system

Abstract

The system according to the embodiment aims to efficiently match parents of infants with each other and help each other with childcare. [Solution] A system according to an embodiment includes a reception unit, a matching unit, a communication unit, and an evaluation unit. The reception unit receives user conditions. The matching unit performs matching based on the conditions received by the reception unit. The communication unit provides a function for parents matched by the matching unit to communicate with each other. The evaluation unit evaluates the results of the communication performed by the communication unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult for parents of infants to efficiently match with each other and help each other with childcare.

[0005] The system according to the embodiment aims to efficiently match parents of infants with each other and help each other with childcare. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a matching unit, a communication unit, and an evaluation unit. The reception unit receives user conditions. The matching unit performs matching based on the conditions received by the reception unit. The communication unit provides a function for parents matched by the matching unit to communicate with each other. The evaluation unit evaluates the results of the communication performed by the communication unit. [Effects of the Invention]

[0007] The system according to the embodiment allows parents of infants and young children to efficiently match with each other and help each other with childcare. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A childcare support system according to an embodiment of the present invention is a system that efficiently matches parents of infants and young children with each other and helps them with childcare. The childcare support system allows a user to input conditions such as a desired play location, gender, and age, and the system searches for other parents who can be matched based on these conditions, allowing the user to select a match. For example, the childcare support system allows a user to set detailed conditions such as a desired play location, gender, and age. Next, the childcare support system analyzes information in a database based on the input conditions to identify parents who meet the conditions. For example, the system can search for parents who want to play in the same park or parents with children of the same age. Next, the childcare support system displays a list of search results, allowing the user to select a match. For example, the user can check the profiles of the parents and information about their children displayed in the search results and select a match. This allows the childcare support system to efficiently match parents of infants and young children with each other and help them with childcare. For example, playing together in a park can deepen interactions between parents and reduce the burden of childcare. Furthermore, parents can help each other with childcare through play at home. Furthermore, chat and messaging functions will be provided as a means of communication after matching. A rating system will also be introduced to increase reliability. An AI-based recommendation system will enable more accurate matching. A location-based matching function will also be added, allowing users to easily match with nearby parents.

[0029] A childcare support system according to an embodiment includes a reception unit, a matching unit, a communication unit, and an evaluation unit. The reception unit receives user conditions. The user conditions include, but are not limited to, desired play locations, gender, and age. The reception unit provides an interface for the user to input desired play locations. The reception unit can also provide options for the user to input the gender and age of the child. For example, the reception unit can specify a specific location, such as when the user wants to play at a park or at home. The matching unit performs matching based on the conditions received by the reception unit. The matching unit can, for example, analyze information in a database to identify parents who meet the conditions. For example, the matching unit can search for parents who want to play at the same park or parents with children of the same age. The matching unit can also use AI to suggest optimal matches based on past matching history and evaluations. For example, the matching unit analyzes past matching history and prioritizes matches with a high success rate. The communication unit provides a function for parents matched by the matching unit to communicate with each other. The communication unit provides, for example, a chat function and a message function. For example, the communication unit provides a real-time chat function, allowing parents to communicate with each other immediately. The communication unit also provides a message function, allowing parents to send messages to each other later. The evaluation unit evaluates the results of the communication performed by the communication unit. For example, the evaluation unit provides an evaluation system that allows users to evaluate their matched partners. For example, the evaluation unit provides an interface that allows users to evaluate their matched partner's profile and communication quality. As a result, the childcare support system according to the embodiment efficiently performs matching based on users' conditions, allowing users to help each other with childcare through communication and evaluation.

[0030] The reception unit can accept conditions such as a desired play location, gender, and age. The reception unit, for example, provides an interface for the user to input the desired play location. For example, the reception unit can provide options such as parks, indoor facilities, and event venues. The reception unit also provides an option for the user to input the child's gender. For example, the reception unit can provide options such as male, female, and other. The reception unit also provides an option for the user to input the child's age. For example, the reception unit can provide age ranges such as 0-3 years old, 4-6 years old, and 7-10 years old. This allows the user to set detailed conditions. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the conditions entered by the user into AI, and the AI ​​can provide an optimal interface based on the conditions.

[0031] The matching unit can use AI to suggest matches based on past matching history and evaluations. The matching unit, for example, analyzes past matching history and prioritizes suggesting matches with a high success rate. For example, the matching unit identifies successful matches under the same conditions from the past matching history and suggests matches under similar conditions. The matching unit can also suggest optimal matches based on user evaluations. For example, the matching unit analyzes the conditions of matches given high ratings by the user and suggests matches under similar conditions. The matching unit can also use AI to suggest optimal matches based on the user's conditions. For example, the matching unit uses AI to analyze the user's conditions and identify the optimal match. This makes it possible to perform optimal matches by utilizing past data. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input past matching history into AI, which then suggests optimal matches.

[0032] The communication unit may provide a chat function or a message function. The communication unit may, for example, provide a real-time chat function. For example, the communication unit may provide a chat interface that allows parents to communicate with each other immediately. The communication unit may also provide a message function that allows parents to send messages to each other later. For example, the communication unit may provide an interface for sending text messages or voice messages. This allows users to easily communicate with each other after matching. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may optimize the chat function or message function using AI to provide an interface that is easier for users to use.

[0033] The evaluation unit can provide a rating system for users to rate match-making partners. The evaluation unit, for example, provides an interface for users to rate match-making partners' profiles and communication quality. For example, the evaluation unit can allow users to give star ratings and comments to match-making partners. The evaluation unit can also provide feedback to improve the next match based on the user's rating of the match-making partner. For example, the evaluation unit can analyze the conditions of match-making partners that users have given high ratings to and reflect these in the next match. This allows users to rate match-making partners, thereby increasing the reliability of the system. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input user evaluation data into AI, which can analyze the evaluation results and reflect them in the next match.

[0034] The childcare support system includes a location information unit that uses location information to enable a user to easily match with nearby parents. The location information unit identifies the user's current location, for example, using GPS or Wi-Fi location information. For example, the location information unit preferentially presents parents close to the user's current location as matching candidates. The location information unit can also provide matching information with nearby parents based on the user's geographic location information. For example, the location information unit provides information on parks and playgrounds close to the user's current location and presents parents who would like to play there as matching candidates. This allows the user to easily match with nearby parents. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input the user's location information into AI, which can then present optimal matching candidates.

[0035] The reception unit can analyze the user's past condition input history and suggest optimal condition inputs to the user. For example, the reception unit automatically displays as candidates conditions that the user frequently input in the past. For example, the reception unit suggests optimal conditions based on the user's past input of desired play locations and children's ages. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit prioritizes voice input. The reception unit can also predict and suggest conditions to be used in a specific time period based on the user's past input history. For example, the reception unit suggests similar conditions based on conditions the user previously input in a specific time period. This makes it possible to suggest optimal condition inputs to the user by utilizing the past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past condition input history into AI, which then suggests optimal condition inputs.

[0036] When inputting conditions, the reception unit can present input candidates based on the user's current living situation and areas of interest. The reception unit proposes related conditions based, for example, on places or events recently visited by the user. For example, the reception unit presents candidates for places the user would like to play based on parks or event venues recently visited by the user. The reception unit can also present input candidates based on the user's areas of interest (e.g., specific parks or playgrounds). For example, the reception unit presents candidates for places the user would like to play based on parks or playgrounds the user is interested in. The reception unit can also propose appropriate conditions based on the user's living situation (e.g., whether the user is on parental leave). For example, if the user is on parental leave, the reception unit presents candidates for places the user would like to play at during the daytime on weekdays. This allows for more appropriate condition input by presenting input candidates based on the user's living situation and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI, which then presents optimal input candidates.

[0037] When inputting conditions, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the conditions using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software and inputs the conditions. Furthermore, if the user selects text input, the reception unit can also input the conditions using a keyboard or touch screen. For example, the reception unit provides an interface for the user to input the conditions using a keyboard. Furthermore, if the user selects image input, the reception unit can also input the conditions using image recognition technology. For example, the reception unit allows the user to upload an image and extracts the conditions using image recognition technology. This improves usability by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to AI, which can then select the optimal input means.

[0038] The matching unit can analyze past matching history and select an optimal matching method. For example, the matching unit analyzes past matching history and selects a matching method with a high success rate. For example, the matching unit identifies a matching method that was successful under the same conditions from the past matching history and selects a similar method. The matching unit can also select an optimal matching method based on user ratings. For example, the matching unit analyzes matching methods that users have given high ratings and selects a similar method. The matching unit can also use AI to select an optimal matching method based on the user's conditions. For example, the matching unit uses AI to analyze the user's conditions and identify the optimal matching method. This makes it possible to provide an optimal matching method by utilizing past data. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input past matching history into AI, which selects the optimal matching method.

[0039] During matching, the matching unit can present matching candidates based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the matching unit prioritizes presenting parents in the same situation. For example, the matching unit prioritizes presenting parents on parental leave as matching candidates. The matching unit can also present related parents based on the user's areas of interest (e.g., a specific child-rearing method). For example, the matching unit presents related parents based on the child-rearing methods in which the user is interested. The matching unit can also present appropriate matching candidates based on the user's living situation (e.g., whether the user is a single parent). For example, the matching unit prioritizes presenting single parents as matching candidates. This enables more appropriate matching by providing matching candidates based on the user's living situation and areas of interest. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input data on the user's living situation and areas of interest into AI, which then presents optimal matching candidates.

[0040] The matching unit can select the optimal matching means according to the user's input method during matching. For example, if the user selects voice input, the matching unit uses voice recognition technology to present matching candidates. For example, the matching unit uses voice recognition software to convert the user's voice into text and presents matching candidates. Alternatively, if the user selects text input, the matching unit can also present matching candidates using a keyboard or touch screen. For example, the matching unit allows the user to input conditions using a keyboard and presents matching candidates. Alternatively, if the user selects image input, the matching unit can also present matching candidates using image recognition technology. For example, the matching unit allows the user to upload an image, extracts conditions using image recognition technology, and presents matching candidates. This improves usability by providing the optimal matching means according to the user's input method. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input the user's input method into AI, which then selects the optimal matching means.

[0041] The communication unit can analyze past communication history and suggest the optimal communication method to the user. For example, the communication unit prioritizes suggesting communication methods that the user has preferred in the past. For example, the communication unit suggests the optimal communication method based on the chat and message history used by the user in the past. The communication unit can also suggest the optimal communication method based on the user's past communication history. For example, the communication unit analyzes communication methods that the user has used successfully in the past and suggests similar methods. The communication unit can also analyze the user's past communication history and suggest the most efficient communication method. For example, the communication unit suggests the optimal method based on communication methods used by the user in the past. In this way, the optimal communication method can be suggested to the user by utilizing the past history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the past communication history into AI, which then suggests the optimal communication method.

[0042] The communication unit can present communication content based on the user's current living situation and areas of interest during communication. For example, if the user is currently on parental leave, the communication unit prioritizes presenting communication with parents in the same situation. For example, the communication unit prioritizes presenting communication content with parents on parental leave. The communication unit can also present related communication content based on the user's areas of interest (e.g., a specific child-rearing method). For example, the communication unit presents related communication content based on the child-rearing method in which the user is interested. The communication unit can also present appropriate communication content based on the user's living situation (e.g., whether the user is a single parent). For example, the communication unit prioritizes presenting communication content with single parents. This enables more appropriate communication by providing communication content based on the user's living situation and areas of interest. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input data on the user's living situation and areas of interest into AI, which can then present optimal communication content.

[0043] The communication unit can select the optimal communication means depending on the user's input method during communication. For example, if the user selects voice input, the communication unit communicates using voice recognition technology. For example, the communication unit uses voice recognition software to convert the user's voice into text and communicate. Also, if the user selects text input, the communication unit can communicate using a keyboard or touch screen. For example, the communication unit provides an interface for the user to communicate using a keyboard. Also, if the user selects image input, the communication unit can communicate using image recognition technology. For example, the communication unit allows the user to upload an image, extracts conditions using image recognition technology, and communicates. This improves usability by providing the optimal communication means depending on the user's input method. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input the user's input method into AI, which can select the optimal communication means.

[0044] The evaluation unit can analyze past evaluation history and suggest the optimal evaluation method to the user. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has previously preferred. For example, the evaluation unit can suggest the optimal evaluation method based on evaluation methods that the user has previously used. The evaluation unit can also suggest the optimal evaluation method based on the user's past evaluation history. For example, the evaluation unit can analyze evaluation methods that the user has previously used successfully and suggest a similar method. The evaluation unit can also analyze the user's past evaluation history and suggest the most efficient evaluation method. For example, the evaluation unit can suggest the optimal method based on evaluation methods that the user has previously used. In this way, the optimal evaluation method can be suggested to the user by utilizing the past history. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation history into AI, which then suggests the optimal evaluation method.

[0045] The evaluation unit can present evaluation content based on the user's current living situation and areas of interest during evaluation. For example, if the user is currently on parental leave, the evaluation unit prioritizes presenting evaluations of parents in the same situation. For example, the evaluation unit prioritizes presenting evaluation content of parents on parental leave. The evaluation unit can also present related evaluation content based on the user's areas of interest (e.g., a specific child-rearing method). For example, the evaluation unit presents related evaluation content based on the child-rearing method in which the user is interested. The evaluation unit can also present appropriate evaluation content based on the user's living situation (e.g., whether the user is a single parent). For example, the evaluation unit prioritizes presenting evaluation content of parents who are single parents. This enables more appropriate evaluations by providing evaluation content based on the user's living situation and areas of interest. Some or all of the above-described processing by the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input data on the user's living situation and areas of interest into AI, which can then present optimal evaluation content.

[0046] The evaluation unit can select the optimal evaluation means depending on the user's input method during evaluation. For example, if the user selects voice input, the evaluation unit performs the evaluation using voice recognition technology. For example, the evaluation unit uses voice recognition software to convert the user's voice into text and perform the evaluation. Furthermore, if the user selects text input, the evaluation unit can also perform the evaluation using a keyboard or touch screen. For example, the evaluation unit provides an interface for the user to perform the evaluation using a keyboard. Furthermore, if the user selects image input, the evaluation unit can also perform the evaluation using image recognition technology. For example, the evaluation unit allows the user to upload an image, extracts conditions using image recognition technology, and performs the evaluation. This improves usability by providing the optimal evaluation means depending on the user's input method. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's input method into AI, which then selects the optimal evaluation means.

[0047] The location information unit can analyze past location information history and suggest an optimal location information acquisition method to the user. The location information unit can suggest an optimal location information acquisition method, for example, based on places the user has visited in the past. For example, the location information unit can suggest an optimal location information acquisition method based on parks or event venues the user has visited in the past. The location information unit can also suggest an efficient location information acquisition method based on the user's past location information history. For example, the location information unit can analyze the history of places the user has visited in the past and suggest an optimal location information acquisition method. The location information unit can also analyze the user's past location information history and suggest the most appropriate location information acquisition method. For example, the location information unit can suggest an optimal location information acquisition method based on the history of places the user has visited in the past. In this way, the past history can be utilized to suggest an optimal location information acquisition method to the user. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input past location information history into AI, which can then suggest an optimal location information acquisition method.

[0048] When acquiring location information, the location information unit can present location information based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the location information unit prioritizes presenting relevant location information. For example, the location information unit prioritizes presenting information about parks and playgrounds relevant to a user on parental leave. The location information unit can also present relevant location information based on the user's areas of interest (e.g., specific parks and playgrounds). For example, the location information unit prioritizes presenting information about parks and playgrounds in which the user is interested. The location information unit can also present appropriate location information based on the user's living situation (e.g., whether the user is a single parent). For example, the location information unit prioritizes presenting information about parks and playgrounds relevant to a single-parent user. This enables more appropriate location information to be acquired by providing location information based on the user's living situation and areas of interest. Some or all of the above-described processing in the location information unit may be performed, for example, using AI or without AI. For example, the location information unit can input data about the user's living situation and areas of interest into the AI, allowing the AI ​​to present the most appropriate location information.

[0049] When acquiring location information, the location information unit can select the optimal acquisition means depending on the user's input method. For example, if the user selects voice input, the location information unit acquires location information using voice recognition technology. For example, the location information unit uses voice recognition software to convert the user's voice into text and acquire location information. Alternatively, if the user selects text input, the location information unit can also acquire location information using a keyboard or touch screen. For example, the location information unit provides an interface for the user to acquire location information using a keyboard. Alternatively, if the user selects image input, the location information unit can also acquire location information using image recognition technology. For example, the location information unit allows the user to upload an image, extracts conditions using image recognition technology, and acquires location information. This improves usability by providing the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without AI. For example, the location information unit can input the user's input method into AI, which then selects the optimal acquisition means.

[0050] When acquiring location information, the location information unit can prioritize presenting highly relevant information taking into account the user's geographical location information. The location information unit, for example, prioritizes presenting information about places close to the user's current location. For example, the location information unit prioritizes presenting information about parks and playgrounds close to the user's current location. The location information unit can also prioritize presenting matching information about nearby parents based on the user's geographical location information. For example, the location information unit prioritizes presenting parents close to the user's current location as matching candidates. The location information unit can also suggest information about optimal play locations based on the user's geographical location information. For example, the location information unit suggests optimal play locations based on information about parks and playgrounds close to the user's current location. This improves usability by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the location information unit may be performed using, or without, AI. For example, the location information unit can input the user's geographical location information into AI, which can then prioritize presenting highly relevant information.

[0051] The location information unit can analyze the user's social media activities when acquiring location information and suggest related information. The location information unit can, for example, suggest information related to places where the user has checked in on social media. For example, the location information unit can suggest information about parks or event venues where the user has checked in on social media. The location information unit can also analyze the content of the user's social media posts and suggest related location information. For example, the location information unit can suggest information about related parks or playgrounds based on the content of the user's social media posts. The location information unit can also suggest information about related places based on the activities of the user's friends on social media. For example, the location information unit can suggest information about related parks or playgrounds based on information about places where the user's friends have checked in on social media. This improves usability by providing related information based on the user's social media activities. Some or all of the above-described processing in the location information unit can be performed using, for example, AI, or without AI. For example, the location information unit can input data about the user's social media activities into AI, which can then suggest related information.

[0052] The location information unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information unit preferentially suggests location information acquisition methods that the user has previously preferred. For example, the location information unit suggests an optimal acquisition method based on location information acquisition methods that the user has previously used. The location information unit can also customize the location information acquisition interface based on the user's past feedback. For example, the location information unit can adjust the interface based on feedback provided by the user in the past. The location information unit can also optimize the location information acquisition procedure by reflecting the user's past feedback. For example, the location information unit can simplify the procedure based on feedback provided by the user in the past. This improves usability by providing an acquisition method based on the user's past feedback. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input the user's past feedback into AI, which can then customize the acquisition method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can analyze the user's past condition input history and suggest optimal condition input. For example, it can automatically display conditions that the user has frequently input in the past as candidates. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest conditions that will be used in a specific time period based on the user's past input history. This makes it possible to utilize past history to suggest optimal condition inputs to the user.

[0055] The matching unit can present match candidates based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, parents in the same situation can be presented preferentially. Related parents can also be presented based on the user's areas of interest (e.g., a specific child-rearing method). Furthermore, appropriate match candidates can be presented based on the user's living situation (e.g., whether the user is a single parent). This allows for more appropriate matching by providing match candidates based on the user's living situation and areas of interest.

[0056] The communication unit can analyze the user's past communication history and suggest the most suitable communication method. For example, it can prioritize and suggest communication methods that the user has preferred in the past. It can also suggest the most suitable communication method based on the user's past communication history. It can also analyze the user's past communication history and suggest the most efficient communication method. In this way, it is possible to use the past history to suggest the most suitable communication method for the user.

[0057] The evaluation unit can present evaluation content based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, evaluations of parents in the same situation can be presented preferentially. Also, related evaluation content can be presented based on the user's areas of interest (e.g., a specific child-rearing method). Furthermore, appropriate evaluation content can be presented based on the user's living situation (e.g., whether the user is a single parent). This allows for more appropriate evaluations by providing evaluation content based on the user's living situation and areas of interest.

[0058] The location information unit can analyze the user's social media activity and suggest related information. For example, it can suggest information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and suggest related location information. It can also suggest related location information based on the activity of the user's friends on social media. This improves usability by providing related information based on the user's social media activity.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception unit receives the user's conditions. The user's conditions include the desired play location, gender, age, etc. The reception unit provides an interface for the user to input the desired play location and options for inputting the gender and age of the child. For example, the user can specify a specific location, such as playing in a park or at home. Step 2: The matching unit performs matching based on the conditions received by the reception unit. The matching unit analyzes the information in the database and identifies parents who meet the conditions. For example, it can search for parents who want to play in the same park, or parents with children of the same age. It can also use AI to suggest optimal matches based on past matching history and evaluations. Step 3: The communication unit provides a function for parents who have been matched by the matching unit to communicate with each other. The communication unit provides a chat function and a message function, allowing parents to communicate with each other in real time. They can also send messages later. Step 4: The evaluation unit evaluates the results of the communication performed by the communication unit. The evaluation unit provides a rating system for users to rate matched partners and an interface for users to rate the matched partner's profile and communication quality.

[0061] (Example 2) A childcare support system according to an embodiment of the present invention is a system that efficiently matches parents of infants and young children with each other and helps them with childcare. The childcare support system allows a user to input conditions such as a desired play location, gender, and age, and the system searches for other parents who can be matched based on these conditions, allowing the user to select a match. For example, the childcare support system allows a user to set detailed conditions such as a desired play location, gender, and age. Next, the childcare support system analyzes information in a database based on the input conditions to identify parents who meet the conditions. For example, the system can search for parents who want to play in the same park or parents with children of the same age. Next, the childcare support system displays a list of search results, allowing the user to select a match. For example, the user can check the profiles of the parents and information about their children displayed in the search results and select a match. This allows the childcare support system to efficiently match parents of infants and young children with each other and help them with childcare. For example, playing together in a park can deepen interactions between parents and reduce the burden of childcare. Furthermore, parents can help each other with childcare through play at home. Furthermore, chat and messaging functions will be provided as a means of communication after matching. A rating system will also be introduced to increase reliability. An AI-based recommendation system will enable more accurate matching. A location-based matching function will also be added, allowing users to easily match with nearby parents.

[0062] A childcare support system according to an embodiment includes a reception unit, a matching unit, a communication unit, and an evaluation unit. The reception unit receives user conditions. The user conditions include, but are not limited to, desired play locations, gender, and age. The reception unit provides an interface for the user to input desired play locations. The reception unit can also provide options for the user to input the gender and age of the child. For example, the reception unit can specify a specific location, such as when the user wants to play at a park or at home. The matching unit performs matching based on the conditions received by the reception unit. The matching unit can, for example, analyze information in a database to identify parents who meet the conditions. For example, the matching unit can search for parents who want to play at the same park or parents with children of the same age. The matching unit can also use AI to suggest optimal matches based on past matching history and evaluations. For example, the matching unit analyzes past matching history and prioritizes matches with a high success rate. The communication unit provides a function for parents matched by the matching unit to communicate with each other. The communication unit provides, for example, a chat function and a message function. For example, the communication unit provides a real-time chat function, allowing parents to communicate with each other immediately. The communication unit also provides a message function, allowing parents to send messages to each other later. The evaluation unit evaluates the results of the communication performed by the communication unit. For example, the evaluation unit provides an evaluation system that allows users to evaluate their matched partners. For example, the evaluation unit provides an interface that allows users to evaluate their matched partner's profile and communication quality. As a result, the childcare support system according to the embodiment efficiently performs matching based on users' conditions, allowing users to help each other with childcare through communication and evaluation.

[0063] The reception unit can accept conditions such as a desired play location, gender, and age. The reception unit, for example, provides an interface for the user to input the desired play location. For example, the reception unit can provide options such as parks, indoor facilities, and event venues. The reception unit also provides an option for the user to input the child's gender. For example, the reception unit can provide options such as male, female, and other. The reception unit also provides an option for the user to input the child's age. For example, the reception unit can provide age ranges such as 0-3 years old, 4-6 years old, and 7-10 years old. This allows the user to set detailed conditions. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the conditions entered by the user into AI, and the AI ​​can provide an optimal interface based on the conditions.

[0064] The matching unit can use AI to suggest matches based on past matching history and evaluations. The matching unit, for example, analyzes past matching history and prioritizes suggesting matches with a high success rate. For example, the matching unit identifies successful matches under the same conditions from the past matching history and suggests matches under similar conditions. The matching unit can also suggest optimal matches based on user evaluations. For example, the matching unit analyzes the conditions of matches given high ratings by the user and suggests matches under similar conditions. The matching unit can also use AI to suggest optimal matches based on the user's conditions. For example, the matching unit uses AI to analyze the user's conditions and identify the optimal match. This makes it possible to perform optimal matches by utilizing past data. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input past matching history into AI, which then suggests optimal matches.

[0065] The communication unit may provide a chat function or a message function. The communication unit may, for example, provide a real-time chat function. For example, the communication unit may provide a chat interface that allows parents to communicate with each other immediately. The communication unit may also provide a message function that allows parents to send messages to each other later. For example, the communication unit may provide an interface for sending text messages or voice messages. This allows users to easily communicate with each other after matching. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may optimize the chat function or message function using AI to provide an interface that is easier for users to use.

[0066] The evaluation unit can provide a rating system for users to rate match-making partners. The evaluation unit, for example, provides an interface for users to rate match-making partners' profiles and communication quality. For example, the evaluation unit can allow users to give star ratings and comments to match-making partners. The evaluation unit can also provide feedback to improve the next match based on the user's rating of the match-making partner. For example, the evaluation unit can analyze the conditions of match-making partners that users have given high ratings to and reflect these in the next match. This allows users to rate match-making partners, thereby increasing the reliability of the system. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input user evaluation data into AI, which can analyze the evaluation results and reflect them in the next match.

[0067] The childcare support system includes a location information unit that uses location information to enable a user to easily match with nearby parents. The location information unit identifies the user's current location, for example, using GPS or Wi-Fi location information. For example, the location information unit preferentially presents parents close to the user's current location as matching candidates. The location information unit can also provide matching information with nearby parents based on the user's geographic location information. For example, the location information unit provides information on parks and playgrounds close to the user's current location and presents parents who would like to play there as matching candidates. This allows the user to easily match with nearby parents. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input the user's location information into AI, which can then present optimal matching candidates.

[0068] The reception unit can estimate the user's emotion and adjust the condition input interface based on the estimated user emotion. For example, the reception unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the interface. The reception unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the interface. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and adjusts the interface. This improves usability by providing an interface that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input user emotion data into AI, which may then adjust the interface.

[0069] The reception unit can analyze the user's past condition input history and suggest optimal condition inputs to the user. For example, the reception unit automatically displays as candidates conditions that the user frequently input in the past. For example, the reception unit suggests optimal conditions based on the user's past input of desired play locations and children's ages. The reception unit can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. For example, if the user has used voice input in the past, the reception unit prioritizes voice input. The reception unit can also predict and suggest conditions to be used in a specific time period based on the user's past input history. For example, the reception unit suggests similar conditions based on conditions the user previously input in a specific time period. This makes it possible to suggest optimal condition inputs to the user by utilizing the past history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past condition input history into AI, which then suggests optimal condition inputs.

[0070] When inputting conditions, the reception unit can present input candidates based on the user's current living situation and areas of interest. The reception unit proposes related conditions based, for example, on places or events recently visited by the user. For example, the reception unit presents candidates for places the user would like to play based on parks or event venues recently visited by the user. The reception unit can also present input candidates based on the user's areas of interest (e.g., specific parks or playgrounds). For example, the reception unit presents candidates for places the user would like to play based on parks or playgrounds the user is interested in. The reception unit can also propose appropriate conditions based on the user's living situation (e.g., whether the user is on parental leave). For example, if the user is on parental leave, the reception unit presents candidates for places the user would like to play at during the daytime on weekdays. This allows for more appropriate condition input by presenting input candidates based on the user's living situation and areas of interest. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit can input data on the user's living situation and areas of interest into AI, which then presents optimal input candidates.

[0071] When inputting conditions, the reception unit can select the optimal input means depending on the user's input method. For example, if the user selects voice input, the reception unit inputs the conditions using voice recognition technology. For example, the reception unit converts the user's voice into text using voice recognition software and inputs the conditions. Furthermore, if the user selects text input, the reception unit can also input the conditions using a keyboard or touch screen. For example, the reception unit provides an interface for the user to input the conditions using a keyboard. Furthermore, if the user selects image input, the reception unit can also input the conditions using image recognition technology. For example, the reception unit allows the user to upload an image and extracts the conditions using image recognition technology. This improves usability by providing the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's input method to AI, which can then select the optimal input means.

[0072] The matching unit can estimate the user's emotion and adjust the matching algorithm based on the estimated user emotion. For example, the matching unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the matching unit calculates an emotion score based on changes in facial expression and adjusts the matching algorithm. The matching unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the matching unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the matching algorithm. The matching unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the matching unit calculates an emotion score based on heart rate fluctuations and adjusts the matching algorithm. This enables more appropriate matching by providing a matching algorithm that corresponds to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without using AI. For example, the matching unit may input user emotion data into AI, which may then adjust the matching algorithm.

[0073] The matching unit can analyze past matching history and select an optimal matching method. For example, the matching unit analyzes past matching history and selects a matching method with a high success rate. For example, the matching unit identifies a matching method that was successful under the same conditions from the past matching history and selects a similar method. The matching unit can also select an optimal matching method based on user ratings. For example, the matching unit analyzes matching methods that users have given high ratings and selects a similar method. The matching unit can also use AI to select an optimal matching method based on the user's conditions. For example, the matching unit uses AI to analyze the user's conditions and identify the optimal matching method. This makes it possible to provide an optimal matching method by utilizing past data. Some or all of the above-mentioned processing in the matching unit may be performed using AI, for example, or may be performed without using AI. For example, the matching unit can input past matching history into AI, which selects the optimal matching method.

[0074] During matching, the matching unit can present matching candidates based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the matching unit prioritizes presenting parents in the same situation. For example, the matching unit prioritizes presenting parents on parental leave as matching candidates. The matching unit can also present related parents based on the user's areas of interest (e.g., a specific child-rearing method). For example, the matching unit presents related parents based on the child-rearing methods in which the user is interested. The matching unit can also present appropriate matching candidates based on the user's living situation (e.g., whether the user is a single parent). For example, the matching unit prioritizes presenting single parents as matching candidates. This enables more appropriate matching by providing matching candidates based on the user's living situation and areas of interest. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input data on the user's living situation and areas of interest into AI, which then presents optimal matching candidates.

[0075] The matching unit can select the optimal matching means according to the user's input method during matching. For example, if the user selects voice input, the matching unit uses voice recognition technology to present matching candidates. For example, the matching unit uses voice recognition software to convert the user's voice into text and presents matching candidates. Alternatively, if the user selects text input, the matching unit can also present matching candidates using a keyboard or touch screen. For example, the matching unit allows the user to input conditions using a keyboard and presents matching candidates. Alternatively, if the user selects image input, the matching unit can also present matching candidates using image recognition technology. For example, the matching unit allows the user to upload an image, extracts conditions using image recognition technology, and presents matching candidates. This improves usability by providing the optimal matching means according to the user's input method. Some or all of the above-described processing in the matching unit may be performed using, for example, AI, or may be performed without AI. For example, the matching unit can input the user's input method into AI, which then selects the optimal matching means.

[0076] The communication unit can estimate the user's emotions and adjust the communication interface based on the estimated user emotions. For example, the communication unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the communication unit calculates an emotion score based on changes in facial expressions and adjusts the interface. The communication unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the communication unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the interface. The communication unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the communication unit calculates an emotion score based on heart rate fluctuations and adjusts the interface. This improves usability by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input user emotion data into AI, which may then adjust the interface.

[0077] The communication unit can analyze past communication history and suggest the optimal communication method to the user. For example, the communication unit prioritizes suggesting communication methods that the user has preferred in the past. For example, the communication unit suggests the optimal communication method based on the chat and message history used by the user in the past. The communication unit can also suggest the optimal communication method based on the user's past communication history. For example, the communication unit analyzes communication methods that the user has used successfully in the past and suggests similar methods. The communication unit can also analyze the user's past communication history and suggest the most efficient communication method. For example, the communication unit suggests the optimal method based on communication methods used by the user in the past. In this way, the optimal communication method can be suggested to the user by utilizing the past history. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the past communication history into AI, which then suggests the optimal communication method.

[0078] The communication unit can present communication content based on the user's current living situation and areas of interest during communication. For example, if the user is currently on parental leave, the communication unit prioritizes presenting communication with parents in the same situation. For example, the communication unit prioritizes presenting communication content with parents on parental leave. The communication unit can also present related communication content based on the user's areas of interest (e.g., a specific child-rearing method). For example, the communication unit presents related communication content based on the child-rearing method in which the user is interested. The communication unit can also present appropriate communication content based on the user's living situation (e.g., whether the user is a single parent). For example, the communication unit prioritizes presenting communication content with single parents. This enables more appropriate communication by providing communication content based on the user's living situation and areas of interest. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input data on the user's living situation and areas of interest into AI, which can then present optimal communication content.

[0079] The communication unit can select the optimal communication means depending on the user's input method during communication. For example, if the user selects voice input, the communication unit communicates using voice recognition technology. For example, the communication unit uses voice recognition software to convert the user's voice into text and communicate. Also, if the user selects text input, the communication unit can communicate using a keyboard or touch screen. For example, the communication unit provides an interface for the user to communicate using a keyboard. Also, if the user selects image input, the communication unit can communicate using image recognition technology. For example, the communication unit allows the user to upload an image, extracts conditions using image recognition technology, and communicates. This improves usability by providing the optimal communication means depending on the user's input method. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without AI. For example, the communication unit can input the user's input method into AI, which can select the optimal communication means.

[0080] The evaluation unit can estimate the user's emotions and adjust the evaluation interface based on the estimated user emotions. For example, the evaluation unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the evaluation unit calculates an emotion score based on changes in facial expressions and adjusts the interface. The evaluation unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the evaluation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the interface. The evaluation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the evaluation unit calculates an emotion score based on heart rate fluctuations and adjusts the interface. This improves usability by providing an interface that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input user emotion data into AI, which may then adjust the interface.

[0081] The evaluation unit can analyze past evaluation history and suggest the optimal evaluation method to the user. For example, the evaluation unit prioritizes suggesting evaluation methods that the user has previously preferred. For example, the evaluation unit can suggest the optimal evaluation method based on evaluation methods that the user has previously used. The evaluation unit can also suggest the optimal evaluation method based on the user's past evaluation history. For example, the evaluation unit can analyze evaluation methods that the user has previously used successfully and suggest a similar method. The evaluation unit can also analyze the user's past evaluation history and suggest the most efficient evaluation method. For example, the evaluation unit can suggest the optimal method based on evaluation methods that the user has previously used. In this way, the optimal evaluation method can be suggested to the user by utilizing the past history. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the past evaluation history into AI, which then suggests the optimal evaluation method.

[0082] The evaluation unit can present evaluation content based on the user's current living situation and areas of interest during evaluation. For example, if the user is currently on parental leave, the evaluation unit prioritizes presenting evaluations of parents in the same situation. For example, the evaluation unit prioritizes presenting evaluation content of parents on parental leave. The evaluation unit can also present related evaluation content based on the user's areas of interest (e.g., a specific child-rearing method). For example, the evaluation unit presents related evaluation content based on the child-rearing method in which the user is interested. The evaluation unit can also present appropriate evaluation content based on the user's living situation (e.g., whether the user is a single parent). For example, the evaluation unit prioritizes presenting evaluation content of parents who are single parents. This enables more appropriate evaluations by providing evaluation content based on the user's living situation and areas of interest. Some or all of the above-described processing by the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input data on the user's living situation and areas of interest into AI, which can then present optimal evaluation content.

[0083] The evaluation unit can select the optimal evaluation means depending on the user's input method during evaluation. For example, if the user selects voice input, the evaluation unit performs the evaluation using voice recognition technology. For example, the evaluation unit uses voice recognition software to convert the user's voice into text and perform the evaluation. Furthermore, if the user selects text input, the evaluation unit can also perform the evaluation using a keyboard or touch screen. For example, the evaluation unit provides an interface for the user to perform the evaluation using a keyboard. Furthermore, if the user selects image input, the evaluation unit can also perform the evaluation using image recognition technology. For example, the evaluation unit allows the user to upload an image, extracts conditions using image recognition technology, and performs the evaluation. This improves usability by providing the optimal evaluation means depending on the user's input method. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without AI. For example, the evaluation unit can input the user's input method into AI, which then selects the optimal evaluation means.

[0084] The location information unit can estimate the user's emotion and adjust the method for acquiring location information based on the estimated user emotion. For example, the location information unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the location information unit calculates an emotion score based on changes in facial expression and adjusts the method for acquiring location information. The location information unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the location information unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the method for acquiring location information. The location information unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the location information unit calculates an emotion score based on fluctuations in heart rate and adjusts the method for acquiring location information. This improves usability by providing a method for acquiring location information that corresponds to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location information unit may be performed using AI, or may be performed without using AI. For example, the location information unit may input user emotion data into the AI, which may then adjust how the location information is acquired.

[0085] The location information unit can analyze past location information history and suggest an optimal location information acquisition method to the user. The location information unit can suggest an optimal location information acquisition method, for example, based on places the user has visited in the past. For example, the location information unit can suggest an optimal location information acquisition method based on parks or event venues the user has visited in the past. The location information unit can also suggest an efficient location information acquisition method based on the user's past location information history. For example, the location information unit can analyze the history of places the user has visited in the past and suggest an optimal location information acquisition method. The location information unit can also analyze the user's past location information history and suggest the most appropriate location information acquisition method. For example, the location information unit can suggest an optimal location information acquisition method based on the history of places the user has visited in the past. In this way, the past history can be utilized to suggest an optimal location information acquisition method to the user. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input past location information history into AI, which can then suggest an optimal location information acquisition method.

[0086] When acquiring location information, the location information unit can present location information based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, the location information unit prioritizes presenting relevant location information. For example, the location information unit prioritizes presenting information about parks and playgrounds relevant to a user on parental leave. The location information unit can also present relevant location information based on the user's areas of interest (e.g., specific parks and playgrounds). For example, the location information unit prioritizes presenting information about parks and playgrounds in which the user is interested. The location information unit can also present appropriate location information based on the user's living situation (e.g., whether the user is a single parent). For example, the location information unit prioritizes presenting information about parks and playgrounds relevant to a single-parent user. This enables more appropriate location information to be acquired by providing location information based on the user's living situation and areas of interest. Some or all of the above-described processing in the location information unit may be performed, for example, using AI or without AI. For example, the location information unit can input data about the user's living situation and areas of interest into the AI, allowing the AI ​​to present the most appropriate location information.

[0087] When acquiring location information, the location information unit can select the optimal acquisition means depending on the user's input method. For example, if the user selects voice input, the location information unit acquires location information using voice recognition technology. For example, the location information unit uses voice recognition software to convert the user's voice into text and acquire location information. Alternatively, if the user selects text input, the location information unit can also acquire location information using a keyboard or touch screen. For example, the location information unit provides an interface for the user to acquire location information using a keyboard. Alternatively, if the user selects image input, the location information unit can also acquire location information using image recognition technology. For example, the location information unit allows the user to upload an image, extracts conditions using image recognition technology, and acquires location information. This improves usability by providing the optimal acquisition means depending on the user's input method. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without AI. For example, the location information unit can input the user's input method into AI, which then selects the optimal acquisition means.

[0088] The location information unit can estimate the user's emotions and prioritize location information based on the estimated user emotions. For example, the location information unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the location information unit calculates an emotion score based on changes in facial expressions and prioritizes the location information. The location information unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the location information unit analyzes the tone and speed of the voice, calculates an emotion score, and prioritizes the location information. The location information unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the location information unit calculates an emotion score based on heart rate fluctuations and prioritizes the location information. This improves usability by providing a priority order for location information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the location information unit may be performed using AI, or may be performed without using AI. For example, the location information unit may input user emotion data into AI, and the AI ​​may determine the priority of the location information.

[0089] When acquiring location information, the location information unit can prioritize presenting highly relevant information taking into account the user's geographical location information. The location information unit, for example, prioritizes presenting information about places close to the user's current location. For example, the location information unit prioritizes presenting information about parks and playgrounds close to the user's current location. The location information unit can also prioritize presenting matching information about nearby parents based on the user's geographical location information. For example, the location information unit prioritizes presenting parents close to the user's current location as matching candidates. The location information unit can also suggest information about optimal play locations based on the user's geographical location information. For example, the location information unit suggests optimal play locations based on information about parks and playgrounds close to the user's current location. This improves usability by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the location information unit may be performed using, or without, AI. For example, the location information unit can input the user's geographical location information into AI, which can then prioritize presenting highly relevant information.

[0090] The location information unit can analyze the user's social media activities when acquiring location information and suggest related information. The location information unit can, for example, suggest information related to places where the user has checked in on social media. For example, the location information unit can suggest information about parks or event venues where the user has checked in on social media. The location information unit can also analyze the content of the user's social media posts and suggest related location information. For example, the location information unit can suggest information about related parks or playgrounds based on the content of the user's social media posts. The location information unit can also suggest information about related places based on the activities of the user's friends on social media. For example, the location information unit can suggest information about related parks or playgrounds based on information about places where the user's friends have checked in on social media. This improves usability by providing related information based on the user's social media activities. Some or all of the above-described processing in the location information unit can be performed using, for example, AI, or without AI. For example, the location information unit can input data about the user's social media activities into AI, which can then suggest related information.

[0091] The location information unit can customize the acquisition method by reflecting the user's past feedback when acquiring location information. For example, the location information unit preferentially suggests location information acquisition methods that the user has previously preferred. For example, the location information unit suggests an optimal acquisition method based on location information acquisition methods that the user has previously used. The location information unit can also customize the location information acquisition interface based on the user's past feedback. For example, the location information unit can adjust the interface based on feedback provided by the user in the past. The location information unit can also optimize the location information acquisition procedure by reflecting the user's past feedback. For example, the location information unit can simplify the procedure based on feedback provided by the user in the past. This improves usability by providing an acquisition method based on the user's past feedback. Some or all of the above-described processing in the location information unit may be performed using, for example, AI, or may be performed without using AI. For example, the location information unit can input the user's past feedback into AI, which can then customize the acquisition method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, matching unit, communication unit, evaluation unit, and location information 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 the user's conditions. The matching unit is realized by the identification processing unit 290 of the data processing device 12 and identifies parents who meet the conditions. The communication unit is realized by the control unit 46A of the smart device 14 and provides a chat function and a message function. The evaluation unit is realized by the identification processing unit 290 of the data processing device 12 and evaluates matching partners. The location information unit is realized by the control unit 46A of the smart device 14 and identifies the user's current location and presents nearby parents as matching candidates. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described reception unit, matching unit, communication unit, evaluation unit, and location information 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 the user's conditions. The matching unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies parents who meet the conditions. The communication unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides a chat function and a message function. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and evaluates matching partners. The location information unit is realized, for example, by the control unit 46A of the smart glasses 214 and identifies the user's current location and presents nearby parents as matching candidates. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, matching unit, communication unit, evaluation unit, and location information 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 the user's conditions. The matching unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies parents who meet the conditions. The communication unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides a chat function and a message function. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and evaluates matching partners. The location information unit is realized, for example, by the control unit 46A of the headset type terminal 314 and identifies the user's current location and presents nearby parents as matching candidates. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, matching unit, communication unit, evaluation unit, and location information 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 the user's conditions. The matching unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and identifies parents who meet the conditions. The communication unit is realized, for example, by the control unit 46A of the robot 414 and provides a chat function and a message function. The evaluation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and evaluates matching partners. The location information unit is realized, for example, by the control unit 46A of the robot 414 and identifies the user's current location and presents nearby parents as matching candidates.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can estimate the user's emotions and adjust the interface for inputting conditions based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can simplify the interface and reduce the number of input items. Also, if the user is relaxed, the reception unit can provide an interface that prompts the user to input detailed conditions. Furthermore, if the user is excited, the color or design of the interface can be changed to enable more intuitive operation. In this way, usability is improved by providing an interface that corresponds to the user's emotions.

[0094] The matching unit can estimate the user's emotions and adjust the matching algorithm based on the estimated emotions. For example, if the user feels anxious, the matching unit can preferentially present parents with high reliability. Also, if the user feels excited, the matching unit can suggest parents with different conditions to provide new encounters. Furthermore, if the user feels relaxed, the matching unit can suggest stable matches based on the past successful matching history. This makes it possible to perform matching according to the user's emotions.

[0095] The communication unit can estimate the user's emotions and adjust the communication interface based on the estimated emotions. For example, if the user is nervous, the communication unit can provide a simple chat interface and display friendly messages. If the user is relaxed, the communication unit can provide an interface that displays detailed profile information and photos. Furthermore, if the user is excited, the communication unit can provide a real-time video chat function to enable instant communication. This enables communication according to the user's emotions.

[0096] The evaluation unit can estimate the user's emotions and adjust the evaluation interface based on the estimated emotions. For example, if the user is dissatisfied, the evaluation unit can provide an interface that requests detailed feedback and allows the user to specifically input areas for improvement. If the user is satisfied, the evaluation unit can provide an interface that allows the user to input a simple star rating or a short comment. Furthermore, if the user is excited, the evaluation unit can provide an interface that encourages positive feedback and allows the user to share it with other users. This makes it possible to evaluate according to the user's emotions.

[0097] The location information unit can estimate the user's emotions and adjust the method of acquiring location information based on the estimated emotions. For example, if the user feels anxious, the location information unit can provide detailed location information to give the user a sense of security. If the user feels relaxed, the location information unit can provide simple location information to allow the user to move freely. Furthermore, if the user feels excited, the location information unit can provide information about new places and events to attract the user's interest. This makes it possible to provide location information according to the user's emotions.

[0098] The reception unit can analyze the user's past condition input history and suggest optimal condition input. For example, it can automatically display conditions that the user has frequently input in the past as candidates. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. It can also predict and suggest conditions that will be used in a specific time period based on the user's past input history. This makes it possible to utilize past history to suggest optimal condition inputs to the user.

[0099] The matching unit can present match candidates based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, parents in the same situation can be presented preferentially. Related parents can also be presented based on the user's areas of interest (e.g., a specific child-rearing method). Furthermore, appropriate match candidates can be presented based on the user's living situation (e.g., whether the user is a single parent). This allows for more appropriate matching by providing match candidates based on the user's living situation and areas of interest.

[0100] The communication unit can analyze the user's past communication history and suggest the most suitable communication method. For example, it can prioritize and suggest communication methods that the user has preferred in the past. It can also suggest the most suitable communication method based on the user's past communication history. It can also analyze the user's past communication history and suggest the most efficient communication method. In this way, it is possible to use the past history to suggest the most suitable communication method for the user.

[0101] The evaluation unit can present evaluation content based on the user's current living situation and areas of interest. For example, if the user is currently on parental leave, evaluations of parents in the same situation can be presented preferentially. Also, related evaluation content can be presented based on the user's areas of interest (e.g., a specific child-rearing method). Furthermore, appropriate evaluation content can be presented based on the user's living situation (e.g., whether the user is a single parent). This allows for more appropriate evaluations by providing evaluation content based on the user's living situation and areas of interest.

[0102] The location information unit can analyze the user's social media activity and suggest related information. For example, it can suggest information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and suggest related location information. It can also suggest related location information based on the activity of the user's friends on social media. This improves usability by providing related information based on the user's social media activity.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception unit receives the user's conditions. The user's conditions include the desired play location, gender, age, etc. The reception unit provides an interface for the user to input the desired play location and options for inputting the gender and age of the child. For example, the user can specify a specific location, such as playing in a park or at home. Step 2: The matching unit performs matching based on the conditions received by the reception unit. The matching unit analyzes the information in the database and identifies parents who meet the conditions. For example, it can search for parents who want to play in the same park, or parents with children of the same age. It can also use AI to suggest optimal matches based on past matching history and evaluations. Step 3: The communication unit provides a function for parents who have been matched by the matching unit to communicate with each other. The communication unit provides a chat function and a message function, allowing parents to communicate with each other in real time. They can also send messages later. Step 4: The evaluation unit evaluates the results of the communication performed by the communication unit. The evaluation unit provides a rating system for users to rate matched partners and an interface for users to rate the matched partner's profile and communication quality.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0119] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0149] 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.

[0150] 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.

[0151] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0152] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0153] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0159] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0160] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0161] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0162] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0163] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0164] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0165] 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.

[0166] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0167] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0168] 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.

[0169] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0170] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0171] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0172] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0173] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0174] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0175] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0176] [Explanation of symbols]

[0177] 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 user conditions; a matching unit that performs matching based on the conditions accepted by the accepting unit; a communication unit for allowing the parents matched by the matching unit to communicate with each other; an evaluation unit that evaluates the results of the communication performed by the communication unit; A system characterized by:

2. The reception unit Accepts conditions for where you want to play, gender, and age 2. The system of claim 1.

3. The matching unit Using AI, we suggest matches based on past matching history and ratings.

2. The system of claim 1.

4. The communication unit Providing chat or messaging functionality 2. The system of claim 1.

5. The evaluation unit Providing a rating system for users to rate their matches 2. The system of claim 1.

6. Using location information, It has a location-based component that allows users to easily match with nearby parents.

2. The system of claim 1.

7. The reception unit Estimating a user's emotion and adjusting a condition input interface based on the estimated user's emotion 2. The system of claim 1.

8. The reception unit Analyzes past condition input history and suggests optimal condition input for the user 2. The system of claim 1.

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