system

Abstract

The system according to this embodiment aims to clarify the conditions that users seek in a partner of the opposite sex and to provide specific improvement advice. [Solution] The system according to this embodiment comprises a consulting unit, an analysis unit, and an advice unit. The consulting unit clarifies the conditions that the user seeks in a partner of the opposite sex. The analysis unit analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting unit. The advice unit provides specific improvement advice based on the analysis performed by the analysis unit.

Description

Technical Field

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

Background Art

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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in matchmaking, the conditions that a user desires in a potential partner have not been sufficiently clarified, and specific improvement advice has not been provided, leaving room for improvement.

[0005] The system according to the embodiment aims to clarify the conditions that a user desires in a potential partner and provide specific improvement advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a consulting unit, an analysis unit, and an advice unit. The consulting unit clarifies the conditions that the user seeks in a romantic partner. The analysis unit analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting unit. The advice unit provides specific improvement advice based on the analysis conducted by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can clarify the conditions a user seeks in a partner of the opposite sex and provide specific improvement advice. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pet-type matchmaking consultant robot system according to an embodiment of the present invention is an innovative system for supporting men and women who desire marriage but are unsuccessful in their search for a partner. This system has a cute appearance in the shape of a cat or dog, while incorporating the methods of a charismatic matchmaking advisor. The system provides rigorous guidance on matters that are difficult to discuss with friends, such as improving the user's appearance and clothing, conversation techniques, and setting up dates. This raises the user's matchmaking quotient and reliably leads to the establishment of a relationship with the intention of marriage. For example, a user rents a robot and begins their search for a partner. The robot uses a generating AI to consult and clarify the conditions the user seeks in a partner. Next, it analyzes the user's clothing, hairstyle, and conversation content using camera footage and recorded conversations to provide specific and practical improvement advice. Furthermore, it analyzes the feelings expressed in messages from the opposite sex and speaks and explains the intent behind their statements. It also learns behaviors that are considered unacceptable in the matchmaking industry and provides strict criticism based on the user's behavior. This robot can alleviate the loneliness of single people while providing support. Having a companion at home makes it harder to ignore the process of finding a partner, helping to maintain motivation. The ability to talk to the robot and offer advice in everyday life is a unique feature of a robot, not an app. Users can even choose the level of bluntness of the advice, providing support tailored to their needs. This service will help create a world where men and women seeking marriage can reliably find a partner, contributing to the solution of the declining birthrate. As the user grows, the robot will return to its "home" (within the app) when its role is complete, providing a touching experience for the user. In this way, the pet-type matchmaking consultant robot system can support men and women who want to get married but are unsuccessful in their search for a partner, raising their matchmaking quotient and leading them to a relationship with the intention of marriage.

[0029] The pet-type matchmaking consultant robot system according to this embodiment comprises a consulting unit, an analysis unit, and an advice unit. The consulting unit clarifies the conditions the user seeks in a partner of the opposite sex. The consulting unit consults on and clarifies the conditions the user seeks in a partner of the opposite sex, for example, using a generative AI. The generative AI generates the conditions the user seeks in a partner of the opposite sex based on the user's input information. For example, the generative AI receives information such as the user's personality, appearance, hobbies, and values ​​as input, and clarifies the conditions the user seeks in a partner based on this information. The analysis unit analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting unit. The analysis unit analyzes the user's clothing, hairstyle, and conversation content, for example, using camera footage and recorded conversation content. The generative AI receives the user's clothing, hairstyle, and conversation content as input and performs analysis based on this information. For example, the generative AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. The advice unit provides specific improvement advice based on the analysis performed by the analysis unit. The advice unit provides specific improvement advice, for example, using a generative AI. The generating AI receives the user's clothing, hairstyle, and conversation content as input, and generates specific improvement advice based on this information. For example, the generating AI provides specific improvement advice on how to choose clothing, how to style one's hair, and how to converse. In this way, the pet-type matchmaking consultant robot system according to the embodiment can support matchmaking by clarifying the conditions the user is looking for in a partner, analyzing their clothing, hairstyle, and conversation content, and providing specific improvement advice.

[0030] The Consulting Department clarifies the criteria that users seek in a romantic partner. Specifically, the Consulting Department uses generative AI to consult with users and clarify the criteria they seek in a romantic partner. The generative AI generates criteria for a romantic partner based on the user's input information. For example, the generative AI receives information such as the user's personality, appearance, hobbies, and values ​​as input, and uses this information to clarify the criteria for a romantic partner. Users input detailed information about their personality, values, hobbies, and past romantic experiences through a dedicated interface. The generative AI analyzes this information using natural language processing technology to deeply understand the user's preferences and values. Furthermore, the generative AI lists specific criteria for a romantic partner based on the user's input information. For example, if a user inputs criteria such as "kind personality" or "likes the outdoors," the generative AI will generate even more detailed criteria based on these. For example, it will suggest specific examples of actions related to "kind personality" or specific activities related to "likes the outdoors." This allows users to understand their criteria for a romantic partner more concretely. Furthermore, the generating AI uses past data and statistical information based on the user's input to suggest the most suitable criteria for a partner. For example, it suggests criteria for a compatible partner based on past success stories and statistical data. This allows users to understand their own desired criteria for a partner more concretely and realistically.

[0031] The analysis department analyzes users' clothing, hairstyles, and conversation content based on conditions clarified by the consulting department. Specifically, the analysis department analyzes users' clothing, hairstyles, and conversation content using camera footage and recorded conversations. The generative AI receives the user's clothing, hairstyle, and conversation content as input and performs analysis based on this information. For example, the generative AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. Users record their clothing, hairstyle, and conversation content through a dedicated camera and microphone. The generative AI analyzes this video and audio data to extract characteristics of the user's clothing, hairstyle, and conversation content. For example, the generative AI analyzes in detail the user's clothing colors and style, hairstyle shape and maintenance, and conversation tone and content. Furthermore, the generative AI compares the user's clothing, hairstyle, and conversation content with data from other users to identify the user's strengths and areas for improvement. For example, the generative AI suggests areas for improvement in the user's clothing, hairstyle, and conversation content based on data from other successful users. This allows users to specifically understand areas for improvement in their own clothing, hairstyle, and conversation content. Furthermore, the generating AI suggests specific ways to improve the user's clothing, hairstyle, and conversation content based on areas for improvement. For example, it might suggest ways to change the user's clothing colors and style, how to maintain their hairstyle, or how to improve the tone and content of their conversations. This allows users to make concrete improvements to their own clothing, hairstyle, and conversation content.

[0032] The advice department provides specific improvement advice based on the analysis conducted by the analysis department. Specifically, the advice department uses generative AI to provide specific improvement advice. The generative AI receives the user's clothing, hairstyle, and conversation content as input and generates specific improvement advice based on this information. For example, the generative AI provides specific improvement advice on how to choose clothing, how to style hair, and how to converse. The user receives advice from the generative AI through a dedicated interface. The generative AI proposes specific improvement methods based on the user's clothing, hairstyle, and conversation content. For example, it suggests how to change the user's clothing colors and style, how to maintain their hair, and how to improve the tone and content of their conversations. Furthermore, the generative AI provides specific action plans based on the user's areas for improvement. For example, it provides a shopping list to improve the user's clothing, how to book a hair salon appointment to improve their hairstyle, and a training plan to improve their conversation content. This allows the user to improve their clothing, hairstyle, and conversation content according to the specific action plan. The generative AI also monitors the user's improvement progress and provides additional advice as needed. For example, after a user improves their clothing, hairstyle, or conversation style, the system evaluates the effect and points out areas that need further improvement. This allows users to continuously improve their clothing, hairstyle, and conversation style, thereby increasing their chances of success in finding a partner.

[0033] The consulting department can analyze a user's past romantic history and propose the ideal partner criteria. For example, the consulting department uses generative AI to analyze a user's past romantic history and propose the ideal partner criteria. The generative AI receives input such as the characteristics of the user's past partners, the duration of the relationship, and the factors that led to the success or failure of the relationship, and performs analysis based on this information. For example, the generative AI analyzes the user's past romantic history and proposes criteria for a partner with many commonalities. The generative AI can also analyze the user's past romantic history and propose criteria to avoid the causes of failure. Furthermore, the generative AI can analyze the user's past romantic history and propose criteria based on successful experiences. In this way, by analyzing the user's past romantic history, the ideal partner criteria can be proposed.

[0034] The consulting department can consider the user's lifestyle and values ​​when clarifying the criteria they seek in a partner. For example, the consulting department uses generative AI to consider the user's lifestyle and values ​​and clarify the criteria they seek in a partner. The generative AI receives the user's daily habits, work style, beliefs, and convictions as input and performs analysis based on this information. For example, the generative AI analyzes the user's lifestyle and proposes criteria for a partner that match their values. The generative AI can also analyze the user's values ​​and propose criteria for a partner that match their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and propose the most suitable criteria for a partner. In this way, by considering the user's lifestyle and values, it is possible to propose more appropriate criteria for a partner.

[0035] The consulting department can incorporate users' hobbies and interests when clarifying the criteria they seek in a partner. For example, the consulting department uses generative AI to reflect users' hobbies and interests and clarify the criteria they seek in a partner. The generative AI takes the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests criteria for a partner of the opposite sex who shares those hobbies. The generative AI can also analyze the user's interests and suggest criteria for a partner who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the most suitable criteria for a partner. In this way, by reflecting the user's hobbies and interests, it is possible to suggest more appropriate criteria for a partner of the opposite sex.

[0036] The consulting department can consider the user's occupation and economic situation when clarifying the criteria for a partner. For example, the consulting department can use generative AI to consider the user's occupation and economic situation and clarify the criteria for a partner. The generative AI takes the user's occupation, income, and financial stability as input and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and suggests criteria for a partner that match that occupation. The generative AI can also analyze the user's economic situation and suggest criteria for a financially stable partner. Furthermore, the generative AI can comprehensively analyze the user's occupation and economic situation and suggest the most suitable criteria for a partner. In this way, by considering the user's occupation and economic situation, it is possible to suggest more appropriate criteria for a partner.

[0037] The analysis department can analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. For example, the analysis department uses generative AI to analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. The generative AI receives the user's past photos, styling records, etc., as input and performs analysis based on this information. For example, the generative AI analyzes a user's past clothing history and proposes successful styles. The generative AI can also analyze a user's past hairstyle history and propose suitable styles. Furthermore, the generative AI can comprehensively analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. In this way, by analyzing a user's past clothing and hairstyle history, the most suitable improvement methods can be proposed.

[0038] The analysis department can consider the user's body type and facial features when analyzing clothing and hairstyles. For example, the analysis department uses generative AI to consider the user's body type and facial features when analyzing clothing and hairstyles. The generative AI receives input such as the user's body type, face shape, and personal color, and performs analysis based on this information. For example, the generative AI analyzes the user's body type and suggests clothing that suits that body type. The generative AI can also analyze the user's facial features and suggest hairstyles that suit those features. Furthermore, the generative AI can comprehensively analyze the user's body type and facial features and suggest the optimal clothing and hairstyle. In this way, by considering the user's body type and facial features, it is possible to suggest more appropriate clothing and hairstyles.

[0039] The analysis department can consider the user's occupation and lifestyle when analyzing clothing and hairstyles. For example, the analysis department uses generative AI to consider the user's occupation and lifestyle when analyzing clothing and hairstyles. The generative AI receives input such as the user's occupation, lifestyle habits, and work style, and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and suggests clothing that suits that occupation. The generative AI can also analyze the user's lifestyle and suggest a hairstyle that suits that lifestyle. Furthermore, the generative AI can comprehensively analyze the user's occupation and lifestyle and suggest the optimal clothing and hairstyle. In this way, by considering the user's occupation and lifestyle, it is possible to suggest more appropriate clothing and hairstyles.

[0040] The analysis unit can incorporate the user's hobbies and interests when analyzing conversation content. For example, the analysis unit uses generative AI to reflect the user's hobbies and interests and analyze the conversation content. The generative AI receives the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests conversation topics with someone of the opposite sex who shares the same hobbies. The generative AI can also analyze the user's interests and suggest conversation topics with someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the most appropriate conversation topics. In this way, by reflecting the user's hobbies and interests, more appropriate conversation topics can be suggested.

[0041] The advice unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the advice unit uses generative AI to refer to the user's past behavioral history and provide optimal advice. The generative AI receives the user's past activity logs, behavioral pattern records, etc., as input and performs analysis based on this information. For example, the generative AI analyzes the user's past behavioral history and provides advice based on successful actions. The generative AI can also analyze the user's past behavioral history and provide advice to avoid failed actions. Furthermore, the generative AI can comprehensively analyze the user's past behavioral history and provide optimal advice. In this way, optimal advice can be provided by referring to the user's past behavioral history.

[0042] The advice function can consider the user's lifestyle and values ​​when providing advice. For example, the advice function can use generative AI to consider the user's lifestyle and values ​​and provide advice. The generative AI receives input such as the user's daily habits, work style, beliefs, and convictions, and performs analysis based on this information. For example, the generative AI analyzes the user's lifestyle and provides advice that matches their values. The generative AI can also analyze the user's values ​​and provide advice that matches their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and provide optimal advice. This allows for the provision of more appropriate advice by considering the user's lifestyle and values.

[0043] The advice function can consider the user's occupation and economic situation when providing advice. For example, the advice function uses generative AI to consider the user's occupation and economic situation and provide advice. The generative AI receives input such as the user's occupation type, income, and economic stability, and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and provides advice that is appropriate for that occupation. The generative AI can also analyze the user's economic situation and provide economically stable advice. Furthermore, the generative AI can comprehensively analyze the user's occupation and economic situation and provide optimal advice. In this way, by considering the user's occupation and economic situation, more appropriate advice can be provided.

[0044] The advice section can reflect the user's hobbies and interests when providing advice. For example, the advice section uses generative AI to reflect the user's hobbies and interests and provide advice. The generative AI receives the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and provides advice on meeting someone of the opposite sex who shares the same hobbies. The generative AI can also analyze the user's interests and provide advice on meeting someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and provide optimal advice. In this way, by reflecting the user's hobbies and interests, more appropriate advice can be provided.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The consulting department can suggest criteria for a partner that take into account the user's health condition. For example, it can use generative AI to analyze the user's health data and suggest criteria for a partner that match their health status. The generative AI takes the user's medical records, fitness data, and dietary habits as input and performs analysis based on this information. For example, if the generative AI has allergies, it will suggest criteria for a partner that take allergies into consideration. Furthermore, if the generative AI has a specific health problem, it can suggest criteria for a partner that understands that health problem. In addition, the generative AI can suggest criteria for a partner that match the user's health goals. In this way, by taking the user's health condition into consideration, it is possible to suggest more appropriate criteria for a partner.

[0047] The analysis department can analyze a user's past dating history and propose the optimal date plan. For example, it can use generative AI to analyze a user's past dating history and propose the optimal date plan. The generative AI receives information such as the location, activities, and success / failure factors of past dates as input, and performs analysis based on this information. For example, the generative AI analyzes a user's past dating history and proposes a successful date plan. The generative AI can also analyze a user's past dating history and make suggestions to avoid unsuccessful date plans. Furthermore, the generative AI can comprehensively analyze a user's past dating history and propose the optimal date plan. In this way, by analyzing a user's past dating history, it is possible to propose the optimal date plan.

[0048] The advice section can suggest date plans that take into account the user's hobbies and interests. For example, it can use generative AI to analyze the user's hobbies and interests and suggest a date plan. The generative AI takes the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests a date plan with someone of the opposite sex who shares the same hobbies. It can also analyze the user's interests and suggest a date plan with someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the optimal date plan. This allows for the suggestion of more appropriate date plans by taking the user's hobbies and interests into consideration.

[0049] The advice section can suggest date plans that take into account the user's occupation and financial situation. For example, it can use generative AI to analyze the user's occupation and financial situation and suggest a date plan. The generative AI receives input such as the user's occupation, income, and financial stability, and performs analysis based on this information. For example, the generative AI can analyze the user's occupation and suggest a date plan that suits that occupation. It can also analyze the user's financial situation and suggest a date plan that is financially feasible. Furthermore, the generative AI can comprehensively analyze the user's occupation and financial situation and suggest the optimal date plan. In this way, by taking into account the user's occupation and financial situation, it can suggest a more appropriate date plan.

[0050] The advice section can propose date plans that take into account the user's lifestyle and values. For example, it can use generative AI to analyze the user's lifestyle and values ​​and propose a date plan. The generative AI receives input such as the user's daily habits, work style, beliefs, and convictions, and performs analysis based on this information. For example, the generative AI can analyze the user's lifestyle and propose a date plan that matches their values. It can also analyze the user's values ​​and propose a date plan that matches their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and propose the optimal date plan. This allows for the proposal of more appropriate date plans by considering the user's lifestyle and values.

[0051] The advice section can provide post-date feedback by referring to the user's past dating history. For example, it can use generative AI to analyze the user's past dating history and provide post-date feedback. The generative AI receives input such as the location, activities, and success / failure factors of the user's past dates, and performs analysis based on this information. For example, the generative AI analyzes the user's past dating history and provides feedback on successful dates. The generative AI can also analyze the user's past dating history and provide feedback pointing out areas for improvement in unsuccessful dates. Furthermore, the generative AI can comprehensively analyze the user's past dating history and provide optimal feedback. In this way, it can provide optimal feedback by referring to the user's past dating history.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The consulting department clarifies the criteria the user is looking for in a partner of the opposite sex. For example, using generative AI, the department receives information such as the user's personality, appearance, hobbies, and values ​​as input, and based on this information, generates and clarifies the criteria the user is looking for in a partner of the opposite sex. Step 2: The analysis department analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting department. For example, they analyze the user's clothing, hairstyle, and conversation content using camera footage and recorded conversations. The generating AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. Step 3: The advice department provides specific improvement advice based on the analysis conducted by the analysis department. For example, it uses generative AI to generate and provide specific improvement advice on topics such as how to choose clothing, how to style one's hair, and how to converse.

[0054] (Example of form 2) The pet-type matchmaking consultant robot system according to an embodiment of the present invention is an innovative system for supporting men and women who desire marriage but are unsuccessful in their search for a partner. This system has a cute appearance in the shape of a cat or dog, while incorporating the methods of a charismatic matchmaking advisor. The system provides rigorous guidance on matters that are difficult to discuss with friends, such as improving the user's appearance and clothing, conversation techniques, and setting up dates. This raises the user's matchmaking quotient and reliably leads to the establishment of a relationship with the intention of marriage. For example, a user rents a robot and begins their search for a partner. The robot uses a generating AI to consult and clarify the conditions the user seeks in a partner. Next, it analyzes the user's clothing, hairstyle, and conversation content using camera footage and recorded conversations to provide specific and practical improvement advice. Furthermore, it analyzes the feelings expressed in messages from the opposite sex and speaks and explains the intent behind their statements. It also learns behaviors that are considered unacceptable in the matchmaking industry and provides strict criticism based on the user's behavior. This robot can alleviate the loneliness of single people while providing support. Having a companion at home makes it harder to ignore the process of finding a partner, helping to maintain motivation. The ability to talk to the robot and offer advice in everyday life is a unique feature of a robot, not an app. Users can even choose the level of bluntness of the advice, providing support tailored to their needs. This service will help create a world where men and women seeking marriage can reliably find a partner, contributing to the solution of the declining birthrate. As the user grows, the robot will return to its "home" (within the app) when its role is complete, providing a touching experience for the user. In this way, the pet-type matchmaking consultant robot system can support men and women who want to get married but are unsuccessful in their search for a partner, raising their matchmaking quotient and leading them to a relationship with the intention of marriage.

[0055] The pet-type matchmaking consultant robot system according to this embodiment comprises a consulting unit, an analysis unit, and an advice unit. The consulting unit clarifies the conditions the user seeks in a partner of the opposite sex. The consulting unit consults on and clarifies the conditions the user seeks in a partner of the opposite sex, for example, using a generative AI. The generative AI generates the conditions the user seeks in a partner of the opposite sex based on the user's input information. For example, the generative AI receives information such as the user's personality, appearance, hobbies, and values ​​as input, and clarifies the conditions the user seeks in a partner based on this information. The analysis unit analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting unit. The analysis unit analyzes the user's clothing, hairstyle, and conversation content, for example, using camera footage and recorded conversation content. The generative AI receives the user's clothing, hairstyle, and conversation content as input and performs analysis based on this information. For example, the generative AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. The advice unit provides specific improvement advice based on the analysis performed by the analysis unit. The advice unit provides specific improvement advice, for example, using a generative AI. The generating AI receives the user's clothing, hairstyle, and conversation content as input, and generates specific improvement advice based on this information. For example, the generating AI provides specific improvement advice on how to choose clothing, how to style one's hair, and how to converse. In this way, the pet-type matchmaking consultant robot system according to the embodiment can support matchmaking by clarifying the conditions the user is looking for in a partner, analyzing their clothing, hairstyle, and conversation content, and providing specific improvement advice.

[0056] The Consulting Department clarifies the criteria that users seek in a romantic partner. Specifically, the Consulting Department uses generative AI to consult with users and clarify the criteria they seek in a romantic partner. The generative AI generates criteria for a romantic partner based on the user's input information. For example, the generative AI receives information such as the user's personality, appearance, hobbies, and values ​​as input, and uses this information to clarify the criteria for a romantic partner. Users input detailed information about their personality, values, hobbies, and past romantic experiences through a dedicated interface. The generative AI analyzes this information using natural language processing technology to deeply understand the user's preferences and values. Furthermore, the generative AI lists specific criteria for a romantic partner based on the user's input information. For example, if a user inputs criteria such as "kind personality" or "likes the outdoors," the generative AI will generate even more detailed criteria based on these. For example, it will suggest specific examples of actions related to "kind personality" or specific activities related to "likes the outdoors." This allows users to understand their criteria for a romantic partner more concretely. Furthermore, the generating AI uses past data and statistical information based on the user's input to suggest the most suitable criteria for a partner. For example, it suggests criteria for a compatible partner based on past success stories and statistical data. This allows users to understand their own desired criteria for a partner more concretely and realistically.

[0057] The analysis department analyzes users' clothing, hairstyles, and conversation content based on conditions clarified by the consulting department. Specifically, the analysis department analyzes users' clothing, hairstyles, and conversation content using camera footage and recorded conversations. The generative AI receives the user's clothing, hairstyle, and conversation content as input and performs analysis based on this information. For example, the generative AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. Users record their clothing, hairstyle, and conversation content through a dedicated camera and microphone. The generative AI analyzes this video and audio data to extract characteristics of the user's clothing, hairstyle, and conversation content. For example, the generative AI analyzes in detail the user's clothing colors and style, hairstyle shape and maintenance, and conversation tone and content. Furthermore, the generative AI compares the user's clothing, hairstyle, and conversation content with data from other users to identify the user's strengths and areas for improvement. For example, the generative AI suggests areas for improvement in the user's clothing, hairstyle, and conversation content based on data from other successful users. This allows users to specifically understand areas for improvement in their own clothing, hairstyle, and conversation content. Furthermore, the generating AI suggests specific ways to improve the user's clothing, hairstyle, and conversation content based on areas for improvement. For example, it might suggest ways to change the user's clothing colors and style, how to maintain their hairstyle, or how to improve the tone and content of their conversations. This allows users to make concrete improvements to their own clothing, hairstyle, and conversation content.

[0058] The advice department provides specific improvement advice based on the analysis conducted by the analysis department. Specifically, the advice department uses generative AI to provide specific improvement advice. The generative AI receives the user's clothing, hairstyle, and conversation content as input and generates specific improvement advice based on this information. For example, the generative AI provides specific improvement advice on how to choose clothing, how to style hair, and how to converse. The user receives advice from the generative AI through a dedicated interface. The generative AI proposes specific improvement methods based on the user's clothing, hairstyle, and conversation content. For example, it suggests how to change the user's clothing colors and style, how to maintain their hair, and how to improve the tone and content of their conversations. Furthermore, the generative AI provides specific action plans based on the user's areas for improvement. For example, it provides a shopping list to improve the user's clothing, how to book a hair salon appointment to improve their hairstyle, and a training plan to improve their conversation content. This allows the user to improve their clothing, hairstyle, and conversation content according to the specific action plan. The generative AI also monitors the user's improvement progress and provides additional advice as needed. For example, after a user improves their clothing, hairstyle, or conversation style, the system evaluates the effect and points out areas that need further improvement. This allows users to continuously improve their clothing, hairstyle, and conversation style, thereby increasing their chances of success in finding a partner.

[0059] The consulting department can estimate a user's emotions and adjust the priority of the criteria a user seeks in a partner based on those estimated emotions. For example, the consulting department uses generative AI to estimate a user's emotions and adjust the priority of the criteria a user seeks in a partner. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the user is depressed, the generative AI estimates their emotions and adjusts the priority to conditions that promote relaxation. The generative AI can also estimate a user's emotions if they are excited and adjust the priority to conditions that promote calm judgment. Furthermore, if the generative AI is feeling anxious, it can estimate their emotions and adjust the priority to conditions that provide a sense of security. By adjusting the priority of the criteria a user seeks in a partner based on their emotions, the consulting department can propose more appropriate criteria. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0060] The consulting department can analyze a user's past romantic history and propose the ideal partner criteria. For example, the consulting department uses generative AI to analyze a user's past romantic history and propose the ideal partner criteria. The generative AI receives input such as the characteristics of the user's past partners, the duration of the relationship, and the factors that led to the success or failure of the relationship, and performs analysis based on this information. For example, the generative AI analyzes the user's past romantic history and proposes criteria for a partner with many commonalities. The generative AI can also analyze the user's past romantic history and propose criteria to avoid the causes of failure. Furthermore, the generative AI can analyze the user's past romantic history and propose criteria based on successful experiences. In this way, by analyzing the user's past romantic history, the ideal partner criteria can be proposed.

[0061] The consulting department can consider the user's lifestyle and values ​​when clarifying the criteria they seek in a partner. For example, the consulting department uses generative AI to consider the user's lifestyle and values ​​and clarify the criteria they seek in a partner. The generative AI receives the user's daily habits, work style, beliefs, and convictions as input and performs analysis based on this information. For example, the generative AI analyzes the user's lifestyle and proposes criteria for a partner that match their values. The generative AI can also analyze the user's values ​​and propose criteria for a partner that match their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and propose the most suitable criteria for a partner. In this way, by considering the user's lifestyle and values, it is possible to propose more appropriate criteria for a partner.

[0062] The consulting department can estimate the user's emotions and adjust the level of detail in the criteria for a partner based on the estimated emotions. For example, the consulting department can use generative AI to estimate the user's emotions and adjust the level of detail in the criteria for a partner. The generative AI receives input such as facial recognition, voice analysis, and survey results from the user and estimates the user's emotions based on this information. For example, if the user is relaxed, the generative AI will estimate their emotions and suggest detailed criteria. The generative AI can also estimate the user's emotions if they are nervous and suggest concise criteria. Furthermore, if the user is excited, the generative AI can estimate their emotions and adjust the level of detail in the criteria to allow for calmer judgment. By adjusting the level of detail in the criteria for a partner based on the user's emotions, more appropriate criteria can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0063] The consulting department can incorporate users' hobbies and interests when clarifying the criteria they seek in a partner. For example, the consulting department uses generative AI to reflect users' hobbies and interests and clarify the criteria they seek in a partner. The generative AI takes the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests criteria for a partner of the opposite sex who shares those hobbies. The generative AI can also analyze the user's interests and suggest criteria for a partner who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the most suitable criteria for a partner. In this way, by reflecting the user's hobbies and interests, it is possible to suggest more appropriate criteria for a partner of the opposite sex.

[0064] The consulting department can consider the user's occupation and economic situation when clarifying the criteria for a partner. For example, the consulting department can use generative AI to consider the user's occupation and economic situation and clarify the criteria for a partner. The generative AI takes the user's occupation, income, and financial stability as input and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and suggests criteria for a partner that match that occupation. The generative AI can also analyze the user's economic situation and suggest criteria for a financially stable partner. Furthermore, the generative AI can comprehensively analyze the user's occupation and economic situation and suggest the most suitable criteria for a partner. In this way, by considering the user's occupation and economic situation, it is possible to suggest more appropriate criteria for a partner.

[0065] The analysis department can estimate the user's emotions and, based on those estimated emotions, prioritize suggesting improvements to clothing and hairstyles. For example, the analysis department can use generative AI to estimate the user's emotions and prioritize suggesting improvements to clothing and hairstyles. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the generative AI is confident, it can estimate the user's emotions and suggest detailed improvements. The generative AI can also estimate the user's emotions if they are feeling anxious and suggest basic improvements. Furthermore, if the generative AI is excited, it can estimate the user's emotions and suggest improvements to help them make calmer judgments. This allows for more appropriate improvements by prioritizing suggestions for clothing and hairstyles based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0066] The analysis department can analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. For example, the analysis department uses generative AI to analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. The generative AI receives the user's past photos, styling records, etc., as input and performs analysis based on this information. For example, the generative AI analyzes a user's past clothing history and proposes successful styles. The generative AI can also analyze a user's past hairstyle history and propose suitable styles. Furthermore, the generative AI can comprehensively analyze a user's past clothing and hairstyle history and propose the most suitable improvement methods. In this way, by analyzing a user's past clothing and hairstyle history, the most suitable improvement methods can be proposed.

[0067] The analysis department can consider the user's body type and facial features when analyzing clothing and hairstyles. For example, the analysis department uses generative AI to consider the user's body type and facial features when analyzing clothing and hairstyles. The generative AI receives input such as the user's body type, face shape, and personal color, and performs analysis based on this information. For example, the generative AI analyzes the user's body type and suggests clothing that suits that body type. The generative AI can also analyze the user's facial features and suggest hairstyles that suit those features. Furthermore, the generative AI can comprehensively analyze the user's body type and facial features and suggest the optimal clothing and hairstyle. In this way, by considering the user's body type and facial features, it is possible to suggest more appropriate clothing and hairstyles.

[0068] The analysis unit can estimate the user's emotions and prioritize identifying areas for improvement in the conversation based on those estimated emotions. For example, the analysis unit can use generative AI to estimate the user's emotions and prioritize identifying areas for improvement in the conversation. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the generative AI is nervous, it can estimate the user's emotions and suggest conversation content that will help them relax. The generative AI can also estimate the user's emotions if they are confident and suggest more detailed conversation content. Furthermore, if the generative AI is feeling anxious, it can estimate the user's emotions and suggest conversation content that will provide reassurance. This allows for more appropriate improvements by prioritizing the identification of areas for improvement in the conversation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The analysis department can consider the user's occupation and lifestyle when analyzing clothing and hairstyles. For example, the analysis department uses generative AI to consider the user's occupation and lifestyle when analyzing clothing and hairstyles. The generative AI receives input such as the user's occupation, lifestyle habits, and work style, and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and suggests clothing that suits that occupation. The generative AI can also analyze the user's lifestyle and suggest a hairstyle that suits that lifestyle. Furthermore, the generative AI can comprehensively analyze the user's occupation and lifestyle and suggest the optimal clothing and hairstyle. In this way, by considering the user's occupation and lifestyle, it is possible to suggest more appropriate clothing and hairstyles.

[0070] The analysis unit can incorporate the user's hobbies and interests when analyzing conversation content. For example, the analysis unit uses generative AI to reflect the user's hobbies and interests and analyze the conversation content. The generative AI receives the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests conversation topics with someone of the opposite sex who shares the same hobbies. The generative AI can also analyze the user's interests and suggest conversation topics with someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the most appropriate conversation topics. In this way, by reflecting the user's hobbies and interests, more appropriate conversation topics can be suggested.

[0071] The advice unit can estimate the user's emotions and adjust the way it expresses advice based on those emotions. For example, the advice unit might use a generative AI to estimate the user's emotions and adjust the way it expresses advice. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the user is depressed, the generative AI estimates their emotions and provides advice in a gentle manner. It can also estimate the user's emotions if they are excited and provide advice in a calm manner. Furthermore, if the user is feeling anxious, the generative AI estimates their emotions and provides advice in a reassuring manner. By adjusting the way advice is expressed based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The advice unit can provide optimal advice by referring to the user's past behavioral history when providing advice. For example, the advice unit uses generative AI to refer to the user's past behavioral history and provide optimal advice. The generative AI receives the user's past activity logs, behavioral pattern records, etc., as input and performs analysis based on this information. For example, the generative AI analyzes the user's past behavioral history and provides advice based on successful actions. The generative AI can also analyze the user's past behavioral history and provide advice to avoid failed actions. Furthermore, the generative AI can comprehensively analyze the user's past behavioral history and provide optimal advice. In this way, optimal advice can be provided by referring to the user's past behavioral history.

[0073] The advice function can consider the user's lifestyle and values ​​when providing advice. For example, the advice function can use generative AI to consider the user's lifestyle and values ​​and provide advice. The generative AI receives input such as the user's daily habits, work style, beliefs, and convictions, and performs analysis based on this information. For example, the generative AI analyzes the user's lifestyle and provides advice that matches their values. The generative AI can also analyze the user's values ​​and provide advice that matches their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and provide optimal advice. This allows for the provision of more appropriate advice by considering the user's lifestyle and values.

[0074] The advice unit can estimate the user's emotions and adjust the level of detail of the advice based on the estimated emotions. For example, the advice unit might use a generative AI to estimate the user's emotions and adjust the level of detail of the advice. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the user is relaxed, the generative AI can estimate their emotions and provide detailed advice. It can also estimate the user's emotions if they are tense and provide concise advice. Furthermore, if the user is excited, the generative AI can estimate their emotions and adjust the level of detail of the advice to help them make calmer judgments. By adjusting the level of detail of the advice based on the user's emotions, more appropriate advice can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0075] The advice function can consider the user's occupation and economic situation when providing advice. For example, the advice function uses generative AI to consider the user's occupation and economic situation and provide advice. The generative AI receives input such as the user's occupation type, income, and economic stability, and performs analysis based on this information. For example, the generative AI analyzes the user's occupation and provides advice that is appropriate for that occupation. The generative AI can also analyze the user's economic situation and provide economically stable advice. Furthermore, the generative AI can comprehensively analyze the user's occupation and economic situation and provide optimal advice. In this way, by considering the user's occupation and economic situation, more appropriate advice can be provided.

[0076] The advice section can reflect the user's hobbies and interests when providing advice. For example, the advice section uses generative AI to reflect the user's hobbies and interests and provide advice. The generative AI receives the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and provides advice on meeting someone of the opposite sex who shares the same hobbies. The generative AI can also analyze the user's interests and provide advice on meeting someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and provide optimal advice. In this way, by reflecting the user's hobbies and interests, more appropriate advice can be provided.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The consulting department can suggest criteria for a partner that take into account the user's health condition. For example, it can use generative AI to analyze the user's health data and suggest criteria for a partner that match their health status. The generative AI takes the user's medical records, fitness data, and dietary habits as input and performs analysis based on this information. For example, if the generative AI has allergies, it will suggest criteria for a partner that take allergies into consideration. Furthermore, if the generative AI has a specific health problem, it can suggest criteria for a partner that understands that health problem. In addition, the generative AI can suggest criteria for a partner that match the user's health goals. In this way, by taking the user's health condition into consideration, it is possible to suggest more appropriate criteria for a partner.

[0079] The consulting department can estimate a user's emotions and adjust the flexibility of the criteria they seek in a partner based on those estimated emotions. For example, it can use generative AI to estimate a user's emotions and adjust the flexibility of the criteria. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the generative AI is feeling stressed, it will estimate the emotion and increase the flexibility of the criteria. Conversely, if the generative AI is relaxed, it can estimate the emotion and decrease the flexibility of the criteria. Furthermore, if the generative AI is excited, it can estimate the emotion and adjust the flexibility of the criteria to allow for calmer judgment. By adjusting the flexibility of the criteria sought in a partner based on the user's emotions, it can propose more appropriate criteria.

[0080] The analysis department can analyze a user's past dating history and propose the optimal date plan. For example, it can use generative AI to analyze a user's past dating history and propose the optimal date plan. The generative AI receives information such as the location, activities, and success / failure factors of past dates as input, and performs analysis based on this information. For example, the generative AI analyzes a user's past dating history and proposes a successful date plan. The generative AI can also analyze a user's past dating history and make suggestions to avoid unsuccessful date plans. Furthermore, the generative AI can comprehensively analyze a user's past dating history and propose the optimal date plan. In this way, by analyzing a user's past dating history, it is possible to propose the optimal date plan.

[0081] The analysis department can estimate the user's emotions and adjust the date plan based on those emotions. For example, it can use generative AI to estimate the user's emotions and adjust the date plan accordingly. The generative AI receives input such as facial recognition, voice analysis, and survey results, and estimates the user's emotions based on this information. For example, if the generative AI is nervous, it can estimate the user's emotions and suggest a relaxing date plan. It can also estimate the user's emotions if they are confident and suggest an active date plan. Furthermore, if the generative AI is feeling anxious, it can estimate their emotions and suggest a reassuring date plan. By adjusting the date plan based on the user's emotions, it can suggest a more appropriate date plan.

[0082] The advice function can estimate the user's emotions and provide advice on what to do during the date based on those estimated emotions. For example, it can use generative AI to estimate the user's emotions and provide advice on what to do during the date. The generative AI receives input such as facial recognition, voice analysis, and survey results from the user, and estimates the user's emotions based on this information. For example, if the generative AI is nervous, it can estimate the user's emotions and provide advice on what to do to help them relax. The generative AI can also estimate the user's emotions if they are confident and provide advice on what to do to help them be more proactive. Furthermore, if the generative AI is feeling anxious, it can estimate the user's emotions and provide advice on what to do to help them feel more secure. By providing advice on what to do during the date based on the user's emotions, it is possible to provide more appropriate advice.

[0083] The advice section can suggest date plans that take into account the user's hobbies and interests. For example, it can use generative AI to analyze the user's hobbies and interests and suggest a date plan. The generative AI takes the user's hobbies and interests, such as sports, music, travel, and reading, as input and performs analysis based on this information. For example, the generative AI analyzes the user's hobbies and suggests a date plan with someone of the opposite sex who shares the same hobbies. It can also analyze the user's interests and suggest a date plan with someone of the opposite sex who shares those interests. Furthermore, the generative AI can comprehensively analyze the user's hobbies and interests and suggest the optimal date plan. This allows for the suggestion of more appropriate date plans by taking the user's hobbies and interests into consideration.

[0084] The advice section can suggest date plans that take into account the user's occupation and financial situation. For example, it can use generative AI to analyze the user's occupation and financial situation and suggest a date plan. The generative AI receives input such as the user's occupation, income, and financial stability, and performs analysis based on this information. For example, the generative AI can analyze the user's occupation and suggest a date plan that suits that occupation. It can also analyze the user's financial situation and suggest a date plan that is financially feasible. Furthermore, the generative AI can comprehensively analyze the user's occupation and financial situation and suggest the optimal date plan. In this way, by taking into account the user's occupation and financial situation, it can suggest a more appropriate date plan.

[0085] The advice section can propose date plans that take into account the user's lifestyle and values. For example, it can use generative AI to analyze the user's lifestyle and values ​​and propose a date plan. The generative AI receives input such as the user's daily habits, work style, beliefs, and convictions, and performs analysis based on this information. For example, the generative AI can analyze the user's lifestyle and propose a date plan that matches their values. It can also analyze the user's values ​​and propose a date plan that matches their lifestyle. Furthermore, the generative AI can comprehensively analyze the user's lifestyle and values ​​and propose the optimal date plan. This allows for the proposal of more appropriate date plans by considering the user's lifestyle and values.

[0086] The advice section can estimate the user's emotions and provide post-date feedback based on those estimated emotions. For example, it can use generative AI to estimate the user's emotions and provide post-date feedback. The generative AI receives input such as facial recognition, voice analysis, and survey results from the user, and estimates the user's emotions based on this information. For example, if the user is satisfied, the generative AI estimates their emotions and provides positive feedback. The generative AI can also estimate the user's emotions if they are dissatisfied and provide feedback that points out areas for improvement. Furthermore, if the user is feeling anxious, the generative AI can estimate their emotions and provide reassuring feedback. This allows for more appropriate feedback to be provided by basing post-date feedback on the user's emotions.

[0087] The advice section can provide post-date feedback by referring to the user's past dating history. For example, it can use generative AI to analyze the user's past dating history and provide post-date feedback. The generative AI receives input such as the location, activities, and success / failure factors of the user's past dates, and performs analysis based on this information. For example, the generative AI analyzes the user's past dating history and provides feedback on successful dates. The generative AI can also analyze the user's past dating history and provide feedback pointing out areas for improvement in unsuccessful dates. Furthermore, the generative AI can comprehensively analyze the user's past dating history and provide optimal feedback. In this way, it can provide optimal feedback by referring to the user's past dating history.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The consulting department clarifies the criteria the user is looking for in a partner of the opposite sex. For example, using generative AI, the department receives information such as the user's personality, appearance, hobbies, and values ​​as input, and based on this information, generates and clarifies the criteria the user is looking for in a partner of the opposite sex. Step 2: The analysis department analyzes the user's clothing, hairstyle, and conversation content based on the conditions clarified by the consulting department. For example, they analyze the user's clothing, hairstyle, and conversation content using camera footage and recorded conversations. The generating AI analyzes the user's clothing choices, hairstyle styling methods, and conversation style. Step 3: The advice department provides specific improvement advice based on the analysis conducted by the analysis department. For example, it uses generative AI to generate and provide specific improvement advice on topics such as how to choose clothing, how to style one's hair, and how to converse.

[0090] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0091] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0092] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0093] Each of the multiple elements described above, including the consulting unit, analysis unit, and advice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the consulting unit is implemented by the control unit 46A of the smart device 14, and uses generating AI to consult and clarify the conditions that the user seeks in a partner of the opposite sex. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, and uses camera images and recorded conversation content to analyze the user's clothing, hairstyle, and conversation content. The advice unit is implemented by the control unit 46A of the smart device 14, and provides specific improvement advice. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0096] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0100] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0101] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0102] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0103] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0104] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0108] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the consulting unit, analysis unit, and advice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the consulting unit is implemented by the control unit 46A of the smart glasses 214, which uses generating AI to consult and clarify the conditions the user seeks in a partner of the opposite sex. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's clothing, hairstyle, and conversation content using camera images and recorded conversation content. The advice unit is implemented by the control unit 46A of the smart glasses 214, which provides specific improvement advice. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the consulting unit, analysis unit, and advice unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the consulting unit is implemented by the control unit 46A of the headset terminal 314, which uses generating AI to consult and clarify the conditions the user seeks in a partner of the opposite sex. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's clothing, hairstyle, and conversation content using camera images and recorded conversation content. The advice unit is implemented by the control unit 46A of the headset terminal 314, which provides specific improvement advice. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0134] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the consulting unit, analysis unit, and advice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the consulting unit is implemented by the control unit 46A of the robot 414, which uses generating AI to consult and clarify the conditions the user seeks in a romantic partner. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the user's clothing, hairstyle, and conversation content using camera images and recorded conversation content. The advice unit is implemented by, for example, the control unit 46A of the robot 414, which provides specific improvement advice. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0143] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0145] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0146] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0147] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0151] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0152] 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.

[0153] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0154] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0155] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0156] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0158] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0159] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0160] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0161] (Note 1) The consulting department clarifies the criteria that users look for in a partner of the opposite sex, Based on the conditions clarified by the aforementioned consulting department, the analysis department analyzes the user's clothing, hairstyle, and conversation content. The system includes an advice unit that provides specific improvement advice based on the analysis performed by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned consulting department, It estimates the user's emotions and adjusts the priority of the criteria they seek in a partner based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned consulting department, It analyzes the user's past dating history and suggests the ideal partner criteria. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned consulting department, When clarifying the qualities you look for in a partner, take into account the user's lifestyle and values. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consulting department, The system estimates the user's emotions and adjusts the level of detail in the criteria they seek in a partner based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consulting department, When clarifying the qualities you look for in a partner, reflect the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned consulting department, When clarifying the criteria you look for in a partner, take into account the user's occupation and economic situation. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is It estimates the user's emotions and, based on those estimated emotions, prioritizes suggesting improvements to clothing and hairstyles. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We analyze the user's past clothing and hairstyle history and suggest the most suitable improvement methods. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When analyzing clothing and hairstyles, the user's body type and facial features are taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is It estimates the user's emotions and prioritizes identifying areas for improvement in the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is When analyzing clothing and hairstyles, we take into account the user's occupation and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is When analyzing conversation content, reflect the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned advice section, When providing advice, we refer to the user's past behavioral history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned advice section, When providing advice, we take into consideration the user's lifestyle and values. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned advice section, It estimates the user's emotions and adjusts the level of detail in the advice based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned advice section, When providing advice, we take into consideration the user's occupation and financial situation. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned advice section, When providing advice, reflect the user's hobbies and interests. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The consulting department clarifies the criteria that users look for in a partner of the opposite sex, Based on the conditions clarified by the aforementioned consulting department, the analysis department analyzes the user's clothing, hairstyle, and conversation content. The system includes an advice unit that provides specific improvement advice based on the analysis performed by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned consulting department, It estimates the user's emotions and adjusts the priority of the criteria they seek in a partner based on those estimated emotions. The system according to feature 1.

3. The aforementioned consulting department, It analyzes the user's past dating history and suggests the ideal partner criteria. The system according to feature 1.

4. The aforementioned consulting department, When clarifying the qualities you look for in a partner, take into account the user's lifestyle and values. The system according to feature 1.

5. The aforementioned consulting department, The system estimates the user's emotions and adjusts the level of detail in the criteria they seek in a partner based on those estimated emotions. The system according to feature 1.

6. The aforementioned consulting department, When clarifying the qualities you look for in a partner, reflect the user's hobbies and interests. The system according to feature 1.

7. The aforementioned consulting department, When clarifying the criteria you look for in a partner, take into account the user's occupation and economic situation. The system according to feature 1.

8. The aforementioned analysis unit is It estimates the user's emotions and, based on those estimated emotions, prioritizes suggesting improvements to clothing and hairstyles. The system according to feature 1.

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