System

Abstract

To provide a system for the early detection of signs of postpartum depression and the provision of appropriate support.SOLUTION: A system according to an embodiment comprises a reception unit, an analysis unit, a provision unit, a detection unit, and a communication unit. The reception unit receives input from a user. The analysis unit analyzes the information received by the reception unit. The provision unit provides advice based on the information analyzed by the analysis unit. The detection unit detects signs of postpartum depression based on the advice provided by the provision unit. The communication unit performs external notification based on the signs detected by the detection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

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

[0004] The problem with conventional technology is that it is not sufficient to detect signs of postpartum depression early and provide appropriate support.

[0005] The system according to the embodiment aims to detect signs of postpartum depression early and provide appropriate support. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a detection unit, and a communication unit. The reception unit receives user input. The analysis unit analyzes the information received by the reception unit. The provision unit provides advice based on the information analyzed by the analysis unit. The detection unit detects signs of postpartum depression based on the advice provided by the provision unit. The communication unit contacts the outside based on the signs detected by the detection unit. Effect of the Invention

[0007] The system according to the embodiment can detect early signs of postpartum depression and provide appropriate support. [Brief description of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​mommy friend system according to the embodiment of the present invention is a system in which a user inputs developmental concerns, child-rearing consultations, and consultations on how to respond when sick into the AI, and the AI ​​analyzes the input and provides appropriate advice and information. The AI ​​mommy friend system can also accept the user's complaints, and if symptoms such as postpartum depression are recognized, the AI ​​can contact the outside. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the AI ​​analyzes the information and provides advice such as "If your child's development is delayed, I recommend that you first consult a specialist." In addition, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the AI ​​determines that there is a possibility of postpartum depression and contacts a specialist institution or family. In this way, the user's mental health can be supported. As a result, the AI ​​mommy friend system can easily ask about developmental concerns, child-rearing consultations, and consultations on how to respond when sick, which are difficult for users to hear in real life. In addition, users can complain as much as they want and contact the other party without considering their convenience. Furthermore, if symptoms such as postpartum depression are recognized, the AI ​​can contact the outside and provide appropriate support.

[0029] The AI ​​mommy friend system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a detection unit, and a communication unit. The reception unit receives user input. The user input includes, for example, text input, voice input, image input, and the like, but is not limited to such examples. The reception unit receives, for example, a question or consultation content input by the user in text format. The reception unit can also receive content dictated by the user using voice input. For example, the user can input a question such as "I feel like my child's development is delayed. What should I do?" by voice. The reception unit can also receive an image taken by the user using image input. For example, the user can take an image showing the child's developmental status and input it to the reception unit. The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using natural language processing or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the user's text input using natural language processing technology and understands the intention of the question. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. Furthermore, the analysis unit can also analyze the image input using image recognition technology and understand the contents of the image. The provision unit provides advice based on the information analyzed by the analysis unit. The advice is provided in the form of, for example, a text message, a voice message, a video message, etc., but is not limited to such examples. For example, the provision unit provides an answer to a user's question by a text message. The provision unit can also provide advice to the user using a voice message. The provision unit can also provide advice to the user using a video message. The detection unit detects a sign of postpartum depression based on the advice provided by the provision unit. The detection is performed, for example, based on the frequency of appearance of a specific keyword or a change in a behavior pattern, but is not limited to such examples. For example, the detection unit detects a case where the user frequently makes statements such as "I'm tired" or "I can't do it anymore" as a sign of postpartum depression. The detection unit can also analyze the user's behavior pattern and detect a sign of postpartum depression.For example, if a user continues to make negative comments for a long period of time, this is detected as a sign of postpartum depression. The contact unit contacts the outside based on the sign detected by the detection unit. The contact is made by means of, for example, telephone, email, a messaging app, etc., but is not limited to such examples. For example, when the contact unit detects a sign of postpartum depression, it contacts a specialist institution by telephone. The contact unit can also contact the family by email. Furthermore, the contact unit can also contact the family using a messaging app. In this way, the AI ​​mommy friend system according to the embodiment can accept and analyze user input, provide advice, detect signs of postpartum depression, and contact the outside.

[0030] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a question or consultation content input by the user in text format. The reception unit can also receive content dictated by the user using voice input. For example, the user can input a question such as "I feel like my child's development is delayed. What should I do?" by voice. The reception unit can also receive images taken by the user using image input. For example, the user can take an image showing the child's developmental status and input it to the reception unit. The reception unit provides a user-friendly interface to receive these inputs. For example, the reception unit allows the user to easily input data through a smartphone app or web application. In the case of voice input, the speech of the user is converted into text using voice recognition technology and transmitted to the analysis unit. In the case of image input, the content of the image is analyzed using image recognition technology and necessary information is extracted. This allows the reception unit to appropriately receive inputs in any format by the user. The reception unit can also temporarily store the user's input content and reuse it as necessary. For example, by referring to the contents of past consultations, the reception unit can provide more appropriate advice, thereby improving the convenience for the user and the efficiency of the entire system.

[0031] The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using natural language processing or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the user's text input using natural language processing technology to understand the intent of the question. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. Furthermore, the analysis unit can analyze the image input using image recognition technology to understand the content of the image. The analysis unit combines these technologies to comprehensively analyze the user's input content and extract information for providing optimal advice. For example, the natural language processing technology is used to analyze the user's question or consultation content and search for related information. A machine learning algorithm is used to generate optimal advice based on past data. The image recognition technology is used to extract important information from the image provided by the user and reflect it in the analysis result. This allows the analysis unit to accurately understand the user's input content and build a foundation for providing appropriate advice. The analysis unit also transmits the analysis result to the provision unit to support the provision of advice to the user. This allows the analysis unit to improve the accuracy and efficiency of the entire system.

[0032] The providing unit provides advice based on the information analyzed by the analyzing unit. The advice is provided in the form of, for example, a text message, a voice message, a video message, or the like, but is not limited to such examples. For example, the providing unit provides an answer to a user's question in the form of a text message. The providing unit can also provide advice to the user using a voice message. Furthermore, the providing unit can also provide advice to the user using a video message. In order to provide these pieces of advice, the providing unit selects an optimal format according to the user's preferences and circumstances. For example, if the user prefers text messages, the advice is provided in text format, and if the user prefers voice messages, the advice is provided in voice format. When a video message is used, detailed advice including visual information can be provided. This allows the providing unit to provide advice to the user in the optimal format, thereby increasing the user's satisfaction. Furthermore, in order to continuously improve the content of the advice, the providing unit can collect user feedback and work with the analyzing unit to improve the accuracy of the advice. This allows the providing unit to always provide the latest and most optimal advice to the user, thereby improving the reliability of the entire system.

[0033] The detection unit detects signs of postpartum depression based on the advice provided by the provision unit. The detection is performed, for example, based on the frequency of appearance of a specific keyword or a change in a behavior pattern, but is not limited to such examples. For example, when the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the detection unit detects this as a sign of postpartum depression. The detection unit can also analyze the user's behavior pattern and detect signs of postpartum depression. For example, when the user continues to make negative statements for a long period of time, the detection unit detects this as a sign of postpartum depression. Based on this information, the detection unit can comprehensively evaluate the user's mental state and detect the risk of postpartum depression early. The detection unit can use a machine learning algorithm to detect signs of postpartum depression based on past data with high accuracy. For example, the risk of postpartum depression is predicted by learning past user data and identifying specific patterns and trends. The detection unit can also monitor the user's input content in real time and detect abnormal patterns early. This allows the detection unit to continuously monitor the user's mental state and take appropriate measures early.

[0034] The contact unit contacts the outside based on the symptom detected by the detection unit. The contact is made by means of, for example, telephone, email, a message app, etc., but is not limited to such examples. For example, when the contact unit detects a symptom of postpartum depression, the contact unit contacts a specialist institution by telephone. The contact unit can also contact the family by email. Furthermore, the contact unit can contact the family using a message app. The contact unit can transmit information quickly and reliably using these means. For example, in an emergency, the specialist institution is directly contacted by telephone to encourage a quick response. Detailed information is provided to the family by email or a message app, and appropriate support is requested. This allows the contact unit to take a quick and appropriate response to protect the safety and health of the user. Furthermore, the contact unit records the contact content so that it can be referenced later. This allows the contact unit to take a more effective response based on the past contact history. Furthermore, the contact unit regularly updates the contact information and always keeps the latest information, thereby realizing quick and reliable contact. This allows the contact unit to always take the optimal response to protect the safety and health of the user.

[0035] The AI ​​mommy friend system includes a receiving unit that has a specific means for receiving the user's complaints. The receiving unit receives the user's complaints. Specific means for receiving the complaints include, but are not limited to, text analysis and emotion analysis. For example, the receiving unit analyzes the complaints input by the user in text format using text analysis technology to understand the user's feelings. The receiving unit can also convert the complaints dictated by the user using voice input into text using voice recognition technology and analyze the text. Furthermore, the receiving unit can analyze the emotions of the user's complaints using emotion analysis technology to understand the user's feelings. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the receiving unit determines that the user is feeling stressed and sympathizes with the user. This allows the AI ​​mommy friend system to receive the user's complaints.

[0036] The analysis unit can analyze the content of the user's statement and generate appropriate advice. For example, the analysis unit can analyze the content of the user's statement using natural language processing technology and understand the intent of the question. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit can analyze the question and generate appropriate advice. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the analysis unit analyzes the statement and determines that the user is feeling stressed. Furthermore, the analysis unit can analyze the image input using image recognition technology and understand the content of the image. For example, if the user takes an image showing the developmental status of his / her child and inputs it to the analysis unit, the analysis unit can analyze the image and generate appropriate advice. This allows the analysis unit to analyze the content of the user's statement and generate appropriate advice.

[0037] The providing unit can provide an appropriate answer to the user's question. The providing unit, for example, provides an answer to the user's question by a text message. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides an answer such as "If your child's development is delayed, I recommend that you first consult a specialist." The providing unit can also provide advice to the user by using a voice message. For example, when the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the providing unit provides advice such as "I understand your feelings. Take a short break and refresh yourself" by using a voice message. Furthermore, the providing unit can also provide advice to the user by using a video message. For example, when the user takes an image showing the developmental status of the child and inputs it to the providing unit, the providing unit provides advice by a video message based on the image. This allows the providing unit to provide an appropriate answer to the user's question.

[0038] The detection unit can analyze the user's statement content or behavioral pattern to detect a sign of postpartum depression. The detection unit can, for example, analyze the user's statement content using text mining technology to detect a sign of postpartum depression. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the detection unit analyzes the statement and detects it as a sign of postpartum depression. The detection unit can also analyze the user's behavioral pattern using behavior log analysis technology to detect a sign of postpartum depression. For example, if the user continues to make negative statements for a long period of time, the detection unit analyzes the behavioral pattern and detects it as a sign of postpartum depression. Furthermore, the detection unit can also detect a sign of postpartum depression early based on the user's behavioral pattern. For example, if the user frequently avoids going out, the detection unit analyzes the behavioral pattern and detects it as a sign of postpartum depression. In this way, the detection unit can analyze the user's statement content and behavioral pattern to detect a sign of postpartum depression.

[0039] The communication unit can contact a specialist institution or a family member when a sign of postpartum depression is detected. For example, the communication unit contacts a specialist institution by telephone when a sign of postpartum depression is detected. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the communication unit analyzes the statements, detects them as a sign of postpartum depression, and contacts a specialist institution by telephone. The communication unit can also contact the family member by email. For example, if the user continues to make negative statements for a long period of time, the communication unit analyzes the behavior pattern, detects them as a sign of postpartum depression, and contacts the family member by email. Furthermore, the communication unit can contact the family member by using a message app. For example, if the user frequently avoids going out, the communication unit analyzes the behavior pattern, detects them as a sign of postpartum depression, and contacts the family member by a message app. In this way, the communication unit can contact a specialist institution or a family member when a sign of postpartum depression is detected.

[0040] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit preferentially suggests text input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, if the user has used voice input at night in the past, the reception unit suggests voice input at night. Furthermore, the reception unit can automatically display related questions and consultation contents by referring to the contents input by the user in the past. For example, if the user has frequently input questions about childcare in the past, the reception unit automatically displays questions and consultation contents related to childcare. In this way, the reception unit can analyze the user's past input history and select the optimal reception method.

[0041] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving input. For example, if the user is raising a child, the reception unit preferentially receives questions and consultation contents related to childcare. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit preferentially receives the question. In addition, if the user is interested in health, the reception unit can also preferentially receive questions and consultation contents related to health. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit preferentially receives the question. Furthermore, if the user wishes to consult about work, the reception unit can also preferentially receive questions and consultation contents related to work. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit preferentially receives the question. This allows the reception unit to perform filtering based on the user's current living situation and areas of interest.

[0042] The reception unit can preferentially receive inputs with high relevance based on the geographical location information of the user when receiving inputs. For example, if the user lives in a specific area, the reception unit preferentially receives information related to that area. For example, if the user inputs a question such as "Where is a recommended pediatrician in this area?", the reception unit preferentially receives the question. In addition, if the user is traveling, the reception unit can also preferentially receive information related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the reception unit preferentially receives the question. Furthermore, if the user is planning to move, the reception unit can also preferentially receive information related to the new place of residence. For example, if the user inputs a question such as "Please tell me about childcare support services in the new place of residence," the reception unit preferentially receives the question. In this way, the reception unit can preferentially receive inputs with high relevance in consideration of the geographical location information of the user.

[0043] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. The reception unit, for example, analyzes the content frequently posted by the user on social media and preferentially receives related questions and consultations. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit preferentially receives the question. The reception unit can also analyze the content of accounts the user follows on social media and preferentially receive related information. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit preferentially receives the question. Furthermore, the reception unit can analyze the content of groups the user participates in on social media and preferentially receive related questions and consultations. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit preferentially receives the question. In this way, the reception unit can analyze the user's social media activity and receive related inputs.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, the analysis unit performs a detailed analysis on input content with high importance. For example, when a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit analyzes the question in detail and provides a detailed analysis result. The analysis unit can also perform a concise analysis on input content with low importance. For example, when a user makes a statement such as "I feel good today," the analysis unit analyzes the statement concisely and provides a concise analysis result. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on input content with medium importance. For example, when a user makes a statement such as "I'm in a hurry," the analysis unit analyzes the statement moderately and provides an analysis result with a moderate level of detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input content.

[0045] The analysis unit can apply different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit applies a childcare-specialized analysis algorithm to input content related to childcare. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit analyzes the question using a childcare-specialized analysis algorithm. The analysis unit can also apply a health-specialized analysis algorithm to input content related to health. For example, if a user inputs a question such as "I'm worried about the results of my health check. What should I do?", the analysis unit analyzes the question using a health-specialized analysis algorithm. Furthermore, the analysis unit can apply a work-specialized analysis algorithm to input content related to work. For example, if a user inputs a question such as "I'm stressed out at work. What should I do?", the analysis unit analyzes the question using a work-specialized analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the input content.

[0046] The analysis unit can determine the priority of analysis based on the submission time of the input contents during analysis. For example, the analysis unit gives priority to analysis of input contents with high urgency. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit gives priority to analyzing the question. The analysis unit can also postpone analysis of input contents submitted earlier. For example, if the user says "I feel good today," the analysis unit will postpone analysis of the statement. The analysis unit can also give priority to analysis of input contents submitted more recently. For example, if the user says "I'm in a hurry," the analysis unit will prioritize analysis of the statement. This allows the analysis unit to determine the priority of analysis based on the submission time of the input contents.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the input contents during analysis. For example, the analysis unit prioritizes analysis of highly relevant input contents. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit prioritizes analysis of the question. The analysis unit can also postpone analysis of less relevant input contents. For example, if a user makes a statement such as "I feel good today," the analysis unit analyzes the statement later. Furthermore, the analysis unit can also analyze input contents with a medium degree of relevance in a moderate order. For example, if a user makes a statement such as "I'm in a hurry," the analysis unit analyzes the statement in a moderate order. This allows the analysis unit to adjust the order of analysis based on the relevance of the input contents.

[0048] The providing unit can adjust the level of detail of the advice based on the importance of the input content when providing the advice. For example, the providing unit provides detailed advice for input content with high importance. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides detailed advice for the question. The providing unit can also provide concise advice for input content with low importance. For example, when the user makes a statement such as "I feel good today," the providing unit provides concise advice for the statement. Furthermore, the providing unit can also provide advice with a moderate level of detail for input content with medium importance. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice with a moderate level of detail for the statement. This allows the providing unit to adjust the level of detail of the advice based on the importance of the input content.

[0049] The providing unit can apply different advice algorithms depending on the category of the input content when providing advice. For example, the providing unit applies a childcare-specialized advice algorithm to the input content related to childcare. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides an answer to the question using a childcare-specialized advice algorithm. The providing unit can also apply a health-specialized advice algorithm to the input content related to health. For example, when the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the providing unit provides an answer to the question using a health-specialized advice algorithm. Furthermore, the providing unit can apply a work-specialized advice algorithm to the input content related to work. For example, when the user inputs a question such as "I'm stressed out at work. What should I do?", the providing unit provides an answer to the question using a work-specialized advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the input content.

[0050] The providing unit can determine the priority of advice based on the submission time of the input contents when providing advice. For example, the providing unit provides advice preferentially for input contents with high urgency. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides advice preferentially for the question. In addition, the providing unit can also provide advice for input contents submitted earlier, with a postponement. For example, when the user makes a statement such as "I feel good today," the providing unit provides advice for the statement with a postponement. In addition, the providing unit can also provide advice for input contents submitted more recently, with a priority. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice for the statement with a priority. In this way, the providing unit can determine the priority of advice based on the submission time of the input contents.

[0051] The providing unit can adjust the order of advice based on the relevance of the input contents when providing advice. For example, the providing unit provides advice preferentially for highly relevant input contents. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides advice preferentially for the question. In addition, the providing unit can also provide advice for less relevant input contents after delay. For example, when the user makes a statement such as "I feel good today," the providing unit provides advice for the statement after delay. Furthermore, the providing unit can also provide advice in a moderate order for input contents with a medium relevance. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice for the statement in a moderate order. In this way, the providing unit can adjust the order of advice based on the relevance of the input contents.

[0052] The detection unit can improve the accuracy of detection when detecting the symptoms by referring to the contents of the user's past statements. The detection unit, for example, detects the symptoms of postpartum depression by referring to the contents of the user's frequent statements in the past. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit detects the symptoms of postpartum depression by referring to the statements. The detection unit can also analyze negative tendencies from the contents of the user's past statements and detect the symptoms of postpartum depression. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit analyzes the statements and detects the symptoms of postpartum depression. Furthermore, the detection unit can also detect the symptoms of postpartum depression early based on the contents of the user's past statements. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit detects the symptoms of postpartum depression early based on the statements. This allows the detection unit to improve the accuracy of detection by referring to the contents of the user's past statements.

[0053] The detection unit can apply different detection algorithms based on the user's behavioral patterns when detecting symptoms. For example, if the user frequently avoids going out, the detection unit applies an algorithm that detects this as a symptom of postpartum depression. For example, if the user frequently utters a statement such as "I'm afraid to go out," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. In addition, the detection unit can also apply an algorithm that detects this as a symptom of postpartum depression when the user complains of lack of sleep. For example, if the user frequently utters a statement such as "I can't sleep," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. In addition, the detection unit can also apply an algorithm that detects this as a symptom of postpartum depression when the user complains of loss of appetite. For example, if the user frequently utters a statement such as "I have no appetite," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. This allows the detection unit to apply different detection algorithms based on the user's behavioral patterns.

[0054] The detection unit can perform detection while taking into consideration the geographical distribution of the user when detecting the symptom. For example, when the user lives in a specific area, the detection unit detects the symptom while taking into consideration the characteristics of the area. For example, when the user makes a statement such as "raising children in this area is difficult," the detection unit analyzes the statement and detects the symptom while taking into consideration the characteristics of the area. In addition, when the user lives in an urban area, the detection unit can also detect the symptom while taking into consideration stress factors specific to the urban area. For example, when the user makes a statement such as "life in an urban area is stressful," the detection unit analyzes the statement and detects the symptom while taking into consideration stress factors specific to the urban area. In addition, when the user lives in a rural area, the detection unit can also detect the symptom while taking into consideration stress factors specific to the rural area. For example, when the user makes a statement such as "life in the rural area is lonely," the detection unit analyzes the statement and detects the symptom while taking into consideration stress factors specific to the rural area. In this way, the detection unit can perform detection while taking into consideration the geographical distribution of the user.

[0055] The detection unit can improve the accuracy of detection when detecting symptoms by referring to related literature of the user. The detection unit, for example, detects symptoms of postpartum depression by referring to related literature read by the user in the past. For example, when the user makes a statement such as "I'm worried about what is written in this book," the detection unit analyzes the statement and detects symptoms of postpartum depression by referring to related literature. The detection unit can also detect symptoms of postpartum depression based on technical books that the user has referred to in the past. For example, when the user makes a statement such as "I'm worried about what is written in the technical book," the detection unit analyzes the statement and detects symptoms of postpartum depression based on the technical book. Furthermore, the detection unit can also detect symptoms of postpartum depression by referring to the contents of seminars that the user has attended in the past. For example, when the user makes a statement such as "I'm worried about what I heard at the seminar," the detection unit analyzes the statement and detects symptoms of postpartum depression by referring to the contents of the seminar. This allows the detection unit to improve the accuracy of detection by referring to related literature of the user.

[0056] The contact unit can select the optimal contact method by referring to the content of the user's past statements when contacting the user. For example, the contact unit preferentially suggests a contact method (email, telephone, etc.) that the user has used in the past. For example, if the user has used email in the past, the contact unit preferentially suggests email. The contact unit can also predict and suggest the optimal contact method from the content of the user's past statements. For example, if the user has previously said something like "I feel most comfortable contacting by phone," the contact unit refers to the statement and suggests contact by phone. Furthermore, the contact unit can select the optimal contact method based on the contact method that the user has used in the past. For example, if the user has frequently used a messaging app in the past, the contact unit preferentially suggests a messaging app. This allows the contact unit to select the optimal contact method by referring to the content of the user's past statements.

[0057] The contact unit can customize the means of contact based on the user's behavioral patterns when contacting. For example, the contact unit preferentially suggests a contact means (email, telephone, etc.) that the user frequently uses. For example, if the user has frequently used email in the past, the contact unit preferentially suggests email. The contact unit can also predict and suggest the optimal means of contact based on the user's behavioral patterns. For example, if the user has previously said something like "I feel most comfortable contacting by phone," the contact unit refers to the statement and suggests contact by phone. Furthermore, the contact unit can customize the optimal means of contact based on the contact means used by the user in the past. For example, if the user has frequently used a messaging app in the past, the contact unit preferentially suggests a messaging app. This allows the contact unit to customize the means of contact based on the user's behavioral patterns.

[0058] The contact unit can select the optimal contact method in consideration of the geographical location information of the user at the time of contact. For example, if the user lives in a specific area, the contact unit preferentially suggests a contact method related to that area. For example, if the user inputs a question such as "Where is a recommended pediatrician in this area?", the contact unit analyzes the question and suggests a contact method related to the area. In addition, if the user is traveling, the contact unit can also preferentially suggest a contact method related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the contact unit analyzes the question and suggests a contact method related to the travel destination. Furthermore, if the user is planning to move, the contact unit can also preferentially suggest a contact method related to the new place of residence. For example, if the user inputs a question such as "Please tell me about childcare support services in my new place of residence," the contact unit analyzes the question and suggests a contact method related to the new place of residence. In this way, the contact unit can select the optimal contact method in consideration of the geographical location information of the user.

[0059] The contact unit can analyze the user's social media activity and suggest a means of contact at the time of contact. The contact unit, for example, analyzes the content frequently posted by the user on social media and preferentially suggests a related means of contact. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the contact unit analyzes the question and suggests a related means of contact. The contact unit can also analyze the content of accounts the user follows on social media and preferentially suggest a related means of contact. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the contact unit analyzes the question and suggests a related means of contact. Furthermore, the contact unit can analyze the content of groups the user participates in on social media and preferentially suggest a related means of contact. For example, if the user inputs a question such as "I'm stressed at work. What should I do?", the contact unit analyzes the question and suggests a related means of contact. In this way, the contact unit can analyze the user's social media activity and suggest a means of contact.

[0060] When receiving a complaint, the reception unit can select the optimal reception method by referring to the contents of the user's past statements. The reception unit, for example, refers to the contents of the user's frequent past statements and selects the optimal reception method. For example, if the user frequently said in the past, "I'm tired," or "I can't do it anymore," the reception unit refers to the statements and selects the optimal reception method. The reception unit can also predict and suggest the optimal reception method from the user's past statements. For example, if the user has said in the past, "I feel most comfortable contacting you by phone," the reception unit refers to the statement and suggests contacting you by phone. Furthermore, the reception unit can select the optimal reception method based on the contents of the user's past statements. For example, if the user has frequently used a messaging app in the past, the reception unit preferentially suggests the messaging app. This allows the reception unit to select the optimal reception method by referring to the contents of the user's past statements.

[0061] The receiving unit can customize the means of receiving complaints based on the user's behavioral patterns when receiving complaints. For example, the receiving unit preferentially suggests a receiving means (such as voice or text) that the user frequently uses. For example, if the user has frequently used text in the past, the receiving unit preferentially suggests text. The receiving unit can also predict and suggest the optimal receiving means based on the user's behavioral patterns. For example, if the user has previously said, "I feel most comfortable contacting by phone," the receiving unit refers to the statement and suggests contact by phone. Furthermore, the receiving unit can customize the optimal receiving means based on the receiving means that the user has previously used. For example, if the user has frequently used a messaging app in the past, the receiving unit preferentially suggests a messaging app. This allows the receiving unit to customize the means of receiving complaints based on the user's behavioral patterns.

[0062] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving a complaint. For example, if the user lives in a specific area, the reception unit preferentially suggests a reception method related to that area. For example, if the user makes a statement such as "raising a child in this area is difficult," the reception unit analyzes the statement and suggests a reception method related to that area. In addition, if the user is traveling, the reception unit can preferentially suggest a reception method related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the reception unit analyzes the question and suggests a reception method related to the travel destination. Furthermore, if the user is planning to move, the reception unit can preferentially suggest a reception method related to the new residence. For example, if the user inputs a question such as "Please tell me about child-rearing support services in the new residence," the reception unit analyzes the question and suggests a reception method related to the new residence. In this way, the reception unit can select the optimal reception method in consideration of the geographical location information of the user.

[0063] When a user complains, the reception unit can analyze the user's social media activity and suggest a method of receiving the complaint. For example, the reception unit analyzes the content frequently posted by the user on social media and preferentially suggests a related method of receiving the complaint. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. The reception unit can also analyze the content of accounts the user follows on social media and preferentially suggest a related method of receiving the complaint. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. Furthermore, the reception unit can analyze the content of groups the user participates in on social media and preferentially suggest a related method of receiving the complaint. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. In this way, the reception unit can analyze the user's social media activity and suggest a method of receiving the complaint.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The AI ​​mommy friend system can further include a rhythm analysis unit that analyzes the user's life rhythm. The rhythm analysis unit analyzes the user's life rhythm and provides advice at an appropriate time. For example, if the user frequently seeks advice at night, the rhythm analysis unit analyzes that pattern and provides advice appropriate for the night. The rhythm analysis unit can also identify time periods when stress is likely to build up based on the user's life rhythm and suggest ways to relax during those times. Furthermore, the rhythm analysis unit can also suggest appropriate rest times according to the user's life rhythm. This allows the AI ​​mommy friend system to provide more effective support based on the user's life rhythm.

[0066] The AI ​​Mama Friend System can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's health condition and provides appropriate advice. For example, if the user complains of feeling unwell, the health monitoring unit analyzes the information and refers the user to an appropriate medical institution. The health monitoring unit can also suggest preventive measures based on the user's health condition. Furthermore, the health monitoring unit can periodically check the user's health condition and respond early if there is an abnormality. This allows the AI ​​Mama Friend System to monitor the user's health condition and provide appropriate support.

[0067] The AI ​​Mommy Friends System can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's hobbies and interests and provides related information. For example, if the user is interested in a particular hobby, the hobby analysis unit analyzes that information and introduces related events and communities. The hobby analysis unit can also suggest new hobbies based on the user's interests. Furthermore, the hobby analysis unit can also suggest activities related to the user's hobbies. This allows the AI ​​Mommy Friends System to support a more fulfilling life based on the user's hobbies and interests.

[0068] The AI ​​mommy friend system can further include a family structure analysis unit that takes into account the user's family structure. The family structure analysis unit analyzes the user's family structure and provides appropriate advice. For example, if the user has multiple children, the family structure analysis unit analyzes that information and provides appropriate advice for each child. The family structure analysis unit can also suggest health management and stress management for the entire family based on the user's family structure. Furthermore, the family structure analysis unit can also suggest activities that the entire family can enjoy according to the user's family structure. This allows the AI ​​mommy friend system to provide more effective support based on the user's family structure.

[0069] The AI ​​Mama Friend System can further include a diet analysis unit that analyzes the user's eating habits. The diet analysis unit analyzes the user's eating habits and provides appropriate advice. For example, if the user requests advice on how to eat a nutritionally balanced diet, the diet analysis unit analyzes the information and proposes a specific meal plan. The diet analysis unit can also provide healthy recipes based on the user's eating habits. Furthermore, the diet analysis unit can periodically check the user's eating habits and suggest improvements. This allows the AI ​​Mama Friend System to support a healthier lifestyle based on the user's eating habits.

[0070] The process flow of the first embodiment will be briefly described below.

[0071] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, and the like. For example, the reception unit receives a question or consultation content input by the user in text format. It can also receive content dictated by the user using voice input. It can also receive an image taken by the user using image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using natural language processing or machine learning algorithms. For example, the analysis unit can analyze the user's text input using natural language processing technology to understand the intent of the question. It can also convert voice input into text using voice recognition technology and analyze that text. It can also analyze image input using image recognition technology to understand the content of the image. Step 3: The providing unit provides advice based on the information analyzed by the analyzing unit. The advice is provided in the form of a text message, a voice message, a video message, or the like. For example, an answer to a user's question is provided by a text message. Advice can also be provided to the user using a voice message or a video message. Step 4: The detection unit detects signs of postpartum depression based on the advice provided by the advice provider. Detection is performed based on the frequency of appearance of specific keywords and changes in behavioral patterns. For example, if the user frequently makes statements such as "I'm tired" or "I can't do this anymore," this is detected as a sign of postpartum depression. In addition, by analyzing the user's behavioral patterns, continued negative statements over a long period of time are also detected as a sign of postpartum depression. Step 5: The communication unit contacts the outside based on the symptoms detected by the detection unit. The communication is performed by means of telephone, email, messaging app, etc. For example, if a symptom of postpartum depression is detected, a specialist institution is contacted by telephone. The family can also be contacted by email. Furthermore, the family can also be contacted using a messaging app.

[0072] (Example 2) The AI ​​mommy friend system according to the embodiment of the present invention is a system in which a user inputs developmental concerns, child-rearing consultations, and consultations on how to respond when sick into the AI, and the AI ​​analyzes the input and provides appropriate advice and information. The AI ​​mommy friend system can also accept the user's complaints, and if symptoms such as postpartum depression are recognized, the AI ​​can contact the outside. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the AI ​​analyzes the information and provides advice such as "If your child's development is delayed, I recommend that you first consult a specialist." In addition, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the AI ​​determines that there is a possibility of postpartum depression and contacts a specialist institution or family. In this way, the user's mental health can be supported. As a result, the AI ​​mommy friend system can easily ask about developmental concerns, child-rearing consultations, and consultations on how to respond when sick, which are difficult for users to hear in real life. In addition, users can complain as much as they want and contact the other party without considering their convenience. Furthermore, if symptoms such as postpartum depression are recognized, the AI ​​can contact the outside and provide appropriate support.

[0073] The AI ​​mommy friend system according to the embodiment includes a reception unit, an analysis unit, a provision unit, a detection unit, and a communication unit. The reception unit receives user input. The user input includes, for example, text input, voice input, image input, and the like, but is not limited to such examples. The reception unit receives, for example, a question or consultation content input by the user in text format. The reception unit can also receive content dictated by the user using voice input. For example, the user can input a question such as "I feel like my child's development is delayed. What should I do?" by voice. The reception unit can also receive an image taken by the user using image input. For example, the user can take an image showing the child's developmental status and input it to the reception unit. The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using natural language processing or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the user's text input using natural language processing technology and understands the intention of the question. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. Furthermore, the analysis unit can also analyze the image input using image recognition technology and understand the contents of the image. The provision unit provides advice based on the information analyzed by the analysis unit. The advice is provided in the form of, for example, a text message, a voice message, a video message, etc., but is not limited to such examples. For example, the provision unit provides an answer to a user's question by a text message. The provision unit can also provide advice to the user using a voice message. The provision unit can also provide advice to the user using a video message. The detection unit detects a sign of postpartum depression based on the advice provided by the provision unit. The detection is performed, for example, based on the frequency of appearance of a specific keyword or a change in a behavior pattern, but is not limited to such examples. For example, the detection unit detects a case where the user frequently makes statements such as "I'm tired" or "I can't do it anymore" as a sign of postpartum depression. The detection unit can also analyze the user's behavior pattern and detect a sign of postpartum depression.For example, if a user continues to make negative comments for a long period of time, this is detected as a sign of postpartum depression. The contact unit contacts the outside based on the sign detected by the detection unit. The contact is made by means of, for example, telephone, email, a messaging app, etc., but is not limited to such examples. For example, when the contact unit detects a sign of postpartum depression, it contacts a specialist institution by telephone. The contact unit can also contact the family by email. Furthermore, the contact unit can also contact the family using a messaging app. In this way, the AI ​​mommy friend system according to the embodiment can accept and analyze user input, provide advice, detect signs of postpartum depression, and contact the outside.

[0074] The reception unit receives user input. Examples of user input include, but are not limited to, text input, voice input, and image input. The reception unit receives, for example, a question or consultation content input by the user in text format. The reception unit can also receive content dictated by the user using voice input. For example, the user can input a question such as "I feel like my child's development is delayed. What should I do?" by voice. The reception unit can also receive images taken by the user using image input. For example, the user can take an image showing the child's developmental status and input it to the reception unit. The reception unit provides a user-friendly interface to receive these inputs. For example, the reception unit allows the user to easily input data through a smartphone app or web application. In the case of voice input, the speech of the user is converted into text using voice recognition technology and transmitted to the analysis unit. In the case of image input, the content of the image is analyzed using image recognition technology and necessary information is extracted. This allows the reception unit to appropriately receive inputs in any format by the user. The reception unit can also temporarily store the user's input content and reuse it as necessary. For example, by referring to the contents of past consultations, the reception unit can provide more appropriate advice, thereby improving the convenience for the user and the efficiency of the entire system.

[0075] The analysis unit analyzes the information received by the reception unit. The analysis is performed, for example, using natural language processing or machine learning algorithms, but is not limited to such examples. For example, the analysis unit analyzes the user's text input using natural language processing technology to understand the intent of the question. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. Furthermore, the analysis unit can analyze the image input using image recognition technology to understand the content of the image. The analysis unit combines these technologies to comprehensively analyze the user's input content and extract information for providing optimal advice. For example, the natural language processing technology is used to analyze the user's question or consultation content and search for related information. A machine learning algorithm is used to generate optimal advice based on past data. The image recognition technology is used to extract important information from the image provided by the user and reflect it in the analysis result. This allows the analysis unit to accurately understand the user's input content and build a foundation for providing appropriate advice. The analysis unit also transmits the analysis result to the provision unit to support the provision of advice to the user. This allows the analysis unit to improve the accuracy and efficiency of the entire system.

[0076] The providing unit provides advice based on the information analyzed by the analyzing unit. The advice is provided in the form of, for example, a text message, a voice message, a video message, or the like, but is not limited to such examples. For example, the providing unit provides an answer to a user's question in the form of a text message. The providing unit can also provide advice to the user using a voice message. Furthermore, the providing unit can also provide advice to the user using a video message. In order to provide these pieces of advice, the providing unit selects an optimal format according to the user's preferences and circumstances. For example, if the user prefers text messages, the advice is provided in text format, and if the user prefers voice messages, the advice is provided in voice format. When a video message is used, detailed advice including visual information can be provided. This allows the providing unit to provide advice to the user in the optimal format, thereby increasing the user's satisfaction. Furthermore, in order to continuously improve the content of the advice, the providing unit can collect user feedback and work with the analyzing unit to improve the accuracy of the advice. This allows the providing unit to always provide the latest and most optimal advice to the user, thereby improving the reliability of the entire system.

[0077] The detection unit detects signs of postpartum depression based on the advice provided by the provision unit. The detection is performed, for example, based on the frequency of appearance of a specific keyword or a change in a behavior pattern, but is not limited to such examples. For example, when the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the detection unit detects this as a sign of postpartum depression. The detection unit can also analyze the user's behavior pattern and detect signs of postpartum depression. For example, when the user continues to make negative statements for a long period of time, the detection unit detects this as a sign of postpartum depression. Based on this information, the detection unit can comprehensively evaluate the user's mental state and detect the risk of postpartum depression early. The detection unit can use a machine learning algorithm to detect signs of postpartum depression based on past data with high accuracy. For example, the risk of postpartum depression is predicted by learning past user data and identifying specific patterns and trends. The detection unit can also monitor the user's input content in real time and detect abnormal patterns early. This allows the detection unit to continuously monitor the user's mental state and take appropriate measures early.

[0078] The contact unit contacts the outside based on the symptom detected by the detection unit. The contact is made by means of, for example, telephone, email, a message app, etc., but is not limited to such examples. For example, when the contact unit detects a symptom of postpartum depression, the contact unit contacts a specialist institution by telephone. The contact unit can also contact the family by email. Furthermore, the contact unit can contact the family using a message app. The contact unit can transmit information quickly and reliably using these means. For example, in an emergency, the specialist institution is directly contacted by telephone to encourage a quick response. Detailed information is provided to the family by email or a message app, and appropriate support is requested. This allows the contact unit to take a quick and appropriate response to protect the safety and health of the user. Furthermore, the contact unit records the contact content so that it can be referenced later. This allows the contact unit to take a more effective response based on the past contact history. Furthermore, the contact unit regularly updates the contact information and always keeps the latest information, thereby realizing quick and reliable contact. This allows the contact unit to always take the optimal response to protect the safety and health of the user.

[0079] The AI ​​mommy friend system includes a receiving unit that has a specific means for receiving the user's complaints. The receiving unit receives the user's complaints. Specific means for receiving the complaints include, but are not limited to, text analysis and emotion analysis. For example, the receiving unit analyzes the complaints input by the user in text format using text analysis technology to understand the user's feelings. The receiving unit can also convert the complaints dictated by the user using voice input into text using voice recognition technology and analyze the text. Furthermore, the receiving unit can analyze the emotions of the user's complaints using emotion analysis technology to understand the user's feelings. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the receiving unit determines that the user is feeling stressed and sympathizes with the user. This allows the AI ​​mommy friend system to receive the user's complaints.

[0080] The analysis unit can analyze the content of the user's statement and generate appropriate advice. For example, the analysis unit can analyze the content of the user's statement using natural language processing technology and understand the intent of the question. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit can analyze the question and generate appropriate advice. The analysis unit can also convert the voice input into text using voice recognition technology and analyze the text. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the analysis unit analyzes the statement and determines that the user is feeling stressed. Furthermore, the analysis unit can analyze the image input using image recognition technology and understand the content of the image. For example, if the user takes an image showing the developmental status of his / her child and inputs it to the analysis unit, the analysis unit can analyze the image and generate appropriate advice. This allows the analysis unit to analyze the content of the user's statement and generate appropriate advice.

[0081] The providing unit can provide an appropriate answer to the user's question. The providing unit, for example, provides an answer to the user's question by a text message. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides an answer such as "If your child's development is delayed, I recommend that you first consult a specialist." The providing unit can also provide advice to the user by using a voice message. For example, when the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the providing unit provides advice such as "I understand your feelings. Take a short break and refresh yourself" by using a voice message. Furthermore, the providing unit can also provide advice to the user by using a video message. For example, when the user takes an image showing the developmental status of the child and inputs it to the providing unit, the providing unit provides advice by a video message based on the image. This allows the providing unit to provide an appropriate answer to the user's question.

[0082] The detection unit can analyze the user's statement content or behavioral pattern to detect a sign of postpartum depression. The detection unit can, for example, analyze the user's statement content using text mining technology to detect a sign of postpartum depression. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the detection unit analyzes the statement and detects it as a sign of postpartum depression. The detection unit can also analyze the user's behavioral pattern using behavior log analysis technology to detect a sign of postpartum depression. For example, if the user continues to make negative statements for a long period of time, the detection unit analyzes the behavioral pattern and detects it as a sign of postpartum depression. Furthermore, the detection unit can also detect a sign of postpartum depression early based on the user's behavioral pattern. For example, if the user frequently avoids going out, the detection unit analyzes the behavioral pattern and detects it as a sign of postpartum depression. In this way, the detection unit can analyze the user's statement content and behavioral pattern to detect a sign of postpartum depression.

[0083] The communication unit can contact a specialist institution or a family member when a sign of postpartum depression is detected. For example, the communication unit contacts a specialist institution by telephone when a sign of postpartum depression is detected. For example, if the user frequently makes statements such as "I'm tired" or "I can't do it anymore," the communication unit analyzes the statements, detects them as a sign of postpartum depression, and contacts a specialist institution by telephone. The communication unit can also contact the family member by email. For example, if the user continues to make negative statements for a long period of time, the communication unit analyzes the behavior pattern, detects them as a sign of postpartum depression, and contacts the family member by email. Furthermore, the communication unit can contact the family member by using a message app. For example, if the user frequently avoids going out, the communication unit analyzes the behavior pattern, detects them as a sign of postpartum depression, and contacts the family member by a message app. In this way, the communication unit can contact a specialist institution or a family member when a sign of postpartum depression is detected.

[0084] The reception unit can estimate the user's emotion and adjust the timing of input reception based on the estimated user's emotion. For example, when the user is feeling stressed, the reception unit delays the timing of input reception to provide time for the user to relax. For example, when the user frequently utters such as "I'm tired" or "I can't do it anymore," the reception unit analyzes the utterance, determines that the user is feeling stressed, and adjusts the timing of input reception. In addition, when the user is relaxed, the reception unit can immediately accept the input and promote smooth dialogue. For example, when the user utters such as "I feel good today," the reception unit analyzes the utterance, determines that the user is relaxed, and immediately accepts the input. Furthermore, when the user is in a hurry, the reception unit can quickly accept the input and immediately start responding. For example, when the user utters such as "I'm in a hurry," the reception unit analyzes the utterance, determines that the user is in a hurry, and quickly accepts the input. This allows the reception unit to adjust the timing of input reception based on the user's emotion.

[0085] The reception unit can analyze the user's past input history and select an appropriate reception method. For example, the reception unit preferentially suggests an input method (such as voice or text) that the user has frequently used in the past. For example, if the user has frequently used text input in the past, the reception unit preferentially suggests text input. The reception unit can also predict and suggest an input method to be used in a specific time period from the user's past input history. For example, if the user has used voice input at night in the past, the reception unit suggests voice input at night. Furthermore, the reception unit can automatically display related questions and consultation contents by referring to the contents input by the user in the past. For example, if the user has frequently input questions about childcare in the past, the reception unit automatically displays questions and consultation contents related to childcare. In this way, the reception unit can analyze the user's past input history and select the optimal reception method.

[0086] The reception unit can perform filtering based on the user's current living situation and areas of interest when receiving input. For example, if the user is raising a child, the reception unit preferentially receives questions and consultation contents related to childcare. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit preferentially receives the question. In addition, if the user is interested in health, the reception unit can also preferentially receive questions and consultation contents related to health. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit preferentially receives the question. Furthermore, if the user wishes to consult about work, the reception unit can also preferentially receive questions and consultation contents related to work. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit preferentially receives the question. This allows the reception unit to perform filtering based on the user's current living situation and areas of interest.

[0087] The reception unit can estimate the user's emotion and determine the priority of inputs to be received based on the estimated user's emotion. For example, when the user is feeling anxious, the reception unit preferentially receives consultation contents with high urgency. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit preferentially receives the question. In addition, when the user is relaxed, the reception unit can also preferentially receive normal consultation contents. For example, when the user says "I feel good today," the reception unit analyzes the statement and preferentially receives normal consultation contents. Furthermore, when the user is in a hurry, the reception unit can also preferentially receive consultation contents that require a quick response. For example, when the user says "I'm in a hurry," the reception unit analyzes the statement and preferentially receives consultation contents that require a quick response. In this way, the reception unit can determine the priority of inputs to be received based on the user's emotion.

[0088] The reception unit can preferentially receive inputs with high relevance based on the geographical location information of the user when receiving inputs. For example, if the user lives in a specific area, the reception unit preferentially receives information related to that area. For example, if the user inputs a question such as "Where is a recommended pediatrician in this area?", the reception unit preferentially receives the question. In addition, if the user is traveling, the reception unit can also preferentially receive information related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the reception unit preferentially receives the question. Furthermore, if the user is planning to move, the reception unit can also preferentially receive information related to the new place of residence. For example, if the user inputs a question such as "Please tell me about childcare support services in the new place of residence," the reception unit preferentially receives the question. In this way, the reception unit can preferentially receive inputs with high relevance in consideration of the geographical location information of the user.

[0089] The reception unit can analyze the user's social media activity and receive related inputs when receiving inputs. The reception unit, for example, analyzes the content frequently posted by the user on social media and preferentially receives related questions and consultations. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit preferentially receives the question. The reception unit can also analyze the content of accounts the user follows on social media and preferentially receive related information. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit preferentially receives the question. Furthermore, the reception unit can analyze the content of groups the user participates in on social media and preferentially receive related questions and consultations. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit preferentially receives the question. In this way, the reception unit can analyze the user's social media activity and receive related inputs.

[0090] The analysis unit can estimate the user's emotions and adjust the way of expressing the analysis based on the estimated user's emotions. For example, if the user feels anxious, the analysis unit provides the analysis result in gentle language. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit analyzes the question and provides an answer in gentle language. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user says "I feel good today," the analysis unit analyzes the statement and provides a detailed analysis result. Furthermore, the analysis unit can also provide a concise and to the point analysis result if the user is in a hurry. For example, if the user says "I'm in a hurry," the analysis unit analyzes the statement and provides a concise and to the point analysis result. This allows the analysis unit to adjust the way of expressing the analysis based on the user's emotions.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the input content during the analysis. For example, the analysis unit performs a detailed analysis on input content with high importance. For example, when a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit analyzes the question in detail and provides a detailed analysis result. The analysis unit can also perform a concise analysis on input content with low importance. For example, when a user makes a statement such as "I feel good today," the analysis unit analyzes the statement concisely and provides a concise analysis result. Furthermore, the analysis unit can perform an analysis with a moderate level of detail on input content with medium importance. For example, when a user makes a statement such as "I'm in a hurry," the analysis unit analyzes the statement moderately and provides an analysis result with a moderate level of detail. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the input content.

[0092] The analysis unit can apply different analysis algorithms depending on the category of the input content during analysis. For example, the analysis unit applies a childcare-specialized analysis algorithm to input content related to childcare. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit analyzes the question using a childcare-specialized analysis algorithm. The analysis unit can also apply a health-specialized analysis algorithm to input content related to health. For example, if a user inputs a question such as "I'm worried about the results of my health check. What should I do?", the analysis unit analyzes the question using a health-specialized analysis algorithm. Furthermore, the analysis unit can apply a work-specialized analysis algorithm to input content related to work. For example, if a user inputs a question such as "I'm stressed out at work. What should I do?", the analysis unit analyzes the question using a work-specialized analysis algorithm. This allows the analysis unit to apply different analysis algorithms depending on the category of the input content.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is in a hurry, the analysis unit provides a short and to the point analysis result. For example, when the user says something like "I'm in a hurry," the analysis unit analyzes the statement and provides a short and to the point analysis result. The analysis unit can also provide a detailed analysis result when the user is relaxed. For example, when the user says something like "I'm in a good mood today," the analysis unit analyzes the statement and provides a detailed analysis result. Furthermore, when the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect added. For example, when the user says something like "I'm very excited," the analysis unit analyzes the statement and provides an analysis result with a visually stimulating effect added. This allows the analysis unit to adjust the length of the analysis based on the user's emotions.

[0094] The analysis unit can determine the priority of analysis based on the submission time of the input contents during analysis. For example, the analysis unit gives priority to analysis of input contents with high urgency. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit gives priority to analyzing the question. The analysis unit can also postpone analysis of input contents submitted earlier. For example, if the user says "I feel good today," the analysis unit will postpone analysis of the statement. The analysis unit can also give priority to analysis of input contents submitted more recently. For example, if the user says "I'm in a hurry," the analysis unit will prioritize analysis of the statement. This allows the analysis unit to determine the priority of analysis based on the submission time of the input contents.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the input contents during analysis. For example, the analysis unit prioritizes analysis of highly relevant input contents. For example, if a user inputs a question such as "I feel like my child's development is delayed. What should I do?", the analysis unit prioritizes analysis of the question. The analysis unit can also postpone analysis of less relevant input contents. For example, if a user makes a statement such as "I feel good today," the analysis unit analyzes the statement later. Furthermore, the analysis unit can also analyze input contents with a medium degree of relevance in a moderate order. For example, if a user makes a statement such as "I'm in a hurry," the analysis unit analyzes the statement in a moderate order. This allows the analysis unit to adjust the order of analysis based on the relevance of the input contents.

[0096] The providing unit can estimate the user's emotions and adjust the way of expressing advice based on the estimated user's emotions. For example, when the user feels anxious, the providing unit provides advice in gentle language. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit analyzes the question and provides an answer in gentle language. In addition, the providing unit can provide detailed advice when the user is relaxed. For example, when the user says "I feel good today," the providing unit analyzes the statement and provides detailed advice. Furthermore, when the user is in a hurry, the providing unit can provide concise advice that hits the point. For example, when the user says "I'm in a hurry," the providing unit analyzes the statement and provides concise advice that hits the point. This allows the providing unit to adjust the way of expressing advice based on the user's emotions.

[0097] The providing unit can adjust the level of detail of the advice based on the importance of the input content when providing the advice. For example, the providing unit provides detailed advice for input content with high importance. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides detailed advice for the question. The providing unit can also provide concise advice for input content with low importance. For example, when the user makes a statement such as "I feel good today," the providing unit provides concise advice for the statement. Furthermore, the providing unit can also provide advice with a moderate level of detail for input content with medium importance. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice with a moderate level of detail for the statement. This allows the providing unit to adjust the level of detail of the advice based on the importance of the input content.

[0098] The providing unit can apply different advice algorithms depending on the category of the input content when providing advice. For example, the providing unit applies a childcare-specialized advice algorithm to the input content related to childcare. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides an answer to the question using a childcare-specialized advice algorithm. The providing unit can also apply a health-specialized advice algorithm to the input content related to health. For example, when the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the providing unit provides an answer to the question using a health-specialized advice algorithm. Furthermore, the providing unit can apply a work-specialized advice algorithm to the input content related to work. For example, when the user inputs a question such as "I'm stressed out at work. What should I do?", the providing unit provides an answer to the question using a work-specialized advice algorithm. This allows the providing unit to apply different advice algorithms depending on the category of the input content.

[0099] The providing unit can estimate the user's emotions and adjust the length of the advice based on the estimated user's emotions. For example, when the user is in a hurry, the providing unit provides short and to the point advice. For example, when the user makes a statement such as "I'm in a hurry," the providing unit analyzes the statement and provides short and to the point advice. In addition, the providing unit can provide detailed advice when the user is relaxed. For example, when the user makes a statement such as "I'm in a good mood today," the providing unit analyzes the statement and provides detailed advice. Furthermore, when the user is excited, the providing unit can provide advice with a visually stimulating effect. For example, when the user makes a statement such as "I'm very excited," the providing unit analyzes the statement and provides advice with a visually stimulating effect. This allows the providing unit to adjust the length of the advice based on the user's emotions.

[0100] The providing unit can determine the priority of advice based on the submission time of the input contents when providing advice. For example, the providing unit provides advice preferentially for input contents with high urgency. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides advice preferentially for the question. In addition, the providing unit can also provide advice for input contents submitted earlier, with a postponement. For example, when the user makes a statement such as "I feel good today," the providing unit provides advice for the statement with a postponement. In addition, the providing unit can also provide advice for input contents submitted more recently, with a priority. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice for the statement with a priority. In this way, the providing unit can determine the priority of advice based on the submission time of the input contents.

[0101] The providing unit can adjust the order of advice based on the relevance of the input contents when providing advice. For example, the providing unit provides advice preferentially for highly relevant input contents. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the providing unit provides advice preferentially for the question. In addition, the providing unit can also provide advice for less relevant input contents after delay. For example, when the user makes a statement such as "I feel good today," the providing unit provides advice for the statement after delay. Furthermore, the providing unit can also provide advice in a moderate order for input contents with a medium relevance. For example, when the user makes a statement such as "I'm in a hurry," the providing unit provides advice for the statement in a moderate order. In this way, the providing unit can adjust the order of advice based on the relevance of the input contents.

[0102] The detection unit can estimate the user's emotions and adjust the criteria for detecting signs of postpartum depression based on the estimated user's emotions. For example, when the user frequently says "I'm tired," the detection unit sets a criterion for detecting it as a sign of postpartum depression. For example, when the user frequently says "I'm tired," the detection unit analyzes the statement and sets a criterion for detecting it as a sign of postpartum depression. The detection unit can also set a criterion for detecting it as a sign of postpartum depression when the user says "I can't do it anymore." For example, when the user says "I can't do it anymore," the detection unit analyzes the statement and sets a criterion for detecting it as a sign of postpartum depression. Furthermore, the detection unit can also set a criterion for detecting it as a sign of postpartum depression when the user continues to make negative statements for a long period of time. For example, when the user continues to make negative statements such as "I'm tired" and "I can't do it anymore" for a long period of time, the detection unit analyzes the statement and sets a criterion for detecting it as a sign of postpartum depression. This allows the detection unit to adjust the criterion for detecting signs of postpartum depression based on the user's emotions.

[0103] The detection unit can improve the accuracy of detection when detecting the symptoms by referring to the contents of the user's past statements. The detection unit, for example, detects the symptoms of postpartum depression by referring to the contents of the user's frequent statements in the past. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit detects the symptoms of postpartum depression by referring to the statements. The detection unit can also analyze negative tendencies from the contents of the user's past statements and detect the symptoms of postpartum depression. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit analyzes the statements and detects the symptoms of postpartum depression. Furthermore, the detection unit can also detect the symptoms of postpartum depression early based on the contents of the user's past statements. For example, if the user frequently said things such as "I'm tired" or "I can't do it anymore" in the past, the detection unit detects the symptoms of postpartum depression early based on the statements. This allows the detection unit to improve the accuracy of detection by referring to the contents of the user's past statements.

[0104] The detection unit can apply different detection algorithms based on the user's behavioral patterns when detecting symptoms. For example, if the user frequently avoids going out, the detection unit applies an algorithm that detects this as a symptom of postpartum depression. For example, if the user frequently utters a statement such as "I'm afraid to go out," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. In addition, the detection unit can also apply an algorithm that detects this as a symptom of postpartum depression when the user complains of lack of sleep. For example, if the user frequently utters a statement such as "I can't sleep," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. In addition, the detection unit can also apply an algorithm that detects this as a symptom of postpartum depression when the user complains of loss of appetite. For example, if the user frequently utters a statement such as "I have no appetite," the detection unit analyzes the statement and applies an algorithm that detects this as a symptom of postpartum depression. This allows the detection unit to apply different detection algorithms based on the user's behavioral patterns.

[0105] The detection unit can estimate the user's emotion and adjust the order of displaying the results of the sign detection based on the estimated user's emotion. For example, when the user feels anxious, the detection unit preferentially displays important symptoms. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the detection unit analyzes the question and preferentially displays important symptoms. In addition, when the user is relaxed, the detection unit can also display detailed symptoms in an orderly manner. For example, when the user makes a statement such as "I feel good today," the detection unit analyzes the statement and displays detailed symptoms in an orderly manner. Furthermore, when the user is in a hurry, the detection unit can also display concise symptoms in a orderly manner. For example, when the user makes a statement such as "I'm in a hurry," the detection unit analyzes the statement and displays concise symptoms in a orderly manner. This allows the detection unit to adjust the order of displaying the results of the sign detection based on the user's emotion.

[0106] The detection unit can perform detection while taking into consideration the geographical distribution of the user when detecting the symptom. For example, when the user lives in a specific area, the detection unit detects the symptom while taking into consideration the characteristics of the area. For example, when the user makes a statement such as "raising children in this area is difficult," the detection unit analyzes the statement and detects the symptom while taking into consideration the characteristics of the area. In addition, when the user lives in an urban area, the detection unit can also detect the symptom while taking into consideration stress factors specific to the urban area. For example, when the user makes a statement such as "life in an urban area is stressful," the detection unit analyzes the statement and detects the symptom while taking into consideration stress factors specific to the urban area. In addition, when the user lives in a rural area, the detection unit can also detect the symptom while taking into consideration stress factors specific to the rural area. For example, when the user makes a statement such as "life in the rural area is lonely," the detection unit analyzes the statement and detects the symptom while taking into consideration stress factors specific to the rural area. In this way, the detection unit can perform detection while taking into consideration the geographical distribution of the user.

[0107] The detection unit can improve the accuracy of detection when detecting symptoms by referring to related literature of the user. The detection unit, for example, detects symptoms of postpartum depression by referring to related literature read by the user in the past. For example, when the user makes a statement such as "I'm worried about what is written in this book," the detection unit analyzes the statement and detects symptoms of postpartum depression by referring to related literature. The detection unit can also detect symptoms of postpartum depression based on technical books that the user has referred to in the past. For example, when the user makes a statement such as "I'm worried about what is written in the technical book," the detection unit analyzes the statement and detects symptoms of postpartum depression based on the technical book. Furthermore, the detection unit can also detect symptoms of postpartum depression by referring to the contents of seminars that the user has attended in the past. For example, when the user makes a statement such as "I'm worried about what I heard at the seminar," the detection unit analyzes the statement and detects symptoms of postpartum depression by referring to the contents of the seminar. This allows the detection unit to improve the accuracy of detection by referring to related literature of the user.

[0108] The communication unit can estimate the user's emotions and adjust the method of contact based on the estimated user's emotions. For example, when the user feels anxious, the communication unit contacts the user using gentle language. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the communication unit analyzes the question and contacts the user using gentle language. In addition, when the user is relaxed, the communication unit can also contact the user including detailed information. For example, when the user makes a statement such as "I feel good today," the communication unit analyzes the statement and contacts the user including detailed information. Furthermore, when the user is in a hurry, the communication unit can also contact the user briefly and to the point. For example, when the user makes a statement such as "I'm in a hurry," the communication unit analyzes the statement and contacts the user briefly and to the point. This allows the communication unit to adjust the method of contact based on the user's emotions.

[0109] The contact unit can select the optimal contact method by referring to the content of the user's past statements when contacting the user. For example, the contact unit preferentially suggests a contact method (email, telephone, etc.) that the user has used in the past. For example, if the user has used email in the past, the contact unit preferentially suggests email. The contact unit can also predict and suggest the optimal contact method from the content of the user's past statements. For example, if the user has previously said something like "I feel most comfortable contacting by phone," the contact unit refers to the statement and suggests contact by phone. Furthermore, the contact unit can select the optimal contact method based on the contact method that the user has used in the past. For example, if the user has frequently used a messaging app in the past, the contact unit preferentially suggests a messaging app. This allows the contact unit to select the optimal contact method by referring to the content of the user's past statements.

[0110] The contact unit can customize the means of contact based on the user's behavioral patterns when contacting. For example, the contact unit preferentially suggests a contact means (email, telephone, etc.) that the user frequently uses. For example, if the user has frequently used email in the past, the contact unit preferentially suggests email. The contact unit can also predict and suggest the optimal means of contact based on the user's behavioral patterns. For example, if the user has previously said something like "I feel most comfortable contacting by phone," the contact unit refers to the statement and suggests contact by phone. Furthermore, the contact unit can customize the optimal means of contact based on the contact means used by the user in the past. For example, if the user has frequently used a messaging app in the past, the contact unit preferentially suggests a messaging app. This allows the contact unit to customize the means of contact based on the user's behavioral patterns.

[0111] The communication unit can estimate the user's emotions and determine the priority of the communication based on the estimated user's emotions. For example, when the user is feeling anxious, the communication unit prioritizes communication with high urgency. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the communication unit analyzes the question and prioritizes communication with high urgency. In addition, the communication unit can also prioritize regular communication when the user is relaxed. For example, when the user says "I feel good today," the communication unit analyzes the statement and prioritizes regular communication. Furthermore, when the user is in a hurry, the communication unit can also prioritize communication that requires a quick response. For example, when the user says "I'm in a hurry," the communication unit analyzes the statement and prioritizes communication that requires a quick response. In this way, the communication unit can determine the priority of the communication based on the user's emotions.

[0112] The contact unit can select the optimal contact method in consideration of the geographical location information of the user at the time of contact. For example, if the user lives in a specific area, the contact unit preferentially suggests a contact method related to that area. For example, if the user inputs a question such as "Where is a recommended pediatrician in this area?", the contact unit analyzes the question and suggests a contact method related to the area. In addition, if the user is traveling, the contact unit can also preferentially suggest a contact method related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the contact unit analyzes the question and suggests a contact method related to the travel destination. Furthermore, if the user is planning to move, the contact unit can also preferentially suggest a contact method related to the new place of residence. For example, if the user inputs a question such as "Please tell me about childcare support services in my new place of residence," the contact unit analyzes the question and suggests a contact method related to the new place of residence. In this way, the contact unit can select the optimal contact method in consideration of the geographical location information of the user.

[0113] The contact unit can analyze the user's social media activity and suggest a means of contact at the time of contact. The contact unit, for example, analyzes the content frequently posted by the user on social media and preferentially suggests a related means of contact. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the contact unit analyzes the question and suggests a related means of contact. The contact unit can also analyze the content of accounts the user follows on social media and preferentially suggest a related means of contact. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the contact unit analyzes the question and suggests a related means of contact. Furthermore, the contact unit can analyze the content of groups the user participates in on social media and preferentially suggest a related means of contact. For example, if the user inputs a question such as "I'm stressed at work. What should I do?", the contact unit analyzes the question and suggests a related means of contact. In this way, the contact unit can analyze the user's social media activity and suggest a means of contact.

[0114] The reception unit can estimate the user's emotions and adjust the way of receiving the complaint based on the estimated user's emotions. For example, when the user feels anxious, the reception unit receives the complaint in a gentle manner. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit analyzes the question and receives the complaint in a gentle manner. In addition, when the user is relaxed, the reception unit can receive detailed complaints. For example, when the user says "I feel good today," the reception unit analyzes the statement and receives detailed complaints. Furthermore, when the user is in a hurry, the reception unit can receive a brief and to the point complaint. For example, when the user says "I'm in a hurry," the reception unit analyzes the statement and receives a brief and to the point complaint. This allows the reception unit to adjust the way of receiving the complaint based on the user's emotions.

[0115] When receiving a complaint, the reception unit can select the optimal reception method by referring to the contents of the user's past statements. The reception unit, for example, refers to the contents of the user's frequent past statements and selects the optimal reception method. For example, if the user frequently said in the past, "I'm tired," or "I can't do it anymore," the reception unit refers to the statements and selects the optimal reception method. The reception unit can also predict and suggest the optimal reception method from the user's past statements. For example, if the user has said in the past, "I feel most comfortable contacting you by phone," the reception unit refers to the statement and suggests contacting you by phone. Furthermore, the reception unit can select the optimal reception method based on the contents of the user's past statements. For example, if the user has frequently used a messaging app in the past, the reception unit preferentially suggests the messaging app. This allows the reception unit to select the optimal reception method by referring to the contents of the user's past statements.

[0116] The receiving unit can customize the means of receiving complaints based on the user's behavioral patterns when receiving complaints. For example, the receiving unit preferentially suggests a receiving means (such as voice or text) that the user frequently uses. For example, if the user has frequently used text in the past, the receiving unit preferentially suggests text. The receiving unit can also predict and suggest the optimal receiving means based on the user's behavioral patterns. For example, if the user has previously said, "I feel most comfortable contacting by phone," the receiving unit refers to the statement and suggests contact by phone. Furthermore, the receiving unit can customize the optimal receiving means based on the receiving means that the user has previously used. For example, if the user has frequently used a messaging app in the past, the receiving unit preferentially suggests a messaging app. This allows the receiving unit to customize the means of receiving complaints based on the user's behavioral patterns.

[0117] The reception unit can estimate the user's emotions and determine the priority of receiving complaints based on the estimated user's emotions. For example, when the user is feeling anxious, the reception unit prioritizes receiving complaints with high urgency. For example, when the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit analyzes the question and prioritizes receiving complaints with high urgency. In addition, when the user is relaxed, the reception unit can also prioritize receiving normal complaints. For example, when the user makes a statement such as "I feel good today," the reception unit analyzes the statement and prioritizes receiving normal complaints. Furthermore, when the user is in a hurry, the reception unit can also prioritize receiving complaints that require a quick response. For example, when the user makes a statement such as "I'm in a hurry," the reception unit analyzes the statement and prioritizes receiving complaints that require a quick response. In this way, the reception unit can determine the priority of receiving complaints based on the user's emotions.

[0118] The reception unit can select the optimal reception method in consideration of the geographical location information of the user when receiving a complaint. For example, if the user lives in a specific area, the reception unit preferentially suggests a reception method related to that area. For example, if the user makes a statement such as "raising a child in this area is difficult," the reception unit analyzes the statement and suggests a reception method related to that area. In addition, if the user is traveling, the reception unit can preferentially suggest a reception method related to the travel destination. For example, if the user inputs a question such as "Please tell me about how to manage my child's health when traveling," the reception unit analyzes the question and suggests a reception method related to the travel destination. Furthermore, if the user is planning to move, the reception unit can preferentially suggest a reception method related to the new residence. For example, if the user inputs a question such as "Please tell me about child-rearing support services in the new residence," the reception unit analyzes the question and suggests a reception method related to the new residence. In this way, the reception unit can select the optimal reception method in consideration of the geographical location information of the user.

[0119] When a user complains, the reception unit can analyze the user's social media activity and suggest a method of receiving the complaint. For example, the reception unit analyzes the content frequently posted by the user on social media and preferentially suggests a related method of receiving the complaint. For example, if the user inputs a question such as "I feel like my child's development is delayed. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. The reception unit can also analyze the content of accounts the user follows on social media and preferentially suggest a related method of receiving the complaint. For example, if the user inputs a question such as "I'm worried about the results of my health check. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. Furthermore, the reception unit can analyze the content of groups the user participates in on social media and preferentially suggest a related method of receiving the complaint. For example, if the user inputs a question such as "I'm stressed out at work. What should I do?", the reception unit analyzes the question and suggests a related method of receiving the complaint. In this way, the reception unit can analyze the user's social media activity and suggest a method of receiving the complaint.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The AI ​​mommy friend system can further include a rhythm analysis unit that analyzes the user's life rhythm. The rhythm analysis unit analyzes the user's life rhythm and provides advice at an appropriate time. For example, if the user frequently seeks advice at night, the rhythm analysis unit analyzes that pattern and provides advice appropriate for the night. The rhythm analysis unit can also identify time periods when stress is likely to build up based on the user's life rhythm and suggest ways to relax during those times. Furthermore, the rhythm analysis unit can also suggest appropriate rest times according to the user's life rhythm. This allows the AI ​​mommy friend system to provide more effective support based on the user's life rhythm.

[0122] The AI ​​Mama Friend System can further include a health monitoring unit that monitors the user's health condition. The health monitoring unit monitors the user's health condition and provides appropriate advice. For example, if the user complains of feeling unwell, the health monitoring unit analyzes the information and refers the user to an appropriate medical institution. The health monitoring unit can also suggest preventive measures based on the user's health condition. Furthermore, the health monitoring unit can periodically check the user's health condition and respond early if there is an abnormality. This allows the AI ​​Mama Friend System to monitor the user's health condition and provide appropriate support.

[0123] The AI ​​Mommy Friends System can further include a hobby analysis unit that analyzes the user's hobbies and interests. The hobby analysis unit analyzes the user's hobbies and interests and provides related information. For example, if the user is interested in a particular hobby, the hobby analysis unit analyzes that information and introduces related events and communities. The hobby analysis unit can also suggest new hobbies based on the user's interests. Furthermore, the hobby analysis unit can also suggest activities related to the user's hobbies. This allows the AI ​​Mommy Friends System to support a more fulfilling life based on the user's hobbies and interests.

[0124] The AI ​​mommy friend system can further include a family structure analysis unit that takes into account the user's family structure. The family structure analysis unit analyzes the user's family structure and provides appropriate advice. For example, if the user has multiple children, the family structure analysis unit analyzes that information and provides appropriate advice for each child. The family structure analysis unit can also suggest health management and stress management for the entire family based on the user's family structure. Furthermore, the family structure analysis unit can also suggest activities that the entire family can enjoy according to the user's family structure. This allows the AI ​​mommy friend system to provide more effective support based on the user's family structure.

[0125] The AI ​​Mama Friend System can further include a diet analysis unit that analyzes the user's eating habits. The diet analysis unit analyzes the user's eating habits and provides appropriate advice. For example, if the user requests advice on how to eat a nutritionally balanced diet, the diet analysis unit analyzes the information and proposes a specific meal plan. The diet analysis unit can also provide healthy recipes based on the user's eating habits. Furthermore, the diet analysis unit can periodically check the user's eating habits and suggest improvements. This allows the AI ​​Mama Friend System to support a healthier lifestyle based on the user's eating habits.

[0126] The AI ​​Mommy Friend System can estimate the user's emotions and suggest relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxing music or meditation methods. Also, if the user is feeling anxious, the system can suggest deep breathing or yoga methods. Furthermore, if the user is relaxed, the system can suggest activities to maintain that state. This allows the AI ​​Mommy Friend System to suggest appropriate relaxation methods based on the user's emotions.

[0127] The AI ​​mommy friend system can estimate the user's emotions and provide encouraging messages based on the estimated emotions. For example, if the user is feeling down, the system can provide encouraging words and positive messages. Also, if the user is tired, the system can provide a message encouraging the user to rest. Furthermore, if the user is working hard, the system can provide a message praising the user's efforts. This allows the AI ​​mommy friend system to provide appropriate encouraging messages based on the user's emotions.

[0128] The AI ​​Mommy Friend System can estimate the user's emotions and suggest appropriate exercises based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxing yoga or stretching. If the user has too much energy, the system can also suggest aerobic exercise or dancing. Furthermore, if the user is tired, the system can suggest light walking or relaxation exercises. This allows the AI ​​Mommy Friend System to suggest appropriate exercises based on the user's emotions.

[0129] The AI ​​Mommy Friends System can estimate the user's emotions and suggest an appropriate reading list based on the estimated emotions. For example, if the user wants to relax, the system can suggest relaxing novels and essays. Also, if the user is looking for self-improvement, the system can also suggest self-improvement books and business books. Furthermore, if the user is looking for entertainment, the system can also suggest interesting manga or original books of movies. This allows the AI ​​Mommy Friends System to suggest an appropriate reading list based on the user's emotions.

[0130] The AI ​​Mommy Friends System can estimate the user's emotions and suggest appropriate hobby activities based on the estimated emotions. For example, if the user is feeling stressed, the system can suggest relaxing crafts or gardening. Also, if the user has too much energy, the system can suggest sports or outdoor activities. Furthermore, if the user is looking for creative activities, the system can suggest painting or music production. This allows the AI ​​Mommy Friends System to suggest appropriate hobby activities based on the user's emotions.

[0131] The process flow of the second embodiment will be briefly described below.

[0132] Step 1: The reception unit receives user input. User input includes text input, voice input, image input, and the like. For example, the reception unit receives a question or consultation content input by the user in text format. It can also receive content dictated by the user using voice input. It can also receive an image taken by the user using image input. Step 2: The analysis unit analyzes the information received by the reception unit. The analysis is performed using natural language processing or machine learning algorithms. For example, the analysis unit can analyze the user's text input using natural language processing technology to understand the intent of the question. It can also convert voice input into text using voice recognition technology and analyze that text. It can also analyze image input using image recognition technology to understand the content of the image. Step 3: The providing unit provides advice based on the information analyzed by the analyzing unit. The advice is provided in the form of a text message, a voice message, a video message, or the like. For example, an answer to a user's question is provided by a text message. Advice can also be provided to the user using a voice message or a video message. Step 4: The detection unit detects signs of postpartum depression based on the advice provided by the advice provider. Detection is performed based on the frequency of appearance of specific keywords and changes in behavioral patterns. For example, if the user frequently makes statements such as "I'm tired" or "I can't do this anymore," this is detected as a sign of postpartum depression. In addition, by analyzing the user's behavioral patterns, continued negative statements over a long period of time are also detected as a sign of postpartum depression. Step 5: The communication unit contacts the outside based on the symptoms detected by the detection unit. The communication is performed by means of telephone, email, messaging app, etc. For example, if a symptom of postpartum depression is detected, a specialist institution is contacted by telephone. The family can also be contacted by email. Furthermore, the family can also be contacted using a messaging app.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0135] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, detection unit, communication unit, and reception unit 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 control unit 46A of the smart device 14 and receives a user's input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides advice based on the analysis result. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects a symptom of postpartum depression. The communication unit is realized, for example, by the control unit 46A of the smart device 14 and communicates with the outside. The reception unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and receives the user's complaints. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0144] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, detection unit, communication unit, and reception unit 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 control unit 46A of the smart glasses 214 and receives a user's input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides advice based on the analysis result. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of postpartum depression. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214 and communicates with the outside. The reception unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and receives the user's complaints. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] 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.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0160] 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.

[0161] 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.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0163] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0168] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, detection unit, communication unit, and reception 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 control unit 46A of the headset type terminal 314 and receives a user's input. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The provision unit is realized by the control unit 46A of the headset type terminal 314 and provides advice based on the analysis result. The detection unit is realized by the specific processing unit 290 of the data processing device 12 and detects a symptom of postpartum depression. The communication unit is realized by the control unit 46A of the headset type terminal 314 and communicates with the outside. The reception unit is realized by the specific processing unit 290 of the data processing device 12 and receives the user's complaints. The correspondence relationship between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 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.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] 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.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] 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.

[0178] 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.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0180] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] Each of the multiple elements including the above-mentioned reception unit, analysis unit, provision unit, detection unit, communication unit, and reception unit 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 control unit 46A of the robot 414 and receives a user's input. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the user's input. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides advice based on the analysis result. The detection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and detects signs of postpartum depression. The communication unit is realized, for example, by the control unit 46A of the robot 414 and communicates with the outside. The reception unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and receives the user's complaints. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0187] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0190] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0191] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0193] 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.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0196] 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.

[0197] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0198] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0202] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0204] (Appendix 1) A reception unit that receives input from a user; an analysis unit that analyzes the information received by the reception unit; a providing unit that provides advice based on the information analyzed by the analyzing unit; A detection unit that detects a sign of postpartum depression based on the advice provided by the provision unit; A communication unit that communicates with the outside based on the symptom detected by the detection unit. A system characterized by: (Appendix 2) The system described in appendix 1, characterized in that it has a receiving section that has a specific means for receiving a user's complaints. (Appendix 3) The analysis unit is Analyze user comments and generate appropriate advice 2. The system of claim 1, (Appendix 4) The system described in Appendix 1, characterized in that the providing unit provides appropriate answers to user questions. (Appendix 5) The system described in Appendix 1, wherein the detection unit analyzes the content of a user's statements or behavioral patterns to detect signs of postpartum depression. (Appendix 6) The system described in Appendix 1, wherein the communication unit contacts a specialist institution or family if it detects signs of postpartum depression. (Appendix 7) The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions. 2. The system of claim 1, (Appendix 8) The system described in Appendix 1, wherein the reception unit analyzes the user's past input history and selects an appropriate reception method. (Appendix 9) The reception unit is As input is received, it is filtered based on the user's current life situation and areas of interest. 2. The system of claim 1, (Appendix 10) The reception unit is Estimate the user's emotions and prioritize inputs based on the estimated user emotions. 2. The system of claim 1, (Appendix 11) The system according to claim 1, wherein the reception unit, when receiving input, preferentially receives input that is highly relevant based on the user's geographical location information. (Appendix 12) The reception unit is When receiving input, analyze the user's social media activity and receive relevant input 2. The system of claim 1, (Appendix 13) The analysis unit is Infer user emotions and adjust the presentation of analytics based on the inferred user emotions 2. The system of claim 1, (Appendix 14) The analysis unit is During analysis, adjust the level of detail in the analysis based on the importance of the input 2. The system of claim 1, (Appendix 15) The analysis unit is During parsing, apply different parsing algorithms depending on the category of input content 2. The system of claim 1, (Appendix 16) The analysis unit is Infer user sentiment and adjust the length of the analysis based on the inferred user sentiment 2. The system of claim 1, (Appendix 17) The analysis unit is During analysis, prioritize analysis based on when inputs were submitted 2. The system of claim 1, (Appendix 18) The analysis unit is During parsing, adjust the order of parsing based on the relevance of the input 2. The system of claim 1, (Appendix 19) The providing unit is Inferring the user's emotions and adjusting the way advice is presented based on the estimated user's emotions 2. The system of claim 1, (Appendix 20) The providing unit is When providing advice, adjust the level of detail of the advice based on the importance of the input 2. The system of claim 1, (Appendix 21) The providing unit is When providing advice, apply different advice algorithms depending on the category of input 2. The system of claim 1, (Appendix 22) The providing unit is Estimate the user's emotions and adjust the length of the advice based on the estimated user emotions. 2. The system of claim 1, (Appendix 23) The providing unit is When providing advice, prioritize advice based on when input was submitted 2. The system of claim 1, (Appendix 24) The providing unit is When giving advice, adjust the order of advice based on the relevance of the input 2. The system of claim 1, (Appendix 25) The detection unit is Estimating a user's emotions and adjusting criteria for detecting signs of postpartum depression based on the estimated user's emotions 2. The system of claim 1, (Appendix 26) The system described in Appendix 1, characterized in that the detection unit improves detection accuracy when detecting symptoms based on the content of the user's past comments. (Appendix 27) The detection unit is When detecting symptoms, different detection algorithms are applied based on the user's behavioral patterns. 2. The system of claim 1, (Appendix 28) The detection unit is Estimating a user's emotion and adjusting the order in which symptom detection results are displayed based on the estimated user's emotion 2. The system of claim 1, (Appendix 29) The detection unit is When detecting symptoms, the geographical distribution of users is taken into account. 2. The system of claim 1, (Appendix 30) The detection unit is When detecting symptoms, the accuracy of detection is improved by referring to the user's related literature. 2. The system of claim 1, (Appendix 31) The communication unit includes: To estimate a user's emotions and adjust a contact method based on the estimated user's emotions 2. The system of claim 1, (Appendix 32) The communication unit includes: When contacting a user, select the most appropriate contact method by referring to the user's past comments. 2. The system of claim 1, (Appendix 33) The communication unit includes: When contacting users, customize the contact method based on their behavioral patterns 2. The system of claim 1, (Appendix 34) The communication unit includes: To estimate a user's emotion and determine a priority of contacts based on the estimated user's emotion. 2. The system of claim 1, (Appendix 35) The communication unit includes: When contacting someone, consider their geographic location to find the best way to contact them 2. The system of claim 1, (Appendix 36) The communication unit includes: When you contact us, we analyze your social media activity to suggest ways to contact you. 2. The system of claim 1, (Appendix 37) The receiving portion is Infer the user's emotions and adjust the way the complaint is handled based on the estimated user's emotions 3. The system of claim 2, (Appendix 38) The receiving portion is When responding to a complaint, the system selects the most appropriate way to respond by referring to the user's past comments. 3. The system of claim 2, (Appendix 39) The receiving portion is Customize the way you handle complaints based on the user's behavioral patterns 3. The system of claim 2, (Appendix 40) The receiving portion is Infer the user's emotions and determine the priority of complaint handling based on the estimated user emotions 3. The system of claim 2, (Appendix 41) The receiving portion is When receiving a complaint, the system selects the most appropriate method of receiving the complaint by taking into account the user's geographic location. 3. The system of claim 2, (Appendix 42) The receiving portion is When acknowledging a complaint, the system analyzes the user's social media activity to suggest ways to respond. 3. The system of claim 2, [Explanation of symbols]

[0205] 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 from a user; an analysis unit that analyzes the information received by the reception unit; a providing unit that provides advice based on the information analyzed by the analyzing unit; A detection unit that detects a sign of postpartum depression based on the advice provided by the provision unit; A communication unit that communicates with the outside based on the symptom detected by the detection unit. A system characterized by:

2. 2. The system according to claim 1, further comprising a receiver having specific means for receiving complaints from users.

3. The analysis unit is Analyze user comments and generate appropriate advice 2. The system of claim 1 .

4. The providing unit is Providing relevant answers to user questions 2. The system of claim 1 .

5. The detection unit is Analyzing user speech or behavior patterns to detect signs of postpartum depression 2. The system of claim 1 .

6. The communication unit includes: Contacting a professional or family member if you detect signs of postnatal depression 2. The system of claim 1 .

7. The reception unit is Estimate the user's emotions and adjust the timing of input acceptance based on the estimated user emotions.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and select the appropriate reception method 2. The system of claim 1 .

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