system

Abstract

The system according to the embodiment aims to efficiently select a compatible partner based on the user's preferences and conditions. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives keywords from the user. The analysis unit analyzes the keywords received by the reception unit. The selection unit selects compatible partners based on the results of the analysis performed by the analysis unit. The presentation unit presents the partners selected by the selection unit to the user.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have not been able to efficiently select compatible partners based on the user's preferences and conditions, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently select a compatible partner based on the user's preferences and conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives keywords from a user. The analysis unit analyzes the keywords received by the reception unit. The selection unit selects a compatible partner based on the analysis results by the analysis unit. The presentation unit presents the partner selected by the selection unit to the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select a compatible partner based on the user's preferences and conditions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The matching system according to an embodiment of the present invention is a system in which a user inputs their likes and dislikes as keywords, and a generating AI analyzes those keywords to select a more compatible partner from among the matching partners. This matching system allows users to be matched with partners who are more compatible in terms of personality type by having the user input their likes and dislikes as keywords, and the generating AI analyzing those keywords to select a more compatible partner from among the matching partners. For example, a user inputs keywords such as "I like movies" or "I dislike sports." These keywords are input into the generating AI. Next, the generating AI analyzes the input keywords. Based on the user's keywords, the generating AI analyzes the profile information of matching partners and selects compatible partners. For example, if a user inputs "I like movies," the generating AI will prioritize selecting partners who have information about movies. The partners selected by the generating AI are presented to the user. The user can further narrow down the presented partners by setting more detailed conditions. For example, by setting conditions such as age and place of residence, a more specific partner can be selected. Through this mechanism, users can be matched with partners who are more compatible in terms of personality type. For example, matching users with shared hobbies and interests can lead to more engaging conversations and deeper relationships. Furthermore, the AI ​​can analyze keywords to identify compatible partners that the user might not have even considered. This allows the matching system to select and present compatible partners based on the user's keywords.

[0029] The matching system according to this embodiment comprises a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives keywords from the user. Keywords from the user include, for example, things they like and dislike, but are not limited to such examples. The reception unit can receive keywords such as "I like movies" and "I dislike sports" entered by the user. The analysis unit analyzes the keywords received by the reception unit using a generation AI. The generation AI analyzes keywords using technologies such as GPT-4 (registered trademark) or Gemini. The analysis unit analyzes the profile information of potential matches based on the keywords entered by the user. The selection unit selects compatible partners based on the results analyzed by the analysis unit. The selection unit analyzes the profile information of potential matches using, for example, a generation AI and selects compatible partners. The presentation unit presents the partners selected by the selection unit to the user. The presentation unit can present the partners selected by the generation AI to the user. As a result, the matching system according to the embodiment can select and present compatible partners based on the user's keywords.

[0030] The analysis unit can analyze keywords using generative AI. Generative AI includes, but is not limited to, GPT-4 and Gemini. For example, the analysis unit inputs keywords entered by the user, such as "I like movies" or "I hate sports," into the generative AI and performs the analysis. The generative AI, for example, analyzes the meaning of the keywords and extracts related information. The generative AI can also analyze the sentiment of the keywords and estimate the user's preferences. For example, from the keyword "I like movies," the generative AI estimates that the user is interested in movies and selects people who have information related to movies. As a result, the accuracy of keyword analysis is improved by using generative AI.

[0031] The selection unit can use the generation AI to analyze the profile information of the matched partner and select a compatible partner. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The selection unit inputs the profile information of the matched partner into the generation AI and performs analysis, for example, based on keywords entered by the user. The generation AI can, for example, analyze the text of the profile information and select a partner whose information matches the user's keywords. The generation AI can also analyze the emotions in the profile information and select a partner whose preferences match the user's preferences. For example, based on the keyword "I like movies," the generation AI can prioritize selecting a partner who has information about movies. This improves the accuracy of selecting a compatible partner by using the generation AI.

[0032] The presentation unit may include a condition setting unit that allows the user to set detailed conditions to narrow down the search candidates. The condition setting unit may set conditions such as age, place of residence, occupation, and hobbies. For example, the user may set an age range to narrow down the search candidates to a specific age range. The user may also set a place of residence to narrow down the search candidates to those living in a specific area. The user may also set an occupation or hobby to narrow down the search candidates to those with a specific occupation or hobby. For example, the user may set conditions such as "in their 20s," "living in Tokyo," "engineer," and "movie lover" to select a more specific partner. This allows the user to set detailed conditions to narrow down the search candidates.

[0033] The condition setting section allows users to set conditions for age, place of residence, occupation, and hobbies. Age conditions include, for example, those in their 20s, 30s, and 40s, but are not limited to these examples. Place of residence conditions include, for example, city names and region names, but are not limited to these examples. Occupation conditions include, for example, engineers, designers, and teachers, but are not limited to these examples. Hobby conditions include, for example, sports, music, and reading, but are not limited to these examples. For example, a user can set an age condition to select a partner in their 20s. A user can also set a place of residence condition to select a partner living in Tokyo. Furthermore, a user can set an occupation condition to select a partner who is an engineer. This allows users to narrow down their search by setting detailed conditions.

[0034] The presentation unit can present users with potential matches selected by a generative AI. Generative AIs include, but are not limited to, GPT-4 and Gemini. The presentation unit, for example, presents users with potential matches selected by a generative AI. For example, based on keywords entered by the user, the presentation unit displays profile information of potential matches selected by the generative AI. The user can review the presented profile information and refine their search by setting detailed criteria. For example, if the user enters the keyword "likes movies," the presentation unit displays profile information of potential matches with information related to movies. This allows the presentation unit to show users potential matches selected by a generative AI.

[0035] The reception desk can analyze a user's past keyword input history and select an appropriate input method. For example, the reception desk can store and analyze the user's past keyword input history in a database. Past keyword input history includes, but is not limited to, the date and time of input, frequency, and content. For example, the reception desk can automatically display keywords that the user has frequently used 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. Furthermore, the reception desk can predict and suggest keywords that the user will use during specific time periods based on their past input history. This improves input efficiency by selecting the optimal input method based on the user's past input history.

[0036] The input system can suggest keywords based on the user's current interests and trends when they enter keywords. For example, the input system can analyze the user's recent search history and social media posts to estimate their current interests. Current interests include, but are not limited to, recent search topics and social media activity. For example, the input system can suggest relevant keywords based on topics the user has recently searched for. The input system can also suggest popular keywords based on current trend information. Furthermore, the input system can analyze the user's social media activity and suggest keywords of interest. This allows for more appropriate keyword input by suggesting keywords based on the user's interests and trends.

[0037] When a keyword is input, the reception unit can prioritize and present highly relevant keywords based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and presents highly relevant keywords. Geographical location information includes, but is not limited to, a current location, past location information, travel destinations, etc. For example, the reception unit prioritizes and displays keywords related to the user's current location. The reception unit can also suggest related keywords based on the user's past location information. Furthermore, if the user is traveling, the reception unit can also prioritize and display keywords related to the travel destination. This allows for more appropriate keyword input by presenting highly relevant keywords based on the user's geographical location information.

[0038] The reception unit can analyze the user's social media activity and present related keywords when the user inputs keywords. The reception unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can present related keywords based on the content recently posted by the user. The reception unit can also suggest keywords based on the topics of accounts the user follows. Furthermore, the reception unit can analyze the user's social media activity history and present keywords of interest. This allows the user to input more appropriate keywords by presenting related keywords based on the user's social media activity.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of keywords during the analysis. For example, the analysis unit evaluates importance based on the frequency and relevance of keywords. Keyword importance includes, but is not limited to, frequency, relevance, and user interest. For example, the analysis unit performs a detailed analysis for high-importance keywords to provide highly accurate results. Conversely, the analysis unit can perform a simplified analysis for low-importance keywords to provide results quickly. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of keywords. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on keyword importance.

[0040] The analysis unit can apply different analysis algorithms depending on the keyword category during analysis. For example, the analysis unit classifies keyword categories into technology, business, entertainment, etc. Keyword categories include, but are not limited to, hobbies, personality, and values. For example, for keywords related to hobbies, the analysis unit can apply an algorithm that prioritizes analyzing people with the same hobby. Furthermore, for keywords related to personality, the analysis unit can apply an algorithm that analyzes compatibility based on personality assessment results. In addition, for keywords related to values, the analysis unit can apply an algorithm that analyzes the degree of shared values. By applying different analysis algorithms depending on the keyword category, more appropriate analysis becomes possible.

[0041] The analysis unit can determine the priority of analysis based on the submission date of keywords during the analysis process. For example, the analysis unit evaluates priority based on the submission date and frequency of keywords. Keyword submission dates include, but are not limited to, recently submitted keywords and frequently submitted keywords. For example, the analysis unit prioritizes the analysis of recently submitted keywords to provide the latest information. The analysis unit can also lower the priority of older keywords. Furthermore, the analysis unit can appropriately allocate analysis resources according to the submission date. This allows for more appropriate analysis by determining the priority of analysis based on the submission date of keywords.

[0042] The analysis unit can adjust the order of analysis based on the relevance of keywords during the analysis process. For example, the analysis unit evaluates relevance based on the co-occurrence frequency and semantic relevance of keywords. Keyword relevance includes, but is not limited to, co-occurrence frequency, semantic relevance, and user interest. For example, the analysis unit prioritizes the analysis of highly relevant keywords to provide highly accurate results. The analysis unit can also postpone the analysis of less relevant keywords. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of keywords. This allows for more appropriate analysis by adjusting the order of analysis based on keyword relevance.

[0043] The selection unit can improve the accuracy of its selection process by considering the relationships between potential matches. For example, the selection unit considers mutual friends and past interaction history between potential matches. Mutual relationships include, but are not limited to, mutual friends, shared hobbies, and past interactions. The selection unit can also consider mutual friends between potential matches. Furthermore, the selection unit can consider shared hobbies and interests between potential matches. In addition, the selection unit can consider past interaction history between potential matches. This improves the accuracy of the selection process by considering the relationships between potential matches.

[0044] The selection unit can make selections by considering the attribute information of potential matches. For example, the selection unit considers attribute information such as the age, gender, occupation, educational background, and hobbies of potential matches. Attribute information includes, but is not limited to, age, gender, occupation, educational background, and hobbies. For example, the selection unit can consider the age and gender of potential matches. The selection unit can also consider the occupation and educational background of potential matches. Furthermore, the selection unit can also consider the hobbies and interests of potential matches. By considering the attribute information of potential matches, a more suitable match can be selected.

[0045] The selection unit can consider the geographical distribution of potential matches when making a selection. For example, the selection unit considers the geographical distribution of potential matches. Geographical distribution includes, but is not limited to, cities, regions, and countries. For example, the selection unit can prioritize selecting matches who live near the user. Also, if the user is traveling, the selection unit can prioritize selecting matches who live in their travel destination. Furthermore, the selection unit can select matches in relevant regions based on the user's past travel history. In this way, a more appropriate match is selected by considering the geographical distribution of potential matches.

[0046] The selection process can improve the accuracy of the selection by referring to relevant literature related to the matching partner. For example, the selection process can refer to literature related to the matching partner's profile. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. The selection process can also refer to literature related to the matching partner's hobbies and interests. Furthermore, the selection process can refer to literature related to the matching partner's occupation and educational background. This improves the accuracy of the selection process by referring to relevant literature related to the matching partner.

[0047] The presentation unit can select the optimal presentation method by referring to the user's past selection history when making a presentation. For example, the presentation unit can store the user's past selection history in a database and perform analysis. Past selection history includes, but is not limited to, selection date and time and selection content. For example, the presentation unit can select the optimal presentation method based on the characteristics of the person the user has selected in the past. The presentation unit can also prioritize presenting relevant people based on the user's past selection history. Furthermore, the presentation unit can analyze the user's past selection history and select the most effective presentation method. As a result, by selecting the optimal presentation method based on the user's past selection history, more appropriate presentations become possible.

[0048] The presentation unit can customize the content presented based on the user's current interests. For example, the presentation unit can analyze the user's recent search history and social media posts to estimate current interests. Current interests include, but are not limited to, recent search topics and social media activity. For example, the presentation unit can present relevant people based on topics the user has recently searched for. It can also analyze the user's social media activity and present people of interest. Furthermore, the presentation unit can customize and present the most suitable people based on the user's current interests. This allows for more appropriate presentations by customizing the content based on the user's current interests.

[0049] The presentation unit can present the most suitable partner by taking into consideration the user's geographical location information. The presentation unit, for example, acquires the user's geographical location information from GPS data or an IP address and presents the most suitable partner. The geographical location information includes, but is not limited to, the user's current location, past location information, travel destination, etc. For example, the presentation unit can preferentially present partners close to the user's current location. Furthermore, if the user is traveling, the presentation unit can preferentially present partners who live in the travel destination. Furthermore, the presentation unit can also present partners in related areas based on the user's past location information. This allows for more appropriate presentation by presenting the most suitable partner based on the user's geographical location information.

[0050] When presenting a match, the presentation unit can analyze the user's social media activity and present the most suitable match. The presentation unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The presentation unit, for example, presents relevant matchmakers based on the content recently posted by the user. The presentation unit can also suggest matchmakers based on the topics of accounts the user follows. Furthermore, the presentation unit can analyze the user's social media activity history and present matchmakers of interest. This allows for more appropriate presentation by presenting the most suitable matchmakers based on the user's social media activity.

[0051] The condition setting unit can select the optimal setting method by referring to the user's past setting history when setting conditions. For example, the condition setting unit can store the user's past setting history in a database and perform analysis. Past setting history includes, but is not limited to, setting date and time and setting content. For example, the condition setting unit can suggest the optimal setting method based on the conditions the user has set in the past. The condition setting unit can also preferentially suggest relevant conditions from the user's past setting history. Furthermore, the condition setting unit can analyze the user's past setting history and select the most effective setting method. As a result, more appropriate settings can be made by selecting the optimal setting method based on the user's past setting history.

[0052] The condition setting unit can customize the settings based on the user's current living situation when setting conditions. For example, the condition setting unit stores and analyzes the user's current living situation in a database. Current living situation includes, but is not limited to, work situation and family situation. For example, if the user has recently moved, the condition setting unit can set conditions based on the new place of residence. Also, if the user has started a new job, the condition setting unit can set conditions related to the workplace. Furthermore, the condition setting unit can customize and set the optimal conditions based on the user's current living situation. This makes it possible to set more appropriate settings by customizing the settings based on the user's current living situation.

[0053] When setting conditions, the condition setting unit can select the optimal setting method taking into account the user's geographical location information. The condition setting unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects the optimal setting method. The geographical location information includes, but is not limited to, the user's current location, past location information, travel destination, etc. The condition setting unit sets the optimal conditions based on the user's current location, for example. Furthermore, if the user is traveling, the condition setting unit can set conditions related to the travel destination. Furthermore, the condition setting unit can also set related conditions based on the user's past location information. This enables more appropriate settings by selecting the optimal setting method based on the user's geographical location information.

[0054] When setting conditions, the condition setting unit can analyze the user's social media activity and suggest an optimal setting method. The condition setting unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The condition setting unit can set relevant conditions based on, for example, the content recently posted by the user. The condition setting unit can also suggest conditions based on the topics of accounts the user follows. Furthermore, the condition setting unit can analyze the user's social media activity history and set conditions of interest. This allows for more appropriate settings by suggesting an optimal setting method based on the user's social media activity.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The reception unit can analyze the user's past matching history and extract characteristics of partners that the user tends to prefer. For example, it collects data such as the age group, hobbies, and occupations of previously matched partners to create a profile of partners that the user tends to prefer. The reception unit can also analyze the content of the user's interactions with previously matched partners to understand what topics the user is interested in. Furthermore, the reception unit can track the progress of the user's relationships with previously matched partners and identify the characteristics of successful matches. This makes it possible to select more suitable partners based on the user's past matching history.

[0057] The analysis unit can analyze images and videos related to keywords entered by the user and select a compatible partner based on visual information. For example, if a user enters "I like movies," the analysis unit can analyze movie posters and trailers to select partners who are interested in similar movies. If a user enters "I don't like sports," the analysis unit can analyze images and videos related to sports and select partners who are not interested in sports. Furthermore, the analysis unit can analyze photos and videos uploaded by the user to visually grasp the user's hobbies and interests. This allows for more accurate matching by selecting a compatible partner based on visual information.

[0058] The reception unit can present input candidates based on the user's current interests and trends. For example, it can analyze the user's recent search history and social media posts to estimate the user's current interests. It can present related keywords based on topics the user has recently searched for. It can also suggest popular keywords based on current trend information. It can also analyze the user's social media activity to present keywords of interest. This allows for more appropriate keyword input by presenting input candidates based on the user's interests and trends.

[0059] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the keywords. For example, the importance is evaluated based on the frequency of keyword appearance and relevance. A detailed analysis is performed on keywords with high importance, providing highly accurate results. A simplified analysis is performed on keywords with low importance, allowing for quick results. Furthermore, analysis resources can be appropriately allocated according to the importance of the keywords. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the keywords.

[0060] The selection unit can improve the accuracy of selection by taking into account the mutual relationships between matched partners during selection. For example, the selection is made by taking into account mutual friends and past interaction history between the matched partners. By taking into account mutual friends during selection, it is possible to select a partner with high reliability. Furthermore, by taking into account common hobbies and interests during selection, it is possible to select a partner with whom there are many common topics to talk about. Furthermore, by taking into account past interaction history during selection, it is possible to select a partner with good compatibility. Thus, by taking into account the mutual relationships between matched partners, the accuracy of selection is improved.

[0061] When presenting a match, the presentation unit can present the most suitable match by taking into consideration the user's geographical location information. For example, the presentation unit can obtain the user's geographical location information from GPS data or IP address and present the most suitable match. By preferentially presenting matches close to the user's current location, the user can select a match that is easy to meet in person. Also, if the user is traveling, it can preferentially present matches who live in the user's travel destination. Furthermore, it can also present matches in related areas based on the user's past location information. This allows for more appropriate presentation by presenting the most suitable match based on the user's geographical location information.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The reception unit receives keywords from the user. Keywords from the user include, but are not limited to, likes and dislikes. For example, the reception unit can receive keywords such as "I like movies" or "I dislike sports" input by the user. Step 2: The analysis unit uses the generation AI to analyze the keywords received by the reception unit. The generation AI analyzes the keywords using technologies such as text generation AI (e.g., GPT-4) or Gemini. The analysis unit analyzes the profile information of the matched partner based on the keywords entered by the user. Step 3: The selection unit selects compatible partners based on the results analyzed by the analysis unit. For example, the selection unit uses a generation AI to analyze the profile information of potential matches and select compatible partners. Step 4: The presentation unit presents the user with the partner selected by the selection unit. The presentation unit can, for example, present the user with a partner selected by a generation AI.

[0064] (Example 2) The matching system according to an embodiment of the present invention is a system in which a user inputs their likes and dislikes as keywords, and a generating AI analyzes those keywords to select a more compatible partner from among the matching partners. This matching system allows users to be matched with partners who are more compatible in terms of personality type by having the user input their likes and dislikes as keywords, and the generating AI analyzing those keywords to select a more compatible partner from among the matching partners. For example, a user inputs keywords such as "I like movies" or "I dislike sports." These keywords are input into the generating AI. Next, the generating AI analyzes the input keywords. Based on the user's keywords, the generating AI analyzes the profile information of matching partners and selects compatible partners. For example, if a user inputs "I like movies," the generating AI will prioritize selecting partners who have information about movies. The partners selected by the generating AI are presented to the user. The user can further narrow down the presented partners by setting more detailed conditions. For example, by setting conditions such as age and place of residence, a more specific partner can be selected. Through this mechanism, users can be matched with partners who are more compatible in terms of personality type. For example, matching users with shared hobbies and interests can lead to more engaging conversations and deeper relationships. Furthermore, the AI ​​can analyze keywords to identify compatible partners that the user might not have even considered. This allows the matching system to select and present compatible partners based on the user's keywords.

[0065] A matching system according to an embodiment includes a reception unit, an analysis unit, a selection unit, and a presentation unit. The reception unit receives keywords from a user. Keywords from the user include, but are not limited to, likes and dislikes. For example, the reception unit can receive keywords such as "I like movies" and "I dislike sports" input by the user. The analysis unit uses a generation AI to analyze the keywords received by the reception unit. The generation AI analyzes the keywords using technologies such as GPT-4 and Gemini. The analysis unit analyzes profile information of a matched partner based on the keywords input by the user. The selection unit selects a compatible partner based on the analysis results by the analysis unit. The selection unit analyzes profile information of a matched partner using, for example, the generation AI and selects a compatible partner. The presentation unit presents the partner selected by the selection unit to the user. For example, the presentation unit can present the partner selected by the generation AI to the user. As a result, the matching system according to an embodiment can select and present compatible partners based on the user's keywords.

[0066] The analysis unit can analyze keywords using generative AI. Generative AI includes, but is not limited to, GPT-4 and Gemini. For example, the analysis unit inputs keywords entered by the user, such as "I like movies" or "I hate sports," into the generative AI and performs the analysis. The generative AI, for example, analyzes the meaning of the keywords and extracts related information. The generative AI can also analyze the sentiment of the keywords and estimate the user's preferences. For example, from the keyword "I like movies," the generative AI estimates that the user is interested in movies and selects people who have information related to movies. As a result, the accuracy of keyword analysis is improved by using generative AI.

[0067] The selection unit can use the generation AI to analyze the profile information of the matched partner and select a compatible partner. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. The selection unit inputs the profile information of the matched partner into the generation AI and performs analysis, for example, based on keywords entered by the user. The generation AI can, for example, analyze the text of the profile information and select a partner whose information matches the user's keywords. The generation AI can also analyze the emotions in the profile information and select a partner whose preferences match the user's preferences. For example, based on the keyword "I like movies," the generation AI can prioritize selecting a partner who has information about movies. This improves the accuracy of selecting a compatible partner by using the generation AI.

[0068] The presentation unit may include a condition setting unit that allows the user to set detailed conditions to narrow down the search candidates. The condition setting unit may set conditions such as age, place of residence, occupation, and hobbies. For example, the user may set an age range to narrow down the search candidates to a specific age range. The user may also set a place of residence to narrow down the search candidates to those living in a specific area. The user may also set an occupation or hobby to narrow down the search candidates to those with a specific occupation or hobby. For example, the user may set conditions such as "in their 20s," "living in Tokyo," "engineer," and "movie lover" to select a more specific partner. This allows the user to set detailed conditions to narrow down the search candidates.

[0069] The condition setting section allows users to set conditions for age, place of residence, occupation, and hobbies. Age conditions include, for example, those in their 20s, 30s, and 40s, but are not limited to these examples. Place of residence conditions include, for example, city names and region names, but are not limited to these examples. Occupation conditions include, for example, engineers, designers, and teachers, but are not limited to these examples. Hobby conditions include, for example, sports, music, and reading, but are not limited to these examples. For example, a user can set an age condition to select a partner in their 20s. A user can also set a place of residence condition to select a partner living in Tokyo. Furthermore, a user can set an occupation condition to select a partner who is an engineer. This allows users to narrow down their search by setting detailed conditions.

[0070] The presentation unit can present users with potential matches selected by a generative AI. Generative AIs include, but are not limited to, GPT-4 and Gemini. The presentation unit, for example, presents users with potential matches selected by a generative AI. For example, based on keywords entered by the user, the presentation unit displays profile information of potential matches selected by the generative AI. The user can review the presented profile information and refine their search by setting detailed criteria. For example, if the user enters the keyword "likes movies," the presentation unit displays profile information of potential matches with information related to movies. This allows the presentation unit to show users potential matches selected by a generative AI.

[0071] The reception unit can estimate the user's emotions and adjust the timing of keyword input based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, if the user is relaxed, the reception unit displays the input screen slowly to allow the user to input calmly. Furthermore, if the user is in a hurry, the reception unit displays the input screen quickly to allow the user to complete input in a short time. Furthermore, if the user is feeling stressed, the reception unit simplifies the input screen and minimizes the input steps. This allows more appropriate input by adjusting the timing of keyword input according to the user's emotions.

[0072] The reception desk can analyze a user's past keyword input history and select an appropriate input method. For example, the reception desk can store and analyze the user's past keyword input history in a database. Past keyword input history includes, but is not limited to, the date and time of input, frequency, and content. For example, the reception desk can automatically display keywords that the user has frequently used 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. Furthermore, the reception desk can predict and suggest keywords that the user will use during specific time periods based on their past input history. This improves input efficiency by selecting the optimal input method based on the user's past input history.

[0073] The input system can suggest keywords based on the user's current interests and trends when they enter keywords. For example, the input system can analyze the user's recent search history and social media posts to estimate their current interests. Current interests include, but are not limited to, recent search topics and social media activity. For example, the input system can suggest relevant keywords based on topics the user has recently searched for. The input system can also suggest popular keywords based on current trend information. Furthermore, the input system can analyze the user's social media activity and suggest keywords of interest. This allows for more appropriate keyword input by suggesting keywords based on the user's interests and trends.

[0074] The reception unit can estimate the user's emotions and determine the priority of input keywords based on the estimated user emotions. The reception unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, if the user is excited, the reception unit can prioritize and display highly relevant keywords. Furthermore, if the user is relaxed, the reception unit can present a wide range of keywords to increase the options. Furthermore, if the user is stressed, the reception unit can prioritize and display simple and intuitively understandable keywords. In this way, by prioritizing keywords according to the user's emotions, more appropriate keywords can be selected.

[0075] When a keyword is input, the reception unit can prioritize and present highly relevant keywords based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and presents highly relevant keywords. Geographical location information includes, but is not limited to, a current location, past location information, travel destinations, etc. For example, the reception unit prioritizes and displays keywords related to the user's current location. The reception unit can also suggest related keywords based on the user's past location information. Furthermore, if the user is traveling, the reception unit can also prioritize and display keywords related to the travel destination. This allows for more appropriate keyword input by presenting highly relevant keywords based on the user's geographical location information.

[0076] The reception unit can analyze the user's social media activity and present related keywords when the user inputs keywords. The reception unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit can present related keywords based on the content recently posted by the user. The reception unit can also suggest keywords based on the topics of accounts the user follows. Furthermore, the reception unit can analyze the user's social media activity history and present keywords of interest. This allows the user to input more appropriate keywords by presenting related keywords based on the user's social media activity.

[0077] The analysis unit can estimate the user's emotions and adjust the keyword analysis method based on the estimated user emotions. The analysis unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or generative AI (e.g., text generation AI or multimodal generation AI). For example, if the user is relaxed, the analysis unit can perform a detailed analysis and suggest a wide range of matching partners. Furthermore, if the user is in a hurry, the analysis unit can perform a quick analysis and prioritize suggesting the most relevant partners. Furthermore, if the user is feeling stressed, the analysis unit can perform a simple analysis and provide results that are easy to understand intuitively. This allows for more appropriate analysis by adjusting the keyword analysis method according to the user's emotions.

[0078] The analysis unit can adjust the level of detail of the analysis based on the importance of keywords during the analysis. For example, the analysis unit evaluates importance based on the frequency and relevance of keywords. Keyword importance includes, but is not limited to, frequency, relevance, and user interest. For example, the analysis unit performs a detailed analysis for high-importance keywords to provide highly accurate results. Conversely, the analysis unit can perform a simplified analysis for low-importance keywords to provide results quickly. Furthermore, the analysis unit can appropriately allocate analysis resources according to the importance of keywords. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on keyword importance.

[0079] The analysis unit can apply different analysis algorithms depending on the keyword category during analysis. For example, the analysis unit classifies keyword categories into technology, business, entertainment, etc. Keyword categories include, but are not limited to, hobbies, personality, and values. For example, for keywords related to hobbies, the analysis unit can apply an algorithm that prioritizes analyzing people with the same hobby. Furthermore, for keywords related to personality, the analysis unit can apply an algorithm that analyzes compatibility based on personality assessment results. In addition, for keywords related to values, the analysis unit can apply an algorithm that analyzes the degree of shared values. By applying different analysis algorithms depending on the keyword category, more appropriate analysis becomes possible.

[0080] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, the analysis unit can analyze the user's facial expressions and voice to estimate emotions. For emotion estimation, for example, an emotion engine or generative AI (e.g., text generation AI or multimodal generation AI) can be used. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible.

[0081] The analysis unit can determine the priority of analysis based on the submission date of keywords during the analysis process. For example, the analysis unit evaluates priority based on the submission date and frequency of keywords. Keyword submission dates include, but are not limited to, recently submitted keywords and frequently submitted keywords. For example, the analysis unit prioritizes the analysis of recently submitted keywords to provide the latest information. The analysis unit can also lower the priority of older keywords. Furthermore, the analysis unit can appropriately allocate analysis resources according to the submission date. This allows for more appropriate analysis by determining the priority of analysis based on the submission date of keywords.

[0082] The analysis unit can adjust the order of analysis based on the relevance of keywords during the analysis process. For example, the analysis unit evaluates relevance based on the co-occurrence frequency and semantic relevance of keywords. Keyword relevance includes, but is not limited to, co-occurrence frequency, semantic relevance, and user interest. For example, the analysis unit prioritizes the analysis of highly relevant keywords to provide highly accurate results. The analysis unit can also postpone the analysis of less relevant keywords. Furthermore, the analysis unit can appropriately allocate analysis resources according to the relevance of keywords. This allows for more appropriate analysis by adjusting the order of analysis based on keyword relevance.

[0083] The selection unit can estimate the user's emotions and adjust the selection criteria for compatible partners based on the estimated user emotions. The selection unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, when the user is relaxed, the selection unit applies broad selection criteria to suggest a variety of partners. Furthermore, when the user is in a hurry, the selection unit can apply carefully selected selection criteria to quickly suggest compatible partners. Furthermore, when the user is stressed, the selection unit can apply simple selection criteria to suggest partners that are easy to intuitively understand. In this way, by adjusting the selection criteria for compatible partners according to the user's emotions, a more suitable partner can be selected.

[0084] The selection unit can improve the accuracy of its selection process by considering the relationships between potential matches. For example, the selection unit considers mutual friends and past interaction history between potential matches. Mutual relationships include, but are not limited to, mutual friends, shared hobbies, and past interactions. The selection unit can also consider mutual friends between potential matches. Furthermore, the selection unit can consider shared hobbies and interests between potential matches. In addition, the selection unit can consider past interaction history between potential matches. This improves the accuracy of the selection process by considering the relationships between potential matches.

[0085] The selection unit can make selections by considering the attribute information of potential matches. For example, the selection unit considers attribute information such as the age, gender, occupation, educational background, and hobbies of potential matches. Attribute information includes, but is not limited to, age, gender, occupation, educational background, and hobbies. For example, the selection unit can consider the age and gender of potential matches. The selection unit can also consider the occupation and educational background of potential matches. Furthermore, the selection unit can also consider the hobbies and interests of potential matches. By considering the attribute information of potential matches, a more suitable match can be selected.

[0086] The selection unit can estimate the user's emotions and adjust the display order of the selection results based on the estimated user emotions. The selection unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, if the user is excited, the selection unit can prioritize displaying highly relevant people. Furthermore, if the user is relaxed, the selection unit can display a wide range of selection results. Furthermore, if the user is stressed, the selection unit can display simple and intuitively understandable selection results. This allows for more appropriate display by adjusting the display order of the selection results according to the user's emotions.

[0087] The selection unit can consider the geographical distribution of potential matches when making a selection. For example, the selection unit considers the geographical distribution of potential matches. Geographical distribution includes, but is not limited to, cities, regions, and countries. For example, the selection unit can prioritize selecting matches who live near the user. Also, if the user is traveling, the selection unit can prioritize selecting matches who live in their travel destination. Furthermore, the selection unit can select matches in relevant regions based on the user's past travel history. In this way, a more appropriate match is selected by considering the geographical distribution of potential matches.

[0088] The selection process can improve the accuracy of the selection by referring to relevant literature related to the matching partner. For example, the selection process can refer to literature related to the matching partner's profile. Relevant literature includes, but is not limited to, academic papers, patent documents, and technical reports. The selection process can also refer to literature related to the matching partner's hobbies and interests. Furthermore, the selection process can refer to literature related to the matching partner's occupation and educational background. This improves the accuracy of the selection process by referring to relevant literature related to the matching partner.

[0089] The presentation unit can estimate the user's emotions and adjust the presentation method of the other party based on the estimated user emotions. The presentation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, if the user is relaxed, the presentation unit can provide a presentation method that includes detailed profile information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise presentation method that focuses on the main points. Furthermore, if the user is feeling stressed, the presentation unit can provide a simple and intuitively understandable presentation method. This enables more appropriate presentation by adjusting the presentation method of the other party according to the user's emotions.

[0090] The presentation unit can select the optimal presentation method by referring to the user's past selection history when making a presentation. For example, the presentation unit can store the user's past selection history in a database and perform analysis. Past selection history includes, but is not limited to, selection date and time and selection content. For example, the presentation unit can select the optimal presentation method based on the characteristics of the person the user has selected in the past. The presentation unit can also prioritize presenting relevant people based on the user's past selection history. Furthermore, the presentation unit can analyze the user's past selection history and select the most effective presentation method. As a result, by selecting the optimal presentation method based on the user's past selection history, more appropriate presentations become possible.

[0091] The presentation unit can customize the content presented based on the user's current interests. For example, the presentation unit can analyze the user's recent search history and social media posts to estimate current interests. Current interests include, but are not limited to, recent search topics and social media activity. For example, the presentation unit can present relevant people based on topics the user has recently searched for. It can also analyze the user's social media activity and present people of interest. Furthermore, the presentation unit can customize and present the most suitable people based on the user's current interests. This allows for more appropriate presentations by customizing the content based on the user's current interests.

[0092] The presentation unit can estimate the user's emotions and determine the priority of the people to be presented based on the estimated user emotions. The presentation unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, if the user is excited, the presentation unit can prioritize and display people who are highly relevant. Furthermore, if the user is relaxed, the presentation unit can display a wide range of selection results. Furthermore, if the user is feeling stressed, the presentation unit can display simple and intuitively understandable selection results. This enables more appropriate presentation by determining the priority of people to be presented according to the user's emotions.

[0093] The presentation unit can present the most suitable partner by taking into consideration the user's geographical location information. The presentation unit, for example, acquires the user's geographical location information from GPS data or an IP address and presents the most suitable partner. The geographical location information includes, but is not limited to, the user's current location, past location information, travel destination, etc. For example, the presentation unit can preferentially present partners close to the user's current location. Furthermore, if the user is traveling, the presentation unit can preferentially present partners who live in the travel destination. Furthermore, the presentation unit can also present partners in related areas based on the user's past location information. This allows for more appropriate presentation by presenting the most suitable partner based on the user's geographical location information.

[0094] When presenting a match, the presentation unit can analyze the user's social media activity and present the most suitable match. The presentation unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The presentation unit, for example, presents relevant matchmakers based on the content recently posted by the user. The presentation unit can also suggest matchmakers based on the topics of accounts the user follows. Furthermore, the presentation unit can analyze the user's social media activity history and present matchmakers of interest. This allows for more appropriate presentation by presenting the most suitable matchmakers based on the user's social media activity.

[0095] The condition setting unit can estimate the user's emotions and adjust the method of setting conditions based on the estimated user emotions. The condition setting unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the condition setting unit can provide detailed condition setting options when the user is relaxed. Furthermore, the condition setting unit can provide simple condition setting options when the user is in a hurry. Furthermore, the condition setting unit can provide simple and intuitively understandable condition setting options when the user is stressed. This allows for more appropriate settings by adjusting the method of setting conditions according to the user's emotions.

[0096] The condition setting unit can select the optimal setting method by referring to the user's past setting history when setting conditions. For example, the condition setting unit can store the user's past setting history in a database and perform analysis. Past setting history includes, but is not limited to, setting date and time and setting content. For example, the condition setting unit can suggest the optimal setting method based on the conditions the user has set in the past. The condition setting unit can also preferentially suggest relevant conditions from the user's past setting history. Furthermore, the condition setting unit can analyze the user's past setting history and select the most effective setting method. As a result, more appropriate settings can be made by selecting the optimal setting method based on the user's past setting history.

[0097] The condition setting unit can customize the settings based on the user's current living situation when setting conditions. For example, the condition setting unit stores and analyzes the user's current living situation in a database. Current living situation includes, but is not limited to, work situation and family situation. For example, if the user has recently moved, the condition setting unit can set conditions based on the new place of residence. Also, if the user has started a new job, the condition setting unit can set conditions related to the workplace. Furthermore, the condition setting unit can customize and set the optimal conditions based on the user's current living situation. This makes it possible to set more appropriate settings by customizing the settings based on the user's current living situation.

[0098] The condition setting unit can estimate the user's emotions and determine the priority of condition settings based on the estimated user emotions. The condition setting unit, for example, analyzes the user's facial expressions and voice to estimate emotions. Emotion estimation can use, for example, an emotion engine or a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, when the user is excited, the condition setting unit can prioritize highly relevant conditions. Furthermore, when the user is relaxed, the condition setting unit can also prioritize simple and intuitively understandable conditions when the user is stressed. This allows for more appropriate settings by determining the priority of condition settings according to the user's emotions.

[0099] When setting conditions, the condition setting unit can select the optimal setting method taking into account the user's geographical location information. The condition setting unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects the optimal setting method. The geographical location information includes, but is not limited to, the user's current location, past location information, travel destination, etc. The condition setting unit sets the optimal conditions based on the user's current location, for example. Furthermore, if the user is traveling, the condition setting unit can set conditions related to the travel destination. Furthermore, the condition setting unit can also set related conditions based on the user's past location information. This enables more appropriate settings by selecting the optimal setting method based on the user's geographical location information.

[0100] When setting conditions, the condition setting unit can analyze the user's social media activity and suggest an optimal setting method. The condition setting unit, for example, stores the user's social media activity in a database and analyzes it. Social media activity includes, for example, but is not limited to, the content of posts, the number of likes, the number of followers, etc. The condition setting unit can set relevant conditions based on, for example, the content recently posted by the user. The condition setting unit can also suggest conditions based on the topics of accounts the user follows. Furthermore, the condition setting unit can analyze the user's social media activity history and set conditions of interest. This allows for more appropriate settings by suggesting an optimal setting method based on the user's social media activity. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reception unit, analysis unit, selection unit, and presentation 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 keywords entered by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the keywords using a generation AI. The selection unit is implemented by the identification processing unit 290 of the data processing unit 12 and selects compatible partners based on the analysis results. The presentation unit is implemented by the output device 40 of the smart device 14 and presents the selected partners to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-described reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives keywords entered by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The selection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and selects a compatible partner based on the analysis results. The presentation unit is realized, for example, by the speaker 240 of the smart glasses 214 and presents the selected partner to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives keywords entered by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The selection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and selects a compatible partner based on the analysis results. The presentation unit is realized, for example, by the display 343 of the headset-type terminal 314 and presents the selected partner to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, selection unit, and presentation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives keywords entered by the user. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the keywords using a generation AI. The selection unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and selects a compatible partner based on the analysis results. The presentation unit is realized, for example, by the speaker 240 of the robot 414 and presents the selected partner to the user.

[0101] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0102] The reception unit can analyze the user's past matching history and extract characteristics of partners that the user tends to prefer. For example, it collects data such as the age group, hobbies, and occupations of previously matched partners to create a profile of partners that the user tends to prefer. The reception unit can also analyze the content of the user's interactions with previously matched partners to understand what topics the user is interested in. Furthermore, the reception unit can track the progress of the user's relationships with previously matched partners and identify the characteristics of successful matches. This makes it possible to select more suitable partners based on the user's past matching history.

[0103] The analysis unit can analyze images and videos related to keywords entered by the user and select a compatible partner based on visual information. For example, if a user enters "I like movies," the analysis unit can analyze movie posters and trailers to select partners who are interested in similar movies. If a user enters "I don't like sports," the analysis unit can analyze images and videos related to sports and select partners who are not interested in sports. Furthermore, the analysis unit can analyze photos and videos uploaded by the user to visually grasp the user's hobbies and interests. This allows for more accurate matching by selecting a compatible partner based on visual information.

[0104] The selection unit can estimate the user's emotions and adjust the selection criteria for compatible partners based on the estimated user emotions. For example, if the user is relaxed, a wide range of selection criteria can be applied to suggest a variety of partners. If the user is in a hurry, carefully selected selection criteria can be applied to quickly suggest compatible partners. Furthermore, if the user is stressed, simple selection criteria can be applied to suggest partners that are easy to understand intuitively. In this way, by adjusting the selection criteria for compatible partners according to the user's emotions, a more suitable partner can be selected.

[0105] The presentation unit can estimate the user's emotions and adjust the presentation method of the other party based on the estimated user's emotions. For example, if the user is relaxed, a presentation method including detailed profile information can be provided. If the user is in a hurry, a concise presentation method that focuses on the main points can be provided. Furthermore, if the user is stressed, a simple and intuitively easy-to-understand presentation method can be provided. This allows for more appropriate presentation by adjusting the presentation method of the other party according to the user's emotions.

[0106] The reception unit can present input candidates based on the user's current interests and trends. For example, it can analyze the user's recent search history and social media posts to estimate the user's current interests. It can present related keywords based on topics the user has recently searched for. It can also suggest popular keywords based on current trend information. It can also analyze the user's social media activity to present keywords of interest. This allows for more appropriate keyword input by presenting input candidates based on the user's interests and trends.

[0107] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the keywords. For example, the importance is evaluated based on the frequency of keyword appearance and relevance. A detailed analysis is performed on keywords with high importance, providing highly accurate results. A simplified analysis is performed on keywords with low importance, allowing for quick results. Furthermore, analysis resources can be appropriately allocated according to the importance of the keywords. This allows for more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the keywords.

[0108] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. This allows for more appropriate display by adjusting the display method of the analysis results according to the user's emotions.

[0109] The selection unit can improve the accuracy of selection by taking into account the mutual relationships between matched partners during selection. For example, the selection is made by taking into account mutual friends and past interaction history between the matched partners. By taking into account mutual friends during selection, it is possible to select a partner with high reliability. Furthermore, by taking into account common hobbies and interests during selection, it is possible to select a partner with whom there are many common topics to talk about. Furthermore, by taking into account past interaction history during selection, it is possible to select a partner with good compatibility. Thus, by taking into account the mutual relationships between matched partners, the accuracy of selection is improved.

[0110] When presenting a match, the presentation unit can present the most suitable match by taking into consideration the user's geographical location information. For example, the presentation unit can obtain the user's geographical location information from GPS data or IP address and present the most suitable match. By preferentially presenting matches close to the user's current location, the user can select a match that is easy to meet in person. Also, if the user is traveling, it can preferentially present matches who live in the user's travel destination. Furthermore, it can also present matches in related areas based on the user's past location information. This allows for more appropriate presentation by presenting the most suitable match based on the user's geographical location information.

[0111] The condition setting unit can estimate the user's emotions and adjust the method of setting conditions based on the estimated user's emotions. For example, if the user is relaxed, detailed condition setting options can be provided. If the user is in a hurry, simple condition setting options can be provided. Furthermore, if the user is stressed, simple and intuitively understandable condition setting options can be provided. This allows for more appropriate settings by adjusting the method of setting conditions according to the user's emotions.

[0112] The processing flow of the second embodiment will be briefly explained below.

[0113] Step 1: The reception unit receives keywords from the user. Keywords from the user include, but are not limited to, likes and dislikes. For example, the reception unit can receive keywords such as "I like movies" or "I dislike sports" input by the user. Step 2: The analysis unit uses the generation AI to analyze the keywords received by the reception unit. The generation AI analyzes the keywords using technologies such as text generation AI (e.g., GPT-4) or Gemini. The analysis unit analyzes the profile information of the matched partner based on the keywords entered by the user. Step 3: The selection unit selects compatible partners based on the results analyzed by the analysis unit. For example, the selection unit uses a generation AI to analyze the profile information of potential matches and select compatible partners. Step 4: The presentation unit presents the user with the partner selected by the selection unit. The presentation unit can, for example, present the user with a partner selected by a generation AI.

[0114] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0116] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0118] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0119] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0134] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0135] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0137] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0138] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0140] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0141] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0142] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0143] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0144] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0145] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0149] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0150] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0154] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0156] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0157] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0158] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0169] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0170] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0171] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0175] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0176] 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.

[0177] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0178] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0179] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0180] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0182] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0183] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0184] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0185] [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a keyword from a user; an analysis unit that analyzes the keywords received by the reception unit; a selection unit that selects a compatible partner based on the analysis result by the analysis unit; a presentation unit that presents the partner selected by the selection unit to the user. A system characterized by:

2. The analysis unit Analyzing keywords using generative AI 2. The system of claim 1.

3. The selection unit Generative AI is used to analyze profile information of potential matches and select compatible partners.

2. The system of claim 1.

4. The presentation unit It has a condition setting section that allows users to set detailed conditions to narrow down the partners 2. The system of claim 1.

5. The condition setting unit You can set conditions for age, place of residence, occupation, and hobbies.

5. The system of claim 4.

6. The presentation unit Present the user with a partner selected by the generation AI 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of keyword input based on the estimated user emotions.

2. The system of claim 1.

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

9. The reception unit As you type, suggestions are provided based on your current interests and trends.

2. The system of claim 1.

10. The reception unit Estimate the user's emotions and prioritize the keywords entered based on the estimated user emotions.

2. The system of claim 1.

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