System

Abstract

To provide a system capable of accurately comprehending the decision-making status of dementia patients, documenting and saving the same.SOLUTION: A system in an embodiment includes an analysis unit, a determination unit, a storage unit, and a feedback unit. The analysis unit analyzes the facial expression or tone of a patient. The determination unit determines the status of the decision based on the results analyzed by the analysis unit. The storage unit documents and stores the will determined by the determination unit. The feedback unit receives feedback based on the document stored by the storage 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, it was difficult to accurately grasp the decision-making state of dementia patients and document and store it.

[0005] The system according to the embodiment aims to accurately grasp the decision-making state of a dementia patient, document it, and store it. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a determination unit, a storage unit, and a feedback unit. The analysis unit analyzes the patient's facial expression or tone of voice. The determination unit determines the state of decision-making based on the result of the analysis by the analysis unit. The storage unit documents and stores the intention determined by the determination unit. The feedback unit receives feedback based on the document stored by the storage unit. Effect of the Invention

[0007] The system according to the embodiment can accurately grasp, document, and store the decision-making state of a dementia patient. [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 decision-making support system for dementia patients according to the embodiment of the present invention is a system in which AI analyzes the facial expressions and tone of speech of dementia patients, judges the state of decision-making, and documents and stores the judged intention. This system aims to provide respect for the will and support to dementia patients and their families. Dementia makes decision-making difficult, but at that time, there are many situations where the family wants to hear the intention. In addition, even if preparations are made in advance, the intention may change depending on the situation. Dementia is not always in an uneasy state, and there are cases where normal judgments can be made, so it is important to judge the situation. For example, AI analyzes the facial expressions and tone of speech of dementia patients in real time. For example, when the patient is smiling or speaking in a calm tone, the AI ​​analyzes the facial expressions and tone of speech and judges the state of decision-making. Next, the judged intention is documented and saved in digital format. This allows the patient's intention to be confirmed later. In addition, the system provides feedback to improve accuracy. For example, family members and medical staff provide feedback on the system's judgment results, and the AI ​​learns based on that feedback. This improves the accuracy of the system and enables more accurate intention judgment. This system provides respect for the will and support to dementia patients and their families. For example, even if a patient has difficulty clearly communicating his or her wishes, the system can determine those wishes and communicate them to the family. Also, even if the patient's wishes change, the system can capture the change and provide the latest wishes to the family. In this way, the present invention is a system for supporting the decision-making of dementia patients and facilitating communication with their families. As a result, the decision-making support system for dementia patients can accurately determine the wishes of dementia patients and communicate them to their families.

[0029] The decision support system for dementia patients according to the embodiment includes an analysis unit, a determination unit, a storage unit, and a feedback unit. The analysis unit analyzes facial expressions or tone of voice. The analysis unit, for example, analyzes facial expressions using a facial expression analysis method. For example, facial expressions such as smile, anger, and sadness of the patient can be classified using face recognition technology. The analysis unit also analyzes tone of voice using a tone of voice analysis method. For example, the tone, speed, and strength of the patient's voice can be analyzed using voice recognition technology. The determination unit determines the state of decision-making based on the result of the analysis by the analysis unit. For example, the determination unit can determine whether the patient's intention is positive or negative based on the analysis result. The storage unit documents and stores the intention determined by the determination unit. The storage unit can digitally store the determined intention in text format or audio format. The feedback unit receives feedback based on the document stored by the storage unit. The feedback unit receives feedback from, for example, family members or medical staff, and the AI ​​can learn based on the feedback. As a result, the decision support system for dementia patients according to the embodiment can accurately determine the intention of the dementia patient and convey it to the family. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input feedback from family members or medical staff to the AI, and the AI ​​may learn based on the feedback.

[0030] The analysis unit analyzes facial expressions or tone of voice. The analysis unit analyzes facial expressions using, for example, a facial expression analysis method. Specifically, the analysis unit detects facial feature points of the patient using face recognition technology and classifies emotions such as smile, anger, and sadness. The facial feature points are calculated based on the positions and shapes of the eyes, mouth, eyebrows, etc., and the patient's emotional state is estimated by analyzing changes in these feature points. The analysis unit also analyzes tone of voice using a tone of voice analysis method. The analysis unit uses voice recognition technology to analyze the tone, speed, strength, etc. of the patient's voice and estimates emotions and intentions. For example, a high tone and a fast voice speed can indicate an excited state, and a low tone and a slow voice speed can indicate a calm state. These analysis results are processed in real time, and the patient's current emotional state and intentions can be quickly grasped. Furthermore, the analysis unit can perform more accurate analysis by taking into account past data and individual characteristics of the patient. For example, the analysis accuracy can be improved by learning the patient's past facial expression and tone of voice data and grasping individual trends and patterns. This enables the analysis unit to accurately analyze the patient's emotions and intentions and provide the necessary information to the determination unit, which is the next step.

[0031] The determination unit determines the state of decision-making based on the results of the analysis by the analysis unit. Specifically, based on the analysis results, it determines whether the patient's intention is positive or negative. For example, based on the data of facial expressions and tone of voice provided by the analysis unit, it determines whether the patient has a positive or negative reaction to a specific question. The determination unit can use a machine learning algorithm to learn past data and patterns and make a more accurate determination. For example, it learns what facial expressions and tone of voice the patient used in the past to show a positive or negative intention, and makes a determination by comparing them with the current analysis results. In addition, the determination unit can integrate multiple analysis results to make a comprehensive determination. For example, it combines the results of the facial expression analysis and the results of the tone of voice analysis to make a more reliable determination of decision-making. As a result, the determination unit can accurately determine the patient's intention and provide the necessary information to the storage unit, which is the next step.

[0032] The storage unit documents and stores the intention determined by the determination unit. Specifically, the determined intention is digitally stored in text or audio format. For example, if the patient indicates a positive intention in response to a specific question, the intention is recorded in text format and stored as digital data. It is also possible to store the intention in audio format, and the patient's voice can be recorded and stored as is. The storage unit safely manages these data and enables rapid access when necessary. For example, the data can be stored using cloud storage and accessed by family members or medical staff. In addition, the stored data has a search function for later reference, and can be quickly searched based on a specific date and time or content. This allows the storage unit to accurately record the patient's intention and enable rapid access when necessary.

[0033] The feedback unit receives feedback based on the documents stored by the storage unit. Specifically, the feedback unit receives feedback from family members and medical staff, and the AI ​​learns based on the feedback. For example, the family members and medical staff check the stored data and evaluate whether the patient's intention has been accurately determined. The feedback unit receives the evaluation result and inputs it into the AI. The AI ​​can learn based on the feedback and improve the accuracy of the next analysis and judgment. For example, based on the feedback, the method of analyzing specific facial expressions and tone of voice can be improved to make a more accurate judgment. In addition, the feedback unit can collect opinions and requests from family members and medical staff and use them to improve the entire system. In this way, the feedback unit can continuously improve the accuracy and reliability of the system and more effectively support patients' decision-making.

[0034] The analysis unit can analyze the facial expression using a facial expression analysis method. The facial expression analysis method includes, for example, face recognition technology and a facial expression recognition algorithm. The analysis unit can classify the facial expressions of the patient, such as smile, anger, and sadness, using, for example, face recognition technology. The analysis unit can also analyze changes in the patient's facial expression in real time using a facial expression recognition algorithm. For example, the facial recognition technology extracts facial feature points based on a face image captured by a camera and classifies the facial expression. The facial expression recognition algorithm analyzes the movement of the facial feature points and detects changes in the facial expression. As a result, the accuracy of the analysis of the facial expression is improved by using the facial expression analysis method. 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 facial recognition technology to AI, which extracts facial feature points and classifies the facial expression.

[0035] The analysis unit can analyze the tone of speech using a tone of speech analysis method. The tone of speech analysis method includes, for example, a voice recognition technology and a voice analysis algorithm. The analysis unit can analyze the tone, speed, strength, and the like of the patient's voice using, for example, a voice recognition technology. The analysis unit can also analyze changes in the patient's tone of speech in real time using a voice analysis algorithm. For example, the voice recognition technology extracts voice features and analyzes the tone of speech based on voice data recorded by a microphone. The voice analysis algorithm analyzes changes in the voice features and detects changes in the tone of speech. As a result, the tone of speech analysis method is used to improve the analysis accuracy of the tone of speech. 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 voice recognition technology to AI, which extracts voice features and analyzes the tone of speech.

[0036] The feedback unit can receive feedback from family members or medical staff, and the AI ​​can learn based on the feedback. The feedback unit can receive comments and evaluations from family members and medical staff, for example, and the AI ​​can learn based on the feedback. For example, a family member can provide a comment on the patient's intention determination result, and the AI ​​can learn based on the comment. Also, a medical staff member can provide an evaluation on the patient's intention determination result, and the AI ​​can learn based on the evaluation. As a result, the AI ​​learns based on the feedback, and the accuracy of the system is improved. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the feedback unit can input feedback from family members and medical staff to the AI, and the AI ​​can learn based on the feedback.

[0037] The storage unit can store the determined intention in a digital format. Digital formats include, for example, PDF format, text format, and audio format. The storage unit can store, for example, the determined intention in a text format. The storage unit can also store the determined intention in an audio format. For example, the storage unit can store the determined intention as a text file so that it can be confirmed later. The storage unit can also store the determined intention as an audio file so that it can be played later. Thereby, by storing in a digital format, it becomes easy to confirm the intention. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the determined intention to AI, and the AI ​​can store it in text format or audio format.

[0038] The judgment unit can judge the state of decision-making based on the analysis result. The analysis result includes, for example, numerical data, graphs, text, and the like. The judgment unit can judge, for example, whether the patient's will is positive or negative based on the analysis result. The judgment unit can also judge, for example, whether the patient's will is normal or unstable based on the analysis result. For example, the judgment unit judges whether the patient's will is positive or negative based on numerical data obtained as the analysis result. The judgment unit judges whether the patient's will is normal or unstable based on a graph obtained as the analysis result. This makes it possible to accurately judge the state of decision-making based on the analysis result. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the analysis result to AI, and the AI ​​can judge the state of decision-making.

[0039] The analysis unit can estimate the patient's emotion and adjust the analysis accuracy of the facial expression and tone of speech based on the estimated emotion. For example, when the patient is relaxed, the analysis unit allows the AI ​​to analyze subtle changes in the facial expression and tone of speech in more detail. In addition, when the patient is nervous, the analysis unit can also allow the AI ​​to analyze large changes in the facial expression and tone of speech with priority. Furthermore, when the patient is excited, the analysis unit can also allow the AI ​​to analyze rapid changes in the facial expression and tone of speech with priority. This improves the accuracy of analysis by adjusting the analysis accuracy based on the 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 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, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the AI, which can estimate the emotion and adjust the analysis accuracy of the facial expression and tone of speech.

[0040] The analysis unit can optimize the analysis algorithm by referring to the data of the patient's past facial expressions and tone of voice. The analysis unit can improve the analysis accuracy of the current smile based on, for example, the data of the patient's past smile. The analysis unit can also improve the analysis accuracy of the current angry facial expression based on the data of the patient's past angry facial expression. Furthermore, the analysis unit can also improve the analysis accuracy of the current tone of voice based on the data of the patient's past calm tone of voice. As a result, the accuracy of the analysis algorithm is improved by referring to the past 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 data of the patient's past facial expressions and tone of voice into AI, and the AI ​​can optimize the analysis algorithm based on the data.

[0041] The analysis unit can perform the analysis by taking into account the patient's current health condition and environmental factors when analyzing facial expressions and tone of voice. For example, when the patient is tired, the analysis unit allows the AI ​​to more carefully analyze changes in facial expressions and tone of voice. In addition, when the patient is in a hospital, the analysis unit can also analyze the tone of voice by taking into account environmental sounds. Furthermore, when the patient is outdoors, the analysis unit can also analyze facial expressions by taking into account the influence of light. This improves the accuracy of the analysis by taking into account the health condition and environmental factors. 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 data on the patient's health condition and environmental factors into the AI, and the AI ​​can perform analysis based on the data.

[0042] The analysis unit can estimate the patient's emotion and determine the priority of the analysis results based on the estimated emotion. For example, when the patient is sad, the analysis unit can display the analysis results of the AI ​​with a sad expression and tone of voice with priority. Furthermore, when the patient is happy, the analysis unit can also display the analysis results of the AI ​​with a happy expression and tone of voice with priority. Furthermore, when the patient is angry, the analysis unit can also display the analysis results of the AI ​​with an angry expression and tone of voice with priority. In this way, important analysis results can be displayed with priority by determining the priority based on the 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 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, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the AI, which can estimate the emotion and determine the priority of the analysis results.

[0043] The analysis unit can perform the analysis based on the geographical location information of the patient when analyzing facial expressions and tone of voice. For example, when the patient is at home, the analysis unit performs the analysis assuming a relaxed environment. Furthermore, when the patient is in a hospital, the analysis unit can also perform the analysis assuming a tense environment. Furthermore, when the patient is in a park, the analysis unit can also perform the analysis assuming a natural environment. This improves the accuracy of the analysis by taking the geographical location information into consideration. 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 geographical location information of the patient to AI, and the AI ​​can perform the analysis based on that information.

[0044] The analysis unit can analyze the patient's social media activity and obtain related data when analyzing facial expressions and tone of voice. The analysis unit estimates the patient's current emotional state based on, for example, photos and comments recently posted by the patient. The analysis unit can also analyze the tone of voice based on expressions and words frequently used by the patient. Furthermore, the analysis unit can analyze changes in emotions based on the patient's friendships on social media. This improves the accuracy of the analysis by analyzing social media activity. 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 data on the patient's social media activity into AI, and the AI ​​can perform analysis based on the data.

[0045] The determination unit can estimate the patient's emotion and determine the state of decision-making based on the estimated emotion. For example, when the patient is relaxed, the determination unit determines that the decision-making is normal. In addition, when the patient is tense, the determination unit can also determine that the decision-making is difficult. Furthermore, when the patient is excited, the determination unit can also determine that the decision-making is unstable. This makes it possible to accurately determine the state of decision-making based on 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input emotion data of the patient to an AI, which estimates the emotion and determines the state of decision-making.

[0046] The judgment unit can optimize the judgment algorithm by referring to the patient's past decision-making history at the time of judgment. The judgment unit judges the current decision based on, for example, a situation in which the patient made a normal decision in the past. The judgment unit can also judge the current decision based on a situation in which the patient made a difficult decision in the past. Furthermore, the judgment unit can also judge the current decision based on a situation in which the patient made an unstable decision in the past. As a result, the accuracy of the judgment algorithm is improved by referring to the past decision-making history. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs the patient's past decision-making history into AI, and the AI ​​can optimize the judgment algorithm based on the history.

[0047] The determination unit can determine the state of decision-making by taking into account the patient's current living situation and areas of interest at the time of determination. For example, if the patient is relaxed in the current living situation, the determination unit determines that the decision-making is normal. In addition, the determination unit can also determine that the decision-making is difficult if the patient is tense in the current living situation. Furthermore, the determination unit can also determine that the decision-making is unstable if the patient is excited in the current living situation. In this way, the state of decision-making can be accurately determined by taking into account the living situation and areas of interest. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can determine the state of decision-making based on the data.

[0048] The judgment unit can estimate the patient's emotion and adjust the display method of the judgment result based on the estimated emotion. For example, when the patient is relaxed, the judgment unit displays a detailed judgment result. Also, when the patient is tense, the judgment unit can display a concise judgment result. Furthermore, when the patient is excited, the judgment unit can provide a visually calm display method. This makes it easier to understand the judgment result by adjusting the display method based on the 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the patient's emotion data to an AI, which estimates the emotion, and adjust the display method of the judgment result.

[0049] The determination unit can determine the state of decision-making based on the geographical location information of the patient at the time of determination. For example, when the patient is at home, the determination unit makes a determination on the premise of a relaxed environment. Furthermore, when the patient is in a hospital, the determination unit can also make a determination on the premise of a tense environment. Furthermore, when the patient is in a park, the determination unit can also make a determination on the premise of a natural environment. In this way, the state of decision-making can be accurately determined by taking into account the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the geographical location information of the patient into AI, and the AI ​​can determine the state of decision-making based on that information.

[0050] At the time of judgment, the judgment unit can analyze the social media activity of the patient and judge the decision-making state by referring to the related data. The judgment unit judges the current decision-making state based on, for example, photos and comments recently posted by the patient. The judgment unit can also judge the decision-making state based on expressions and words frequently used by the patient. Furthermore, the judgment unit can judge the decision-making state based on the friend relationships on social media of the patient. In this way, the decision-making state can be accurately judged by analyzing the social media activity. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input data of the patient's social media activity into AI, and the AI ​​can judge the decision-making state based on the data.

[0051] The storage unit can estimate the patient's emotion and adjust the format of the document to be stored based on the estimated emotion. For example, when the patient is relaxed, the storage unit stores the document in a detailed document format. Also, when the patient is nervous, the storage unit can store the document in a concise document format. Furthermore, when the patient is excited, the storage unit can store the document in a visually calm document format. This makes it easier to understand the stored document by adjusting the document format based on the 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the patient's emotion data into an AI, which estimates the emotion and adjusts the document format.

[0052] The storage unit can optimize the storage algorithm by referring to the patient's past decision-making history at the time of storage. The storage unit stores the current document based on, for example, a situation in which the patient made a normal decision in the past. The storage unit can also store the current document based on a situation in which the patient made a difficult decision in the past. Furthermore, the storage unit can also store the current document based on a situation in which the patient made an unstable decision in the past. This improves the accuracy of the storage algorithm by referring to the past decision-making history. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the patient's past decision-making history into AI, and the AI ​​can optimize the storage algorithm based on the history.

[0053] The storage unit can store the document taking into consideration the patient's current living situation and areas of interest at the time of storage. For example, if the patient is relaxed in the current living situation, the storage unit stores a detailed document. In addition, if the patient is tense in the current living situation, the storage unit can store a concise document. Furthermore, if the patient is excited in the current living situation, the storage unit can store a visually calming document. This improves the accuracy of the stored document by taking into consideration the living situation and areas of interest. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can store the document based on the data.

[0054] The storage unit can estimate the patient's emotion and determine the priority of documents to be stored based on the estimated emotion. For example, when the patient is relaxed, the storage unit preferentially stores detailed documents. In addition, when the patient is nervous, the storage unit can also preferentially store concise documents. Furthermore, when the patient is excited, the storage unit can also preferentially store visually calm documents. In this way, important documents can be preferentially stored by determining the priority based on 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the patient's emotion data into an AI, which can estimate the emotion and determine the priority of the documents.

[0055] The storage unit can store the document based on the geographical location information of the patient at the time of storage. For example, when the patient is at home, the storage unit stores the document on the premise of a relaxed environment. Also, when the patient is in a hospital, the storage unit can store the document on the premise of a tense environment. Furthermore, when the patient is in a park, the storage unit can store the document on the premise of a natural environment. This improves the accuracy of the stored document by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the patient to AI, and the AI ​​can store the document based on that information.

[0056] The storage unit can analyze the patient's social media activity and store the document by referring to the related data when storing. The storage unit can store the current document based on, for example, photos and comments recently posted by the patient. The storage unit can also store the document based on expressions and words frequently used by the patient. Furthermore, the storage unit can store the document based on the patient's friendships on social media. This improves the accuracy of the stored document by analyzing the social media activity. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input data of the patient's social media activity into AI, and the AI ​​can store the document based on the data.

[0057] The feedback unit can estimate the patient's emotion and adjust the content of the feedback based on the estimated emotion. For example, the feedback unit provides detailed feedback when the patient is relaxed. In addition, the feedback unit can also provide brief feedback when the patient is tense. Furthermore, the feedback unit can provide visually calming feedback when the patient is excited. This allows appropriate feedback to be provided by adjusting the content of the feedback based on the 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the patient's emotion data into an AI, which estimates the emotion and adjusts the content of the feedback.

[0058] The feedback unit can optimize the feedback algorithm by referring to the past feedback history of the family and medical staff at the time of feedback. The feedback unit can adjust the current feedback content based on the feedback provided by the family in the past, for example. The feedback unit can also adjust the current feedback content based on the feedback provided by the medical staff in the past. Furthermore, the feedback unit can analyze the feedback history of the family and medical staff and suggest an optimal feedback method. As a result, the accuracy of the feedback algorithm is improved by referring to the past feedback history. A part or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the feedback history of the family and medical staff into the AI, and the AI ​​can optimize the feedback algorithm based on the history.

[0059] The feedback unit can provide feedback while taking into consideration the patient's current living situation and areas of interest. For example, when the patient is relaxed in the current living situation, the feedback unit provides detailed feedback. In addition, when the patient is tense in the current living situation, the feedback unit can also provide concise feedback. Furthermore, when the patient is excited in the current living situation, the feedback unit can also provide visually calming feedback. This allows appropriate feedback to be provided by taking into consideration the living situation and areas of interest. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can provide feedback based on the data.

[0060] The feedback unit can estimate the patient's emotion and determine the priority of the feedback based on the estimated emotion. For example, when the patient is relaxed, the feedback unit can preferentially provide detailed feedback. In addition, when the patient is tense, the feedback unit can also preferentially provide brief feedback. Furthermore, when the patient is excited, the feedback unit can also preferentially provide visually calm feedback. In this way, important feedback can be preferentially provided by determining the priority based on the 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the patient's emotion data into an AI, which can estimate the emotion and determine the priority of the feedback.

[0061] The feedback unit can provide feedback based on the geographical location information of the patient at the time of feedback. For example, when the patient is at home, the feedback unit provides feedback on the premise of a relaxed environment. Also, when the patient is in a hospital, the feedback unit can provide feedback on the premise of a tense environment. Furthermore, when the patient is in a park, the feedback unit can provide feedback on the premise of a natural environment. In this way, appropriate feedback can be provided by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information of the patient to AI, and the AI ​​can provide feedback based on that information.

[0062] At the time of providing feedback, the feedback unit can analyze the social media activity of the patient and provide feedback by referring to related data. The feedback unit can adjust the current feedback content based on, for example, photos and comments recently posted by the patient. The feedback unit can also adjust the feedback content based on expressions and words frequently used by the patient. Furthermore, the feedback unit can also adjust the feedback content based on the patient's friendships on social media. In this way, appropriate feedback can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input data on the patient's social media activity into AI, and the AI ​​can provide feedback based on the data.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The analysis unit can acquire the patient's biometric data and reflect it in the analysis results. For example, biometric data such as heart rate, blood pressure, and body temperature can be acquired in real time to more accurately determine the patient's emotions and decision-making state. The analysis unit can also analyze the patient's sleep patterns and consider the impact of lack of sleep and excessive stress on decision-making. Furthermore, the analysis unit can analyze the patient's amount of exercise and consider the impact of lack of exercise and excessive exercise on decision-making. In this way, the accuracy of the analysis can be improved by utilizing biometric data.

[0065] The determination unit can determine the state of decision-making by taking into account the social relationships of the patient. For example, if the patient has good relationships with family and friends, it can determine that the decision-making is normal. In addition, if the patient is isolated, it can also determine that the decision-making is difficult. Furthermore, if the patient is building new social relationships, it can also determine that the decision-making is unstable. In this way, by taking into account the social relationships, it is possible to more accurately determine the state of decision-making.

[0066] The storage unit can perform access control for the stored documents. For example, it can limit the range of access that family members and medical staff can have, thereby protecting privacy. The storage unit can also record the access history and track who accessed the document and when. Furthermore, the storage unit can dynamically change the access authority, adding or deleting the access authority as necessary. This improves the security of the stored documents.

[0067] The feedback unit can visually display the feedback content. For example, the feedback content can be displayed in a graph or chart to make it easier to understand visually. The feedback unit can also display the feedback content in animation to dynamically convey information. Furthermore, the feedback unit can display the feedback content in infographics to convey complex information concisely. This makes the feedback content easier to understand.

[0068] The analysis unit can perform analysis taking into account the patient's hobbies and interests. For example, if the patient likes music, the facial expression and tone of voice when listening to music can be analyzed to determine whether the patient is relaxed. If the patient likes reading, the facial expression and tone of voice while reading can be analyzed to determine whether the patient is concentrating. Furthermore, if the patient likes sports, the facial expression and tone of voice while watching sports can be analyzed to determine whether the patient is excited. In this way, the accuracy of the analysis can be improved by taking into account hobbies and interests.

[0069] The process flow of the first embodiment will be briefly described below.

[0070] Step 1: The analysis unit analyzes facial expressions or tone of voice. For example, the analysis unit analyzes facial expressions using a facial expression analysis method. Using face recognition technology, the patient's facial expressions such as smile, anger, sadness, etc. can be classified. The analysis unit also analyzes tone of voice using a tone of voice analysis method. Using voice recognition technology, the tone, speed, strength, etc. of the patient's voice can be analyzed. Step 2: The determination unit determines the state of decision-making based on the results of the analysis by the analysis unit. The determination unit can determine, for example, whether the patient's will is positive or negative based on the analysis results. Step 3: The storage unit documents and stores the intention determined by the determination unit. The storage unit can digitally store the determined intention in text or audio format, for example. Step 4: The feedback unit receives feedback based on the documents stored by the storage unit. The feedback unit receives feedback from, for example, family members or medical staff, and the AI ​​can learn based on that feedback. This allows the system to accurately determine the wishes of the dementia patient and convey them to the family.

[0071] (Example 2) The decision-making support system for dementia patients according to the embodiment of the present invention is a system in which AI analyzes the facial expressions and tone of speech of dementia patients, judges the state of decision-making, and documents and stores the judged intention. This system aims to provide respect for the will and support to dementia patients and their families. Dementia makes decision-making difficult, but at that time, there are many situations where the family wants to hear the intention. In addition, even if preparations are made in advance, the intention may change depending on the situation. Dementia is not always in an uneasy state, and there are cases where normal judgments can be made, so it is important to judge the situation. For example, AI analyzes the facial expressions and tone of speech of dementia patients in real time. For example, when the patient is smiling or speaking in a calm tone, the AI ​​analyzes the facial expressions and tone of speech and judges the state of decision-making. Next, the judged intention is documented and saved in digital format. This allows the patient's intention to be confirmed later. In addition, the system provides feedback to improve accuracy. For example, family members and medical staff provide feedback on the system's judgment results, and the AI ​​learns based on that feedback. This improves the accuracy of the system and enables more accurate intention judgment. This system provides respect for the will and support to dementia patients and their families. For example, even if a patient has difficulty clearly communicating his or her wishes, the system can determine those wishes and communicate them to the family. Also, even if the patient's wishes change, the system can capture the change and provide the latest wishes to the family. In this way, the present invention is a system for supporting the decision-making of dementia patients and facilitating communication with their families. As a result, the decision-making support system for dementia patients can accurately determine the wishes of dementia patients and communicate them to their families.

[0072] The decision support system for dementia patients according to the embodiment includes an analysis unit, a determination unit, a storage unit, and a feedback unit. The analysis unit analyzes facial expressions or tone of voice. The analysis unit, for example, analyzes facial expressions using a facial expression analysis method. For example, facial expressions such as smile, anger, and sadness of the patient can be classified using face recognition technology. The analysis unit also analyzes tone of voice using a tone of voice analysis method. For example, the tone, speed, and strength of the patient's voice can be analyzed using voice recognition technology. The determination unit determines the state of decision-making based on the result of the analysis by the analysis unit. For example, the determination unit can determine whether the patient's intention is positive or negative based on the analysis result. The storage unit documents and stores the intention determined by the determination unit. The storage unit can digitally store the determined intention in text format or audio format. The feedback unit receives feedback based on the document stored by the storage unit. The feedback unit receives feedback from, for example, family members or medical staff, and the AI ​​can learn based on the feedback. As a result, the decision support system for dementia patients according to the embodiment can accurately determine the intention of the dementia patient and convey it to the family. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input feedback from family members or medical staff to the AI, and the AI ​​may learn based on the feedback.

[0073] The analysis unit analyzes facial expressions or tone of voice. The analysis unit analyzes facial expressions using, for example, a facial expression analysis method. Specifically, the analysis unit detects facial feature points of the patient using face recognition technology and classifies emotions such as smile, anger, and sadness. The facial feature points are calculated based on the positions and shapes of the eyes, mouth, eyebrows, etc., and the patient's emotional state is estimated by analyzing changes in these feature points. The analysis unit also analyzes tone of voice using a tone of voice analysis method. The analysis unit uses voice recognition technology to analyze the tone, speed, strength, etc. of the patient's voice and estimates emotions and intentions. For example, a high tone and a fast voice speed can indicate an excited state, and a low tone and a slow voice speed can indicate a calm state. These analysis results are processed in real time, and the patient's current emotional state and intentions can be quickly grasped. Furthermore, the analysis unit can perform more accurate analysis by taking into account past data and individual characteristics of the patient. For example, the analysis accuracy can be improved by learning the patient's past facial expression and tone of voice data and grasping individual trends and patterns. This enables the analysis unit to accurately analyze the patient's emotions and intentions and provide the necessary information to the determination unit, which is the next step.

[0074] The determination unit determines the state of decision-making based on the results of the analysis by the analysis unit. Specifically, based on the analysis results, it determines whether the patient's intention is positive or negative. For example, based on the data of facial expressions and tone of voice provided by the analysis unit, it determines whether the patient has a positive or negative reaction to a specific question. The determination unit can use a machine learning algorithm to learn past data and patterns and make a more accurate determination. For example, it learns what facial expressions and tone of voice the patient used in the past to show a positive or negative intention, and makes a determination by comparing them with the current analysis results. In addition, the determination unit can integrate multiple analysis results to make a comprehensive determination. For example, it combines the results of the facial expression analysis and the results of the tone of voice analysis to make a more reliable determination of decision-making. As a result, the determination unit can accurately determine the patient's intention and provide the necessary information to the storage unit, which is the next step.

[0075] The storage unit documents and stores the intention determined by the determination unit. Specifically, the determined intention is digitally stored in text or audio format. For example, if the patient indicates a positive intention in response to a specific question, the intention is recorded in text format and stored as digital data. It is also possible to store the intention in audio format, and the patient's voice can be recorded and stored as is. The storage unit safely manages these data and enables rapid access when necessary. For example, the data can be stored using cloud storage and accessed by family members or medical staff. In addition, the stored data has a search function for later reference, and can be quickly searched based on a specific date and time or content. This allows the storage unit to accurately record the patient's intention and enable rapid access when necessary.

[0076] The feedback unit receives feedback based on the documents stored by the storage unit. Specifically, the feedback unit receives feedback from family members and medical staff, and the AI ​​learns based on the feedback. For example, the family members and medical staff check the stored data and evaluate whether the patient's intention has been accurately determined. The feedback unit receives the evaluation result and inputs it into the AI. The AI ​​can learn based on the feedback and improve the accuracy of the next analysis and judgment. For example, based on the feedback, the method of analyzing specific facial expressions and tone of voice can be improved to make a more accurate judgment. In addition, the feedback unit can collect opinions and requests from family members and medical staff and use them to improve the entire system. In this way, the feedback unit can continuously improve the accuracy and reliability of the system and more effectively support patients' decision-making.

[0077] The analysis unit can analyze the facial expression using a facial expression analysis method. The facial expression analysis method includes, for example, face recognition technology and a facial expression recognition algorithm. The analysis unit can classify the facial expressions of the patient, such as smile, anger, and sadness, using, for example, face recognition technology. The analysis unit can also analyze changes in the patient's facial expression in real time using a facial expression recognition algorithm. For example, the facial recognition technology extracts facial feature points based on a face image captured by a camera and classifies the facial expression. The facial expression recognition algorithm analyzes the movement of the facial feature points and detects changes in the facial expression. As a result, the accuracy of the analysis of the facial expression is improved by using the facial expression analysis method. 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 facial recognition technology to AI, which extracts facial feature points and classifies the facial expression.

[0078] The analysis unit can analyze the tone of speech using a tone of speech analysis method. The tone of speech analysis method includes, for example, a voice recognition technology and a voice analysis algorithm. The analysis unit can analyze the tone, speed, strength, and the like of the patient's voice using, for example, a voice recognition technology. The analysis unit can also analyze changes in the patient's tone of speech in real time using a voice analysis algorithm. For example, the voice recognition technology extracts voice features and analyzes the tone of speech based on voice data recorded by a microphone. The voice analysis algorithm analyzes changes in the voice features and detects changes in the tone of speech. As a result, the tone of speech analysis method is used to improve the analysis accuracy of the tone of speech. 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 voice recognition technology to AI, which extracts voice features and analyzes the tone of speech.

[0079] The feedback unit can receive feedback from family members or medical staff, and the AI ​​can learn based on the feedback. The feedback unit can receive comments and evaluations from family members and medical staff, for example, and the AI ​​can learn based on the feedback. For example, a family member can provide a comment on the patient's intention determination result, and the AI ​​can learn based on the comment. Also, a medical staff member can provide an evaluation on the patient's intention determination result, and the AI ​​can learn based on the evaluation. As a result, the AI ​​learns based on the feedback, and the accuracy of the system is improved. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the feedback unit can input feedback from family members and medical staff to the AI, and the AI ​​can learn based on the feedback.

[0080] The storage unit can store the determined intention in a digital format. Digital formats include, for example, PDF format, text format, and audio format. The storage unit can store, for example, the determined intention in a text format. The storage unit can also store the determined intention in an audio format. For example, the storage unit can store the determined intention as a text file so that it can be confirmed later. The storage unit can also store the determined intention as an audio file so that it can be played later. Thereby, by storing in a digital format, it becomes easy to confirm the intention. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the determined intention to AI, and the AI ​​can store it in text format or audio format.

[0081] The judgment unit can judge the state of decision-making based on the analysis result. The analysis result includes, for example, numerical data, graphs, text, and the like. The judgment unit can judge, for example, whether the patient's will is positive or negative based on the analysis result. The judgment unit can also judge, for example, whether the patient's will is normal or unstable based on the analysis result. For example, the judgment unit judges whether the patient's will is positive or negative based on numerical data obtained as the analysis result. The judgment unit judges whether the patient's will is normal or unstable based on a graph obtained as the analysis result. This makes it possible to accurately judge the state of decision-making based on the analysis result. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input the analysis result to AI, and the AI ​​can judge the state of decision-making.

[0082] The analysis unit can estimate the patient's emotion and adjust the analysis accuracy of the facial expression and tone of speech based on the estimated emotion. For example, when the patient is relaxed, the analysis unit allows the AI ​​to analyze subtle changes in the facial expression and tone of speech in more detail. In addition, when the patient is nervous, the analysis unit can also allow the AI ​​to analyze large changes in the facial expression and tone of speech with priority. Furthermore, when the patient is excited, the analysis unit can also allow the AI ​​to analyze rapid changes in the facial expression and tone of speech with priority. This improves the accuracy of analysis by adjusting the analysis accuracy based on the 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 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, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the AI, which can estimate the emotion and adjust the analysis accuracy of the facial expression and tone of speech.

[0083] The analysis unit can optimize the analysis algorithm by referring to the data of the patient's past facial expressions and tone of voice. The analysis unit can improve the analysis accuracy of the current smile based on, for example, the data of the patient's past smile. The analysis unit can also improve the analysis accuracy of the current angry facial expression based on the data of the patient's past angry facial expression. Furthermore, the analysis unit can also improve the analysis accuracy of the current tone of voice based on the data of the patient's past calm tone of voice. As a result, the accuracy of the analysis algorithm is improved by referring to the past 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 data of the patient's past facial expressions and tone of voice into AI, and the AI ​​can optimize the analysis algorithm based on the data.

[0084] The analysis unit can perform the analysis by taking into account the patient's current health condition and environmental factors when analyzing facial expressions and tone of voice. For example, when the patient is tired, the analysis unit allows the AI ​​to more carefully analyze changes in facial expressions and tone of voice. In addition, when the patient is in a hospital, the analysis unit can also analyze the tone of voice by taking into account environmental sounds. Furthermore, when the patient is outdoors, the analysis unit can also analyze facial expressions by taking into account the influence of light. This improves the accuracy of the analysis by taking into account the health condition and environmental factors. 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 data on the patient's health condition and environmental factors into the AI, and the AI ​​can perform analysis based on the data.

[0085] The analysis unit can estimate the patient's emotion and determine the priority of the analysis results based on the estimated emotion. For example, when the patient is sad, the analysis unit can display the analysis results of the AI ​​with a sad expression and tone of voice with priority. Furthermore, when the patient is happy, the analysis unit can also display the analysis results of the AI ​​with a happy expression and tone of voice with priority. Furthermore, when the patient is angry, the analysis unit can also display the analysis results of the AI ​​with an angry expression and tone of voice with priority. In this way, important analysis results can be displayed with priority by determining the priority based on the 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 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, for example, using an AI, or may be performed without using an AI. For example, the analysis unit can input the patient's emotion data to the AI, which can estimate the emotion and determine the priority of the analysis results.

[0086] The analysis unit can perform the analysis based on the geographical location information of the patient when analyzing facial expressions and tone of voice. For example, when the patient is at home, the analysis unit performs the analysis assuming a relaxed environment. Furthermore, when the patient is in a hospital, the analysis unit can also perform the analysis assuming a tense environment. Furthermore, when the patient is in a park, the analysis unit can also perform the analysis assuming a natural environment. This improves the accuracy of the analysis by taking the geographical location information into consideration. 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 geographical location information of the patient to AI, and the AI ​​can perform the analysis based on that information.

[0087] The analysis unit can analyze the patient's social media activity and obtain related data when analyzing facial expressions and tone of voice. The analysis unit estimates the patient's current emotional state based on, for example, photos and comments recently posted by the patient. The analysis unit can also analyze the tone of voice based on expressions and words frequently used by the patient. Furthermore, the analysis unit can analyze changes in emotions based on the patient's friendships on social media. This improves the accuracy of the analysis by analyzing social media activity. 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 data on the patient's social media activity into AI, and the AI ​​can perform analysis based on the data.

[0088] The determination unit can estimate the patient's emotion and determine the state of decision-making based on the estimated emotion. For example, when the patient is relaxed, the determination unit determines that the decision-making is normal. In addition, when the patient is tense, the determination unit can also determine that the decision-making is difficult. Furthermore, when the patient is excited, the determination unit can also determine that the decision-making is unstable. This makes it possible to accurately determine the state of decision-making based on 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 determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input emotion data of the patient to an AI, which estimates the emotion and determines the state of decision-making.

[0089] The judgment unit can optimize the judgment algorithm by referring to the patient's past decision-making history at the time of judgment. The judgment unit judges the current decision based on, for example, a situation in which the patient made a normal decision in the past. The judgment unit can also judge the current decision based on a situation in which the patient made a difficult decision in the past. Furthermore, the judgment unit can also judge the current decision based on a situation in which the patient made an unstable decision in the past. As a result, the accuracy of the judgment algorithm is improved by referring to the past decision-making history. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit inputs the patient's past decision-making history into AI, and the AI ​​can optimize the judgment algorithm based on the history.

[0090] The determination unit can determine the state of decision-making by taking into account the patient's current living situation and areas of interest at the time of determination. For example, if the patient is relaxed in the current living situation, the determination unit determines that the decision-making is normal. In addition, the determination unit can also determine that the decision-making is difficult if the patient is tense in the current living situation. Furthermore, the determination unit can also determine that the decision-making is unstable if the patient is excited in the current living situation. In this way, the state of decision-making can be accurately determined by taking into account the living situation and areas of interest. Part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can determine the state of decision-making based on the data.

[0091] The judgment unit can estimate the patient's emotion and adjust the display method of the judgment result based on the estimated emotion. For example, when the patient is relaxed, the judgment unit displays a detailed judgment result. Also, when the patient is tense, the judgment unit can display a concise judgment result. Furthermore, when the patient is excited, the judgment unit can provide a visually calm display method. This makes it easier to understand the judgment result by adjusting the display method based on the 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 judgment unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the judgment unit can input the patient's emotion data to an AI, which estimates the emotion, and adjust the display method of the judgment result.

[0092] The determination unit can determine the state of decision-making based on the geographical location information of the patient at the time of determination. For example, when the patient is at home, the determination unit makes a determination on the premise of a relaxed environment. Furthermore, when the patient is in a hospital, the determination unit can also make a determination on the premise of a tense environment. Furthermore, when the patient is in a park, the determination unit can also make a determination on the premise of a natural environment. In this way, the state of decision-making can be accurately determined by taking into account the geographical location information. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit inputs the geographical location information of the patient into AI, and the AI ​​can determine the state of decision-making based on that information.

[0093] At the time of judgment, the judgment unit can analyze the social media activity of the patient and judge the decision-making state by referring to the related data. The judgment unit judges the current decision-making state based on, for example, photos and comments recently posted by the patient. The judgment unit can also judge the decision-making state based on expressions and words frequently used by the patient. Furthermore, the judgment unit can judge the decision-making state based on the friend relationships on social media of the patient. In this way, the decision-making state can be accurately judged by analyzing the social media activity. A part or all of the above-mentioned processing in the judgment unit may be performed, for example, using AI, or may be performed without using AI. For example, the judgment unit can input data of the patient's social media activity into AI, and the AI ​​can judge the decision-making state based on the data.

[0094] The storage unit can estimate the patient's emotion and adjust the format of the document to be stored based on the estimated emotion. For example, when the patient is relaxed, the storage unit stores the document in a detailed document format. Also, when the patient is nervous, the storage unit can store the document in a concise document format. Furthermore, when the patient is excited, the storage unit can store the document in a visually calm document format. This makes it easier to understand the stored document by adjusting the document format based on the 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the patient's emotion data into an AI, which estimates the emotion and adjusts the document format.

[0095] The storage unit can optimize the storage algorithm by referring to the patient's past decision-making history at the time of storage. The storage unit stores the current document based on, for example, a situation in which the patient made a normal decision in the past. The storage unit can also store the current document based on a situation in which the patient made a difficult decision in the past. Furthermore, the storage unit can also store the current document based on a situation in which the patient made an unstable decision in the past. This improves the accuracy of the storage algorithm by referring to the past decision-making history. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the patient's past decision-making history into AI, and the AI ​​can optimize the storage algorithm based on the history.

[0096] The storage unit can store the document taking into consideration the patient's current living situation and areas of interest at the time of storage. For example, if the patient is relaxed in the current living situation, the storage unit stores a detailed document. In addition, if the patient is tense in the current living situation, the storage unit can store a concise document. Furthermore, if the patient is excited in the current living situation, the storage unit can store a visually calming document. This improves the accuracy of the stored document by taking into consideration the living situation and areas of interest. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can store the document based on the data.

[0097] The storage unit can estimate the patient's emotion and determine the priority of documents to be stored based on the estimated emotion. For example, when the patient is relaxed, the storage unit preferentially stores detailed documents. In addition, when the patient is nervous, the storage unit can also preferentially store concise documents. Furthermore, when the patient is excited, the storage unit can also preferentially store visually calm documents. In this way, important documents can be preferentially stored by determining the priority based on 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 storage unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the storage unit can input the patient's emotion data into an AI, which can estimate the emotion and determine the priority of the documents.

[0098] The storage unit can store the document based on the geographical location information of the patient at the time of storage. For example, when the patient is at home, the storage unit stores the document on the premise of a relaxed environment. Also, when the patient is in a hospital, the storage unit can store the document on the premise of a tense environment. Furthermore, when the patient is in a park, the storage unit can store the document on the premise of a natural environment. This improves the accuracy of the stored document by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the storage unit may be performed, for example, using AI, or may be performed without using AI. For example, the storage unit can input the geographical location information of the patient to AI, and the AI ​​can store the document based on that information.

[0099] The storage unit can analyze the patient's social media activity and store the document by referring to the related data when storing. The storage unit can store the current document based on, for example, photos and comments recently posted by the patient. The storage unit can also store the document based on expressions and words frequently used by the patient. Furthermore, the storage unit can store the document based on the patient's friendships on social media. This improves the accuracy of the stored document by analyzing the social media activity. Some or all of the above-mentioned processing in the storage unit can be performed using, for example, AI, or can be performed without using AI. For example, the storage unit can input data of the patient's social media activity into AI, and the AI ​​can store the document based on the data.

[0100] The feedback unit can estimate the patient's emotion and adjust the content of the feedback based on the estimated emotion. For example, the feedback unit provides detailed feedback when the patient is relaxed. In addition, the feedback unit can also provide brief feedback when the patient is tense. Furthermore, the feedback unit can provide visually calming feedback when the patient is excited. This allows appropriate feedback to be provided by adjusting the content of the feedback based on the 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the patient's emotion data into an AI, which estimates the emotion and adjusts the content of the feedback.

[0101] The feedback unit can optimize the feedback algorithm by referring to the past feedback history of the family and medical staff at the time of feedback. The feedback unit can adjust the current feedback content based on the feedback provided by the family in the past, for example. The feedback unit can also adjust the current feedback content based on the feedback provided by the medical staff in the past. Furthermore, the feedback unit can analyze the feedback history of the family and medical staff and suggest an optimal feedback method. As a result, the accuracy of the feedback algorithm is improved by referring to the past feedback history. A part or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the feedback history of the family and medical staff into the AI, and the AI ​​can optimize the feedback algorithm based on the history.

[0102] The feedback unit can provide feedback while taking into consideration the patient's current living situation and areas of interest. For example, when the patient is relaxed in the current living situation, the feedback unit provides detailed feedback. In addition, when the patient is tense in the current living situation, the feedback unit can also provide concise feedback. Furthermore, when the patient is excited in the current living situation, the feedback unit can also provide visually calming feedback. This allows appropriate feedback to be provided by taking into consideration the living situation and areas of interest. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input data on the patient's living situation and areas of interest into AI, and the AI ​​can provide feedback based on the data.

[0103] The feedback unit can estimate the patient's emotion and determine the priority of the feedback based on the estimated emotion. For example, when the patient is relaxed, the feedback unit can preferentially provide detailed feedback. In addition, when the patient is tense, the feedback unit can also preferentially provide brief feedback. Furthermore, when the patient is excited, the feedback unit can also preferentially provide visually calm feedback. In this way, important feedback can be preferentially provided by determining the priority based on the 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 feedback unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the feedback unit can input the patient's emotion data into an AI, which can estimate the emotion and determine the priority of the feedback.

[0104] The feedback unit can provide feedback based on the geographical location information of the patient at the time of feedback. For example, when the patient is at home, the feedback unit provides feedback on the premise of a relaxed environment. Also, when the patient is in a hospital, the feedback unit can provide feedback on the premise of a tense environment. Furthermore, when the patient is in a park, the feedback unit can provide feedback on the premise of a natural environment. In this way, appropriate feedback can be provided by taking the geographical location information into consideration. A part or all of the above-mentioned processing in the feedback unit may be performed, for example, using AI, or may be performed without using AI. For example, the feedback unit can input the geographical location information of the patient to AI, and the AI ​​can provide feedback based on that information.

[0105] At the time of providing feedback, the feedback unit can analyze the social media activity of the patient and provide feedback by referring to related data. The feedback unit can adjust the current feedback content based on, for example, photos and comments recently posted by the patient. The feedback unit can also adjust the feedback content based on expressions and words frequently used by the patient. Furthermore, the feedback unit can also adjust the feedback content based on the patient's friendships on social media. In this way, appropriate feedback can be provided by analyzing social media activity. Some or all of the above-mentioned processing in the feedback unit can be performed, for example, using AI or can be performed without using AI. For example, the feedback unit can input data on the patient's social media activity into AI, and the AI ​​can provide feedback based on the data.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The analysis unit can acquire the patient's biometric data and reflect it in the analysis results. For example, biometric data such as heart rate, blood pressure, and body temperature can be acquired in real time to more accurately determine the patient's emotions and decision-making state. The analysis unit can also analyze the patient's sleep patterns and consider the impact of lack of sleep and excessive stress on decision-making. Furthermore, the analysis unit can analyze the patient's amount of exercise and consider the impact of lack of exercise and excessive exercise on decision-making. In this way, the accuracy of the analysis can be improved by utilizing biometric data.

[0108] The determination unit can determine the state of decision-making by taking into account the social relationships of the patient. For example, if the patient has good relationships with family and friends, it can determine that the decision-making is normal. In addition, if the patient is isolated, it can also determine that the decision-making is difficult. Furthermore, if the patient is building new social relationships, it can also determine that the decision-making is unstable. In this way, by taking into account the social relationships, it is possible to more accurately determine the state of decision-making.

[0109] The storage unit can perform access control for the stored documents. For example, it can limit the range of access that family members and medical staff can have, thereby protecting privacy. The storage unit can also record the access history and track who accessed the document and when. Furthermore, the storage unit can dynamically change the access authority, adding or deleting the access authority as necessary. This improves the security of the stored documents.

[0110] The feedback unit can visually display the feedback content. For example, the feedback content can be displayed in a graph or chart to make it easier to understand visually. The feedback unit can also display the feedback content in animation to dynamically convey information. Furthermore, the feedback unit can display the feedback content in infographics to convey complex information concisely. This makes the feedback content easier to understand.

[0111] The analysis unit can perform analysis taking into account the patient's hobbies and interests. For example, if the patient likes music, the facial expression and tone of voice when listening to music can be analyzed to determine whether the patient is relaxed. If the patient likes reading, the facial expression and tone of voice while reading can be analyzed to determine whether the patient is concentrating. Furthermore, if the patient likes sports, the facial expression and tone of voice while watching sports can be analyzed to determine whether the patient is excited. In this way, the accuracy of the analysis can be improved by taking into account hobbies and interests.

[0112] The determination unit can estimate the patient's emotions and provide decision-making support based on the estimated emotions. For example, if the patient feels anxious, advice to relax can be provided. Also, if the patient feels happy, activities to maintain that emotion can be suggested. Furthermore, if the patient feels angry, a method to calm down can be provided. This improves the quality of decision-making by providing appropriate support based on emotions.

[0113] The analysis unit can estimate the patient's emotion and filter the analysis results based on the estimated emotion. For example, if the patient is sad, analysis results related to the emotion of sadness can be preferentially displayed. Also, if the patient is excited, analysis results related to the emotion of excitement can be preferentially displayed. Furthermore, if the patient is relaxed, analysis results related to the emotion of relaxation can be preferentially displayed. In this way, important information can be provided preferentially by filtering the analysis results based on emotion.

[0114] The storage unit can estimate the emotion of the patient and adjust the content of the document to be stored based on the estimated emotion. For example, if the patient is relaxed, a detailed document can be stored. Also, if the patient is tense, a concise document can be stored. Furthermore, if the patient is excited, a visually calm document can be stored. In this way, by adjusting the content of the document based on the emotion, the stored document can be easily understood.

[0115] The feedback unit can estimate the emotion of the patient and adjust the timing of the feedback based on the estimated emotion. For example, if the patient is relaxed, the feedback can be provided immediately. If the patient is tense, the feedback can be delayed. Furthermore, if the patient is excited, the feedback can be provided in stages. In this way, the feedback timing can be adjusted based on the emotion, so that the feedback can be provided at an appropriate timing.

[0116] The determination unit can estimate the patient's emotions and adjust the way in which the decision-making results are displayed based on the estimated emotions. For example, if the patient is relaxed, detailed results can be displayed. Also, if the patient is tense, concise results can be displayed. Furthermore, if the patient is excited, visually calm results can be displayed. In this way, by adjusting the way in which the results are displayed based on emotions, the results can be easily understood.

[0117] The process flow of the second embodiment will be briefly described below.

[0118] Step 1: The analysis unit analyzes facial expressions or tone of voice. For example, the analysis unit analyzes facial expressions using a facial expression analysis method. Using face recognition technology, the patient's facial expressions such as smile, anger, sadness, etc. can be classified. The analysis unit also analyzes tone of voice using a tone of voice analysis method. Using voice recognition technology, the tone, speed, strength, etc. of the patient's voice can be analyzed. Step 2: The determination unit determines the state of decision-making based on the results of the analysis by the analysis unit. The determination unit can determine, for example, whether the patient's will is positive or negative based on the analysis results. Step 3: The storage unit documents and stores the intention determined by the determination unit. The storage unit can digitally store the determined intention in text or audio format, for example. Step 4: The feedback unit receives feedback based on the documents stored by the storage unit. The feedback unit receives feedback from, for example, family members or medical staff, and the AI ​​can learn based on that feedback. This allows the system to accurately determine the wishes of the dementia patient and convey them to the family.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements including the above-mentioned analysis unit, judgment unit, storage unit, and feedback unit is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the analysis unit analyzes the facial expression and tone of the patient using the camera 42 and microphone 38B of the smart device 14. The judgment unit judges the state of decision-making based on the analysis result by, for example, the specific processing unit 290 of the data processing device 12. The storage unit documents and stores the judged intention in, for example, the storage 32 of the data processing device 12. The feedback unit receives feedback from family members and medical staff using, for example, the control unit 46A of the smart device 14, and the AI ​​learns based on the feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements including the above-mentioned analysis unit, determination unit, storage unit, and feedback unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit analyzes the facial expression and tone of the patient using the camera 42 and microphone 238 of the smart glasses 214. The determination unit determines the state of decision-making based on the analysis result by, for example, the specific processing unit 290 of the data processing device 12. The storage unit documents and stores the determined intention in, for example, the storage 32 of the data processing device 12. The feedback unit receives feedback from family members and medical staff using, for example, the control unit 46A of the smart glasses 214, and the AI ​​learns based on the feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements including the above-mentioned analysis unit, determination unit, storage unit, and feedback unit is realized, for example, in at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit analyzes the facial expression and tone of the patient using the camera 42 and microphone 238 of the headset type terminal 314. The determination unit determines the state of decision-making based on the analysis result by, for example, the specific processing unit 290 of the data processing device 12. The storage unit documents and stores the determined intention in, for example, the storage 32 of the data processing device 12. The feedback unit receives feedback from family members and medical staff using, for example, the control unit 46A of the headset type terminal 314, and the AI ​​learns based on the feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements including the above-mentioned analysis unit, judgment unit, storage unit, and feedback unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit analyzes the facial expression and tone of the patient using the camera 42 and microphone 238 of the robot 414. The judgment unit judges the state of decision-making based on the analysis result by, for example, the specific processing unit 290 of the data processing device 12. The storage unit documents and stores the judged intention in, for example, the storage 32 of the data processing device 12. The feedback unit receives feedback from family members and medical staff using, for example, the control unit 46A of the robot 414, and the AI ​​learns based on the feedback. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Appendix 1) An analysis unit that analyzes a facial expression or tone of voice of a patient; a determination unit that determines a state of decision-making based on the result of the analysis by the analysis unit; a storage unit that documents and stores the intention determined by the determination unit; a feedback unit that receives feedback based on the document stored by the storage unit. A system characterized by: (Appendix 2) The analysis unit is Analyze facial expressions using facial expression analysis methods 2. The system of claim 1, (Appendix 3) The analysis unit is Analyzing tone of speech using tone analysis method 2. The system of claim 1, (Appendix 4) The determination unit is The AI ​​determines the state of the decision-making process, The feedback unit is The AI ​​learns from feedback received from family members or medical staff. 2. The system of claim 1, (Appendix 5) The storage unit includes: Store the determined intention in digital form 2. The system of claim 1, (Appendix 6) The determination unit is Determine the state of decision making based on analysis results 2. The system of claim 1, (Appendix 7) The analysis unit is Estimate the patient's emotions and adjust the accuracy of facial expression and tone of voice analysis based on the estimated emotions. 2. The system of claim 1, (Appendix 8) The analysis unit is Optimize the analysis algorithm by referencing the patient's past facial expressions and tone of voice data 2. The system of claim 1, (Appendix 9) The analysis unit is When analyzing facial expressions or tone of voice, the analysis takes into account the patient's current health status or environmental factors 2. The system of claim 1, (Appendix 10) The analysis unit is Infer the patient's emotions and prioritize the analysis results based on the inferred emotions. 2. The system of claim 1, (Appendix 11) The analysis unit is When analyzing facial expressions or tone of voice, the analysis is based on the geographic location of the patient. 2. The system of claim 1, (Appendix 12) The analysis unit is Analyze the patient's social media activity and obtain relevant data when analyzing facial expressions and tone of voice 2. The system of claim 1, (Appendix 13) The determination unit is Estimating a patient's emotions and determining the decision-making state based on the estimated emotions 2. The system of claim 1, (Appendix 14) The determination unit is When making a decision, the decision algorithm is optimized by referring to the patient's past decision-making history. 2. The system of claim 1, (Appendix 15) The determination unit is At the time of decision-making, the patient's current life situation or areas of interest are considered to determine the decision-making status. 2. The system of claim 1, (Appendix 16) The determination unit is Inferring the patient's emotions and adjusting the way the results are displayed based on the inferred emotions 2. The system of claim 1, (Appendix 17) The determination unit is At the time of decision making, the decision status is determined based on the patient's geographic location. 2. The system of claim 1, (Appendix 18) The determination unit is At the time of decision, the patient's social media activity is analyzed and relevant data is referenced to determine the decision status. 2. The system of claim 1, (Appendix 19) The storage unit includes: Estimate the patient's emotions and adjust the format of the document to be stored based on the estimated emotions 2. The system of claim 1, (Appendix 20) The storage unit includes: At the time of storage, the storage algorithm is optimized by referencing the patient's past decision-making history. 2. The system of claim 1, (Appendix 21) The storage unit includes: When saving, documents are saved taking into account the patient's current life situation or areas of interest. 2. The system of claim 1, (Appendix 22) The storage unit includes: Estimate the patient's emotions and prioritize documents to be stored based on the estimated emotions 2. The system of claim 1, (Appendix 23) The storage unit includes: Save documents based on the patient's geographic location when saved 2. The system of claim 1, (Appendix 24) The storage unit includes: At the time of storage, the document is saved by analyzing the patient's social media activity and referencing relevant data. 2. The system of claim 1, (Appendix 25) The feedback unit is Infer patient emotions and tailor feedback based on those emotions 2. The system of claim 1, (Appendix 26) The feedback unit is When providing feedback, the feedback algorithm is optimized by referring to the past feedback history of family members and medical staff. 2. The system of claim 1, (Appendix 27) The feedback unit is When providing feedback, take into account the patient's current life situation or areas of interest 2. The system of claim 1, (Appendix 28) The feedback unit is Estimate the patient's emotions and prioritize feedback based on the estimated emotions 2. The system of claim 1, (Appendix 29) The feedback unit is When providing feedback, provide feedback based on the patient's geographic location 2. The system of claim 1, (Appendix 30) The feedback unit is When providing feedback, analyze the patient's social media activity and refer to relevant data to provide feedback. 2. The system of claim 1, [Explanation of symbols]

[0191] 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. An analysis unit that analyzes a facial expression or tone of voice of a patient; a determination unit that determines a state of decision-making based on the result of the analysis by the analysis unit; a storage unit that documents and stores the intention determined by the determination unit; a feedback unit that receives feedback based on the document stored by the storage unit. A system characterized by:

2. The determination unit is The AI ​​determines the state of the decision-making; The AI ​​receives feedback from family members or medical staff and learns based on that feedback.

2. The system of claim 1 .

3. The storage unit includes: Store the determined intention in digital form 2. The system of claim 1 .

4. The analysis unit is When analyzing the facial expression or tone of the patient, the analysis is performed based on the geographic location information of the patient.

2. The system of claim 1 .

5. The analysis unit is Estimating the patient's emotion and adjusting the analysis accuracy of facial expressions and tone of voice based on the estimated emotion.

2. The system of claim 1 .

6. The analysis unit is Optimize the analysis algorithm by referring to the patient's past facial expressions and tone of voice data 2. The system of claim 1 .

7. The analysis unit is When analyzing facial expressions or tone of voice, the analysis takes into account the patient's current health status or environmental factors 2. The system of claim 1 .

8. The analysis unit is Estimating the patient's emotions and prioritizing the analysis results based on the estimated emotions.

2. The system of claim 1 .

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