech data is also analyzed to estimate what the user wants to say. This allows the system to understand the user's intentions and emotions. Furthermore, appropriate information is output based on the estimated intentions and emotions. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the user's intentions from the user's eye gaze and tone of speech and convey "I want water" to the other person. Also, if the user wants to obtain necessary information, the AI ​​can understand the user's intentions and provide appropriate information. This allows people with speech disorders to communicate more smoothly and improve their quality of life. This allows the assistance system to allow people with speech disorders to communicate smoothly and improve their quality of life.

[0066] The support system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, and a provision unit. The collection unit collects gaze data and tone of speech data in the user's daily life. The user's daily life includes, for example, at home, at work, and in public places, but is not limited to such examples. The collection unit collects gaze data using, for example, an eye tracking device or camera data. The collection unit can also collect tone of speech data using voice recording or voice analysis. For example, the collection unit collects gaze data when the user is gazing at a specific object. The collection unit also collects the tone of speech in which the user is speaking. This makes it possible to grasp the gaze and tone of speech data in the user's daily life. The analysis unit analyzes the gaze data and tone of speech data collected by the collection unit. The analysis is performed, for example, using data preprocessing and an analysis algorithm, but is not limited to such examples. For example, the analysis unit analyzes the collected gaze data and estimates whether the user wants a specific object. The analysis unit also analyzes the collected tone of speech data and estimates what the user wants to say. This allows the user's intention and emotions to be understood. The estimation unit estimates the user's intention and emotions based on the data analyzed by the analysis unit. The estimation is performed, for example, using an emotion recognition algorithm or an intention estimation model, but is not limited to such examples. For example, when a user wants to say "I want water" but is unable to express it well due to slurred speech, the estimation unit can understand the intention from the user's gaze and tone of voice and convey "I want water" to the other person. The provision unit outputs appropriate information based on the intention and emotions estimated by the estimation unit. The output is performed, for example, using text display or voice output, but is not limited to such examples. For example, even when a user wants to obtain necessary information, the provision unit uses the AI ​​to understand the intention and provide appropriate information. This allows the support system to allow people with language disabilities to communicate more smoothly, improving their quality of life. This allows the support system according to the embodiment to allow people with language disabilities to communicate smoothly, improving their quality of life.

[0067] The collection unit collects gaze data and tone of speech data in the user's daily life. The user's daily life includes, but is not limited to, for example, at home, at work, and in public places. The collection unit collects gaze data using, for example, an eye tracking device or camera data. The eye tracking device tracks the user's eye movements with high accuracy and records in real time which direction the user is looking and which object the user is paying attention to. The camera data plays a role in complementing the gaze direction by analyzing the user's face direction and facial expression. The collection unit can also collect tone of speech data using voice recording and voice analysis. The voice recording device records the user's speech with high quality, and the voice analysis algorithm analyzes the voice data to extract features such as pitch, strength, and rhythm. For example, the collection unit collects gaze data when the user is gazing at a specific object. This makes it possible to understand what the user is interested in and what he or she needs. The collection unit also collects the tone of speech in which the user is speaking. This makes it possible to more accurately understand the user's emotions and intentions. Furthermore, the collection unit manages these data in a centralized manner and allows the analysis unit and estimation unit to access them. By adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively and improve the performance of the entire system.

[0068] The analysis unit analyzes the gaze data and tone of speech data collected by the collection unit. The analysis is performed, for example, using data preprocessing and an analysis algorithm, but is not limited to such examples. Data preprocessing includes noise removal and data normalization, which improves the accuracy of the analysis. For example, the analysis unit analyzes the collected gaze data and estimates whether the user wants a specific object. The gaze data is analyzed using a method of analyzing the gaze fixation time and gaze movement pattern. This makes it possible to estimate with high accuracy which object the user is interested in and which object the user needs. In addition, the analysis unit analyzes the collected tone of speech data and estimates what the user wants to say. The tone of speech data is analyzed using voice recognition technology and natural language processing technology to analyze the user's speech content and emotions. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the analysis unit can understand the user's intention from the user's gaze and tone of speech and convey "I want water" to the other person. This makes it possible to grasp the user's intention and emotions. In addition, the analysis unit can also use historical data and statistical information to analyze long-term trends and patterns, making it possible to predict changes in user behavior and emotions and provide more appropriate support.

[0069] The estimation unit estimates the user's intention and emotion based on the data analyzed by the analysis unit. The estimation is performed, for example, using an emotion recognition algorithm or an intention estimation model, but is not limited to such examples. The emotion recognition algorithm analyzes the user's gaze data and tone of speech data to estimate the user's emotional state with high accuracy. For example, if a user wants to say "I want water" but is unable to express it well due to slurred speech, the estimation unit can understand the user's intention from the user's gaze and tone of speech and convey "I want water" to the other person. The intention estimation model analyzes the user's behavior and speech content to estimate what the user wants and what the user intends. For example, if the user is gazing at a specific object, the gaze data is analyzed to estimate whether the user wants that object. This allows the estimation unit to estimate the user's intention and emotion with high accuracy and provide information for providing appropriate support. Furthermore, the estimation unit can continuously correct the estimation result based on data updated in real time to respond to the latest situation. This allows the estimation unit to always provide highly accurate estimation based on the latest information and support quick and appropriate response.

[0070] The providing unit outputs appropriate information based on the intention and emotion estimated by the estimating unit. The output is performed, for example, using text display or voice output, but is not limited to such examples. The text display is a means of visually conveying the user's intention and emotion, and is displayed on a display or smartphone screen. The voice output is a means of audibly conveying the user's intention and emotion, and is output through a speaker or earphones. For example, even when the user wants to obtain necessary information, the providing unit uses the AI ​​to understand the intention and provide appropriate information. This allows the user to quickly and accurately obtain the necessary information. Furthermore, the providing unit can collect user feedback and continuously improve the accuracy and effectiveness of the output. For example, the providing unit records how the user responded to the provided information, and reviews the content and method of the output based on the data. In addition, the providing unit can reliably transmit information using multiple communication means. For example, important information is reliably delivered by using not only text display but also voice output and vibration notification. This allows the providing unit to quickly and reliably provide information to the user, maximizing the effectiveness of the entire support system.

[0071] The collection unit can collect what kind of gaze the user is directing. The collection unit collects the gaze data of the user using, for example, an eye tracking device. For example, when the user is gazing at a specific object, the collection unit collects the gaze data. The collection unit can also collect the gaze data of the user using camera data. For example, when the user is looking in a specific direction, the collection unit collects the gaze data. This allows the collection unit to collect what kind of gaze the user is directing. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the gaze data acquired by the eye tracking device to AI and have the AI ​​perform analysis of the gaze data.

[0072] The collection unit can collect the tone of speech of the user. The collection unit collects the tone of speech data of the user using, for example, voice recording. For example, the collection unit collects the tone of speech of the user. The collection unit can also collect the tone of speech data of the user using voice analysis. For example, the collection unit collects the speech pattern in which the user speaks. In this way, the collection unit can collect the tone of speech of the user. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the tone of speech data acquired by voice recording to AI and have the AI ​​perform analysis of the tone of speech data.

[0073] The analysis unit can analyze the collected gaze data and estimate whether the user wants a specific item. The analysis unit, for example, analyzes the collected gaze data and estimates whether the user wants a specific item. For example, when the user is gazing at a specific object, the analysis unit analyzes the gaze data and estimates that the user wants that object. The analysis unit can also estimate whether the user wants a specific item based on the collected gaze data. For example, when the user is looking in a specific direction, the analysis unit analyzes the gaze data and estimates that the user wants an object in that direction. This allows the analysis unit to analyze the collected gaze data and estimate whether the user wants a specific item. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected gaze data to AI and cause the AI ​​to execute analysis of the gaze data.

[0074] The analysis unit can analyze the collected tone of speech data and estimate the specific content of the user's speech. The analysis unit, for example, analyzes the collected tone of speech data and estimates the specific content of the user's speech. For example, the analysis unit analyzes the tone of speech in which the user speaks and estimates what the user wants to say. The analysis unit can also estimate the specific content of the user's speech based on the collected tone of speech data. For example, the analysis unit analyzes the speech pattern in which the user speaks and estimates what the user wants to say. In this way, the analysis unit can analyze the collected tone of speech data and estimate the specific content of the user's speech. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected tone of speech data to AI and cause AI to execute analysis of the tone of speech data.

[0075] The providing unit can output information required by the user based on the estimated intention and emotion. The providing unit outputs information required by the user based on, for example, the estimated intention and emotion. For example, when the user wants to say "I want water" but is unable to express it well due to slurred speech, the AI ​​can understand the intention from the user's gaze and tone of voice and convey "I want water" to the other person. In addition, when the user wants to obtain necessary information, the providing unit can also understand the intention and provide appropriate information. For example, when the user wants to know the weather forecast, the providing unit understands the intention and provides weather forecast information. In this way, the providing unit can output information required by the user based on the estimated intention and emotion. Part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the estimated intention and emotion to the AI ​​and cause the AI ​​to execute the output of information.

[0076] The collection unit can estimate the user's emotion and adjust the collection timing of the eye gaze data and tone of speech data based on the estimated user's emotion. For example, when the user is relaxed, the collection unit periodically collects the eye gaze data and tone of speech data. Also, when the user is stressed, the collection unit can reduce the collection frequency to reduce the burden on the user. Furthermore, when the user is excited, the collection unit can increase the collection frequency to acquire detailed data. As a result, the collection unit can reduce the burden on the user and efficiently collect data by adjusting the collection timing based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The collection unit can analyze the user's past gaze data and tone of speech data and select an efficient collection method. The collection unit can set a collection point for gaze data based on, for example, an object that the user frequently gazed at in the past. The collection unit can also analyze the user's past tone of speech data and collect the data by focusing on specific phrases and words. Furthermore, the collection unit can optimize collection timing from the user's past data and efficiently collect data. As a result, the collection unit can select an optimal collection method and efficiently collect data by analyzing the user's past data. A part or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or can be performed without using AI. For example, the collection unit can input the user's past gaze data and tone of speech data into AI and cause AI to select an efficient collection method.

[0078] The collection unit can perform filtering based on the user's current activity status and environment when collecting the eye movement data and speech tone data. For example, when the user is in a quiet environment, the collection unit collects detailed speech tone data. In addition, when the user is moving, the collection unit can prioritize collection of eye movement data and respond to the movement. Furthermore, when the user is talking, the collection unit can filter data based on the content of the conversation and collect important information. In this way, the collection unit can efficiently collect important information by filtering data based on the user's current activity status and environment. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input the user's current activity status and environmental data to AI and have the AI ​​perform filtering.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is relaxed, the collection unit preferentially collects eye gaze data. In addition, when the user is stressed, the collection unit can also preferentially collect tone data. Furthermore, when the user is excited, the collection unit can collect both data in a balanced manner. In this way, the collection unit can prioritize data collection based on the user's emotion, thereby allowing important data to be preferentially collected. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of the data.

[0080] When collecting the gaze data and the tone of speech data, the collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the user. For example, when the user is in a specific location, the collection unit preferentially collects gaze data related to the location. In addition, when the user is moving, the collection unit can also preferentially collect tone of speech data related to the destination. Furthermore, when the user is participating in a specific event, the collection unit can also preferentially collect data related to the event. Thereby, the collection unit can preferentially collect highly relevant data by considering the geographical location information of the user. Part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the geographical location information of the user to the AI ​​and cause the AI ​​to collect highly relevant data.

[0081] The collection unit can analyze the user's social media activity and collect related data when collecting the eye gaze data and tone data. For example, when the user is talking about a specific topic on social media, the collection unit collects data related to the topic. In addition, when the user is viewing a specific image on social media, the collection unit can also collect eye gaze data related to the image. Furthermore, when the user is watching a specific video on social media, the collection unit can also collect tone data related to the video. This allows the collection unit to efficiently collect related data by analyzing the user's social media activity. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into AI and cause AI to collect related data.

[0082] The analysis unit can estimate the user's emotion and adjust the analysis method of the eye movement data and tone data based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit performs a detailed analysis to capture minute changes. In addition, when the user is stressed, the analysis unit can also perform a simplified analysis to quickly obtain results. Furthermore, when the user is excited, the analysis unit can perform an analysis according to changes in emotion to capture dynamic data. This allows the analysis unit to analyze data efficiently by adjusting the analysis method based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the analysis method.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data with high importance to improve accuracy. The analysis unit can also perform a simplified analysis on data with low importance to emphasize efficiency. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. This allows the analysis unit to analyze data efficiently by adjusting the level of detail of the analysis based on the importance of the data. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to AI and cause AI to adjust the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies an eye tracking algorithm to eye gaze data. The analysis unit can also apply a voice analysis algorithm to tone data. Furthermore, the analysis unit can select and apply an optimal analysis algorithm depending on the category of data. In this way, the analysis unit can improve the accuracy of analysis by applying an optimal analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to AI and cause the AI ​​to select and apply an optimal analysis algorithm.

[0085] The analysis unit can estimate the user's emotion and determine the priority of analysis based on the estimated user's emotion. For example, when the user is relaxed, the analysis unit prioritizes the analysis of the eye gaze data. In addition, when the user is stressed, the analysis unit can also prioritize the analysis of the tone of voice data. Furthermore, when the user is excited, the analysis unit can analyze both data in a balanced manner. In this way, the analysis unit can prioritize the analysis of important data by determining the priority of analysis based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of analysis.

[0086] The analysis unit can determine the priority of the analysis based on the time when the data was collected during the analysis. For example, the analysis unit prioritizes the analysis of the latest data and provides real-time information. The analysis unit can also refer to past data to grasp long-term trends. Furthermore, the analysis unit can dynamically adjust the priority of the analysis according to the time when the data was collected. As a result, the analysis unit can provide real-time information by determining the priority of the analysis based on the time when the data was collected. A part or all of the above-mentioned processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the time when the data was collected into the AI ​​and have the AI ​​execute the determination of the priority of the analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. The analysis unit determines the order of analysis, for example, by considering the relevance of eye movement data and tone data. The analysis unit can also prioritize analysis of highly relevant data to improve efficiency. Furthermore, the analysis unit can optimally allocate analysis resources based on the relevance of data. This allows the analysis unit to analyze data efficiently by adjusting the order of analysis based on the relevance of data. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance of data to AI and cause AI to adjust the order of analysis.

[0088] The estimation unit can estimate the user's emotion and adjust the estimation method of the intention and emotion based on the estimated user's emotion. For example, when the user is relaxed, the estimation unit performs detailed estimation to capture subtle intention. In addition, when the user is stressed, the estimation unit can perform simplified estimation to quickly obtain a result. Furthermore, when the user is excited, the estimation unit can perform estimation according to changes in emotion and capture dynamic intention. Thereby, the estimation unit can efficiently estimate the intention and emotion by adjusting the estimation method based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the estimation method.

[0089] The estimation unit can optimize the estimation algorithm by referring to past estimation data at the time of estimation. The estimation unit can improve the accuracy of the algorithm based on, for example, past estimation data. The estimation unit can also analyze past estimation data and adjust parameters of the algorithm. Furthermore, the estimation unit can also improve the reliability of the estimation result by referring to past estimation data. As a result, the estimation unit can improve the accuracy of the estimation algorithm by referring to the past estimation data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input past estimation data to AI and cause AI to execute optimization of the estimation algorithm.

[0090] The estimation unit can improve the accuracy of the estimation by considering the interrelationship of data at the time of estimation. The estimation unit can improve the accuracy of the estimation by considering the interrelationship of eye movement data and tone of voice data, for example. The estimation unit can also analyze the interrelationship of data and optimize the estimation algorithm. Furthermore, the estimation unit can improve the reliability of the estimation result based on the interrelationship of data. Thereby, the estimation unit can improve the accuracy of the estimation by considering the interrelationship of data. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the interrelationship of data to AI and cause the AI ​​to execute the improvement of the accuracy of the estimation.

[0091] The estimation unit can estimate the user's emotion and adjust the display method of the estimation result based on the estimated user's emotion. For example, when the user is relaxed, the estimation unit displays a detailed estimation result. Also, when the user is stressed, the estimation unit can display a simplified estimation result. Furthermore, when the user is excited, the estimation unit can provide a visually stimulating display method. In this way, the estimation unit can provide an optimal display method for the user by adjusting the display method based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the estimation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the estimation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the display method.

[0092] The estimation unit can perform estimation taking into account the geographical distribution of data when making an estimation. For example, when the user is in a specific location, the estimation unit performs estimation based on data related to the location. In addition, when the user is moving, the estimation unit can also perform estimation based on data related to the destination. Furthermore, when the user is participating in a specific event, the estimation unit can also perform estimation based on data related to the event. In this way, the estimation unit can perform more accurate estimation by taking into account the geographical distribution of data. Part or all of the above-mentioned processing in the estimation unit may be performed using, for example, AI, or may be performed without using AI. For example, the estimation unit can input the geographical distribution of data to AI and cause the AI ​​to perform the estimation.

[0093] The estimation unit can improve the accuracy of the estimation by referring to related literature of the data at the time of estimation. The estimation unit can improve the accuracy of the estimation algorithm, for example, based on related literature. The estimation unit can also improve the reliability of the estimation result by referring to related literature. Furthermore, the estimation unit can adjust the parameters of the estimation algorithm based on related literature. Thereby, the estimation unit can improve the accuracy of the estimation by referring to related literature. A part or all of the above-mentioned processing in the estimation unit can be performed, for example, using AI, or can be performed without using AI. For example, the estimation unit can input related literature to AI and cause AI to execute the improvement of the accuracy of the estimation algorithm.

[0094] The providing unit can estimate the user's emotion and adjust the information output method based on the estimated user's emotion. For example, when the user is relaxed, the providing unit provides detailed information. Furthermore, when the user is stressed, the providing unit can also provide simplified information. Furthermore, when the user is excited, the providing unit can also provide visually stimulating information. In this way, the providing unit can provide optimal information for the user by adjusting the output method based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the adjustment of the output method.

[0095] The providing unit can provide optimal information by referring to the user's past behavioral history at the time of output. The providing unit provides optimal information based on, for example, information frequently requested by the user in the past. The providing unit can also analyze the user's past behavioral history and provide related information. Furthermore, the providing unit can predict and provide information required in a specific time period from the user's past behavioral history. As a result, the providing unit can provide optimal information by referring to the user's past behavioral history. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's past behavioral history into AI and cause AI to provide optimal information.

[0096] The providing unit can customize the method of providing information based on the user's current situation at the time of output. For example, when the user is in a quiet environment, the providing unit provides detailed information. In addition, when the user is moving, the providing unit can also provide simplified information. Furthermore, when the user is talking, the providing unit can also provide information related to the content of the conversation. In this way, the providing unit can provide optimal information for the user by customizing the method of providing information based on the user's current situation. Part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the user's current situation data into AI and cause AI to customize the method of providing information.

[0097] The providing unit can estimate the user's emotion and determine the priority of information based on the estimated user's emotion. For example, when the user is relaxed, the providing unit can provide detailed information preferentially. Furthermore, when the user is stressed, the providing unit can also provide simplified information preferentially. Furthermore, when the user is excited, the providing unit can also provide visually stimulating information preferentially. In this way, the providing unit can determine the priority of information based on the user's emotion, thereby providing important information preferentially. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of information.

[0098] The providing unit can provide optimal information by taking into account the geographical location information of the user at the time of output. For example, when the user is in a specific location, the providing unit provides information related to the location. In addition, when the user is moving, the providing unit can also provide information related to the destination. Furthermore, when the user is participating in a specific event, the providing unit can also provide information related to the event. Thereby, the providing unit can provide optimal information by taking into account the geographical location information of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the geographical location information of the user to AI and cause the AI ​​to provide optimal information.

[0099] The providing unit can provide related information by analyzing the social media activity of the user at the time of output. For example, when the user is talking about a specific topic on social media, the providing unit provides information related to the topic. In addition, when the user is viewing a specific image on social media, the providing unit can also provide information related to the image. Furthermore, when the user is watching a specific video on social media, the providing unit can also provide information related to the video. In this way, the providing unit can efficiently provide related information by analyzing the social media activity of the user. A part or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. For example, the providing unit can input the social media activity data of the user to AI and cause the AI ​​to provide related information.

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

[0101] The assistance system can also collect and analyze the user's physical motion data. For example, the collection unit can collect the user's hand movements and walking patterns. The analysis unit can analyze these data and estimate what the user is trying to do. For example, if the user is reaching out, it can be estimated from the movement that the user is trying to pick something up. Also, if the user changes his or her walking pattern, it can be estimated from the change that the user is tired or in a hurry. In this way, the assistance system can more accurately grasp the user's intentions and state by utilizing the user's physical motion data.

[0102] The collection unit can also collect and analyze biometric data of the user. For example, the heart rate and electrodermal activity can be collected. The analysis unit can analyze these data and estimate the user's stress level and relaxation state. For example, if the heart rate is increasing, it can be estimated that the user is tense. Also, if the electrodermal activity is changing, it can be estimated that the user is excited. This allows the assistance system to utilize the user's biometric data to more accurately grasp the user's emotions and state.

[0103] The collection unit can also collect and analyze the user's environmental sound data. For example, the surrounding noise level and specific sounds can be collected. The analysis unit can analyze these data and estimate the type of environment the user is in. For example, if the surroundings are quiet, it can be estimated that the user is relaxed. Also, if the surroundings are noisy, it can be estimated that the user is feeling stressed. In this way, the support system can utilize the user's environmental sound data to more accurately grasp the user's condition and emotions.

[0104] The analysis unit can learn the user's past behavioral patterns and predict future behavior. For example, if a user repeats a certain behavior at a certain time of day, the analysis unit can learn that pattern and predict that the user will behave in a similar manner the next time. This allows the assistance system to anticipate the user's behavior and provide necessary support in advance. For example, if the user has a habit of drinking coffee every morning, the system can prompt the user to prepare coffee at that time of day. Also, if the user has a habit of going to a certain place on a certain day of the week, the system can provide information related to that place in advance.

[0105] The analysis unit can analyze the user's social network data to grasp the user's interests and human relationships. For example, it can analyze the people with whom the user frequently communicates and the topics that the user frequently talks about. This allows the assistance system to provide appropriate information based on the user's interests. For example, if the user frequently communicates with a specific friend, it can provide information related to that friend. Also, if the user is interested in a specific topic, it can provide the latest information related to that topic.

[0106] The providing unit can estimate the user's emotion and adjust the information presentation method based on the estimated emotion. For example, when the user is relaxed, detailed information can be provided. When the user is stressed, brief information can be provided. When the user is excited, visually stimulating information can be provided. In this way, the assistance system can select the optimal information presentation method according to the user's emotion and reduce the burden on the user.

[0107] The providing unit can customize the method of providing information based on the user's current activity status. For example, when the user is in a quiet environment, detailed information can be provided. When the user is moving, concise information can be provided. Furthermore, when the user is talking, information related to the content of the conversation can be provided. In this way, the assistance system can select the optimal information providing method according to the user's current status and improve the user's convenience.

[0108] The providing unit can provide optimal information by referring to the user's past behavioral history. For example, optimal information can be provided based on information that the user frequently requested in the past. The providing unit can also analyze the user's past behavioral history and provide related information. Furthermore, information required during a specific time period can be predicted and provided based on the user's past behavioral history. In this way, the support system can provide optimal information for the user by utilizing the user's past behavioral history.

[0109] The providing unit can provide optimal information taking into consideration the geographical location information of the user. For example, when the user is in a specific location, information related to the location can be provided. Also, when the user is moving, information related to the destination can be provided. Furthermore, when the user is participating in a specific event, information related to the event can be provided. In this way, the assistance system can provide optimal information for the user by utilizing the geographical location information of the user.

[0110] The providing unit can provide related information by analyzing the user's social media activity. For example, if the user is talking about a specific topic on social media, information related to the topic can be provided. Also, if the user is viewing a specific image on social media, information related to the image can be provided. Furthermore, if the user is watching a specific video on social media, information related to the video can be provided. In this way, the assistance system can utilize the user's social media activity to provide the most suitable information for the user.

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

[0112] Step 1: The collection unit collects gaze data and tone of voice data in the user's daily life. The user's daily life includes at home, at work, and in public places. The collection unit collects gaze data using an eye tracking device or camera data, and collects tone of voice data using voice recording and voice analysis. For example, when a user is looking at a specific object, the collection unit collects the gaze data and collects the tone of voice in which the user is speaking. Step 2: The analysis unit analyzes the gaze data and speech tone data collected by the collection unit. The analysis is performed using data pre-processing and analysis algorithms. For example, the collected gaze data is analyzed to estimate whether the user wants a specific item, and the collected speech tone data is analyzed to estimate what the user wants to say. Step 3: The estimation unit infers the user's intentions and emotions based on the data analyzed by the analysis unit. The estimation is performed using an emotion recognition algorithm and an intention estimation model. For example, if a user wants to say "I want water" but is unable to express themselves clearly due to slurred speech, the estimation unit can infer the user's intention from their gaze and tone of voice and convey "I want water" to the other person. Step 4: The provision unit outputs appropriate information based on the intent and emotion estimated by the estimation unit. The output is done using text display or voice output. For example, when a user wants to obtain necessary information, the AI ​​will understand the intent and provide the appropriate information.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation unit, and provision unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects eye gaze data and tone of voice data in the user's daily life using the camera 42 and microphone 38B of the smart device 14. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's intention and emotion. The estimation unit, for example, estimates the user's intention and emotion based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, for example, outputs appropriate information based on the intention and emotion estimated by the control unit 46A of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects eye gaze data and tone of voice data in the user's daily life using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's intention and emotion. The estimation unit, for example, estimates the user's intention and emotion based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, for example, outputs appropriate information based on the intention and emotion estimated by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0135] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects eye gaze data and tone of voice data in the user's daily life using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's intention and emotion. The estimation unit, for example, estimates the user's intention and emotion based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, for example, outputs appropriate information based on the intention and emotion estimated by the control unit 46A of the headset type terminal 314. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

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

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] Each of the multiple elements including the above-mentioned collection unit, analysis unit, estimation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects eye gaze data and tone of voice data in the user's daily life using the camera 42 and microphone 238 of the robot 414. The analysis unit, for example, analyzes the data collected by the specific processing unit 290 of the data processing device 12 to grasp the user's intention and emotions. The estimation unit, for example, estimates the user's intention and emotions based on the data analyzed by the specific processing unit 290 of the data processing device 12. The provision unit, for example, outputs appropriate information based on the intention and emotions estimated by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

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

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

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

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

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

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

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

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

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

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

[0184] (Appendix 1) A collection unit that collects eye gaze data and tone of speech data in the user's daily life; an analysis unit that analyzes the gaze data and tone of voice data collected by the collection unit; an estimation unit that estimates a user's intention or emotion based on the data analyzed by the analysis unit; A providing unit that outputs information that meets the needs of a user based on the intention or emotion estimated by the estimation unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect information on where users are looking 2. The system of claim 1, (Appendix 3) The collecting unit includes: Collect information about the user's tone of voice 2. The system of claim 1, (Appendix 4) The analysis unit is Analyze collected gaze data to infer whether the user wants a specific item 2. The system of claim 1, (Appendix 5) The analysis unit is Analyze collected speech data and estimate the specific content of what the user said 2. The system of claim 1, (Appendix 6) The providing unit is Output the information the user needs based on the estimated intent and emotion 2. The system of claim 1, (Appendix 7) The collecting unit includes: To estimate a user's emotion and adjust a collection timing of eye movement data and tone of voice data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Analyze the user's past gaze data and tone of voice data and select an efficient collection method 2. The system of claim 1, (Appendix 9) The collecting unit includes: When collecting eye gaze and tone data, filtering is performed based on the user's current activity and environment. 2. The system of claim 1, (Appendix 10) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The collecting unit includes: When collecting eye gaze data and tone of voice data, the system takes into account the user's geographic location information to prioritize collection of data that is more relevant. 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting eye contact data and tone of voice data, analyze users' social media activity and collect related data. 2. The system of claim 1, (Appendix 13) The analysis unit is To estimate a user's emotion and adjust an analysis method of eye movement data and tone of speech data based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail based on the importance of the data 2. The system of claim 1, (Appendix 15) The analysis unit is During analysis, apply different analysis algorithms depending on the category of data 2. The system of claim 1, (Appendix 16) The analysis unit is Estimate user emotions and determine analysis priorities based on the estimated user emotions 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when the data was collected 2. The system of claim 1, (Appendix 18) The analysis unit is During analysis, adjust the order of analysis based on data relevance 2. The system of claim 1, (Appendix 19) The estimation unit is Estimate the user's emotions and adjust the estimation method of the intention and emotions based on the estimated user emotions. 2. The system of claim 1, (Appendix 20) The estimation unit is When making an estimation, the estimation algorithm is optimized by referring to past estimation data. 2. The system of claim 1, (Appendix 21) The estimation unit is Improve the accuracy of estimation by considering correlations in the data during estimation 2. The system of claim 1, (Appendix 22) The estimation unit is Inferring a user's emotions and adjusting the display method of the inferred results based on the inferred user's emotions 2. The system of claim 1, (Appendix 23) The estimation unit is When making estimates, the geographic distribution of the data is taken into account. 2. The system of claim 1, (Appendix 24) The estimation unit is When estimating, refer to relevant literature on the data to improve the accuracy of the estimation 2. The system of claim 1, (Appendix 25) The providing unit is Infer the user's emotions and adjust the way information is output based on the inferred user emotions 2. The system of claim 1, (Appendix 26) The providing unit is At the time of output, provide useful information to the user by referring to the user's past behavior history. 2. The system of claim 1, (Appendix 27) The providing unit is Upon output, customize how information is presented based on the user's current context 2. The system of claim 1, (Appendix 28) The providing unit is Estimate user emotions and prioritize information based on the estimated user emotions 2. The system of claim 1, (Appendix 29) The providing unit is When outputting, provide optimal information by considering the user's geographic location information 2. The system of claim 1, (Appendix 30) The providing unit is At the time of output, the user's social media activity is analyzed to provide relevant information 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects eye gaze data and tone of speech data in the user's daily life; an analysis unit that analyzes the gaze data and tone of voice data collected by the collection unit; an estimation unit that estimates a user's intention or emotion based on the data analyzed by the analysis unit; A providing unit that outputs information that meets the needs of a user based on the intention or emotion estimated by the estimation unit. A system characterized by:

2. The collecting unit includes: Collect information on where users are looking 2. The system of claim 1 .

3. The collecting unit includes: Collect information about the user's tone of voice 2. The system of claim 1 .

4. The analysis unit is Analyzing the gaze data collected by the collection unit to estimate whether the user desires a specific item.

2. The system of claim 1 .

5. The analysis unit is The speech tone data collected by the collecting unit is analyzed, and specific contents of the user's speech are estimated.

2. The system of claim 1 .

6. The collecting unit includes: To estimate a user's emotion and adjust a collection timing of eye movement data and tone of voice data based on the estimated user's emotion.

2. The system of claim 1 .

7. The collecting unit includes: Analyze the user's past gaze data and tone of voice data and select an efficient collection method 2. The system of claim 1 .

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