system

Abstract

The system according to this embodiment aims to efficiently find a date partner that the user desires. [Solution] The system according to the embodiment comprises a reception unit, a search unit, a presentation unit, and an information provision unit. The reception unit receives input from the user regarding the desired conditions for a date partner. The search unit searches for date partners based on the conditions entered by the reception unit. The presentation unit presents the candidates found by the search unit. The information provision unit provides detailed information about the candidates presented by the presentation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to efficiently find a dating partner they desire.

[0005] The system according to an embodiment aims to efficiently find a dating partner desired by a user.

Means for Solving the Problems

[0006] [[ID= forty-five]] [[ID= forty-six]]The system according to an embodiment includes a reception unit, a search unit, a presentation unit, and an information providing unit. The reception unit inputs the conditions of a dating partner desired by the user. The search unit searches for a dating partner based on the conditions input by the reception unit. The presentation unit presents the candidates searched by the search unit. The information providing unit provides detailed information on the candidates presented by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to efficiently find a date partner they desire. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The dating partner matching system according to an embodiment of the present invention is a system that allows users to easily find a date partner they desire. In this dating partner matching system, the user inputs the conditions for their desired date partner, and a generating AI searches for a date partner based on those conditions and presents the most suitable candidates. Furthermore, the generating AI provides detailed information about the presented candidates, allowing the user to understand the characteristics and profiles of the other party. For example, the user inputs the conditions for their desired date partner. For example, they input conditions such as age, hobbies, occupation, and place of residence. This information is input into the generating AI. Next, the generating AI searches for a date partner based on the input conditions. The generating AI extracts candidates that match the conditions from a vast database and presents the most suitable candidates. For example, if the user inputs conditions such as "around 30 years old, hobbies include watching movies, occupation is an engineer, and place of residence is Tokyo," candidates that match those conditions will be presented. Furthermore, the generating AI provides detailed information about the presented candidates. For example, information such as the candidate's profile, hobbies, and past dating experience is provided. This allows the user to understand the characteristics and profiles of the other party and determine whether they are suitable as a date partner. Through this mechanism, users can easily find a date partner they desire. Furthermore, knowing detailed information about your date beforehand increases the chances of a successful date. For example, finding someone with shared hobbies will provide plenty of topics to talk about on the date, leading to a more enjoyable experience. This dating partner matching system is available to everyone looking for a date. For example, it's ideal for those who want to find a date in between busy work schedules or those seeking new encounters. By utilizing generational AI, you can efficiently find a date and expand your opportunities for meeting people. As a result, the dating partner matching system can efficiently search for a date that matches the user's preferences and provide detailed information.

[0029] The dating partner matching system according to this embodiment comprises a reception unit, a search unit, a presentation unit, and an information provision unit. The reception unit receives the user's desired dating partner criteria. These criteria include, but are not limited to, age, hobbies, occupation, and place of residence. The reception unit accepts, for example, the user's entered criteria such as age, hobbies, occupation, and place of residence. The search unit searches for dating partners based on the criteria entered by the reception unit. The search unit extracts candidates that match the criteria from a vast database, for example. The search unit uses a generative AI to search for the most suitable candidate based on the user's criteria. The presentation unit presents the candidates found by the search unit. The presentation unit displays the search results to the user, for example. The presentation unit uses a generative AI to present the search results to the user. The information provision unit provides detailed information about the candidates presented by the presentation unit. The information provision unit provides information such as the candidate's profile, hobbies, and past dating experience. The information provision unit uses a generative AI to provide detailed information about the candidates. As a result, the dating partner matching system according to this embodiment can efficiently search for a date partner that the user desires and provide detailed information about them.

[0030] The reception desk accepts the user's desired criteria for a date. These criteria may include, but are not limited to, age, hobbies, occupation, and place of residence. The reception desk accepts the user's entered criteria, such as age, hobbies, occupation, and place of residence. Specifically, the reception desk provides an intuitive interface to allow users to easily enter criteria. For example, it uses dropdown menus, checkboxes, and sliders to allow users to select their desired criteria. It also provides a free-form input field, allowing users to describe specific criteria in detail. Furthermore, the reception desk has a function to remember criteria entered by the user in the past and automatically suggest them when the user uses the service again. This saves the user the trouble of entering the same criteria every time. The reception desk verifies the entered criteria in real time, checking for inappropriate input or contradictory conditions. For example, if the age range is inappropriate or the choices for hobbies or occupation are contradictory, it provides appropriate feedback to the user and encourages correction. In this way, the reception desk supports users in entering accurate and appropriate criteria.

[0031] The search unit searches for potential dates based on the criteria entered by the reception unit. For example, the search unit extracts candidates that match the criteria from a vast database. The search unit uses generative AI to find the best candidate based on the user's criteria. Specifically, the search unit analyzes the criteria entered by the user and matches them with candidate information in the database. The generative AI uses natural language processing technology to understand the user's criteria and identify the best candidate. For example, if a user enters criteria such as "a 30-something engineer who likes the outdoors," the generative AI analyzes the profile information in the database and extracts the corresponding candidate. The generative AI also considers the user's past search history and matching history to provide more accurate search results. Furthermore, the search unit updates the database in real time, instantly reflecting newly registered candidate information. This ensures that users always receive search results based on the latest information. The search unit displays the search results in a ranking format, showing candidates that the user is most likely to be interested in at the top. The ranking is calculated based on factors such as the degree of match with the user's criteria and the activity level of the candidates. This allows the search unit to quickly and accurately provide users with the best possible date.

[0032] The presentation section presents candidates found by the search section. For example, the presentation section displays search results to the user. The presentation section uses generative AI to present search results to the user. Specifically, the presentation section displays search results in a format that is easy for the user to understand visually. For example, it displays basic information such as a candidate's profile picture, name, age, hobbies, and occupation in a card format, allowing the user to easily compare candidates. It also provides links and buttons to access detailed information about candidates, allowing the user to further explore information about candidates they are interested in. Using generative AI, the presentation section optimizes the display order of search results, taking into account the user's interests and past behavior history. For example, if a user has previously shown interest in candidates with specific hobbies or occupations, it prioritizes displaying candidates with similar criteria. The presentation section also provides functions for users to filter and sort search results. For example, search results can be narrowed down by criteria such as age, location, and hobbies. This allows users to efficiently find candidates that best match their preferences. Furthermore, the presentation section provides an interface for users to send messages to candidates. This allows users to directly approach candidates from the search results and initiate communication.

[0033] The Information Provider section provides detailed information about the candidates presented by the Presentation section. For example, it provides information such as the candidate's profile, hobbies, and past dating experience. The Information Provider section uses generative AI to provide detailed candidate information. Specifically, it displays candidate profile information in detail, allowing users to gain a deeper understanding of the candidate. For example, it displays the candidate's self-introduction, hobbies and skills, occupation and education, past dating experience, and ratings. It also displays photos and videos uploaded by the candidate, allowing users to visually assess the candidate. The Information Provider section uses generative AI to organize candidate information and prioritizes displaying information important to the user. For example, it highlights hobbies and skills that the user is likely to be particularly interested in, as well as common friends and events of interest. The Information Provider section also provides an interface for users to send questions to candidates. This allows users to ask specific questions and obtain more detailed information. Furthermore, the Information Provider section regularly updates candidate information to provide the latest information. For example, if a candidate adds new hobbies or skills, or changes their profile picture, the information is immediately reflected. This allows the information provision department to always provide users with the latest and most accurate information, supporting them in selecting a date partner.

[0034] The reception desk can accept conditions entered by the user, such as age, hobbies, occupation, and place of residence. For example, the reception desk can accept an age range entered by the user. The reception desk can also accept a type of hobby entered by the user. The reception desk can also accept a category of occupation entered by the user. The reception desk can also accept a range of place of residence entered by the user. This allows for more accurate searches by enabling users to enter detailed conditions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the conditions entered by the user into the AI, which can then analyze and accept the conditions.

[0035] The search unit can extract candidates that match the conditions entered by the reception unit from a vast database. For example, the search unit efficiently extracts candidates that match the conditions from a vast database. The search unit uses generative AI to search for the optimal candidate based on the user's conditions. The search unit performs searches considering, for example, the type of database and the amount of data. The search unit can also perform searches considering, for example, the update frequency of the database. This allows for the efficient extraction of candidates that match the conditions from a vast database. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs the user's conditions into the generative AI, and the generative AI extracts candidates that match the conditions from the database.

[0036] The presentation unit can present candidates found by the search unit to the user. The presentation unit, for example, displays the search results to the user. The presentation unit presents the search results to the user using a generation AI. The presentation unit presents the search results considering, for example, the display format and priority. The presentation unit can also, for example, display the search results in a visually easy-to-understand manner. This allows the searched candidates to be presented to the user efficiently. Some or all of the above processing in the presentation unit is performed using a generation AI. For example, the presentation unit inputs the candidates found by the search unit into the generation AI, and the generation AI presents the search results to the user.

[0037] The information provision unit can provide detailed information about the candidates presented by the presentation unit, such as their profiles, hobbies, and past dating experience. For example, the information provision unit can provide the candidate's profile information. The information provision unit can also provide detailed information about the candidate's hobbies. The information provision unit can also provide information about the candidate's past dating experience. By providing detailed information about the candidates, it makes it easier for users to understand the characteristics and profiles of the other party. Some or all of the above processing in the information provision unit is performed using a generating AI. For example, the information provision unit inputs the candidate information presented by the presentation unit into the generating AI, and the generating AI provides the detailed information.

[0038] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest conditions to be used during specific time periods based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input history into the AI, and the AI ​​suggests the optimal input method.

[0039] The input field can automatically complete input suggestions based on the user's current lifestyle and areas of interest during input. For example, the input field can automatically complete relevant conditions based on the user's recent hobbies and activities. The input field can also suggest appropriate conditions based on the user's current occupation and lifestyle. For example, the input field can analyze the user's social media activity and automatically complete relevant conditions. This streamlines the input process by automatically completing input suggestions based on the user's lifestyle and areas of interest. Some or all of the above processing in the input field may be performed using AI or not. For example, the input field can input data on the user's lifestyle and areas of interest into the AI, which then automatically completes input suggestions.

[0040] The input field can prioritize displaying highly relevant input suggestions by considering the user's geographical location information during input. For example, if the user lives in a specific region, the input field can prioritize displaying conditions related to that region. For example, if the user is traveling, the input field can also suggest appropriate conditions based on the user's current location. For example, if the user is interested in a specific city, the input field can also prioritize displaying conditions related to that city. This allows the input field to provide highly relevant input suggestions by considering the user's geographical location information. Some or all of the above processing in the input field may be performed using AI or not. For example, the input field can input the user's geographical location information into the AI, and the AI ​​can prioritize displaying highly relevant input suggestions.

[0041] The reception unit can analyze the user's social media activity during input and suggest relevant input candidates. For example, the reception unit can suggest relevant conditions based on the hobbies and activities the user frequently mentions on social media. The reception unit can also analyze the user's social media friendships and suggest people with similar hobbies and interests. The reception unit can also analyze the content of the user's social media posts and suggest relevant conditions. In this way, by analyzing social media activity, it is possible to provide input candidates relevant to the user. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs data on the user's social media activity into the AI, and the AI ​​suggests relevant input candidates.

[0042] The search unit can provide optimal search results by referring to the user's past search history during a search. For example, the search unit can prioritize displaying relevant candidates based on the conditions the user has previously searched for. For example, the search unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, the search unit can analyze the user's past search history and provide the most suitable candidates. This improves the user's search efficiency by providing optimal search results based on past search history. Some or all of the above processes in the search unit are performed using generative AI. For example, the search unit inputs the user's past search history into the generative AI, which then provides the optimal search results.

[0043] The search unit can filter search results based on the user's current lifestyle and areas of interest during a search. For example, the search unit prioritizes displaying relevant suggestions based on the user's recent hobbies and activities. The search unit can also suggest appropriate suggestions based on the user's current occupation and lifestyle. For example, the search unit can analyze the user's social media activity and prioritize displaying relevant suggestions. This allows the search unit to provide highly relevant suggestions by filtering search results based on the user's lifestyle and areas of interest. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then filters the search results.

[0044] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location during a search. For example, if the user lives in a specific region, the search unit will prioritize displaying suggestions related to that region. If the user is traveling, the search unit can also suggest appropriate suggestions based on their current location. If the user is interested in a specific city, the search unit can also prioritize displaying suggestions related to that city. In this way, highly relevant search results can be provided by considering the user's geographical location. Some or all of the above processing in the search unit is performed using a generative AI. For example, the search unit inputs the user's geographical location information into the generative AI, which then prioritizes displaying highly relevant search results.

[0045] The search unit can analyze a user's social media activity during a search and provide relevant search results. For example, the search unit can suggest relevant candidates based on hobbies and activities that the user frequently mentions on social media. The search unit can also analyze a user's social media friendships and suggest people with similar hobbies and interests. The search unit can also analyze a user's social media posts and suggest relevant candidates. In this way, by analyzing social media activity, it can provide search results relevant to the user. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs data on the user's social media activity into the generative AI, which then provides relevant search results.

[0046] The presentation unit can prioritize displaying the most suitable candidates by referring to the user's past selection history when presenting options. For example, the presentation unit can prioritize displaying relevant candidates based on conditions previously selected by the user. For example, the presentation unit can predict and suggest conditions to be used during a specific time period based on the user's past selection history. For example, the presentation unit can analyze the user's past selection history and provide the most suitable candidates. This improves the user's selection efficiency by providing optimal candidates based on past selection history. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit inputs the user's past selection history into the generative AI, and the generative AI provides optimal candidates.

[0047] The presentation unit can filter candidates based on the user's current lifestyle and areas of interest when presenting them. For example, the presentation unit prioritizes displaying relevant candidates based on the user's recent hobbies and activities. The presentation unit can also suggest appropriate candidates based on the user's current occupation and lifestyle. For example, the presentation unit can analyze the user's social media activity and prioritize displaying relevant candidates. This allows the presentation unit to provide highly relevant candidates by filtering them based on the user's lifestyle and areas of interest. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then filters the candidates.

[0048] The suggestion unit can prioritize displaying highly relevant candidates by considering the user's geographical location information during the suggestion process. For example, if the user lives in a specific region, the suggestion unit will prioritize displaying candidates related to that region. For example, if the user is traveling, the suggestion unit can also suggest appropriate candidates based on the user's current location. For example, if the user is interested in a specific city, the suggestion unit can prioritize displaying candidates related to that city. In this way, highly relevant candidates can be provided by considering the user's geographical location information. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then prioritizes displaying highly relevant candidates.

[0049] The suggestion unit can analyze the user's social media activity and suggest relevant candidates at the time of suggestion. For example, the suggestion unit can suggest relevant candidates based on the hobbies and activities that the user frequently mentions on social media. For example, the suggestion unit can analyze the user's social media friendships and suggest people with similar hobbies and interests. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant candidates. In this way, by analyzing social media activity, it can provide candidates relevant to the user. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, and the generative AI suggests relevant candidates.

[0050] The information provision unit can provide the most relevant information by referring to the user's past browsing history when providing information. For example, the information provision unit can prioritize providing relevant information based on the profile information the user has previously viewed. For example, the information provision unit can predict and suggest information that the user will view at a specific time based on their past browsing history. For example, the information provision unit can analyze the user's past browsing history and provide the most suitable information. This improves the user's information acquisition efficiency by providing the most relevant information based on past browsing history. Some or all of the above processing in the information provision unit is performed using a generation AI. For example, the information provision unit inputs the user's past browsing history into the generation AI, and the generation AI provides the most relevant information.

[0051] The information provision unit can customize information based on the user's current lifestyle and areas of interest when providing it. For example, the information provision unit can provide relevant information based on the user's recent hobbies and activities. The information provision unit can also provide appropriate information based on the user's current occupation and lifestyle. For example, the information provision unit can analyze the user's social media activity and provide relevant information. This allows for the provision of highly relevant information by customizing it based on the user's lifestyle and areas of interest. Some or all of the above processing in the information provision unit is performed using a generative AI. For example, the information provision unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then customizes the information.

[0052] The information provision unit can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user lives in a specific region, the information provision unit will prioritize providing information related to that region. For example, if the user is traveling, the information provision unit can also provide appropriate information based on the user's current location. For example, if the user is interested in a specific city, the information provision unit can also prioritize providing information related to that city. In this way, highly relevant information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit is performed using a generating AI. For example, the information provision unit inputs the user's geographical location information into the generating AI, and the generating AI prioritizes providing highly relevant information.

[0053] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can provide relevant information based on hobbies and activities that the user frequently mentions on social media. For example, the information provision unit can also analyze a user's social media friendships and provide information about people who share common hobbies and interests. For example, the information provision unit can analyze the content of a user's social media posts and provide relevant information. In this way, by analyzing social media activity, it is possible to provide information relevant to the user. Some or all of the above processing in the information provision unit is performed using a generative AI. For example, the information provision unit inputs data on the user's social media activity into the generative AI, and the generative AI provides relevant information.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The dating matching system can analyze a user's past dating history and suggest the most suitable date plan. For example, it can suggest relevant date plans based on places the user has visited and events they have attended. It can also prioritize suggesting date plans the user has enjoyed in the past, and exclude date plans the user has avoided in the past. This allows the system to improve the user's dating experience by suggesting the most suitable date plan based on their past dating history. Some or all of the above processing in the suggestion section is performed using a generative AI. For example, the suggestion section inputs the user's past dating history into the generative AI, which then suggests the most suitable date plan.

[0056] The dating matching system can customize dating partner suggestions based on the user's current lifestyle and interests. For example, it can suggest relevant dating partners based on the user's recent hobbies and activities. It can also suggest suitable dating partners based on the user's current occupation and lifestyle. It can even analyze the user's social media activity and suggest relevant dating partners. By customizing dating partner suggestions based on the user's lifestyle and interests, it can improve user satisfaction. Some or all of the above processing in the suggestion section is performed using a generative AI. For example, the suggestion section inputs data on the user's lifestyle and interests into the generative AI, which then customizes the dating partner suggestions.

[0057] The dating matching system can suggest dating partners while considering the user's geographical location. For example, if a user lives in a specific area, it can prioritize suggesting dating partners related to that area. If a user is traveling, it can also suggest suitable dating partners based on their current location. If a user is interested in a specific city, it can also prioritize suggesting dating partners related to that city. In this way, by considering the user's geographical location, it can provide highly relevant dating partners. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then suggests highly relevant dating partners.

[0058] The dating matching system can analyze a user's social media activity and suggest relevant dating partners. For example, it can suggest relevant dating partners based on hobbies and activities that the user frequently mentions on social media. It can also analyze a user's social media friendships and suggest partners with similar hobbies and interests. It can also analyze the content of a user's social media posts and suggest relevant dating partners. In this way, by analyzing social media activity, it can provide users with relevant dating partners. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, which then suggests relevant dating partners.

[0059] The dating matching system can suggest the most suitable date by referring to the user's past selection history. For example, it can prioritize suggesting relevant date partners based on the conditions the user has previously selected. It can also predict and suggest conditions to be used during specific time periods based on the user's past selection history. It can also analyze the user's past selection history and suggest the most suitable date partner. This improves the user's selection efficiency by suggesting the most suitable date partner based on past selection history. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's past selection history into the generative AI, which then suggests the most suitable date partner.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The reception desk inputs the user's desired criteria for a date. These criteria may include, for example, age, hobbies, occupation, and place of residence. The reception desk receives these criteria from the user. Step 2: The search unit searches for a date based on the conditions entered by the reception unit. The search unit extracts candidates that match the conditions from a vast database and uses a generation AI to find the best candidate. Step 3: The presentation unit presents the candidates found by the search unit. The presentation unit displays the search results to the user and presents the search results to the user using a generation AI. Step 4: The Information Provision Department provides detailed information about the candidates presented by the Presentation Department. The Information Provision Department provides information such as the candidate's profile, hobbies, and past dating experience, and uses a generative AI to provide detailed information.

[0062] (Example of form 2) The dating partner matching system according to an embodiment of the present invention is a system that allows users to easily find a date partner they desire. In this dating partner matching system, the user inputs the conditions for their desired date partner, and a generating AI searches for a date partner based on those conditions and presents the most suitable candidates. Furthermore, the generating AI provides detailed information about the presented candidates, allowing the user to understand the characteristics and profiles of the other party. For example, the user inputs the conditions for their desired date partner. For example, they input conditions such as age, hobbies, occupation, and place of residence. This information is input into the generating AI. Next, the generating AI searches for a date partner based on the input conditions. The generating AI extracts candidates that match the conditions from a vast database and presents the most suitable candidates. For example, if the user inputs conditions such as "around 30 years old, hobbies include watching movies, occupation is an engineer, and place of residence is Tokyo," candidates that match those conditions will be presented. Furthermore, the generating AI provides detailed information about the presented candidates. For example, information such as the candidate's profile, hobbies, and past dating experience is provided. This allows the user to understand the characteristics and profiles of the other party and determine whether they are suitable as a date partner. Through this mechanism, users can easily find a date partner they desire. Furthermore, knowing detailed information about your date beforehand increases the chances of a successful date. For example, finding someone with shared hobbies will provide plenty of topics to talk about on the date, leading to a more enjoyable experience. This dating partner matching system is available to everyone looking for a date. For example, it's ideal for those who want to find a date in between busy work schedules or those seeking new encounters. By utilizing generational AI, you can efficiently find a date and expand your opportunities for meeting people. As a result, the dating partner matching system can efficiently search for a date that matches the user's preferences and provide detailed information.

[0063] The dating partner matching system according to this embodiment comprises a reception unit, a search unit, a presentation unit, and an information provision unit. The reception unit receives the user's desired dating partner criteria. These criteria include, but are not limited to, age, hobbies, occupation, and place of residence. The reception unit accepts, for example, the user's entered criteria such as age, hobbies, occupation, and place of residence. The search unit searches for dating partners based on the criteria entered by the reception unit. The search unit extracts candidates that match the criteria from a vast database, for example. The search unit uses a generative AI to search for the most suitable candidate based on the user's criteria. The presentation unit presents the candidates found by the search unit. The presentation unit displays the search results to the user, for example. The presentation unit uses a generative AI to present the search results to the user. The information provision unit provides detailed information about the candidates presented by the presentation unit. The information provision unit provides information such as the candidate's profile, hobbies, and past dating experience. The information provision unit uses a generative AI to provide detailed information about the candidates. As a result, the dating partner matching system according to this embodiment can efficiently search for a date partner that the user desires and provide detailed information about them.

[0064] The reception desk accepts the user's desired criteria for a date. These criteria may include, but are not limited to, age, hobbies, occupation, and place of residence. The reception desk accepts the user's entered criteria, such as age, hobbies, occupation, and place of residence. Specifically, the reception desk provides an intuitive interface to allow users to easily enter criteria. For example, it uses dropdown menus, checkboxes, and sliders to allow users to select their desired criteria. It also provides a free-form input field, allowing users to describe specific criteria in detail. Furthermore, the reception desk has a function to remember criteria entered by the user in the past and automatically suggest them when the user uses the service again. This saves the user the trouble of entering the same criteria every time. The reception desk verifies the entered criteria in real time, checking for inappropriate input or contradictory conditions. For example, if the age range is inappropriate or the choices for hobbies or occupation are contradictory, it provides appropriate feedback to the user and encourages correction. In this way, the reception desk supports users in entering accurate and appropriate criteria.

[0065] The search unit searches for potential dates based on the criteria entered by the reception unit. For example, the search unit extracts candidates that match the criteria from a vast database. The search unit uses generative AI to find the best candidate based on the user's criteria. Specifically, the search unit analyzes the criteria entered by the user and matches them with candidate information in the database. The generative AI uses natural language processing technology to understand the user's criteria and identify the best candidate. For example, if a user enters criteria such as "a 30-something engineer who likes the outdoors," the generative AI analyzes the profile information in the database and extracts the corresponding candidate. The generative AI also considers the user's past search history and matching history to provide more accurate search results. Furthermore, the search unit updates the database in real time, instantly reflecting newly registered candidate information. This ensures that users always receive search results based on the latest information. The search unit displays the search results in a ranking format, showing candidates that the user is most likely to be interested in at the top. The ranking is calculated based on factors such as the degree of match with the user's criteria and the activity level of the candidates. This allows the search unit to quickly and accurately provide users with the best possible date.

[0066] The presentation section presents candidates found by the search section. For example, the presentation section displays search results to the user. The presentation section uses generative AI to present search results to the user. Specifically, the presentation section displays search results in a format that is easy for the user to understand visually. For example, it displays basic information such as a candidate's profile picture, name, age, hobbies, and occupation in a card format, allowing the user to easily compare candidates. It also provides links and buttons to access detailed information about candidates, allowing the user to further explore information about candidates they are interested in. Using generative AI, the presentation section optimizes the display order of search results, taking into account the user's interests and past behavior history. For example, if a user has previously shown interest in candidates with specific hobbies or occupations, it prioritizes displaying candidates with similar criteria. The presentation section also provides functions for users to filter and sort search results. For example, search results can be narrowed down by criteria such as age, location, and hobbies. This allows users to efficiently find candidates that best match their preferences. Furthermore, the presentation section provides an interface for users to send messages to candidates. This allows users to directly approach candidates from the search results and initiate communication.

[0067] The Information Provider section provides detailed information about the candidates presented by the Presentation section. For example, it provides information such as the candidate's profile, hobbies, and past dating experience. The Information Provider section uses generative AI to provide detailed candidate information. Specifically, it displays candidate profile information in detail, allowing users to gain a deeper understanding of the candidate. For example, it displays the candidate's self-introduction, hobbies and skills, occupation and education, past dating experience, and ratings. It also displays photos and videos uploaded by the candidate, allowing users to visually assess the candidate. The Information Provider section uses generative AI to organize candidate information and prioritizes displaying information important to the user. For example, it highlights hobbies and skills that the user is likely to be particularly interested in, as well as common friends and events of interest. The Information Provider section also provides an interface for users to send questions to candidates. This allows users to ask specific questions and obtain more detailed information. Furthermore, the Information Provider section regularly updates candidate information to provide the latest information. For example, if a candidate adds new hobbies or skills, or changes their profile picture, the information is immediately reflected. This allows the information provision department to always provide users with the latest and most accurate information, supporting them in selecting a date partner.

[0068] The reception desk can accept conditions entered by the user, such as age, hobbies, occupation, and place of residence. For example, the reception desk can accept an age range entered by the user. The reception desk can also accept a type of hobby entered by the user. The reception desk can also accept a category of occupation entered by the user. The reception desk can also accept a range of place of residence entered by the user. This allows for more accurate searches by enabling users to enter detailed conditions. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the conditions entered by the user into the AI, which can then analyze and accept the conditions.

[0069] The search unit can extract candidates that match the conditions entered by the reception unit from a vast database. For example, the search unit efficiently extracts candidates that match the conditions from a vast database. The search unit uses generative AI to search for the optimal candidate based on the user's conditions. The search unit performs searches considering, for example, the type of database and the amount of data. The search unit can also perform searches considering, for example, the update frequency of the database. This allows for the efficient extraction of candidates that match the conditions from a vast database. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs the user's conditions into the generative AI, and the generative AI extracts candidates that match the conditions from the database.

[0070] The presentation unit can present candidates found by the search unit to the user. The presentation unit, for example, displays the search results to the user. The presentation unit presents the search results to the user using a generation AI. The presentation unit presents the search results considering, for example, the display format and priority. The presentation unit can also, for example, display the search results in a visually easy-to-understand manner. This allows the searched candidates to be presented to the user efficiently. Some or all of the above processing in the presentation unit is performed using a generation AI. For example, the presentation unit inputs the candidates found by the search unit into the generation AI, and the generation AI presents the search results to the user.

[0071] The information provision unit can provide detailed information about the candidates presented by the presentation unit, such as their profiles, hobbies, and past dating experience. For example, the information provision unit can provide the candidate's profile information. The information provision unit can also provide detailed information about the candidate's hobbies. The information provision unit can also provide information about the candidate's past dating experience. By providing detailed information about the candidates, it makes it easier for users to understand the characteristics and profiles of the other party. Some or all of the above processing in the information provision unit is performed using a generating AI. For example, the information provision unit inputs the candidate information presented by the presentation unit into the generating AI, and the generating AI provides the detailed information.

[0072] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process more enjoyable. For example, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. This improves the user's input experience by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs the user's facial expression data into the generative AI, which estimates the emotions and changes the interface design.

[0073] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display conditions that the user has frequently entered in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest conditions to be used during specific time periods based on the user's past input history. This improves the user's input efficiency by suggesting the optimal input method based on past input history. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's past input history into the AI, and the AI ​​suggests the optimal input method.

[0074] The input field can automatically complete input suggestions based on the user's current lifestyle and areas of interest during input. For example, the input field can automatically complete relevant conditions based on the user's recent hobbies and activities. The input field can also suggest appropriate conditions based on the user's current occupation and lifestyle. For example, the input field can analyze the user's social media activity and automatically complete relevant conditions. This streamlines the input process by automatically completing input suggestions based on the user's lifestyle and areas of interest. Some or all of the above processing in the input field may be performed using AI or not. For example, the input field can input data on the user's lifestyle and areas of interest into the AI, which then automatically completes input suggestions.

[0075] The reception desk can estimate the user's emotions and dynamically change the priority of input fields based on the estimated emotions. For example, if the user is in a hurry, the reception desk will prioritize displaying the most important input fields. For example, if the user is relaxed, the reception desk may also display detailed input fields. For example, if the user is stressed, the reception desk may also simplify the display of input fields. This improves the user's input experience by changing the priority of input fields according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk inputs the user's emotion data into the generative AI, which estimates the emotions and changes the priority of input fields.

[0076] The input field can prioritize displaying highly relevant input suggestions by considering the user's geographical location information during input. For example, if the user lives in a specific region, the input field can prioritize displaying conditions related to that region. For example, if the user is traveling, the input field can also suggest appropriate conditions based on the user's current location. For example, if the user is interested in a specific city, the input field can also prioritize displaying conditions related to that city. This allows the input field to provide highly relevant input suggestions by considering the user's geographical location information. Some or all of the above processing in the input field may be performed using AI or not. For example, the input field can input the user's geographical location information into the AI, and the AI ​​can prioritize displaying highly relevant input suggestions.

[0077] The reception unit can analyze the user's social media activity during input and suggest relevant input candidates. For example, the reception unit can suggest relevant conditions based on the hobbies and activities the user frequently mentions on social media. The reception unit can also analyze the user's social media friendships and suggest people with similar hobbies and interests. The reception unit can also analyze the content of the user's social media posts and suggest relevant conditions. In this way, by analyzing social media activity, it is possible to provide input candidates relevant to the user. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit inputs data on the user's social media activity into the AI, and the AI ​​suggests relevant input candidates.

[0078] The search unit can estimate the user's emotions and dynamically adjust the parameters of the search algorithm based on the estimated emotions. For example, if the user is relaxed, the search unit adjusts the search algorithm to present a wide range of results. If the user is in a hurry, the search unit can also adjust the search algorithm to quickly present the most relevant results. If the user is excited, the search unit can also adjust the search algorithm to prioritize visually appealing results. By adjusting the search algorithm according to the user's emotions, the system can provide optimal search results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit inputs user emotion data into the generative AI, which estimates the emotions and adjusts the parameters of the search algorithm.

[0079] The search unit can provide optimal search results by referring to the user's past search history during a search. For example, the search unit can prioritize displaying relevant candidates based on the conditions the user has previously searched for. For example, the search unit can predict and suggest conditions to be used during a specific time period based on the user's past search history. For example, the search unit can analyze the user's past search history and provide the most suitable candidates. This improves the user's search efficiency by providing optimal search results based on past search history. Some or all of the above processes in the search unit are performed using generative AI. For example, the search unit inputs the user's past search history into the generative AI, which then provides the optimal search results.

[0080] The search unit can filter search results based on the user's current lifestyle and areas of interest during a search. For example, the search unit prioritizes displaying relevant suggestions based on the user's recent hobbies and activities. The search unit can also suggest appropriate suggestions based on the user's current occupation and lifestyle. For example, the search unit can analyze the user's social media activity and prioritize displaying relevant suggestions. This allows the search unit to provide highly relevant suggestions by filtering search results based on the user's lifestyle and areas of interest. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then filters the search results.

[0081] The search unit can estimate the user's emotions and dynamically change the display order of search results based on the estimated emotions. For example, if the user is in a hurry, the search unit will display the most relevant results at the top. If the user is relaxed, the search unit may also display a wide range of results. If the user is excited, the search unit may also display visually appealing results at the top. This allows the system to provide the user with the most suitable results by changing the display order of search results according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the search unit are performed using generative AI. For example, the search unit inputs user emotion data into the generative AI, which estimates the emotions and changes the display order of search results.

[0082] The search unit can prioritize displaying highly relevant search results by considering the user's geographical location during a search. For example, if the user lives in a specific region, the search unit will prioritize displaying suggestions related to that region. If the user is traveling, the search unit can also suggest appropriate suggestions based on their current location. If the user is interested in a specific city, the search unit can also prioritize displaying suggestions related to that city. In this way, highly relevant search results can be provided by considering the user's geographical location. Some or all of the above processing in the search unit is performed using a generative AI. For example, the search unit inputs the user's geographical location information into the generative AI, which then prioritizes displaying highly relevant search results.

[0083] The search unit can analyze a user's social media activity during a search and provide relevant search results. For example, the search unit can suggest relevant candidates based on hobbies and activities that the user frequently mentions on social media. The search unit can also analyze a user's social media friendships and suggest people with similar hobbies and interests. The search unit can also analyze a user's social media posts and suggest relevant candidates. In this way, by analyzing social media activity, it can provide search results relevant to the user. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs data on the user's social media activity into the generative AI, which then provides relevant search results.

[0084] The presentation unit can estimate the user's emotions and dynamically change the display method of the suggested options based on the estimated emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible display method. If the user is relaxed, the presentation unit can also provide a display method that includes detailed information. If the user is in a hurry, the presentation unit can also provide a display method that gets straight to the point. By changing the display method according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit is performed using generative AI. For example, the presentation unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and changes the display method.

[0085] The presentation unit can prioritize displaying the most suitable candidates by referring to the user's past selection history when presenting options. For example, the presentation unit can prioritize displaying relevant candidates based on conditions previously selected by the user. For example, the presentation unit can predict and suggest conditions to be used during a specific time period based on the user's past selection history. For example, the presentation unit can analyze the user's past selection history and provide the most suitable candidates. This improves the user's selection efficiency by providing optimal candidates based on past selection history. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit inputs the user's past selection history into the generative AI, and the generative AI provides optimal candidates.

[0086] The presentation unit can filter candidates based on the user's current lifestyle and areas of interest when presenting them. For example, the presentation unit prioritizes displaying relevant candidates based on the user's recent hobbies and activities. The presentation unit can also suggest appropriate candidates based on the user's current occupation and lifestyle. For example, the presentation unit can analyze the user's social media activity and prioritize displaying relevant candidates. This allows the presentation unit to provide highly relevant candidates by filtering them based on the user's lifestyle and areas of interest. Some or all of the above processing in the presentation unit is performed using a generative AI. For example, the presentation unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then filters the candidates.

[0087] The presentation unit can estimate the user's emotions and dynamically change the priority of the suggested candidates based on the estimated emotions. For example, if the user is in a hurry, the presentation unit will display the most suitable candidates at the top. If the user is relaxed, the presentation unit can also display a wide range of candidates. If the user is excited, the presentation unit can also display visually appealing candidates at the top. This allows the presentation unit to provide the user with the most suitable candidates by changing the priority of candidates according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit is performed using generative AI. For example, the presentation unit inputs user emotion data into the generative AI, which estimates the emotions and changes the priority of the candidates.

[0088] The suggestion unit can prioritize displaying highly relevant candidates by considering the user's geographical location information during the suggestion process. For example, if the user lives in a specific region, the suggestion unit will prioritize displaying candidates related to that region. For example, if the user is traveling, the suggestion unit can also suggest appropriate candidates based on the user's current location. For example, if the user is interested in a specific city, the suggestion unit can prioritize displaying candidates related to that city. In this way, highly relevant candidates can be provided by considering the user's geographical location information. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then prioritizes displaying highly relevant candidates.

[0089] The suggestion unit can analyze the user's social media activity and suggest relevant candidates at the time of suggestion. For example, the suggestion unit can suggest relevant candidates based on the hobbies and activities that the user frequently mentions on social media. For example, the suggestion unit can analyze the user's social media friendships and suggest people with similar hobbies and interests. For example, the suggestion unit can analyze the content of the user's social media posts and suggest relevant candidates. In this way, by analyzing social media activity, it can provide candidates relevant to the user. Some or all of the above processing in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, and the generative AI suggests relevant candidates.

[0090] The information provider can estimate the user's emotions and dynamically adjust the level of detail of the information provided based on the estimated emotions. For example, if the user is relaxed, the information provider can provide detailed profile information. If the user is in a hurry, the information provider can also provide concise information that gets straight to the point. If the user is excited, the information provider can also provide visually appealing information. This allows the information provider to provide the most relevant information to the user by adjusting the level of detail according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provider are performed using generative AI. For example, the information provider inputs user emotion data into the generative AI, which estimates the emotions and adjusts the level of detail of the information.

[0091] The information provision unit can provide the most relevant information by referring to the user's past browsing history when providing information. For example, the information provision unit can prioritize providing relevant information based on the profile information the user has previously viewed. For example, the information provision unit can predict and suggest information that the user will view at a specific time based on their past browsing history. For example, the information provision unit can analyze the user's past browsing history and provide the most suitable information. This improves the user's information acquisition efficiency by providing the most relevant information based on past browsing history. Some or all of the above processing in the information provision unit is performed using a generation AI. For example, the information provision unit inputs the user's past browsing history into the generation AI, and the generation AI provides the most relevant information.

[0092] The information provision unit can customize information based on the user's current lifestyle and areas of interest when providing it. For example, the information provision unit can provide relevant information based on the user's recent hobbies and activities. The information provision unit can also provide appropriate information based on the user's current occupation and lifestyle. For example, the information provision unit can analyze the user's social media activity and provide relevant information. This allows for the provision of highly relevant information by customizing it based on the user's lifestyle and areas of interest. Some or all of the above processing in the information provision unit is performed using a generative AI. For example, the information provision unit inputs data on the user's lifestyle and areas of interest into the generative AI, which then customizes the information.

[0093] The information provider can estimate the user's emotions and dynamically change how the information is displayed based on the estimated emotions. For example, if the user is nervous, the information provider can provide a simple and highly visible display method. If the user is relaxed, the information provider can also provide a display method that includes detailed information. If the user is in a hurry, the information provider can also provide a display method that gets straight to the point. By changing the display method according to the user's emotions, the system can provide the user with the most optimal information display. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the information provider are performed using the generative AI. For example, the information provider inputs the user's emotion data into the generative AI, which estimates the emotion and changes the display method.

[0094] The information provision unit can prioritize providing highly relevant information by considering the user's geographical location when providing information. For example, if the user lives in a specific region, the information provision unit will prioritize providing information related to that region. For example, if the user is traveling, the information provision unit can also provide appropriate information based on the user's current location. For example, if the user is interested in a specific city, the information provision unit can also prioritize providing information related to that city. In this way, highly relevant information can be provided by considering the user's geographical location. Some or all of the above processing in the information provision unit is performed using a generating AI. For example, the information provision unit inputs the user's geographical location information into the generating AI, and the generating AI prioritizes providing highly relevant information.

[0095] The information provision unit can analyze a user's social media activity and provide relevant information when providing information. For example, the information provision unit can provide relevant information based on hobbies and activities that the user frequently mentions on social media. For example, the information provision unit can also analyze a user's social media friendships and provide information about people who share common hobbies and interests. For example, the information provision unit can analyze the content of a user's social media posts and provide relevant information. In this way, by analyzing social media activity, it is possible to provide information relevant to the user. Some or all of the above processing in the information provision unit is performed using a generative AI. For example, the information provision unit inputs data on the user's social media activity into the generative AI, and the generative AI provides relevant information.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] A dating matching system can estimate a user's emotions and dynamically adjust dating suggestions based on those emotions. For example, if a user is nervous, it can prioritize suggesting someone with relaxing hobbies. If a user is excited, it can suggest someone with active hobbies. If a user is depressed, it can suggest someone with high empathy. This improves user satisfaction by suggesting the most suitable dating partner according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the search unit is performed using generative AI. For example, the search unit inputs user emotion data into the generative AI, which estimates the emotions and adjusts dating suggestions.

[0098] The dating matching system can analyze a user's past dating history and suggest the most suitable date plan. For example, it can suggest relevant date plans based on places the user has visited and events they have attended. It can also prioritize suggesting date plans the user has enjoyed in the past, and exclude date plans the user has avoided in the past. This allows the system to improve the user's dating experience by suggesting the most suitable date plan based on their past dating history. Some or all of the above processing in the suggestion section is performed using a generative AI. For example, the suggestion section inputs the user's past dating history into the generative AI, which then suggests the most suitable date plan.

[0099] A dating matching system can estimate a user's emotions and suggest a suitable date based on those emotions. For example, if a user is stressed, it can suggest a time to relax. If a user is energetic, it can suggest an active date. If a user is tired, it can suggest a time to rest. By suggesting the optimal date timing according to the user's emotions, the success rate of dates can be increased. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotion data into the generative AI, which estimates the emotion and suggests a date timing.

[0100] The dating matching system can customize dating partner suggestions based on the user's current lifestyle and interests. For example, it can suggest relevant dating partners based on the user's recent hobbies and activities. It can also suggest suitable dating partners based on the user's current occupation and lifestyle. It can even analyze the user's social media activity and suggest relevant dating partners. By customizing dating partner suggestions based on the user's lifestyle and interests, it can improve user satisfaction. Some or all of the above processing in the suggestion section is performed using a generative AI. For example, the suggestion section inputs data on the user's lifestyle and interests into the generative AI, which then customizes the dating partner suggestions.

[0101] A dating matching system can estimate a user's emotions and dynamically change how dating profiles are displayed based on those emotions. For example, if a user is nervous, a simple and highly visible profile display method can be provided. If a user is relaxed, a profile display method including detailed information can be provided. If a user is in a hurry, a profile display method that gets straight to the point can be provided. This allows the system to provide the user with the most relevant information by changing the profile display method according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display unit is performed using generative AI. For example, the display unit inputs user emotion data into the generative AI, which estimates the emotion and changes the profile display method.

[0102] The dating matching system can suggest dating partners while considering the user's geographical location. For example, if a user lives in a specific area, it can prioritize suggesting dating partners related to that area. If a user is traveling, it can also suggest suitable dating partners based on their current location. If a user is interested in a specific city, it can also prioritize suggesting dating partners related to that city. In this way, by considering the user's geographical location, it can provide highly relevant dating partners. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's geographical location information into the generative AI, which then suggests highly relevant dating partners.

[0103] A dating matching system can estimate a user's emotions and dynamically adjust the level of detail in a dating partner's profile information based on those emotions. For example, if the user is relaxed, detailed profile information can be provided. If the user is in a hurry, concise profile information can be provided. If the user is excited, visually appealing profile information can be provided. This allows the system to provide the user with the most relevant information by adjusting the level of detail in the profile information according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the information provision unit is performed using generative AI. For example, the information provision unit inputs the user's emotion data into the generative AI, which estimates the emotion and adjusts the level of detail in the profile information.

[0104] The dating matching system can analyze a user's social media activity and suggest relevant dating partners. For example, it can suggest relevant dating partners based on hobbies and activities that the user frequently mentions on social media. It can also analyze a user's social media friendships and suggest partners with similar hobbies and interests. It can also analyze the content of a user's social media posts and suggest relevant dating partners. In this way, by analyzing social media activity, it can provide users with relevant dating partners. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs data on the user's social media activity into the generative AI, which then suggests relevant dating partners.

[0105] The dating matching system can estimate the user's emotions and dynamically change the order of suggested dating partners based on those emotions. For example, if the user is in a hurry, the most suitable dating partner will be displayed at the top. If the user is relaxed, a wider range of dating partners may be displayed. If the user is excited, visually attractive dating partners may be displayed at the top. This allows the system to provide the user with the best possible dating partner by changing the order of suggested partners according to their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit is performed using generative AI. For example, the suggestion unit inputs the user's emotion data into the generative AI, which estimates the emotions and changes the order of suggested dating partners.

[0106] The dating matching system can suggest the most suitable date by referring to the user's past selection history. For example, it can prioritize suggesting relevant date partners based on the conditions the user has previously selected. It can also predict and suggest conditions to be used during specific time periods based on the user's past selection history. It can also analyze the user's past selection history and suggest the most suitable date partner. This improves the user's selection efficiency by suggesting the most suitable date partner based on past selection history. Some or all of the above processing in the suggestion unit is performed using a generative AI. For example, the suggestion unit inputs the user's past selection history into the generative AI, which then suggests the most suitable date partner.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The reception desk inputs the user's desired criteria for a date. These criteria may include, for example, age, hobbies, occupation, and place of residence. The reception desk receives these criteria from the user. Step 2: The search unit searches for a date based on the conditions entered by the reception unit. The search unit extracts candidates that match the conditions from a vast database and uses a generation AI to find the best candidate. Step 3: The presentation unit presents the candidates found by the search unit. The presentation unit displays the search results to the user and presents the search results to the user using a generation AI. Step 4: The Information Provision Department provides detailed information about the candidates presented by the Presentation Department. The Information Provision Department provides information such as the candidate's profile, hobbies, and past dating experience, and uses a generative AI to provide detailed information.

[0109] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0110] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0111] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0112] Each of the multiple elements described above, including the reception unit, search unit, presentation unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives conditions entered by the user. The search unit is implemented by the identification processing unit 290 of the data processing unit 12 and searches for candidates that match the conditions from a vast database. The presentation unit is implemented by the output device 40 of the smart device 14 and displays the search results to the user. The information provision unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides detailed information about the candidates. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0119] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0120] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0121] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0122] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0123] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0125] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0127] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the reception unit, search unit, presentation unit, and information provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the conditions entered by the user. The search unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and searches for candidates that match the conditions from a vast database. The presentation unit is implemented, for example, by the speaker 240 of the smart glasses 214 and presents the search results to the user. The information provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides detailed information about the candidates. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0136] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the reception unit, search unit, presentation unit, and information provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives conditions entered by the user. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for candidates that match the conditions from a vast database. The presentation unit is implemented by, for example, the display 343 of the headset terminal 314 and displays the search results to the user. The information provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides detailed information about the candidates. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] As shown in Figure 7, the 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0152] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0153] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the reception unit, search unit, presentation unit, and information provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives conditions entered by the user. The search unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and searches for candidates that match the conditions from a vast database. The presentation unit is implemented by, for example, the speaker 240 of the robot 414 and presents the search results to the user. The information provision unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides detailed information about the candidates. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0164] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0165] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0166] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0170] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0171] 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.

[0172] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0173] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0174] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0175] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0177] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0178] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0179] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0180] (Note 1) A reception desk where users input the criteria for their desired date, A search unit that searches for a date based on the conditions entered by the reception unit, A presentation unit that presents candidates found by the search unit, The system includes an information providing unit that provides detailed information about the candidates presented by the presentation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system accepts user-provided information such as age, hobbies, occupation, and place of residence. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned search unit, Based on the conditions entered by the reception unit, candidates that match the conditions are extracted from a vast database. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, The search unit presents the search results to the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned information provision unit, The aforementioned display unit provides detailed information such as the profile, hobbies, and past dating experience of the candidates presented. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is As you type, the system automatically completes input suggestions based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of input fields based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When you input text, the system prioritizes displaying the most relevant input suggestions, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is As you input data, the system analyzes your social media activity and suggests relevant input options. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, It estimates the user's emotions and dynamically adjusts the search algorithm parameters based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When a user searches, the system provides the most relevant search results by referencing their past search history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned search unit, When searching, the search results are filtered based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned search unit, It estimates the user's emotions and dynamically changes the display order of search results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned search unit, When searching, the system prioritizes displaying more relevant search results by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned search unit, When a user searches, the system analyzes their social media activity and provides relevant search results. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned display unit is, It estimates the user's emotions and dynamically changes how suggested options are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned display unit is, When presenting options, the system prioritizes displaying the most suitable candidates by referencing the user's past selection history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is, When presenting options, the system filters candidates based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is, It estimates the user's emotions and dynamically changes the priority of the suggested options based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned display unit is, When presenting suggestions, the system prioritizes displaying the most relevant options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, When presenting suggestions, the system analyzes the user's social media activity and proposes relevant options. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned information provision unit, It estimates the user's emotions and dynamically adjusts the level of detail of the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned information provision unit, When providing information, we refer to the user's past browsing history to provide the most relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned information provision unit, When providing information, customize the information based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned information provision unit, It estimates the user's emotions and dynamically changes how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned information provision unit, When providing information, we prioritize providing highly relevant information by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned information provision unit, When providing information, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users input the criteria for their desired date, A search unit that searches for a date based on the conditions entered by the reception unit, A presentation unit that presents candidates found by the search unit, The system includes an information providing unit that provides detailed information about the candidates presented by the presentation unit. A system characterized by the following features.

2. The aforementioned reception unit is The system accepts user-provided information such as age, hobbies, occupation, and place of residence. The system according to feature 1.

3. The aforementioned search unit, Based on the conditions entered by the reception unit, candidates that match the conditions are extracted from a vast database. The system according to feature 1.

4. The aforementioned display unit is, The search unit presents the search results to the user. The system according to feature 1.

5. The aforementioned information provision unit, The aforementioned display unit provides detailed information such as the profile, hobbies, and past dating experience of the candidates presented. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the design of the input interface based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is As you type, the system automatically completes input suggestions based on the user's current lifestyle and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of input fields based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When you input text, the system prioritizes displaying the most relevant input suggestions, taking into account the user's geographical location. The system according to feature 1.

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