system

Abstract

The system according to the embodiment aims to solve the problems of lack of communication and supervision for elderly people living alone. [Solution] The system according to the embodiment includes a reception unit, a dialogue unit, a recognition unit, an operation unit, and a monitoring unit. The reception unit accepts input in natural language. The dialogue unit conducts a dialogue based on the input in natural language accepted by the reception unit. The recognition unit recognizes gestures using a camera. The operation unit performs an operation based on the gestures recognized by the recognition unit. The monitoring unit checks on the condition of the elderly person using a remote camera.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has not adequately addressed the issues of lack of communication and supervision among elderly people living alone, and there is room for improvement.

[0005] The system according to the embodiment aims to solve the problems of lack of communication and supervision for elderly people living alone. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a dialogue unit, a recognition unit, an operation unit, and a monitoring unit. The reception unit receives input in natural language. The dialogue unit conducts a dialogue based on the input in natural language received by the reception unit. The recognition unit recognizes gestures using a camera. The operation unit performs an operation based on the gestures recognized by the recognition unit. The monitoring unit checks on the condition of the elderly person using a remote camera. [Effects of the Invention]

[0007] The system according to the embodiment can solve the problems of lack of communication and supervision for elderly people living alone. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A talking pet robot system for the elderly according to an embodiment of the present invention is a system for reducing loneliness and the risk of dementia in the elderly. The system includes a reception unit that accepts natural language input, a dialogue unit that engages in dialogue based on the natural language input accepted by the reception unit, a recognition unit that recognizes gestures using a camera, an operation unit that performs actions based on the gestures recognized by the recognition unit, and a monitoring unit that monitors the elderly's condition using a remote camera. For example, the system can engage in two-way dialogue with the elderly using natural language processing technology. When the elderly speaks to the robot, the robot responds appropriately. For example, if the elderly asks, "How was your day today?", the robot responds, "It was a sunny, pleasant day today." This function allows the elderly to enjoy everyday conversations. The system can also recognize the elderly's gestures using a camera. For example, when the elderly waves, the robot waves back. Furthermore, when the elderly smiles, the robot smiles back. This function allows the elderly to enjoy interacting with the robot. Furthermore, family members can use the remote camera to check on the elderly's condition via a smartphone or computer. For example, even when family members are out, they can check the robot's camera footage on their smartphone to ensure that the elderly are staying safe. This function allows family members to remotely monitor the safety of the elderly. This robot has an adorable, pet-like design that provides comfort to the elderly. For example, the robot is shaped like a dog or cat and can move its ears and tail. This design allows the elderly to feel like they are a pet, providing emotional comfort. In this way, the talking pet robot system for the elderly is an effective tool for reducing loneliness among the elderly and lowering the risk of dementia. Families can watch over the elderly with peace of mind, and the elderly themselves can enjoy their daily lives. This means that the talking pet robot system for the elderly can reduce loneliness among the elderly and lower the risk of dementia.

[0029] A talking pet robot system for the elderly according to an embodiment includes a reception unit, a dialogue unit, a recognition unit, an operation unit, and a monitoring unit. The reception unit accepts input in natural language. For example, the reception unit can convert words spoken by the elderly into text data using voice recognition technology. The reception unit can also support multiple languages. For example, the reception unit can accept input in Japanese, English, and other languages. The dialogue unit engages in two-way dialogue with the elderly using natural language processing technology. For example, the dialogue unit can understand what the elderly says and generate an appropriate response using technologies such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit can realize natural dialogue using generative AI (e.g., text generation AI or LLM). The recognition unit recognizes the elderly's gestures using a camera. For example, the recognition unit can capture hand movements and facial expressions with a camera and analyze them using image recognition technology. The recognition unit can recognize gestures such as waving, pointing, and smiling. The operation unit performs an action based on the recognized gesture. For example, the operation unit can perform actions such as waving, smiling, and bowing. The operation unit receives gesture information from the recognition unit and performs an appropriate operation. The monitoring unit checks the elderly person's condition using a remote camera. For example, the monitoring unit allows family members to check the elderly person's condition in real time via a smartphone or computer. The monitoring unit has a function to send an alert if the elderly person falls. For example, the monitoring unit can detect the elderly person's fall using a fall detection sensor and notify family members. This makes the talking pet robot system for the elderly according to the embodiment an effective tool for reducing feelings of loneliness and the risk of dementia in the elderly. The system has an adorable pet-like design and is shaped like a dog or cat. For example, the system can move its ears and tail, providing comfort to the elderly. The system's pet-like design can bring peace of mind to the elderly.

[0030] The dialogue unit can use natural language processing technology to engage in two-way dialogue with the elderly. For example, the dialogue unit can use morphological analysis to break down the elderly's utterances and grammatical analysis to understand the structure of the sentences. The dialogue unit can also use semantic analysis to understand the meaning of the utterances and generate an appropriate response. For example, when the elderly asks, "What kind of day was it today?", the dialogue unit can respond, "It was a sunny and pleasant day today." The dialogue unit can also use generative AI (e.g., text generation AI or LLM) to realize natural dialogue. For example, the generative AI can receive the elderly's utterances as input and generate an appropriate response. This makes it possible to have more natural dialogue using natural language processing technology.

[0031] The recognition unit can recognize the hand movements or facial expressions of the elderly person using a camera. For example, the recognition unit can capture hand movements with a camera and analyze them using image recognition technology. For example, the recognition unit can recognize an elderly person's waving action and perform a waving back action. The recognition unit can also capture facial expressions with a camera and analyze them using image recognition technology. For example, when the elderly person smiles, the recognition unit can perform a smiling back action. This allows for richer interactions by recognizing the elderly person's hand movements and facial expressions.

[0032] The operation unit can perform an action such as waving or smiling based on the recognized gesture. For example, the operation unit can receive gesture information from the recognition unit and perform a waving action. For example, when an elderly person waves, the operation unit can perform an action of waving back. Furthermore, the operation unit can receive gesture information from the recognition unit and perform an action of smiling back. For example, when an elderly person smiles, the operation unit can perform an action of smiling back. This makes interaction with the elderly more natural and enjoyable.

[0033] The monitoring unit uses a remote camera to allow family members to check on the elderly person's condition via a smartphone or computer. The monitoring unit can, for example, use a remote camera to check on the elderly person's condition in real time. For example, even when family members are out, the monitoring unit can check the robot's camera footage on a smartphone to ensure that the elderly person is staying safe. The monitoring unit also has a function to send an alert if the elderly person falls. For example, the monitoring unit can use a fall detection sensor to detect when the elderly person has fallen and notify the family member. This allows family members to check on the elderly person's condition remotely, providing peace of mind.

[0034] The monitoring unit can have a function to send an alert if the elderly person falls. For example, the monitoring unit can detect the elderly person's fall using a fall detection sensor and notify family members. For example, the monitoring unit can send a voice alert or a text message if the elderly person falls. This allows for quick action to ensure the elderly person's safety.

[0035] The system may have a cute pet-like design. For example, the system may be shaped like a dog or a cat and may move its ears and tail. For example, the system may move its ears to greet the elderly person. The system may also wag its tail to express joy to the elderly person. This provides comfort to the elderly and brings them peace of mind.

[0036] The system is in the shape of a dog or cat and can move its ears and tail. For example, the system may be in the shape of a dog and can move its ears to greet the elderly. Alternatively, the system may be in the shape of a cat and can wag its tail to express joy to the elderly. This allows the elderly to feel like they have a pet and provides emotional comfort.

[0037] The reception unit can analyze the elderly person's past conversation history and select an appropriate reception method. For example, the reception unit can prioritize reception of phrases that the elderly person has frequently used in the past. For example, the reception unit can prioritize reception of related inputs based on topics that the elderly person has liked to talk about in the past. The reception unit can also predict topics that the elderly person will like to talk about in a specific time period from the elderly person's past conversation history and adjust the reception method. This makes it possible to provide a more appropriate reception method based on the elderly person's past conversation history.

[0038] The reception unit can perform filtering based on the elderly person's current health condition and areas of interest when receiving natural language input. For example, if the elderly person's health condition is good, the reception unit can preferentially receive input related to the areas of interest. For example, if the elderly person's health condition is poor, the reception unit can preferentially receive health advice and support. Furthermore, if the elderly person's areas of interest change, the reception unit can preferentially receive input related to the new areas of interest. This makes it possible to receive appropriate input according to the elderly person's health condition and areas of interest.

[0039] When receiving natural language input, the reception unit can prioritize receiving highly relevant input based on the geographical location information of the elderly person. For example, when the elderly person is at home, the reception unit can prioritize receiving household topics. For example, when the elderly person is out, the reception unit can prioritize receiving information related to the destination. Furthermore, when the elderly person is in a specific facility, the reception unit can prioritize receiving information related to the facility. This makes it possible to prioritize receiving highly relevant input based on the geographical location information of the elderly person.

[0040] The reception unit can analyze the elderly person's social media activities and receive related inputs when receiving natural language inputs. For example, the reception unit can prioritize receiving topics that the elderly person frequently talks about on social media. For example, the reception unit can prioritize receiving information related to accounts that the elderly person follows on social media. The reception unit can also prioritize receiving events and news that the elderly person is interested in from the elderly person's social media activities. This makes it possible to prioritize receiving related inputs based on the elderly person's social media activities.

[0041] The dialogue unit can adjust the level of detail of the dialogue based on the importance of the conversation during the dialogue. For example, the dialogue unit can provide a detailed explanation in the case of an important conversation. For example, the dialogue unit can provide a concise response in the case of an everyday conversation. Furthermore, the dialogue unit can provide an in-depth dialogue in the case of a topic that is of particular interest to the elderly. This enables an appropriate dialogue according to the importance of the conversation.

[0042] During a dialogue, the dialogue unit can apply different dialogue algorithms depending on the category of the conversation. For example, in a conversation about health, the dialogue unit can apply an algorithm with specialized knowledge. For example, in a conversation about hobbies, the dialogue unit can apply an algorithm that attracts interest. Furthermore, in a conversation about family, the dialogue unit can apply an algorithm that emphasizes emotional connections. This enables appropriate dialogue depending on the category of the conversation.

[0043] During a dialogue, the dialogue unit can determine the priority of the dialogue based on the time of submission of the dialogue. For example, the dialogue unit can prioritize the most recently submitted dialogue. For example, the dialogue unit can prioritize the dialogue of a conversation submitted by an elderly person during a specific time period. Furthermore, the dialogue unit can prioritize the dialogue of a conversation frequently submitted by an elderly person. This enables an appropriate dialogue according to the time of submission of the conversation.

[0044] The dialogue unit can adjust the order of dialogue based on the relevance of the dialogue during dialogue. For example, the dialogue unit can prioritize dialogue related to the content that the elderly person has just spoken. For example, the dialogue unit can prioritize dialogue related to topics that the elderly person is interested in. Furthermore, the dialogue unit can prioritize dialogue related to the content that the elderly person has spoken in the past. This enables appropriate dialogue according to the relevance of the conversation.

[0045] The recognition unit can improve the accuracy of gesture recognition by taking into account the interrelationships between actions. For example, the recognition unit can improve the accuracy by simultaneously recognizing a hand waving action and a facial direction. For example, the recognition unit can improve the accuracy by simultaneously recognizing a smile and a hand movement. Furthermore, the recognition unit can improve the accuracy by simultaneously recognizing a head movement and a hand gesture. In this way, the accuracy of gesture recognition is improved by taking into account the interrelationships between actions.

[0046] The recognition unit can recognize gestures while taking into account attribute information of the elderly person. For example, the recognition unit can recognize gestures while taking into account the height and physique of the elderly person. For example, the recognition unit can recognize gestures while taking into account the characteristics of the elderly person's movements. Furthermore, the recognition unit can recognize gestures while taking into account the elderly person's health condition. This enables appropriate gesture recognition based on the attribute information of the elderly person.

[0047] The recognition unit can perform gesture recognition taking into account the geographical distribution of elderly people. For example, when an elderly person is at home, the recognition unit can prioritize recognizing gestures made at home. For example, when an elderly person is out, the recognition unit can prioritize recognizing gestures made while out. Furthermore, when an elderly person is in a specific facility, the recognition unit can prioritize recognizing gestures made at the facility. This enables appropriate gesture recognition based on the geographical distribution of elderly people.

[0048] The recognition unit can improve the accuracy of gesture recognition by referring to related literature. For example, the recognition unit can improve the gesture recognition algorithm by referring to the latest research papers. For example, the recognition unit can improve the accuracy of gesture recognition by referring to past research data. The recognition unit can also improve the accuracy of gesture recognition by referring to data from other research institutions. In this way, the accuracy of gesture recognition is improved by referring to related literature.

[0049] When performing an action, the action unit can select an appropriate action method by referring to the past action history. For example, the action unit can prioritize actions that the elderly person has previously preferred. For example, the action unit can prioritize actions that the elderly person has responded well to in the past. The action unit can also select the optimal action method from the elderly person's past action history. This enables appropriate actions based on the past action history.

[0050] The movement unit can customize the movement means based on the elderly person's current health condition during movement. For example, if the elderly person's health condition is good, the movement unit can perform a vigorous movement. For example, if the elderly person's health condition is poor, the movement unit can perform a gentle movement. The movement unit can also adjust the intensity of the movement according to the elderly person's health condition. This allows for appropriate movement according to the elderly person's health condition.

[0051] The operating unit can select the optimal operating method in consideration of the geographical location information of the elderly person when operating. For example, when the elderly person is at home, the operating unit can prioritize operations within the home. For example, when the elderly person is out, the operating unit can prioritize operations outside the home. Furthermore, when the elderly person is in a specific facility, the operating unit can prioritize operations at that facility. This enables appropriate operations based on the geographical location information of the elderly person.

[0052] The action unit can analyze the elderly person's social media activity and suggest an action during the action. For example, the action unit can perform an action related to a topic that the elderly person frequently talks about on social media. For example, the action unit can perform an action related to an account that the elderly person follows on social media. The action unit can also perform an action related to an event or news that the elderly person is interested in based on the elderly person's social media activity. This enables appropriate action based on the elderly person's social media activity.

[0053] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. For example, the monitoring unit can prioritize monitoring methods that the elderly person has preferred in the past. For example, the monitoring unit can prioritize monitoring methods that the elderly person has responded well to in the past. Furthermore, the monitoring unit can select the optimal monitoring method from the elderly person's past monitoring data. This enables appropriate monitoring based on past monitoring data.

[0054] The monitoring unit can customize the monitoring method based on the elderly person's current health condition during monitoring. For example, if the elderly person's health condition is good, the monitoring unit can reduce the frequency of monitoring. For example, if the elderly person's health condition is poor, the monitoring unit can increase the frequency of monitoring. The monitoring unit can also adjust the monitoring method according to the elderly person's health condition. This enables appropriate monitoring according to the elderly person's health condition.

[0055] When monitoring, the monitoring unit can select the optimal monitoring method by taking into consideration the geographical location information of the elderly person. For example, when the elderly person is at home, the monitoring unit can prioritize monitoring within the home. For example, when the elderly person is out, the monitoring unit can prioritize monitoring while the elderly person is away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can prioritize monitoring at that facility. This makes it possible to monitor the elderly person appropriately based on their geographical location information.

[0056] During monitoring, the monitoring unit can analyze the elderly person's social media activity and suggest monitoring methods. For example, the monitoring unit can monitor topics frequently discussed by the elderly person on social media. For example, the monitoring unit can monitor accounts that the elderly person follows on social media. The monitoring unit can also monitor events and news that interest the elderly person based on the elderly person's social media activity. This makes it possible to provide appropriate monitoring based on the elderly person's social media activity.

[0057] When designing, the design unit can select the optimal design method by referring to past design history. For example, the design unit can preferentially provide designs that have been favored by elderly people in the past. For example, the design unit can preferentially provide designs that have received a good response from elderly people in the past. Furthermore, the design unit can select the optimal design method from the elderly person's past design history. This makes it possible to create an appropriate design based on past design history.

[0058] The design unit can customize the design means based on the current health condition of the elderly person at the time of design. For example, if the elderly person's health condition is good, the design unit can provide an active design. For example, if the elderly person's health condition is poor, the design unit can provide a gentle design. The design unit can also adjust the intensity of the design depending on the elderly person's health condition. This enables an appropriate design according to the elderly person's health condition.

[0059] The design unit can select the optimal design method by taking into consideration the geographical location information of the elderly person when designing. For example, if the elderly person is at home, the design unit can provide a design for the home preferentially. For example, if the elderly person is out, the design unit can provide a design for when the elderly person is out preferentially. Furthermore, if the elderly person is in a specific facility, the design unit can provide a design for that facility preferentially. This makes it possible to provide an appropriate design based on the geographical location information of the elderly person.

[0060] The design department can analyze the social media activity of seniors and suggest design methods during design. For example, the design department can provide designs related to topics that seniors frequently discuss on social media. For example, the design department can provide designs related to accounts that seniors follow on social media. The design department can also provide designs related to events or news that seniors are interested in based on their social media activity. This enables appropriate designs based on the seniors' social media activity.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The talking pet robot system for the elderly can further include a health management unit. The health management unit can monitor the elderly's health condition and send an alert if an abnormality is detected. For example, the health management unit can measure the heart rate and blood pressure and notify family members or medical institutions if an abnormal value is detected. The health management unit can also conduct regular health checks and record the results. Furthermore, the health management unit can provide health advice and support the elderly in maintaining their health. This allows the elderly's health condition to be constantly monitored and prompt action to be taken.

[0063] The recognition unit can improve recognition accuracy by referring to the user's past gesture history during gesture recognition. For example, the recognition unit can prioritize recognition of gestures that the user has frequently performed in the past. The recognition unit can also improve recognition accuracy by learning specific gesture patterns from the past gesture history. Furthermore, the recognition unit can grasp the characteristics of the user's gestures based on the past gesture history and perform more accurate recognition. This allows for more accurate recognition of the user's gestures.

[0064] The talking pet robot system for the elderly can further include an entertainment unit. The entertainment unit can provide content that the elderly can enjoy. For example, the entertainment unit can play music to provide an environment where the elderly can relax. The entertainment unit can also provide simple games to stimulate the elderly's cognitive functions. Furthermore, the entertainment unit can provide news and weather forecasts so that the elderly can get the latest information. This makes the lives of the elderly richer and more enjoyable.

[0065] The talking pet robot system for the elderly can further include a learning unit. The learning unit can support the elderly in learning new knowledge and skills. For example, the learning unit can provide language learning content to help the elderly learn a new language. The learning unit can also provide information about hobbies to encourage the elderly to start a new hobby. Furthermore, the learning unit can provide knowledge about health to help the elderly manage their health. This can support the elderly in improving their knowledge and skills.

[0066] The talking pet robot system for the elderly can further include a reminder unit. The reminder unit can help the elderly not forget important appointments or tasks. For example, the reminder unit can notify them of when to take their medicine, helping them take their medicine at the appropriate time. The reminder unit can also notify them of appointments at medical institutions, helping them not forget their appointments. Furthermore, the reminder unit can notify them of daily tasks, helping them to live their daily lives smoothly. This makes the lives of the elderly more convenient and secure.

[0067] The talking pet robot system for the elderly may further include a communication unit. The communication unit may support the elderly to easily communicate with family and friends. For example, the communication unit may provide voice and video calling functions, allowing the elderly to talk face-to-face with far-away family and friends. The communication unit may also provide a message sending and receiving function, allowing the elderly to easily send messages. Furthermore, the communication unit may support the use of social media, allowing the elderly to enjoy online interactions. This may help the elderly maintain social connections and reduce feelings of loneliness.

[0068] The talking pet robot system for the elderly can further include an environmental sensor unit. The environmental sensor unit can monitor the elderly's living environment and provide support to maintain a comfortable environment. For example, the environmental sensor unit can measure room temperature and humidity and send alerts to maintain the appropriate range. The environmental sensor unit can also monitor air quality and prompt ventilation as needed. Furthermore, the environmental sensor unit can adjust the brightness of lighting to provide a comfortable environment for the elderly. This ensures that the elderly's living environment is always comfortable and safe.

[0069] The processing flow of the first embodiment will be briefly explained below.

[0070] Step 1: The reception unit accepts input in natural language. For example, the reception unit can convert the words spoken by the elderly person into text data using voice recognition technology. The reception unit can also support multiple languages. For example, it can accept input in Japanese, English, and other languages. Step 2: The dialogue unit uses natural language processing technology to engage in two-way dialogue with the elderly. For example, the dialogue unit can use technologies such as morphological analysis, grammatical analysis, and semantic analysis to understand what the elderly person is saying and generate an appropriate response. The dialogue unit can also use generative AI (e.g., text generation AI or LLM) to achieve natural dialogue. Step 3: The recognition unit uses a camera to recognize the elderly person's gestures. For example, the recognition unit can capture hand movements and facial expressions with a camera and analyze them using image recognition technology. The recognition unit can recognize gestures such as waving, pointing, and smiling. Step 4: The action unit performs an action based on the recognized gesture. For example, the action unit can perform actions such as waving, smiling, bowing, etc. The action unit receives the gesture information from the recognition unit and performs the appropriate action. Step 5: The monitoring unit checks on the elderly person's condition using a remote camera. For example, the monitoring unit allows family members to check on the elderly person's condition in real time via a smartphone or computer. The monitoring unit has a function to send an alert if the elderly person falls. For example, the monitoring unit can use a fall detection sensor to detect when the elderly person falls and notify the family.

[0071] (Example 2) A talking pet robot system for the elderly according to an embodiment of the present invention is a system for reducing loneliness and the risk of dementia in the elderly. The system includes a reception unit that accepts natural language input, a dialogue unit that engages in dialogue based on the natural language input accepted by the reception unit, a recognition unit that recognizes gestures using a camera, an operation unit that performs actions based on the gestures recognized by the recognition unit, and a monitoring unit that monitors the elderly's condition using a remote camera. For example, the system can engage in two-way dialogue with the elderly using natural language processing technology. When the elderly speaks to the robot, the robot responds appropriately. For example, if the elderly asks, "How was your day today?", the robot responds, "It was a sunny, pleasant day today." This function allows the elderly to enjoy everyday conversations. The system can also recognize the elderly's gestures using a camera. For example, when the elderly waves, the robot waves back. Furthermore, when the elderly smiles, the robot smiles back. This function allows the elderly to enjoy interacting with the robot. Furthermore, family members can use the remote camera to check on the elderly's condition via a smartphone or computer. For example, even when family members are out, they can check the robot's camera footage on their smartphone to ensure that the elderly are staying safe. This function allows family members to remotely monitor the safety of the elderly. This robot has an adorable, pet-like design that provides comfort to the elderly. For example, the robot is shaped like a dog or cat and can move its ears and tail. This design allows the elderly to feel like they are a pet, providing emotional comfort. In this way, the talking pet robot system for the elderly is an effective tool for reducing loneliness among the elderly and lowering the risk of dementia. Families can watch over the elderly with peace of mind, and the elderly themselves can enjoy their daily lives. This means that the talking pet robot system for the elderly can reduce loneliness among the elderly and lower the risk of dementia.

[0072] A talking pet robot system for the elderly according to an embodiment includes a reception unit, a dialogue unit, a recognition unit, an operation unit, and a monitoring unit. The reception unit accepts input in natural language. For example, the reception unit can convert words spoken by the elderly into text data using voice recognition technology. The reception unit can also support multiple languages. For example, the reception unit can accept input in Japanese, English, and other languages. The dialogue unit engages in two-way dialogue with the elderly using natural language processing technology. For example, the dialogue unit can understand what the elderly says and generate an appropriate response using technologies such as morphological analysis, grammatical analysis, and semantic analysis. The dialogue unit can realize natural dialogue using generative AI (e.g., text generation AI or LLM). The recognition unit recognizes the elderly's gestures using a camera. For example, the recognition unit can capture hand movements and facial expressions with a camera and analyze them using image recognition technology. The recognition unit can recognize gestures such as waving, pointing, and smiling. The operation unit performs an action based on the recognized gesture. For example, the operation unit can perform actions such as waving, smiling, and bowing. The operation unit receives gesture information from the recognition unit and performs an appropriate operation. The monitoring unit checks the elderly person's condition using a remote camera. For example, the monitoring unit allows family members to check the elderly person's condition in real time via a smartphone or computer. The monitoring unit has a function to send an alert if the elderly person falls. For example, the monitoring unit can detect the elderly person's fall using a fall detection sensor and notify family members. This makes the talking pet robot system for the elderly according to the embodiment an effective tool for reducing feelings of loneliness and the risk of dementia in the elderly. The system has an adorable pet-like design and is shaped like a dog or cat. For example, the system can move its ears and tail, providing comfort to the elderly. The system's pet-like design can bring peace of mind to the elderly.

[0073] The dialogue unit can use natural language processing technology to engage in two-way dialogue with the elderly. For example, the dialogue unit can use morphological analysis to break down the elderly's utterances and grammatical analysis to understand the structure of the sentences. The dialogue unit can also use semantic analysis to understand the meaning of the utterances and generate an appropriate response. For example, when the elderly asks, "What kind of day was it today?", the dialogue unit can respond, "It was a sunny and pleasant day today." The dialogue unit can also use generative AI (e.g., text generation AI or LLM) to realize natural dialogue. For example, the generative AI can receive the elderly's utterances as input and generate an appropriate response. This makes it possible to have more natural dialogue using natural language processing technology.

[0074] The recognition unit can recognize the hand movements or facial expressions of the elderly person using a camera. For example, the recognition unit can capture hand movements with a camera and analyze them using image recognition technology. For example, the recognition unit can recognize an elderly person's waving action and perform a waving back action. The recognition unit can also capture facial expressions with a camera and analyze them using image recognition technology. For example, when the elderly person smiles, the recognition unit can perform a smiling back action. This allows for richer interactions by recognizing the elderly person's hand movements and facial expressions.

[0075] The operation unit can perform an action such as waving or smiling based on the recognized gesture. For example, the operation unit can receive gesture information from the recognition unit and perform a waving action. For example, when an elderly person waves, the operation unit can perform an action of waving back. Furthermore, the operation unit can receive gesture information from the recognition unit and perform an action of smiling back. For example, when an elderly person smiles, the operation unit can perform an action of smiling back. This makes interaction with the elderly more natural and enjoyable.

[0076] The monitoring unit uses a remote camera to allow family members to check on the elderly person's condition via a smartphone or computer. The monitoring unit can, for example, use a remote camera to check on the elderly person's condition in real time. For example, even when family members are out, the monitoring unit can check the robot's camera footage on a smartphone to ensure that the elderly person is staying safe. The monitoring unit also has a function to send an alert if the elderly person falls. For example, the monitoring unit can use a fall detection sensor to detect when the elderly person has fallen and notify the family member. This allows family members to check on the elderly person's condition remotely, providing peace of mind.

[0077] The monitoring unit can have a function to send an alert if the elderly person falls. For example, the monitoring unit can detect the elderly person's fall using a fall detection sensor and notify family members. For example, the monitoring unit can send a voice alert or a text message if the elderly person falls. This allows for quick action to ensure the elderly person's safety.

[0078] The system may have a cute pet-like design. For example, the system may be shaped like a dog or a cat and may move its ears and tail. For example, the system may move its ears to greet the elderly person. The system may also wag its tail to express joy to the elderly person. This provides comfort to the elderly and brings them peace of mind.

[0079] The system is in the shape of a dog or cat and can move its ears and tail. For example, the system may be in the shape of a dog and can move its ears to greet the elderly. Alternatively, the system may be in the shape of a cat and can wag its tail to express joy to the elderly. This allows the elderly to feel like they have a pet and provides emotional comfort.

[0080] The reception unit can estimate the user's emotions and adjust the timing of accepting natural language input based on the estimated user emotions. The reception unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, if the user is feeling stressed, the reception unit can delay the timing of accepting input to allow the user to relax. Furthermore, if the user is relaxed, the reception unit can accelerate the timing of accepting input to promote smooth dialogue. Furthermore, if the user is in a hurry, the reception unit can immediately accept input and provide a quick response. This makes it possible to accept input at an appropriate timing according to the user's emotions.

[0081] The reception unit can analyze the elderly person's past conversation history and select an appropriate reception method. For example, the reception unit can prioritize reception of phrases that the elderly person has frequently used in the past. For example, the reception unit can prioritize reception of related inputs based on topics that the elderly person has liked to talk about in the past. The reception unit can also predict topics that the elderly person will like to talk about in a specific time period from the elderly person's past conversation history and adjust the reception method. This makes it possible to provide a more appropriate reception method based on the elderly person's past conversation history.

[0082] The reception unit can perform filtering based on the elderly person's current health condition and areas of interest when receiving natural language input. For example, if the elderly person's health condition is good, the reception unit can preferentially receive input related to the areas of interest. For example, if the elderly person's health condition is poor, the reception unit can preferentially receive health advice and support. Furthermore, if the elderly person's areas of interest change, the reception unit can preferentially receive input related to the new areas of interest. This makes it possible to receive appropriate input according to the elderly person's health condition and areas of interest.

[0083] The reception unit can estimate the user's emotion and determine the priority of natural language to be received based on the estimated user's emotion. The reception unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the reception unit can prioritize receiving important information when the user is excited. Furthermore, the reception unit can prioritize receiving detailed information when the user is calm. Furthermore, the reception unit can prioritize receiving information that provides a sense of security when the user is feeling anxious. This makes it possible to prioritize receiving important information according to the user's emotion.

[0084] When receiving natural language input, the reception unit can prioritize receiving highly relevant input based on the geographical location information of the elderly person. For example, when the elderly person is at home, the reception unit can prioritize receiving household topics. For example, when the elderly person is out, the reception unit can prioritize receiving information related to the destination. Furthermore, when the elderly person is in a specific facility, the reception unit can prioritize receiving information related to the facility. This makes it possible to prioritize receiving highly relevant input based on the geographical location information of the elderly person.

[0085] The reception unit can analyze the elderly person's social media activities and receive related inputs when receiving natural language inputs. For example, the reception unit can prioritize receiving topics that the elderly person frequently talks about on social media. For example, the reception unit can prioritize receiving information related to accounts that the elderly person follows on social media. The reception unit can also prioritize receiving events and news that the elderly person is interested in from the elderly person's social media activities. This makes it possible to prioritize receiving related inputs based on the elderly person's social media activities.

[0086] The dialogue unit can estimate the user's emotions and adjust the way the dialogue is expressed based on the estimated user's emotions. The dialogue unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, if the user is sad, the dialogue unit can use a comforting expression. If the user is happy, the dialogue unit can use a sympathetic expression. If the user is angry, the dialogue unit can use a calm expression to continue the dialogue. This makes it possible to have an appropriate dialogue according to the user's emotions.

[0087] The dialogue unit can adjust the level of detail of the dialogue based on the importance of the conversation during the dialogue. For example, the dialogue unit can provide a detailed explanation in the case of an important conversation. For example, the dialogue unit can provide a concise response in the case of an everyday conversation. Furthermore, the dialogue unit can provide an in-depth dialogue in the case of a topic that is of particular interest to the elderly. This enables an appropriate dialogue according to the importance of the conversation.

[0088] During a dialogue, the dialogue unit can apply different dialogue algorithms depending on the category of the conversation. For example, in a conversation about health, the dialogue unit can apply an algorithm with specialized knowledge. For example, in a conversation about hobbies, the dialogue unit can apply an algorithm that attracts interest. Furthermore, in a conversation about family, the dialogue unit can apply an algorithm that emphasizes emotional connections. This enables appropriate dialogue depending on the category of the conversation.

[0089] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated user's emotions. The dialogue unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the dialogue unit can hold a short dialogue when the user is tired. Furthermore, the dialogue unit can hold a longer dialogue when the user is relaxed. Furthermore, the dialogue unit can hold a short dialogue that focuses on the main points when the user is in a hurry. This makes it possible to provide an appropriate length of dialogue according to the user's emotions.

[0090] During a dialogue, the dialogue unit can determine the priority of the dialogue based on the time of submission of the dialogue. For example, the dialogue unit can prioritize the most recently submitted dialogue. For example, the dialogue unit can prioritize the dialogue of a conversation submitted by an elderly person during a specific time period. Furthermore, the dialogue unit can prioritize the dialogue of a conversation frequently submitted by an elderly person. This enables an appropriate dialogue according to the time of submission of the conversation.

[0091] The dialogue unit can adjust the order of dialogue based on the relevance of the dialogue during dialogue. For example, the dialogue unit can prioritize dialogue related to the content that the elderly person has just spoken. For example, the dialogue unit can prioritize dialogue related to topics that the elderly person is interested in. Furthermore, the dialogue unit can prioritize dialogue related to the content that the elderly person has spoken in the past. This enables appropriate dialogue according to the relevance of the conversation.

[0092] The recognition unit can estimate the user's emotion and adjust the criteria for gesture recognition based on the estimated user's emotion. The recognition unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, the recognition unit can adjust the criteria to recognize even fine gestures when the user is excited. Furthermore, the recognition unit can adjust the criteria to recognize rough gestures when the user is relaxed. Furthermore, the recognition unit can adjust the criteria to recognize simple gestures when the user is tired. This enables appropriate gesture recognition according to the user's emotion.

[0093] The recognition unit can improve the accuracy of gesture recognition by taking into account the interrelationships between actions. For example, the recognition unit can improve the accuracy by simultaneously recognizing a hand waving action and a facial direction. For example, the recognition unit can improve the accuracy by simultaneously recognizing a smile and a hand movement. Furthermore, the recognition unit can improve the accuracy by simultaneously recognizing a head movement and a hand gesture. In this way, the accuracy of gesture recognition is improved by taking into account the interrelationships between actions.

[0094] The recognition unit can recognize gestures while taking into account attribute information of the elderly person. For example, the recognition unit can recognize gestures while taking into account the height and physique of the elderly person. For example, the recognition unit can recognize gestures while taking into account the characteristics of the elderly person's movements. Furthermore, the recognition unit can recognize gestures while taking into account the elderly person's health condition. This enables appropriate gesture recognition based on the attribute information of the elderly person.

[0095] The recognition unit can estimate the user's emotion and adjust the display order of the recognition results based on the estimated user's emotion. The recognition unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, if the user is excited, the recognition unit can prioritize displaying important recognition results. Furthermore, if the user is relaxed, the recognition unit can prioritize displaying detailed recognition results. Furthermore, if the user is feeling anxious, the recognition unit can prioritize displaying recognition results that give a sense of security. This makes it possible to display appropriate recognition results according to the user's emotion.

[0096] The recognition unit can perform gesture recognition taking into account the geographical distribution of elderly people. For example, when an elderly person is at home, the recognition unit can prioritize recognizing gestures made at home. For example, when an elderly person is out, the recognition unit can prioritize recognizing gestures made while out. Furthermore, when an elderly person is in a specific facility, the recognition unit can prioritize recognizing gestures made at the facility. This enables appropriate gesture recognition based on the geographical distribution of elderly people.

[0097] The recognition unit can improve the accuracy of gesture recognition by referring to related literature. For example, the recognition unit can improve the gesture recognition algorithm by referring to the latest research papers. For example, the recognition unit can improve the accuracy of gesture recognition by referring to past research data. The recognition unit can also improve the accuracy of gesture recognition by referring to data from other research institutions. In this way, the accuracy of gesture recognition is improved by referring to related literature.

[0098] The operation unit can estimate the user's emotion and adjust the method of operation based on the estimated user's emotion. The operation unit can estimate the user's emotion using, for example, facial expression recognition technology. For example, if the user is sad, the operation unit can perform a comforting action. Also, if the user is happy, the operation unit can perform a sympathetic action. Furthermore, if the user is angry, the operation unit can perform a calm action to continue the dialogue. This makes it possible to perform appropriate actions according to the user's emotion.

[0099] When performing an action, the action unit can select an appropriate action method by referring to the past action history. For example, the action unit can prioritize actions that the elderly person has previously preferred. For example, the action unit can prioritize actions that the elderly person has responded well to in the past. The action unit can also select the optimal action method from the elderly person's past action history. This enables appropriate actions based on the past action history.

[0100] The movement unit can customize the movement means based on the elderly person's current health condition during movement. For example, if the elderly person's health condition is good, the movement unit can perform a vigorous movement. For example, if the elderly person's health condition is poor, the movement unit can perform a gentle movement. The movement unit can also adjust the intensity of the movement according to the elderly person's health condition. This allows for appropriate movement according to the elderly person's health condition.

[0101] The operation unit can estimate the user's emotions and determine the priority of operations based on the estimated user's emotions. The operation unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, if the user is excited, the operation unit can prioritize performing important operations. Furthermore, if the user is relaxed, the operation unit can prioritize performing detailed operations. Furthermore, if the user is feeling anxious, the operation unit can prioritize performing operations that provide a sense of security. This makes it possible to provide appropriate priorities for operations according to the user's emotions.

[0102] The operating unit can select the optimal operating method in consideration of the geographical location information of the elderly person when operating. For example, when the elderly person is at home, the operating unit can prioritize operations within the home. For example, when the elderly person is out, the operating unit can prioritize operations outside the home. Furthermore, when the elderly person is in a specific facility, the operating unit can prioritize operations at that facility. This enables appropriate operations based on the geographical location information of the elderly person.

[0103] The action unit can analyze the elderly person's social media activity and suggest an action during the action. For example, the action unit can perform an action related to a topic that the elderly person frequently talks about on social media. For example, the action unit can perform an action related to an account that the elderly person follows on social media. The action unit can also perform an action related to an event or news that the elderly person is interested in based on the elderly person's social media activity. This enables appropriate action based on the elderly person's social media activity.

[0104] The monitoring unit can estimate the user's emotions and adjust the monitoring method based on the estimated user's emotions. The monitoring unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the monitoring unit can monitor the user more frequently if the user is feeling anxious. Furthermore, the monitoring unit can reduce the frequency of monitoring if the user is relaxed. Furthermore, the monitoring unit can flexibly adjust the monitoring method if the user is excited. This enables appropriate monitoring according to the user's emotions.

[0105] During monitoring, the monitoring unit can select the optimal monitoring method by referring to past monitoring data. For example, the monitoring unit can prioritize monitoring methods that the elderly person has preferred in the past. For example, the monitoring unit can prioritize monitoring methods that the elderly person has responded well to in the past. Furthermore, the monitoring unit can select the optimal monitoring method from the elderly person's past monitoring data. This enables appropriate monitoring based on past monitoring data.

[0106] The monitoring unit can customize the monitoring method based on the elderly person's current health condition during monitoring. For example, if the elderly person's health condition is good, the monitoring unit can reduce the frequency of monitoring. For example, if the elderly person's health condition is poor, the monitoring unit can increase the frequency of monitoring. The monitoring unit can also adjust the monitoring method according to the elderly person's health condition. This enables appropriate monitoring according to the elderly person's health condition.

[0107] The monitoring unit can estimate the user's emotions and determine the monitoring priority based on the estimated user's emotions. The monitoring unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the monitoring unit can increase the monitoring priority if the user is feeling anxious. Furthermore, the monitoring unit can decrease the monitoring priority if the user is relaxed. Furthermore, the monitoring unit can flexibly adjust the monitoring priority if the user is excited. This makes it possible to provide an appropriate monitoring priority according to the user's emotions.

[0108] When monitoring, the monitoring unit can select the optimal monitoring method by taking into consideration the geographical location information of the elderly person. For example, when the elderly person is at home, the monitoring unit can prioritize monitoring within the home. For example, when the elderly person is out, the monitoring unit can prioritize monitoring while the elderly person is away from home. Furthermore, when the elderly person is in a specific facility, the monitoring unit can prioritize monitoring at that facility. This makes it possible to monitor the elderly person appropriately based on their geographical location information.

[0109] During monitoring, the monitoring unit can analyze the elderly person's social media activity and suggest monitoring methods. For example, the monitoring unit can monitor topics frequently discussed by the elderly person on social media. For example, the monitoring unit can monitor accounts that the elderly person follows on social media. The monitoring unit can also monitor events and news that interest the elderly person based on the elderly person's social media activity. This makes it possible to provide appropriate monitoring based on the elderly person's social media activity.

[0110] The design unit can estimate the user's emotions and adjust the design method based on the estimated user's emotions. The design unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, the design unit can provide a design with bright colors if the user is relaxed. Furthermore, the design unit can provide a design with calm colors if the user is nervous. Furthermore, the design unit can provide a colorful and fun design if the user is having fun. This makes it possible to provide an appropriate design according to the user's emotions.

[0111] When designing, the design unit can select the optimal design method by referring to past design history. For example, the design unit can preferentially provide designs that have been favored by elderly people in the past. For example, the design unit can preferentially provide designs that have received a good response from elderly people in the past. Furthermore, the design unit can select the optimal design method from the elderly person's past design history. This makes it possible to create an appropriate design based on past design history.

[0112] The design unit can customize the design means based on the current health condition of the elderly person at the time of design. For example, if the elderly person's health condition is good, the design unit can provide an active design. For example, if the elderly person's health condition is poor, the design unit can provide a gentle design. The design unit can also adjust the intensity of the design depending on the elderly person's health condition. This enables an appropriate design according to the elderly person's health condition.

[0113] The design unit can estimate the user's emotions and determine the priority of designs based on the estimated user's emotions. The design unit can estimate the user's emotions using, for example, facial expression recognition technology. For example, if the user is excited, the design unit can prioritize providing important designs. Furthermore, if the user is relaxed, the design unit can prioritize providing detailed designs. Furthermore, if the user is anxious, the design unit can prioritize providing designs that give a sense of security. This makes it possible to provide appropriate design priorities according to the user's emotions.

[0114] The design unit can select the optimal design method by taking into consideration the geographical location information of the elderly person when designing. For example, if the elderly person is at home, the design unit can provide a design for the home preferentially. For example, if the elderly person is out, the design unit can provide a design for when the elderly person is out preferentially. Furthermore, if the elderly person is in a specific facility, the design unit can provide a design for that facility preferentially. This makes it possible to provide an appropriate design based on the geographical location information of the elderly person.

[0115] The design department can analyze the social media activity of seniors and suggest design methods during design. For example, the design department can provide designs related to topics that seniors frequently discuss on social media. For example, the design department can provide designs related to accounts that seniors follow on social media. The design department can also provide designs related to events or news that seniors are interested in based on their social media activity. This enables appropriate designs based on the seniors' social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, dialogue unit, recognition unit, operation unit, and monitoring unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the microphone 38B and control unit 46A of the smart device 14 and receives input in natural language. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and engages in two-way dialogue with the elderly person using natural language processing technology. The recognition unit is realized by the camera 42 and control unit 46A of the smart device 14 and recognizes the elderly person's gestures. The operation unit is realized by the control unit 46A of the smart device 14 and performs an operation based on the recognized gestures. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and checks the elderly person's condition using a remote camera. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, dialogue unit, recognition unit, operation unit, and monitoring unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the smart glasses 214 and receives natural language input. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and engages in two-way dialogue with the elderly person using natural language processing technology. The recognition unit is realized by the camera 42 and control unit 46A of the smart glasses 214 and recognizes the elderly person's gestures. The operation unit is realized by the control unit 46A of the smart glasses 214 and performs an operation based on the recognized gestures. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and checks the elderly person's condition using a remote camera. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, dialogue unit, recognition unit, operation unit, and monitoring unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the headset type terminal 314 and receives input in natural language. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and engages in two-way dialogue with the elderly person using natural language processing technology. The recognition unit is realized by the camera 42 and control unit 46A of the headset type terminal 314 and recognizes the elderly person's gestures. The operation unit is realized by the control unit 46A of the headset type terminal 314 and performs an operation based on the recognized gestures. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and checks the elderly person's condition using a remote camera. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, dialogue unit, recognition unit, operation unit, and monitoring unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 and control unit 46A of the robot 414 and receives input in natural language. The dialogue unit is realized by the specific processing unit 290 of the data processing device 12 and engages in two-way dialogue with the elderly person using natural language processing technology. The recognition unit is realized by the camera 42 and control unit 46A of the robot 414 and recognizes the elderly person's gestures. The operation unit is realized by the control unit 46A of the robot 414 and performs an operation based on the recognized gestures. The monitoring unit is realized by the specific processing unit 290 of the data processing device 12 and checks the elderly person's condition using a remote camera.

[0116] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0117] The talking pet robot system for the elderly can further include a health management unit. The health management unit can monitor the elderly's health condition and send an alert if an abnormality is detected. For example, the health management unit can measure the heart rate and blood pressure and notify family members or medical institutions if an abnormal value is detected. The health management unit can also conduct regular health checks and record the results. Furthermore, the health management unit can provide health advice and support the elderly in maintaining their health. This allows the elderly's health condition to be constantly monitored and prompt action to be taken.

[0118] The dialogue unit can estimate the user's emotions and adjust the tone of the dialogue based on the estimated user's emotions. For example, if the user is sad, the dialogue unit can speak to the user in a gentle tone. If the user is excited, the dialogue unit can speak to the user in a calm tone. If the user is relaxed, the dialogue unit can speak to the user in a friendly tone. This makes it possible to have an appropriate dialogue according to the user's emotions.

[0119] The recognition unit can improve recognition accuracy by referring to the user's past gesture history during gesture recognition. For example, the recognition unit can prioritize recognition of gestures that the user has frequently performed in the past. The recognition unit can also improve recognition accuracy by learning specific gesture patterns from the past gesture history. Furthermore, the recognition unit can grasp the characteristics of the user's gestures based on the past gesture history and perform more accurate recognition. This allows for more accurate recognition of the user's gestures.

[0120] The operation unit can estimate the user's emotion and adjust the speed of the operation based on the estimated user's emotion. For example, the operation unit can perform a slow operation when the user is relaxed. Alternatively, the operation unit can perform a quick operation when the user is in a hurry. Furthermore, the operation unit can adjust the speed of the operation to calm the user when the user is excited. This makes it possible to provide an appropriate operation speed according to the user's emotion.

[0121] The monitoring unit can estimate the user's emotions and adjust the frequency of monitoring based on the estimated user emotions. For example, the monitoring unit can monitor the user more frequently if the user feels anxious. Furthermore, the monitoring unit can reduce the frequency of monitoring if the user feels relaxed. Furthermore, the monitoring unit can flexibly adjust the monitoring method if the user feels excited. This enables appropriate monitoring according to the user's emotions.

[0122] The talking pet robot system for the elderly can further include an entertainment unit. The entertainment unit can provide content that the elderly can enjoy. For example, the entertainment unit can play music to provide an environment where the elderly can relax. The entertainment unit can also provide simple games to stimulate the elderly's cognitive functions. Furthermore, the entertainment unit can provide news and weather forecasts so that the elderly can get the latest information. This makes the lives of the elderly richer and more enjoyable.

[0123] The talking pet robot system for the elderly can further include a learning unit. The learning unit can support the elderly in learning new knowledge and skills. For example, the learning unit can provide language learning content to help the elderly learn a new language. The learning unit can also provide information about hobbies to encourage the elderly to start a new hobby. Furthermore, the learning unit can provide knowledge about health to help the elderly manage their health. This can support the elderly in improving their knowledge and skills.

[0124] The talking pet robot system for the elderly can further include a reminder unit. The reminder unit can help the elderly not forget important appointments or tasks. For example, the reminder unit can notify them of when to take their medicine, helping them take their medicine at the appropriate time. The reminder unit can also notify them of appointments at medical institutions, helping them not forget their appointments. Furthermore, the reminder unit can notify them of daily tasks, helping them to live their daily lives smoothly. This makes the lives of the elderly more convenient and secure.

[0125] The talking pet robot system for the elderly may further include a communication unit. The communication unit may support the elderly to easily communicate with family and friends. For example, the communication unit may provide voice and video calling functions, allowing the elderly to talk face-to-face with far-away family and friends. The communication unit may also provide a message sending and receiving function, allowing the elderly to easily send messages. Furthermore, the communication unit may support the use of social media, allowing the elderly to enjoy online interactions. This may help the elderly maintain social connections and reduce feelings of loneliness.

[0126] The talking pet robot system for the elderly can further include an environmental sensor unit. The environmental sensor unit can monitor the elderly's living environment and provide support to maintain a comfortable environment. For example, the environmental sensor unit can measure room temperature and humidity and send alerts to maintain the appropriate range. The environmental sensor unit can also monitor air quality and prompt ventilation as needed. Furthermore, the environmental sensor unit can adjust the brightness of lighting to provide a comfortable environment for the elderly. This ensures that the elderly's living environment is always comfortable and safe.

[0127] The processing flow of the second embodiment will be briefly explained below.

[0128] Step 1: The reception unit accepts input in natural language. For example, the reception unit can convert the words spoken by the elderly person into text data using voice recognition technology. The reception unit can also support multiple languages. For example, it can accept input in Japanese, English, and other languages. Step 2: The dialogue unit uses natural language processing technology to engage in two-way dialogue with the elderly. For example, the dialogue unit can use technologies such as morphological analysis, grammatical analysis, and semantic analysis to understand what the elderly person is saying and generate an appropriate response. The dialogue unit can also use generative AI (e.g., text generation AI or LLM) to achieve natural dialogue. Step 3: The recognition unit uses a camera to recognize the elderly person's gestures. For example, the recognition unit can capture hand movements and facial expressions with a camera and analyze them using image recognition technology. The recognition unit can recognize gestures such as waving, pointing, and smiling. Step 4: The action unit performs an action based on the recognized gesture. For example, the action unit can perform actions such as waving, smiling, bowing, etc. The action unit receives the gesture information from the recognition unit and performs the appropriate action. Step 5: The monitoring unit checks on the elderly person's condition using a remote camera. For example, the monitoring unit allows family members to check on the elderly person's condition in real time via a smartphone or computer. The monitoring unit has a function to send an alert if the elderly person falls. For example, the monitoring unit can use a fall detection sensor to detect when the elderly person falls and notify the family.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0131] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

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

[0136] 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.

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

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

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

[0140] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0143] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0145] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0149] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0150] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0152] 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.

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

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

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

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0159] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0160] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0161] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0163] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0164] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0165] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0168] 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.

[0169] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0170] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0171] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0172] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0173] 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.

[0174] 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.

[0175] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0176] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0177] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0178] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0179] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0180] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0181] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0182] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0183] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0184] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0185] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0186] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0187] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0188] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0189] 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.

[0190] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0191] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0192] 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.

[0193] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0194] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0195] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0196] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0197] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0198] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0199] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0200] [Explanation of symbols]

[0201] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input in natural language; a dialogue unit that conducts dialogue based on the input of natural language accepted by the accepting unit; a recognition unit that recognizes gestures using a camera; an action unit that performs an action based on the gesture recognized by the recognition unit; A monitoring unit that checks the condition of the elderly person using a remote camera. A system characterized by:

2. The dialogue unit Using natural language processing technology to have two-way conversations with the elderly 2. The system of claim 1.

3. The recognition unit Recognizing hand movements or facial expressions of elderly people using a camera 2. The system of claim 1.

4. The operating unit Wave, smile, and more based on recognized gestures 2. The system of claim 1.

5. The monitoring unit Using a remote camera, family members can check on the elderly via their smartphones or computers.

2. The system of claim 1.

6. The monitoring unit Equipped with a function that sends an alert if an elderly person falls 2. The system of claim 1.

7. The system comprises: It has a cute pet-like design 2. The system of claim 1.

8. The system comprises: It has the shape of a dog or cat and can move its ears and tail.

2. The system of claim 1.

Images