system

Abstract

The system according to this embodiment aims to analyze the user's profile information and propose and improve the optimal date plan. [Solution] The system according to the embodiment comprises a profile analysis unit, a plan proposal unit, a feedback collection unit, and an improvement unit. The profile analysis unit analyzes the user's profile information. The plan proposal unit proposes a date plan based on the information analyzed by the profile analysis unit. The feedback collection unit collects feedback on the date plan proposed by the plan proposal unit. The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 prior art, there was a problem that it took time and effort to consider a date plan and it was difficult to propose a plan that suits each other's preferences.

[0005] The system according to the embodiment aims to analyze the user's profile information and propose and improve an optimal date plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a profile analysis unit, a plan proposal unit, a feedback collection unit, and an improvement unit. The profile analysis unit analyzes the user's profile information. The plan proposal unit proposes a date plan based on the information analyzed by the profile analysis unit. The feedback collection unit collects feedback on the date plan proposed by the plan proposal unit. The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's profile information and propose and improve the optimal date plan. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 reception 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 reception 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 date plan suggestion system according to an embodiment of the present invention is a system that suggests date plans using generative AI. This date plan suggestion system can improve user satisfaction by analyzing the user's profile information, suggesting date plans, collecting feedback, and improving the next date plan. For example, the date plan suggestion system performs profile analysis based on user information from a matching app. Specifically, it analyzes information such as the user's hobbies, common interests, and place of residence, and selects recommended date spots. This analysis is performed by generative AI. Next, the date plan suggestion system uses generative AI to analyze each user's preferences and proposes individual date plans. For example, it proposes customized plans such as restaurant reservations, event information, and picnic spots. Furthermore, the date plan suggestion system collects feedback after the date, and the generative AI reflects this in the next suggestion. This allows for the provision of more satisfying plans through continuous learning. This reduces the effort required to prepare for dates and improves user satisfaction. In addition, by providing individually optimized date plans, users can share new experiences with their partners and deepen their relationships. Furthermore, it is expected that the app's retention rate will also improve. This allows the date plan suggestion system to automatically propose date plans to users, collect feedback, and improve the next date plan.

[0029] The date plan suggestion system according to the embodiment comprises a profile analysis unit, a plan suggestion unit, a feedback collection unit, and an improvement unit. The profile analysis unit analyzes the user's profile information. For example, the profile analysis unit analyzes information such as the user's hobbies, common interests, and place of residence. The profile analysis unit can analyze the user's profile information using a generative AI. For example, the profile analysis unit analyzes the information using a generative AI model that takes information such as the user's hobbies, common interests, and place of residence as input and outputs recommended date spots. The plan suggestion unit proposes a date plan based on the information analyzed by the profile analysis unit. For example, the plan suggestion unit proposes a customized plan, such as restaurant reservations, event information, and picnic spots. The plan suggestion unit can propose a date plan that matches the user's wishes using a generative AI. For example, the plan suggestion unit proposes a plan using a generative AI model that takes the user's wishes as input and outputs a date plan. The feedback collection unit collects feedback on the date plan proposed by the plan suggestion unit. For example, the feedback collection unit collects feedback after the date. The feedback collection unit can collect feedback using generative AI. For example, the feedback collection unit collects feedback using a generative AI model that takes post-date feedback as input and outputs feedback data. The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit. The improvement unit improves the next date plan based on the collected feedback. The improvement unit can improve the next date plan using generative AI. For example, the improvement unit improves the next date plan using a generative AI model that takes the collected feedback as input and outputs an improved date plan. As a result, the date plan suggestion system according to the embodiment can automatically suggest a date plan for the user, collect feedback, and improve the next date plan.

[0030] The profile analysis unit analyzes user profile information. Specifically, it collects detailed information provided by users, such as hobbies, interests, place of residence, age, gender, and occupation, and uses this data to understand the user's preferences and lifestyle. By using generative AI, this information can be analyzed at a high level to recommend the most suitable dating spots and activities to the user. For example, if a user enjoys outdoor activities, the generative AI will suggest nearby hiking trails and campsites. If a user enjoys visiting art museums, the generative AI will suggest the latest exhibition information and special events at museums. The generative AI can also take into account the user's past dating history and feedback to provide more personalized suggestions. Furthermore, the generative AI can collect data from the user's social media accounts and online activities to gain a deeper understanding of the user's interests and preferences. As a result, the profile analysis unit can integrate diverse user information and perform highly accurate analysis.

[0031] The plan proposal department proposes date plans based on information analyzed by the profile analysis department. Specifically, it considers information such as the user's hobbies, interests, and place of residence, and proposes customized plans including restaurant reservations, event information, and picnic spots. By using generative AI, it is possible to automatically generate date plans that match the user's wishes. For example, if the user wants a romantic dinner, the generative AI will search for highly-rated restaurants in the vicinity and suggest available reservation times. If the user wants an active date, the generative AI will suggest sports events or outdoor activities. The generative AI can also take into account the user's past dating history and feedback to provide more personalized suggestions. Furthermore, the generative AI can also take into account external factors such as weather, season, and specific events or festivals to propose the optimal date plan. As a result, the plan proposal department can provide the best date plan for the user and increase the success rate of the date.

[0032] The Feedback Collection Unit collects feedback on the date plans proposed by the Plan Proposal Unit. Specifically, it collects feedback from users after the date to understand their satisfaction level and areas for improvement. By using Generative AI, feedback can be collected and analyzed efficiently. For example, a questionnaire is sent to users after the date to collect evaluations and comments on each element of the date. The Generative AI analyzes this feedback data to identify which parts of the date plan were successful and which parts could be improved. Furthermore, based on user feedback, the Generative AI can make suggestions to make the next date plan even better. In addition, by anonymizing user feedback and storing it in a database, the Feedback Collection Unit can also help improve other users' date plans. In this way, the Feedback Collection Unit can play an important role in increasing user satisfaction.

[0033] The Improvement Department improves the next date plan based on the feedback collected by the Feedback Collection Department. Specifically, it analyzes the collected feedback and incorporates it into the next date plan. By using Generative AI, feedback can be efficiently analyzed and the next date plan can be automatically improved. For example, if a user was not satisfied with a particular restaurant on the last date, the Generative AI will remove that restaurant from the next date plan and suggest another highly-rated restaurant instead. Also, if a user enjoyed a particular activity, the Generative AI can include a similar activity in the next date plan. Based on user feedback, the Generative AI can optimize each element of the date plan and make suggestions to increase user satisfaction. Furthermore, by continuously collecting user feedback and iteratively improving the date plan, the Improvement Department can provide the optimal date plan for the user. In this way, the Improvement Department can play a crucial role in improving the user's dating experience.

[0034] The profile analysis unit can analyze information such as a user's hobbies, common interests, and place of residence. For example, the profile analysis unit can analyze information such as a user's hobbies, common interests, and place of residence using a generative AI model that takes the user's hobbies, common interests, and place of residence as input and outputs recommended date spots. This allows for the suggestion of more appropriate date plans by analyzing the user's hobbies, common interests, and place of residence.

[0035] The plan proposal department can customize and propose specific plans such as restaurant reservations, event information, and picnic spots. For example, the plan proposal department can use generative AI to propose date plans that match the user's preferences. This allows the department to provide date plans that are tailored to the user's desires by customizing and proposing specific plans.

[0036] The feedback collection unit can collect feedback after a date. For example, the feedback collection unit collects feedback after a date. The feedback collection unit can collect feedback using generative AI. For example, the feedback collection unit collects feedback using a generative AI model that takes post-date feedback as input and outputs feedback data. This allows for the collection of post-date feedback, which can then be used to improve future date plans.

[0037] The improvement unit can improve the next date plan based on the collected feedback. For example, the improvement unit can improve the next date plan based on the collected feedback. The improvement unit can improve the next date plan using generative AI. For example, the improvement unit can improve the next date plan using a generative AI model that takes the collected feedback as input and outputs an improved date plan. This improves user satisfaction by improving the next date plan based on the collected feedback.

[0038] The profile analysis unit can analyze a user's past dating history and select the optimal analysis method. For example, the profile analysis unit can use its generating AI to suggest new dating spots based on the user's ratings of past dating locations. The profile analysis unit can also analyze patterns of dating plans the user has preferred in the past, and the generating AI can suggest similar plans. The profile analysis unit can also exclude dating spots the user has avoided in the past, and the generating AI can make new suggestions. In this way, by analyzing the user's past dating history, more appropriate dating plans can be suggested. Some or all of the above processes in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input the user's past dating history data into the generating AI and have the generating AI select the optimal analysis method.

[0039] The profile analysis unit can filter the user's profile based on their current lifestyle and areas of interest. For example, the profile analysis unit can use a generating AI to suggest date spots related to a hobby the user has recently become interested in. The profile analysis unit can also use a generating AI to suggest a relaxing date plan, taking into account the user's current work situation. The profile analysis unit can also analyze the user's recent social media activity and use a generating AI to suggest events of interest. This allows for the suggestion of a more appropriate date plan by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the profile analysis unit may be performed using AI, for example, or not. For example, the profile analysis unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the filtering.

[0040] The profile analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location during profile analysis. For example, the profile analysis unit can have the generating AI prioritize the analysis of date spots around the user's place of residence. The profile analysis unit can also have the generating AI prioritize the analysis of lunch spots around the user's workplace. The profile analysis unit can also have the generating AI prioritize the analysis of tourist spots at the user's travel destinations. By prioritizing the analysis of highly relevant information while considering the user's geographical location, it can propose a more appropriate date plan. Some or all of the above processing in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input the user's geographical location information into the generating AI and have the generating AI perform the priority analysis of highly relevant information.

[0041] The profile analysis unit can analyze a user's social media activity and extract relevant information during profile analysis. For example, the profile analysis unit can use a generating AI to analyze places a user has shared on social media and suggest them as date spots. The profile analysis unit can also use a generating AI to analyze events a user has shown interest in on social media and incorporate them into a date plan. The profile analysis unit can also use a generating AI to analyze places visited by a user's social media followers and suggest them as date spots. This allows for the suggestion of more appropriate date plans by analyzing the user's social media activity. Some or all of the above-described processes in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input user social media activity data into a generating AI and have the generating AI perform the analysis of relevant information.

[0042] The plan proposal unit can adjust the level of detail in its proposals based on the importance of the date plan. For example, the generation AI can provide detailed proposals for special anniversary date plans. For everyday date plans, the generation AI can provide concise proposals. For first dates, the generation AI can provide carefully detailed proposals. By adjusting the level of detail in proposals based on the importance of the date plan, the unit can propose more appropriate date plans. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or not using AI. For example, the plan proposal unit can input date plan importance data into the generation AI and have the generation AI adjust the level of detail in the proposals.

[0043] The plan suggestion unit can apply different suggestion algorithms depending on the category of the date plan when making suggestions. For example, in the case of a restaurant date, the generating AI can make suggestions based on the type of cuisine and atmosphere. In the case of an outdoor date, the generating AI can also make suggestions based on the weather and season. In the case of an event date, the generating AI can also make suggestions based on the type of event and location. This allows for the suggestion of more appropriate date plans by applying different suggestion algorithms depending on the category of the date plan. Some or all of the above processing in the plan suggestion unit may be performed using AI, for example, or without AI. For example, the plan suggestion unit can input date plan category data into the generating AI and have the generating AI execute the application of different suggestion algorithms.

[0044] The plan proposal unit can determine the priority of proposals based on the timing of the date plan's implementation. For example, the plan proposal unit may prioritize suggesting the most recent date plan. The plan proposal unit may also prioritize suggesting date plans appropriate for the season. The plan proposal unit may also prioritize suggesting date plans tailored to specific events. By prioritizing proposals based on the timing of the date plan's implementation, it is possible to propose more appropriate date plans. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or not using AI. For example, the plan proposal unit can input date plan implementation timing data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0045] The plan suggestion unit can adjust the order of suggestions based on the relevance of the date plans. For example, the plan suggestion unit may prioritize suggesting date plans related to the user's hobbies. It may also prioritize suggesting date plans close to the user's place of residence. It may also prioritize suggesting date plans that are highly relevant based on the user's past dating history. By adjusting the order of suggestions based on the relevance of the date plans, it is possible to suggest more appropriate date plans. Some or all of the above processing in the plan suggestion unit may be performed using AI, for example, or not using AI. For example, the plan suggestion unit can input date plan relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0046] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the generating AI can request detailed feedback. If the user has provided concise feedback in the past, the generating AI can also request concise feedback. If the user has provided emotional feedback in the past, the generating AI can also request emotional feedback. This allows for the collection of more appropriate feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's past feedback history data into the generating AI and have the generating AI select the optimal collection method.

[0047] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the generating AI can provide a mobile-friendly feedback form. If the user is using a tablet, the generating AI can also provide a feedback form optimized for a large screen. If the user is using a desktop, the generating AI can also provide a detailed feedback form. This allows for the collection of more appropriate feedback by taking into account the user's device information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's device information into the generating AI and have the generating AI select the optimal collection method.

[0048] The improvement unit can analyze the user's past date plans and select the optimal improvement method during the improvement process. For example, the improvement unit can use a generating AI to suggest improvements based on date plans the user has liked in the past. The improvement unit can also exclude date plans the user has avoided in the past and have the generating AI suggest new ones. The improvement unit can also use feedback from the user on date plans they have previously evaluated to suggest improvements. This allows for more appropriate improvement suggestions by analyzing the user's past date plans. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's past date plan data into the generating AI and have the generating AI select the optimal improvement method.

[0049] The improvement unit can customize the means of improvement based on the user's current lifestyle when making improvements. For example, if the user is busy, the generation AI can provide concise improvement suggestions. If the user is relaxed, the generation AI can also provide detailed improvement suggestions. If the user has started a new hobby, the generation AI can also provide improvement suggestions related to that hobby. This allows for more appropriate improvement suggestions to be made by customizing the means of improvement based on the user's current lifestyle. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input data on the user's current lifestyle into the generation AI and have the generation AI customize the means of improvement.

[0050] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, the improvement unit can have the generating AI prioritize suggesting improvement options for date spots near the user's place of residence. The improvement unit can also have the generating AI prioritize suggesting improvement options for lunch spots near the user's workplace. The improvement unit can also have the generating AI prioritize suggesting improvement options for tourist spots at the user's travel destinations. By considering the user's geographical location information, more appropriate improvement suggestions can be made. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into the generating AI and have the generating AI select the optimal improvement method.

[0051] The improvement unit can analyze users' social media activity and propose improvement measures during the improvement process. For example, the improvement unit can use a generating AI to analyze places shared by users on social media and incorporate them into improvement suggestions. The improvement unit can also use a generating AI to analyze events that users have shown interest in on social media and incorporate them into improvement suggestions. The improvement unit can also use a generating AI to analyze places visited by users' social media followers and incorporate them into improvement suggestions. This allows for more appropriate improvement suggestions to be made by analyzing users' social media activity. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user social media activity data into a generating AI and have the generating AI execute suggestions for improvement measures.

[0052] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0053] The date plan suggestion system can consider a user's past dating history when analyzing their profile information. For example, the generating AI can suggest new spots based on the user's ratings of previously visited date spots. Furthermore, it can analyze patterns of date plans the user has preferred in the past and suggest similar plans. It can also exclude date spots the user has avoided in the past and generate new suggestions. In this way, by analyzing the user's past dating history, it can suggest more appropriate date plans.

[0054] The date plan suggestion system can filter suggestions based on the user's current lifestyle and interests. For example, the AI ​​can suggest date spots related to a hobby the user has recently taken an interest in. It can also suggest relaxing date plans considering the user's current work situation. Furthermore, it can analyze the user's recent social media activity and suggest events of interest. This allows for more appropriate date plan suggestions by filtering based on the user's current lifestyle and interests.

[0055] The date plan suggestion system can prioritize analyzing highly relevant information by considering the user's geographical location. For example, the AI ​​can prioritize analyzing date spots near the user's place of residence. It can also prioritize analyzing lunch spots near the user's workplace. It can also prioritize analyzing tourist spots at the user's travel destination. By prioritizing the analysis of highly relevant information while considering the user's geographical location, the system can suggest more appropriate date plans.

[0056] The date plan suggestion system can analyze a user's social media activity and extract relevant information. For example, the generating AI can analyze places a user has shared on social media and suggest them as date spots. The generating AI can also analyze events a user has shown interest in on social media and incorporate them into the date plan. The generating AI can also analyze places visited by a user's social media followers and suggest them as date spots. In this way, by analyzing a user's social media activity, the system can suggest more appropriate date plans.

[0057] The date plan suggestion system can apply different suggestion algorithms depending on the category of the date plan. For example, for a restaurant date, the generating AI will suggest based on the type of cuisine and atmosphere. For an outdoor date, the generating AI can also suggest based on the weather and season. For an event date, the generating AI can also suggest based on the type of event and location. By applying different suggestion algorithms depending on the category of the date plan, the system can suggest more appropriate date plans.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The profile analysis unit analyzes the user's profile information. For example, it analyzes information such as the user's hobbies, common interests, and place of residence, and uses a generation AI to output recommended date spots. Step 2: The plan proposal unit proposes a date plan based on the information analyzed by the profile analysis unit. For example, it customizes and proposes specific plans such as restaurant reservations, event information, and picnic spots, and uses a generation AI to propose a date plan that matches the user's preferences. Step 3: The feedback collection unit collects feedback on the date plan proposed by the plan proposal unit. For example, it collects feedback after the date and outputs feedback data using a generation AI. Step 4: The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit. For example, it takes the collected feedback as input and uses a generation AI to output an improved date plan.

[0060] (Example of form 2) The date plan suggestion system according to an embodiment of the present invention is a system that suggests date plans using generative AI. This date plan suggestion system can improve user satisfaction by analyzing the user's profile information, suggesting date plans, collecting feedback, and improving the next date plan. For example, the date plan suggestion system performs profile analysis based on user information from a matching app. Specifically, it analyzes information such as the user's hobbies, common interests, and place of residence, and selects recommended date spots. This analysis is performed by generative AI. Next, the date plan suggestion system uses generative AI to analyze each user's preferences and proposes individual date plans. For example, it proposes customized plans such as restaurant reservations, event information, and picnic spots. Furthermore, the date plan suggestion system collects feedback after the date, and the generative AI reflects this in the next suggestion. This allows for the provision of more satisfying plans through continuous learning. This reduces the effort required to prepare for dates and improves user satisfaction. In addition, by providing individually optimized date plans, users can share new experiences with their partners and deepen their relationships. Furthermore, it is expected that the app's retention rate will also improve. This allows the date plan suggestion system to automatically propose date plans to users, collect feedback, and improve the next date plan.

[0061] The date plan suggestion system according to the embodiment comprises a profile analysis unit, a plan suggestion unit, a feedback collection unit, and an improvement unit. The profile analysis unit analyzes the user's profile information. For example, the profile analysis unit analyzes information such as the user's hobbies, common interests, and place of residence. The profile analysis unit can analyze the user's profile information using a generative AI. For example, the profile analysis unit analyzes the information using a generative AI model that takes information such as the user's hobbies, common interests, and place of residence as input and outputs recommended date spots. The plan suggestion unit proposes a date plan based on the information analyzed by the profile analysis unit. For example, the plan suggestion unit proposes a customized plan, such as restaurant reservations, event information, and picnic spots. The plan suggestion unit can propose a date plan that matches the user's wishes using a generative AI. For example, the plan suggestion unit proposes a plan using a generative AI model that takes the user's wishes as input and outputs a date plan. The feedback collection unit collects feedback on the date plan proposed by the plan suggestion unit. For example, the feedback collection unit collects feedback after the date. The feedback collection unit can collect feedback using generative AI. For example, the feedback collection unit collects feedback using a generative AI model that takes post-date feedback as input and outputs feedback data. The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit. The improvement unit improves the next date plan based on the collected feedback. The improvement unit can improve the next date plan using generative AI. For example, the improvement unit improves the next date plan using a generative AI model that takes the collected feedback as input and outputs an improved date plan. As a result, the date plan suggestion system according to the embodiment can automatically suggest a date plan for the user, collect feedback, and improve the next date plan.

[0062] The profile analysis unit analyzes user profile information. Specifically, it collects detailed information provided by users, such as hobbies, interests, place of residence, age, gender, and occupation, and uses this data to understand the user's preferences and lifestyle. By using generative AI, this information can be analyzed at a high level to recommend the most suitable dating spots and activities to the user. For example, if a user enjoys outdoor activities, the generative AI will suggest nearby hiking trails and campsites. If a user enjoys visiting art museums, the generative AI will suggest the latest exhibition information and special events at museums. The generative AI can also take into account the user's past dating history and feedback to provide more personalized suggestions. Furthermore, the generative AI can collect data from the user's social media accounts and online activities to gain a deeper understanding of the user's interests and preferences. As a result, the profile analysis unit can integrate diverse user information and perform highly accurate analysis.

[0063] The plan proposal department proposes date plans based on information analyzed by the profile analysis department. Specifically, it considers information such as the user's hobbies, interests, and place of residence, and proposes customized plans including restaurant reservations, event information, and picnic spots. By using generative AI, it is possible to automatically generate date plans that match the user's wishes. For example, if the user wants a romantic dinner, the generative AI will search for highly-rated restaurants in the vicinity and suggest available reservation times. If the user wants an active date, the generative AI will suggest sports events or outdoor activities. The generative AI can also take into account the user's past dating history and feedback to provide more personalized suggestions. Furthermore, the generative AI can also take into account external factors such as weather, season, and specific events or festivals to propose the optimal date plan. As a result, the plan proposal department can provide the best date plan for the user and increase the success rate of the date.

[0064] The Feedback Collection Unit collects feedback on the date plans proposed by the Plan Proposal Unit. Specifically, it collects feedback from users after the date to understand their satisfaction level and areas for improvement. By using Generative AI, feedback can be collected and analyzed efficiently. For example, a questionnaire is sent to users after the date to collect evaluations and comments on each element of the date. The Generative AI analyzes this feedback data to identify which parts of the date plan were successful and which parts could be improved. Furthermore, based on user feedback, the Generative AI can make suggestions to make the next date plan even better. In addition, by anonymizing user feedback and storing it in a database, the Feedback Collection Unit can also help improve other users' date plans. In this way, the Feedback Collection Unit can play an important role in increasing user satisfaction.

[0065] The Improvement Department improves the next date plan based on the feedback collected by the Feedback Collection Department. Specifically, it analyzes the collected feedback and incorporates it into the next date plan. By using Generative AI, feedback can be efficiently analyzed and the next date plan can be automatically improved. For example, if a user was not satisfied with a particular restaurant on the last date, the Generative AI will remove that restaurant from the next date plan and suggest another highly-rated restaurant instead. Also, if a user enjoyed a particular activity, the Generative AI can include a similar activity in the next date plan. Based on user feedback, the Generative AI can optimize each element of the date plan and make suggestions to increase user satisfaction. Furthermore, by continuously collecting user feedback and iteratively improving the date plan, the Improvement Department can provide the optimal date plan for the user. In this way, the Improvement Department can play a crucial role in improving the user's dating experience.

[0066] The profile analysis unit can analyze information such as a user's hobbies, common interests, and place of residence. For example, the profile analysis unit can analyze information such as a user's hobbies, common interests, and place of residence using a generative AI model that takes the user's hobbies, common interests, and place of residence as input and outputs recommended date spots. This allows for the suggestion of more appropriate date plans by analyzing the user's hobbies, common interests, and place of residence.

[0067] The plan proposal department can customize and propose specific plans such as restaurant reservations, event information, and picnic spots. For example, the plan proposal department can use generative AI to propose date plans that match the user's preferences. This allows the department to provide date plans that are tailored to the user's desires by customizing and proposing specific plans.

[0068] The feedback collection unit can collect feedback after a date. For example, the feedback collection unit collects feedback after a date. The feedback collection unit can collect feedback using generative AI. For example, the feedback collection unit collects feedback using a generative AI model that takes post-date feedback as input and outputs feedback data. This allows for the collection of post-date feedback, which can then be used to improve future date plans.

[0069] The improvement unit can improve the next date plan based on the collected feedback. For example, the improvement unit can improve the next date plan based on the collected feedback. The improvement unit can improve the next date plan using generative AI. For example, the improvement unit can improve the next date plan using a generative AI model that takes the collected feedback as input and outputs an improved date plan. This improves user satisfaction by improving the next date plan based on the collected feedback.

[0070] The profile analysis unit can estimate the user's emotions and adjust the accuracy of the analysis results based on the estimated emotions. For example, if the user is stressed, the profile analysis unit's generating AI can prioritize analyzing relaxing date spots. If the user is excited, the profile analysis unit can also prioritize analyzing active date spots. If the user is depressed, the profile analysis unit can also prioritize analyzing healing date spots. By adjusting the accuracy of the analysis results based on the user's emotions, a more appropriate date plan can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the profile analysis unit may be performed using AI, or not using AI. For example, the profile analysis unit can input user emotion data into the generating AI and have the generating AI perform an adjustment of the accuracy of the analysis results based on emotions.

[0071] The profile analysis unit can analyze a user's past dating history and select the optimal analysis method. For example, the profile analysis unit can use its generating AI to suggest new dating spots based on the user's ratings of past dating locations. The profile analysis unit can also analyze patterns of dating plans the user has preferred in the past, and the generating AI can suggest similar plans. The profile analysis unit can also exclude dating spots the user has avoided in the past, and the generating AI can make new suggestions. In this way, by analyzing the user's past dating history, more appropriate dating plans can be suggested. Some or all of the above processes in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input the user's past dating history data into the generating AI and have the generating AI select the optimal analysis method.

[0072] The profile analysis unit can filter the user's profile based on their current lifestyle and areas of interest. For example, the profile analysis unit can use a generating AI to suggest date spots related to a hobby the user has recently become interested in. The profile analysis unit can also use a generating AI to suggest a relaxing date plan, taking into account the user's current work situation. The profile analysis unit can also analyze the user's recent social media activity and use a generating AI to suggest events of interest. This allows for the suggestion of a more appropriate date plan by filtering based on the user's current lifestyle and areas of interest. Some or all of the above processing in the profile analysis unit may be performed using AI, for example, or not. For example, the profile analysis unit can input the user's current lifestyle data into a generating AI and have the generating AI perform the filtering.

[0073] The profile analysis unit can estimate the user's emotions and determine the priority of information to analyze based on the estimated emotions. For example, if the user is stressed, the profile analysis unit can have the generating AI prioritize information that promotes relaxation. If the user is excited, the profile analysis unit can have the generating AI prioritize information that promotes activity. If the user is depressed, the profile analysis unit can have the generating AI prioritize information that promotes comfort. By prioritizing the information to analyze based on the user's emotions, a more appropriate date plan can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the profile analysis unit may be performed using AI, or not using AI. For example, the profile analysis unit can input user emotion data into the generating AI and have the generating AI perform emotion-based information prioritization.

[0074] The profile analysis unit can prioritize the analysis of highly relevant information by considering the user's geographical location during profile analysis. For example, the profile analysis unit can have the generating AI prioritize the analysis of date spots around the user's place of residence. The profile analysis unit can also have the generating AI prioritize the analysis of lunch spots around the user's workplace. The profile analysis unit can also have the generating AI prioritize the analysis of tourist spots at the user's travel destinations. By prioritizing the analysis of highly relevant information while considering the user's geographical location, it can propose a more appropriate date plan. Some or all of the above processing in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input the user's geographical location information into the generating AI and have the generating AI perform the priority analysis of highly relevant information.

[0075] The profile analysis unit can analyze a user's social media activity and extract relevant information during profile analysis. For example, the profile analysis unit can use a generating AI to analyze places a user has shared on social media and suggest them as date spots. The profile analysis unit can also use a generating AI to analyze events a user has shown interest in on social media and incorporate them into a date plan. The profile analysis unit can also use a generating AI to analyze places visited by a user's social media followers and suggest them as date spots. This allows for the suggestion of more appropriate date plans by analyzing the user's social media activity. Some or all of the above-described processes in the profile analysis unit may be performed using AI, for example, or without AI. For example, the profile analysis unit can input user social media activity data into a generating AI and have the generating AI perform the analysis of relevant information.

[0076] The plan suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is relaxed, the generation AI can suggest a date plan using calm language. If the user is excited, the generation AI can suggest a date plan using energetic language. If the user is depressed, the generation AI can suggest a date plan using encouraging language. By adjusting the way suggestions are expressed based on the user's emotions, a more appropriate date plan can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan suggestion unit may be performed using AI or not. For example, the plan suggestion unit can input user emotion data into the generation AI and have the generation AI adjust the way suggestions are expressed based on those emotions.

[0077] The plan proposal unit can adjust the level of detail in its proposals based on the importance of the date plan. For example, the generation AI can provide detailed proposals for special anniversary date plans. For everyday date plans, the generation AI can provide concise proposals. For first dates, the generation AI can provide carefully detailed proposals. By adjusting the level of detail in proposals based on the importance of the date plan, the unit can propose more appropriate date plans. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or not using AI. For example, the plan proposal unit can input date plan importance data into the generation AI and have the generation AI adjust the level of detail in the proposals.

[0078] The plan suggestion unit can apply different suggestion algorithms depending on the category of the date plan when making suggestions. For example, in the case of a restaurant date, the generating AI can make suggestions based on the type of cuisine and atmosphere. In the case of an outdoor date, the generating AI can also make suggestions based on the weather and season. In the case of an event date, the generating AI can also make suggestions based on the type of event and location. This allows for the suggestion of more appropriate date plans by applying different suggestion algorithms depending on the category of the date plan. Some or all of the above processing in the plan suggestion unit may be performed using AI, for example, or without AI. For example, the plan suggestion unit can input date plan category data into the generating AI and have the generating AI execute the application of different suggestion algorithms.

[0079] The plan suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is in a hurry, the generating AI can provide a short, to-the-point suggestion. If the user is relaxed, the generating AI can provide a more detailed suggestion. If the user is excited, the generating AI can provide an energetic suggestion. By adjusting the length of suggestions based on the user's emotions, a more appropriate date plan can be suggested. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the plan suggestion unit may be performed using AI or not. For example, the plan suggestion unit can input user emotion data into the generating AI and have the generating AI adjust the length of suggestions based on those emotions.

[0080] The plan proposal unit can determine the priority of proposals based on the timing of the date plan's implementation. For example, the plan proposal unit may prioritize suggesting the most recent date plan. The plan proposal unit may also prioritize suggesting date plans appropriate for the season. The plan proposal unit may also prioritize suggesting date plans tailored to specific events. By prioritizing proposals based on the timing of the date plan's implementation, it is possible to propose more appropriate date plans. Some or all of the above processing in the plan proposal unit may be performed using AI, for example, or not using AI. For example, the plan proposal unit can input date plan implementation timing data into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0081] The plan suggestion unit can adjust the order of suggestions based on the relevance of the date plans. For example, the plan suggestion unit may prioritize suggesting date plans related to the user's hobbies. It may also prioritize suggesting date plans close to the user's place of residence. It may also prioritize suggesting date plans that are highly relevant based on the user's past dating history. By adjusting the order of suggestions based on the relevance of the date plans, it is possible to suggest more appropriate date plans. Some or all of the above processing in the plan suggestion unit may be performed using AI, for example, or not using AI. For example, the plan suggestion unit can input date plan relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0082] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. For example, if the user is relaxed, the feedback collection unit may have the generative AI request detailed feedback. If the user is in a hurry, the feedback collection unit may have the generative AI request concise feedback. If the user is excited, the feedback collection unit may have the generative AI request emotional feedback. This allows for the collection of more appropriate feedback by adjusting the feedback collection method based on the user's emotions. 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, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI, or not using AI. For example, the feedback collection unit can input user emotion data into the generative AI and have the generative AI adjust the feedback collection method based on emotions.

[0083] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, if the user has provided detailed feedback in the past, the generating AI can request detailed feedback. If the user has provided concise feedback in the past, the generating AI can also request concise feedback. If the user has provided emotional feedback in the past, the generating AI can also request emotional feedback. This allows for the collection of more appropriate feedback by referring to the user's past feedback history. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's past feedback history data into the generating AI and have the generating AI select the optimal collection method.

[0084] The feedback collection unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is relaxed, the feedback collection unit's generative AI can prioritize collecting detailed feedback. If the user is in a hurry, the feedback collection unit's generative AI can also prioritize collecting concise feedback. If the user is excited, the feedback collection unit's generative AI can also prioritize collecting emotional feedback. This allows for the collection of more appropriate feedback by prioritizing feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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. Some or all of the above processing in the feedback collection unit may be performed using AI, or not using AI. For example, the feedback collection unit can input user emotion data into the generative AI and have the generative AI determine the priority of emotion-based feedback.

[0085] The feedback collection unit can select the optimal collection method when collecting feedback, taking into account the user's device information. For example, if the user is using a smartphone, the generating AI can provide a mobile-friendly feedback form. If the user is using a tablet, the generating AI can also provide a feedback form optimized for a large screen. If the user is using a desktop, the generating AI can also provide a detailed feedback form. This allows for the collection of more appropriate feedback by taking into account the user's device information. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's device information into the generating AI and have the generating AI select the optimal collection method.

[0086] The improvement unit can estimate the user's emotions and adjust the improvement methods based on the estimated emotions. For example, if the user is relaxed, the improvement unit's generating AI can make gentle improvement suggestions. If the user is excited, the improvement unit's generating AI can also make energetic improvement suggestions. If the user is depressed, the improvement unit's generating AI can also make encouraging improvement suggestions. In this way, by adjusting the improvement methods based on the user's emotions, more appropriate improvement suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user emotion data into the generating AI and have the generating AI perform adjustments to the improvement methods based on the emotions.

[0087] The improvement unit can analyze the user's past date plans and select the optimal improvement method during the improvement process. For example, the improvement unit can use a generating AI to suggest improvements based on date plans the user has liked in the past. The improvement unit can also exclude date plans the user has avoided in the past and have the generating AI suggest new ones. The improvement unit can also use feedback from the user on date plans they have previously evaluated to suggest improvements. This allows for more appropriate improvement suggestions by analyzing the user's past date plans. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's past date plan data into the generating AI and have the generating AI select the optimal improvement method.

[0088] The improvement unit can customize the means of improvement based on the user's current lifestyle when making improvements. For example, if the user is busy, the generation AI can provide concise improvement suggestions. If the user is relaxed, the generation AI can also provide detailed improvement suggestions. If the user has started a new hobby, the generation AI can also provide improvement suggestions related to that hobby. This allows for more appropriate improvement suggestions to be made by customizing the means of improvement based on the user's current lifestyle. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input data on the user's current lifestyle into the generation AI and have the generation AI customize the means of improvement.

[0089] The improvement unit can estimate the user's emotions and determine improvement priorities based on those emotions. For example, if the user is relaxed, the improvement unit's generative AI may prioritize detailed improvement suggestions. If the user is in a hurry, the improvement unit may also prioritize concise improvement suggestions. If the user is excited, the improvement unit may also prioritize energetic improvement suggestions. This allows for more appropriate improvement suggestions by determining improvement priorities 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI or not. For example, the improvement unit can input user emotion data into the generative AI and have the generative AI determine improvement priorities based on emotions.

[0090] The improvement unit can select the optimal improvement method by considering the user's geographical location information during the improvement process. For example, the improvement unit can have the generating AI prioritize suggesting improvement options for date spots near the user's place of residence. The improvement unit can also have the generating AI prioritize suggesting improvement options for lunch spots near the user's workplace. The improvement unit can also have the generating AI prioritize suggesting improvement options for tourist spots at the user's travel destinations. By considering the user's geographical location information, more appropriate improvement suggestions can be made. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into the generating AI and have the generating AI select the optimal improvement method.

[0091] The improvement unit can analyze users' social media activity and propose improvement measures during the improvement process. For example, the improvement unit can use a generating AI to analyze places shared by users on social media and incorporate them into improvement suggestions. The improvement unit can also use a generating AI to analyze events that users have shown interest in on social media and incorporate them into improvement suggestions. The improvement unit can also use a generating AI to analyze places visited by users' social media followers and incorporate them into improvement suggestions. This allows for more appropriate improvement suggestions to be made by analyzing users' social media activity. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user social media activity data into a generating AI and have the generating AI execute suggestions for improvement measures.

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

[0093] The date plan suggestion system can consider a user's past dating history when analyzing their profile information. For example, the generating AI can suggest new spots based on the user's ratings of previously visited date spots. Furthermore, it can analyze patterns of date plans the user has preferred in the past and suggest similar plans. It can also exclude date spots the user has avoided in the past and generate new suggestions. In this way, by analyzing the user's past dating history, it can suggest more appropriate date plans.

[0094] The date plan suggestion system can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is relaxed, the generating AI will suggest a date plan using calm language. If the user is excited, the generating AI can suggest a date plan using energetic language. If the user is depressed, the generating AI can suggest a date plan using encouraging language. By adjusting the way suggestions are presented based on the user's emotions, the system can suggest a more appropriate date plan.

[0095] The date plan suggestion system can filter suggestions based on the user's current lifestyle and interests. For example, the AI ​​can suggest date spots related to a hobby the user has recently taken an interest in. It can also suggest relaxing date plans considering the user's current work situation. Furthermore, it can analyze the user's recent social media activity and suggest events of interest. This allows for more appropriate date plan suggestions by filtering based on the user's current lifestyle and interests.

[0096] The date plan suggestion system can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the generating AI will make a short, to-the-point suggestion. If the user is relaxed, the generating AI can make a more detailed suggestion. If the user is excited, the generating AI can make an energetic suggestion. By adjusting the length of suggestions based on the user's emotions, the system can suggest a more appropriate date plan.

[0097] The date plan suggestion system can prioritize analyzing highly relevant information by considering the user's geographical location. For example, the AI ​​can prioritize analyzing date spots near the user's place of residence. It can also prioritize analyzing lunch spots near the user's workplace. It can also prioritize analyzing tourist spots at the user's travel destination. By prioritizing the analysis of highly relevant information while considering the user's geographical location, the system can suggest more appropriate date plans.

[0098] The date plan suggestion system can estimate the user's emotions and adjust how feedback is collected based on those emotions. For example, if the user is relaxed, the generating AI may request detailed feedback. If the user is in a hurry, the generating AI may request concise feedback. If the user is excited, the generating AI may request emotional feedback. This allows for the collection of more appropriate feedback by adjusting how feedback is collected based on the user's emotions.

[0099] The date plan suggestion system can analyze a user's social media activity and extract relevant information. For example, the generating AI can analyze places a user has shared on social media and suggest them as date spots. The generating AI can also analyze events a user has shown interest in on social media and incorporate them into the date plan. The generating AI can also analyze places visited by a user's social media followers and suggest them as date spots. In this way, by analyzing a user's social media activity, the system can suggest more appropriate date plans.

[0100] The date plan suggestion system can estimate the user's emotions and adjust the improvement methods based on those emotions. For example, if the user is relaxed, the generating AI will offer gentle improvement suggestions. If the user is excited, the generating AI can offer energetic suggestions. If the user is depressed, the generating AI can offer encouraging suggestions. By adjusting the improvement methods based on the user's emotions, the system can provide more appropriate improvement suggestions.

[0101] The date plan suggestion system can apply different suggestion algorithms depending on the category of the date plan. For example, for a restaurant date, the generating AI will suggest based on the type of cuisine and atmosphere. For an outdoor date, the generating AI can also suggest based on the weather and season. For an event date, the generating AI can also suggest based on the type of event and location. By applying different suggestion algorithms depending on the category of the date plan, the system can suggest more appropriate date plans.

[0102] The date plan suggestion system can estimate the user's emotions and prioritize improvements based on those emotions. For example, if the user is relaxed, the generating AI will prioritize detailed improvement suggestions. If the user is in a hurry, the generating AI can prioritize concise suggestions. If the user is excited, the generating AI can prioritize energetic suggestions. By prioritizing improvements based on the user's emotions, the system can provide more appropriate suggestions.

[0103] The following briefly describes the processing flow for example form 2.

[0104] Step 1: The profile analysis unit analyzes the user's profile information. For example, it analyzes information such as the user's hobbies, common interests, and place of residence, and uses a generation AI to output recommended date spots. Step 2: The plan proposal unit proposes a date plan based on the information analyzed by the profile analysis unit. For example, it customizes and proposes specific plans such as restaurant reservations, event information, and picnic spots, and uses a generation AI to propose a date plan that matches the user's preferences. Step 3: The feedback collection unit collects feedback on the date plan proposed by the plan proposal unit. For example, it collects feedback after the date and outputs feedback data using a generation AI. Step 4: The improvement unit improves the next date plan based on the feedback collected by the feedback collection unit. For example, it takes the collected feedback as input and uses a generation AI to output an improved date plan.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0106] 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.

[0107] 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.

[0108] Each of the multiple elements described above, including the profile analysis unit, plan proposal unit, feedback collection unit, and improvement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the profile analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes information such as the user's hobbies, common interests, and place of residence. The plan proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a date plan that suits the user's wishes using generated AI. The feedback collection unit is implemented by the control unit 46A of the smart device 14 and collects feedback after the date. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the next date plan based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0122] 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.

[0123] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0124] Each of the multiple elements described above, including the profile analysis unit, plan proposal unit, feedback collection unit, and improvement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the profile analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes information such as the user's hobbies, common interests, and place of residence. The plan proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a date plan that suits the user's wishes using generated AI. The feedback collection unit is implemented by the control unit 46A of the smart glasses 214 and collects feedback after the date. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the next date plan based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0140] Each of the multiple elements described above, including the profile analysis unit, plan proposal unit, feedback collection unit, and improvement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the profile analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes information such as the user's hobbies, common interests, and place of residence. The plan proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a date plan that suits the user's wishes using generated AI. The feedback collection unit is implemented by the control unit 46A of the headset terminal 314 and collects feedback after the date. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the next date plan based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0155] 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.

[0156] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0157] Each of the multiple elements described above, including the profile analysis unit, plan proposal unit, feedback collection unit, and improvement unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the profile analysis unit is implemented by the control unit 46A of the robot 414 and analyzes information such as the user's hobbies, common interests, and place of residence. The plan proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a date plan that suits the user's wishes using generated AI. The feedback collection unit is implemented by the control unit 46A of the robot 414 and collects feedback after the date. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves the next date plan based on the collected feedback. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

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

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] (Note 1) The profile analysis unit analyzes the user's profile information, A plan proposal unit proposes a date plan based on the information analyzed by the aforementioned profile analysis unit, A feedback collection unit collects feedback on the date plan proposed by the aforementioned plan proposal unit, The system includes an improvement unit that improves the next date plan based on the feedback collected by the feedback collection unit. A system characterized by the following features. (Note 2) The aforementioned profile analysis unit, Analyzes user information such as hobbies, common interests, and place of residence. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned plan proposal department, We offer customized suggestions for specific plans, such as restaurant reservations, event information, and picnic spots. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback collection unit is Collect feedback after the date. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned improvement unit is, We will improve our next date plan based on the feedback we receive. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned profile analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned profile analysis unit, Analyze the user's past dating history and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned profile analysis unit, During profile analysis, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned profile analysis unit, It estimates the user's emotions and determines the priority of information to analyze based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned profile analysis unit, During profile analysis, the system prioritizes analyzing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned profile analysis unit, During profile analysis, the system analyzes the user's social media activity and extracts relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned plan proposal department, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned plan proposal department, When making a proposal, adjust the level of detail based on the importance of the date plan. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned plan proposal department, When making suggestions, different suggestion algorithms are applied depending on the category of the date plan. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned plan proposal department, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned plan proposal department, When making proposals, prioritize them based on when the date plan will be implemented. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned plan proposal department, When making suggestions, adjust the order of suggestions based on the relevance of the date plan. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned feedback collection unit is We estimate the user's emotions and adjust the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback collection unit is When collecting feedback, the system selects the optimal collection method by referring to the user's past feedback history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback collection unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback collection unit is When collecting feedback, the optimal collection method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned improvement unit is, When making improvements, we analyze the user's past dating plans to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned improvement unit is, When making improvements, customize the methods of improvement based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned improvement unit is, We estimate user emotions and determine improvement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned improvement unit is, When making improvements, the optimal improvement method will be selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned improvement unit is, During the improvement process, we analyze users' social media activity and propose ways to make improvements. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0177] 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 profile analysis unit analyzes the user's profile information, A plan proposal unit proposes a date plan based on the information analyzed by the aforementioned profile analysis unit, A feedback collection unit collects feedback on the date plan proposed by the aforementioned plan proposal unit, The system includes an improvement unit that improves the next date plan based on the feedback collected by the feedback collection unit. A system characterized by the following features.

2. The aforementioned profile analysis unit, Analyzes user information such as hobbies, common interests, and place of residence. The system according to feature 1.

3. The aforementioned plan proposal department, We offer customized suggestions for specific plans, such as restaurant reservations, event information, and picnic spots. The system according to feature 1.

4. The aforementioned feedback collection unit is Collect feedback after the date. The system according to feature 1.

5. The aforementioned improvement unit is, We will improve our next date plan based on the feedback we receive. The system according to feature 1.

6. The aforementioned profile analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis results based on the estimated user emotions. The system according to feature 1.

7. The aforementioned profile analysis unit, Analyze the user's past dating history and select the optimal analysis method. The system according to feature 1.

8. The aforementioned profile analysis unit, During profile analysis, filtering is performed based on the user's current lifestyle and areas of interest. The system according to feature 1.

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