system

Abstract

The system according to the embodiment aims to generate and provide an ideal date plan based on the user's preferences and budget. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or budget. The generation unit analyzes the information received by the reception unit and generates a date plan. The provision unit provides the date plan generated by the generation unit. The learning unit learns from the date plan generated by the generation unit and reflects this in the next proposal.

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that it is difficult to efficiently generate and provide date plans based on a user's preferences and budget.

[0005] The system according to the embodiment aims to generate and provide an ideal date plan based on the user's preferences and budget. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit receives input of a user's preferences or budget. The generation unit analyzes the information received by the reception unit and generates a date plan. The provision unit provides the date plan generated by the generation unit. The learning unit learns the date plan generated by the generation unit and reflects it in the next proposal. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide an ideal date plan based on the user's preferences and budget. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A date plan suggestion system according to an embodiment of the present invention proposes an ideal date plan, taking into account a user's preferences and budget. The date plan suggestion system accepts input from the user regarding preferences and budget, and a generation AI analyzes this information to present new ideas and recommended locations. The generation AI learns from past date plans and user feedback to generate an optimal plan. For example, if a user inputs "a place where I can enjoy nature within a budget of 5,000 yen," the generation AI will suggest places where you can enjoy nature. The generation AI generates a plan to provide a special experience by taking into account the user's preferences and past dating history. For example, it suggests new locations and activities based on the user's past visits and favorite activities. This allows users to discover new locations and activities and makes it easier to create date plans. Furthermore, the date plan suggestion system allows couples to share special moments and build deeper bonds. For example, the plans proposed by the generation AI include activities that can be enjoyed together and romantic locations, helping couples deepen their relationship. Users can also customize the plans proposed by the generation AI to create date plans tailored to their preferences. As a result, the date plan suggestion system is a groundbreaking solution for providing couples with a special experience and making it easy to create date plans. As a result, the date plan suggestion system can suggest ideal date plans based on the user's preferences and budget, providing a special experience. For example, the system can enable users to discover new places and activities and share special time. It can also help couples deepen their relationships.

[0029] A date plan suggestion system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit accepts input of a user's preferences or budget. The user's preferences include, but are not limited to, for example, food preferences, activity preferences, and location preferences. The budget includes, but is not limited to, for example, in units of 1,000 yen or 10,000 yen. The generation unit uses a generation AI to analyze the information accepted by the reception unit and generate a date plan. The generation AI may use a natural language generation model such as GPT-4 (registered trademark) or Gemini. The generation unit uses the generation AI to present new ideas and recommended locations. For example, the generation AI may learn from past date plans and user feedback to generate an optimal plan. The provision unit provides the date plan generated by the generation unit to the user. Methods of providing the date plan include, but are not limited to, for example, email, app notification, and website display. The learning unit uses the generation AI to learn from the date plan generated by the generation unit and reflect the learning in the next proposal. The learning method may include, but is not limited to, a machine learning algorithm, a feedback capturing method, etc. As a result, the date plan suggestion system according to the embodiment can suggest an ideal date plan based on the user's preferences and budget, and provide a special experience.

[0030] The generation unit can present new ideas and recommended places using a generation AI. The generation unit presents new ideas and recommended places using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit learns from past date plans and user feedback to generate an optimal plan. For example, the generation AI extracts new ideas from past data and presents them to the user. The generation AI can also select recommended places taking into account the user's preferences and past dating history. In this way, new ideas and recommended places can be effectively presented using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can present ideas and places using a generation AI model that inputs the user's preferences and budget and outputs new ideas and recommended places.

[0031] The generation unit can learn the user's past dating history and feedback and generate an appropriate plan. For example, the generation unit can learn the user's past dating history and feedback and generate an appropriate plan. Past dating history includes, but is not limited to, the date, time, location, and activity details. Feedback includes, but is not limited to, survey results and user ratings. The generation unit uses a generation AI to learn the past dating history and feedback and generate an optimal plan. For example, the generation AI can analyze the user's preferences and tendencies based on the past dating history and generate an appropriate plan. The generation AI can also incorporate user feedback and reflect it in its next proposal. This allows for the generation of a more appropriate date plan by taking the user's past dating history and feedback into consideration. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can generate a plan using a generation AI model that inputs the user's past dating history and feedback and outputs an appropriate plan.

[0032] The providing unit can provide the generated date plan to the user. For example, the providing unit provides the date plan generated by the generating unit to the user. The providing method includes, but is not limited to, email, app notification, website display, etc. The providing unit can also select the optimal providing method for the user using a generating AI. For example, the providing unit selects the optimal providing method based on the user's past usage history. The providing unit can also incorporate user feedback and improve the providing method. This allows the user to easily use the date plan by providing the generated date plan to the user. Some or all of the above-mentioned processing in the providing unit is performed using a generating AI. For example, the providing unit can provide the plan using a generating AI model that inputs the date plan generated by the generating unit and outputs the optimal providing method.

[0033] The learning unit can learn user feedback using the generation AI and reflect it in the next proposal. The learning unit, for example, uses the generation AI to learn user feedback and reflect it in the next proposal. Feedback includes, for example, survey results and user ratings, but is not limited to these examples. The learning unit uses the generation AI to incorporate user feedback and reflect it in the next proposal. For example, the generation AI can improve the accuracy of the next proposal based on the user feedback. The generation AI can also analyze the content of the feedback and identify areas for improvement in the proposal. In this way, by learning user feedback, the accuracy of the next proposal can be improved. Some or all of the above-mentioned processing in the learning unit is performed using the generation AI. For example, the learning unit can learn the feedback using a generative AI model that inputs user feedback and outputs data to be reflected in the next proposal.

[0034] The provision unit may provide a function that allows a user to customize a proposed plan. For example, the provision unit may provide a function that allows a user to customize a proposed plan. Customization may include, but is not limited to, changing the schedule, adding or deleting activities, and the like. The provision unit may also support customization to suit the user's preferences using a generative AI. For example, the provision unit may suggest optimal customization options based on the user's past customization history. The provision unit may also incorporate user feedback and improve the customization function. This allows the user to customize the proposed plan to suit their preferences. Some or all of the above-described processing in the provision unit is performed using a generative AI. For example, the provision unit may customize the plan using a generative AI model that inputs the proposed plan and outputs customization options.

[0035] The reception unit can analyze the user's past input history and propose an optimal input format. The reception unit, for example, analyzes the user's past input history and proposes an optimal input format. The input history includes, for example, past input data, input date and time, etc., but is not limited to these examples. The reception unit uses a generation AI to analyze the past input history and propose an optimal input format. For example, preferences and budgets frequently entered by the user in the past can be automatically displayed as candidates. The reception unit can also prioritize proposals based on input methods (voice, text, etc.) used by the user in the past. Furthermore, preferences and budgets to be used in specific time periods can be predicted and proposed based on the user's past input history. In this way, by analyzing the past input history, the optimal input format can be provided to the user. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can propose an input format using a generation AI model that inputs the past input history and outputs an optimal input format.

[0036] The reception unit can dynamically change input items based on the user's current situation. The reception unit dynamically changes input items based on, for example, the user's current situation (e.g., weather or time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The reception unit uses a generation AI to analyze the user's current situation and dynamically change the input items. For example, when it rains, input items for indoor date plans can be displayed preferentially. Also, at night, input items for places with enjoyable night views can be displayed preferentially. Furthermore, on holidays, input items for long date plans can be displayed preferentially. In this way, by changing the input items according to the current situation, more appropriate date plans can be proposed. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can dynamically change input items using a generation AI model that inputs current situation data and outputs optimal input items.

[0037] The reception unit can select the optimal input means depending on the user's input method. The reception unit selects the optimal input means depending on, for example, the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. The reception unit uses a generative AI to analyze the user's input method and select the optimal input means. For example, if the user selects voice input, the reception unit uses voice recognition technology to input preferences and budget. Also, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also use image analysis technology to input preferences and budget. This improves input convenience by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can select the input means using a generative AI model that receives the user's input method data as input and outputs the optimal input means.

[0038] The reception unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. For example, the reception unit prioritizes displaying highly relevant input items by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. The reception unit uses a generation AI to analyze the user's geographical location information and prioritize displaying highly relevant input items. For example, if the user is in an urban area, the reception unit can prioritize displaying input items for date plans within the city. Also, if the user is in a suburban area, the reception unit can prioritize displaying input items for date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the reception unit can prioritize displaying input items for date plans that include tourist spots. This allows the user to be provided with highly relevant input items by taking into account the geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can prioritize displaying input items by using a generation AI model that inputs geographical location information and outputs highly relevant input items.

[0039] The reception unit can analyze the user's social media activity and suggest related input items. The reception unit, for example, analyzes the user's social media activity and suggests related input items. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit uses a generative AI to analyze the user's social media activity and suggest related input items. For example, based on the location where the user checked in on social media, the reception unit can suggest related input items for a date plan. The reception unit can also analyze the content of the user's social media posts and suggest input items for related activities. Furthermore, the reception unit can suggest related input items for a date plan based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest related input items for the user. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can suggest input items using a generative AI model that inputs social media activity data and outputs related input items.

[0040] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. Feedback includes, for example, survey results and user evaluations, but is not limited to these examples. The reception unit uses a generative AI to analyze the user's past feedback and customize the input method. For example, the reception unit may preferentially suggest input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. Furthermore, the reception unit can also adjust the input interface to avoid input methods that the user has previously dissatisfied with. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can customize the input method using a generative AI model that inputs past feedback data and outputs the optimal input method.

[0041] The generation unit can adjust the level of detail of the date plan based on the user's preferences and budget when generating the date plan. For example, the generation unit adjusts the level of detail of the plan based on the user's preferences and budget when generating the date plan. Preferences include, but are not limited to, food preferences, activity preferences, and location preferences. Budgets include, but are not limited to, increments of 1,000 yen and 10,000 yen. The generation unit uses a generation AI to analyze the user's preferences and budget and adjust the level of detail of the plan. For example, if the user's budget is limited, the generation unit can generate a cost-effective date plan. It can also generate a date plan that includes detailed activity descriptions based on the user's preferences. Furthermore, if the user has a larger budget, the generation unit can generate a luxurious date plan. By adjusting the level of detail of the plan based on the user's preferences and budget, a more appropriate date plan can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the level of detail of the plan using a generation AI model that inputs the user's preferences and budget and outputs the level of detail of the plan.

[0042] When generating a date plan, the generation unit can improve the accuracy of the plan by referring to the user's past dating history. For example, when generating a date plan, the generation unit can improve the accuracy of the plan by referring to the user's past dating history. Past dating history includes, but is not limited to, dates, locations, and activity details. The generation unit uses a generation AI to analyze the user's past dating history and improve the accuracy of the plan. For example, a new date plan can be generated based on places the user has visited in the past. A date plan including preferred activities can also be generated from the user's past dating history. Furthermore, the generation unit can analyze the user's past dating history and generate an optimal date plan. By referring to the past dating history, the accuracy of the plan can be improved. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can generate a plan using a generation AI model that inputs the user's past dating history and outputs data to improve the accuracy of the plan.

[0043] The generation unit can customize a date plan based on the user's current situation when generating the plan. For example, when generating a date plan, the generation unit customizes the plan based on the user's current situation (e.g., weather and time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The generation unit uses a generation AI to analyze the user's current situation and customize the plan. For example, when it rains, the generation unit generates a date plan that can be enjoyed indoors. Also, at night, the generation unit can generate a date plan that allows you to enjoy the night view. Furthermore, on holidays, the generation unit can generate a date plan that can be enjoyed for a long time. In this way, by customizing the plan according to the current situation, a more appropriate date plan can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can customize the plan using a generation AI model that inputs current situation data and outputs an optimal plan.

[0044] When generating a date plan, the generation unit can propose an optimal plan by taking into account the user's geographical location information. For example, when generating a date plan, the generation unit proposes an optimal plan by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. The generation unit uses a generation AI to analyze the user's geographical location information and propose an optimal plan. For example, if the user is in an urban area, the generation unit can propose a date plan within the city. Also, if the user is in the suburbs, the generation unit can propose a date plan that allows users to enjoy nature. Furthermore, if the user is in a tourist destination, the generation unit can propose a date plan that includes tourist spots. This allows the user to be provided with an optimal date plan by taking into account the geographical location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can propose a plan using a generation AI model that inputs geographical location information and outputs an optimal plan.

[0045] The generation unit can analyze the user's social media activity and suggest related plans when generating a date plan. For example, the generation unit can analyze the user's social media activity and suggest related plans when generating a date plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The generation unit can analyze the user's social media activity and suggest related plans using a generation AI. For example, the generation unit can suggest related date plans based on the locations the user has checked in to on social media. The generation unit can also analyze the content of the user's social media posts and suggest related activities. Furthermore, the generation unit can suggest related date plans based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide relevant date plans to the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can suggest plans using a generation AI model that inputs social media activity data and outputs related plans.

[0046] The generation unit can customize a date plan by reflecting the user's past feedback when generating the plan. For example, the generation unit customizes the plan by reflecting the user's past feedback when generating the date plan. Feedback includes, but is not limited to, survey results and user ratings. The generation unit uses a generation AI to analyze the user's past feedback and customize the plan. For example, a new plan can be generated based on date plans that the user liked in the past. The content of the plan can also be improved based on the user's past feedback. Furthermore, adjustments can be made to avoid plans that the user was dissatisfied with in the past. By reflecting past feedback, the optimal date plan can be provided to the user. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can customize the plan using a generation AI model that inputs past feedback data and outputs an optimal plan.

[0047] The provision unit can select the optimal provision method by referring to the user's past usage history when providing a date plan. For example, when providing a date plan, the provision unit selects the optimal provision method by referring to the user's past usage history. The usage history includes, for example, past usage data, usage dates and times, etc., but is not limited to these examples. The provision unit uses a generation AI to analyze the user's past usage history and select the optimal provision method. For example, the provision unit prioritizes the selection of a provision method that the user has preferred in the past. The provision unit can also suggest the optimal provision method based on the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most efficient provision method. In this way, the optimal provision method for the user can be selected by referring to the past usage history. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can select the provision method using a generation AI model that inputs past usage history and outputs the optimal provision method.

[0048] The provision unit can customize the content to be provided based on the user's current situation when providing a date plan. For example, when providing a date plan, the provision unit customizes the content to be provided based on the user's current situation (e.g., weather and time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The provision unit uses a generation AI to analyze the user's current situation and customize the content to be provided. For example, when it rains, the provision unit provides a date plan that can be enjoyed indoors. Also, at night, the provision unit can provide a date plan that allows for enjoying a night view. Furthermore, on holidays, the provision unit can provide a date plan that can be enjoyed for a long time. In this way, by customizing the content to be provided based on the current situation, a more appropriate date plan can be provided. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can customize the content to be provided using a generation AI model that inputs current situation data and outputs optimal content to be provided.

[0049] The provision unit can improve the provision method by reflecting user feedback when providing a date plan. For example, when providing a date plan, the provision unit improves the provision method by reflecting user feedback. Feedback includes, but is not limited to, survey results and user ratings. The provision unit uses a generation AI to analyze user feedback and improve the provision method. For example, the provision unit can suggest a new provision method based on a provision method that the user has previously preferred. The provision interface can also be improved based on the user's past feedback. Furthermore, it can be adjusted to avoid a provision method that the user has previously dissatisfied with. In this way, by reflecting feedback, the provision method can be improved and the optimal date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can improve the provision method using a generation AI model that inputs feedback data and outputs an optimal provision method.

[0050] The providing unit can select the optimal provision method by taking into consideration the user's geographical location information when providing a date plan. For example, when providing a date plan, the providing unit selects the optimal provision method by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. The providing unit uses a generation AI to analyze the user's geographical location information and select the optimal provision method. For example, if the user is in an urban area, the providing unit can prioritize providing date plans within the city. Also, if the user is in a suburban area, the providing unit can prioritize providing date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the providing unit can prioritize providing date plans that include tourist spots. In this way, the optimal provision method for the user can be selected by taking into consideration the geographical location information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit can select the provision method using a generation AI model that inputs geographical location information and outputs the optimal provision method.

[0051] The provision unit can analyze the user's social media activity and provide a relevant plan when providing a date plan. For example, when providing a date plan, the provision unit can analyze the user's social media activity and provide a relevant plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The provision unit uses a generative AI to analyze the user's social media activity and provide a relevant plan. For example, the provision unit can provide a relevant date plan based on the location where the user checked in on social media. The provision unit can also analyze the content of the user's social media posts and provide relevant activities. Furthermore, the provision unit can provide a relevant date plan based on the activity of the user's friends on social media. In this way, by analyzing social media activity, a relevant date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generative AI. For example, the provision unit can provide a plan using a generative AI model that inputs social media activity data and outputs a relevant plan.

[0052] The provision unit can customize the provision method by reflecting the user's past feedback when providing a date plan. For example, when providing a date plan, the provision unit customizes the provision method by reflecting the user's past feedback. Feedback includes, but is not limited to, survey results and user ratings. The provision unit uses a generation AI to analyze the user's past feedback and customize the provision method. For example, a new provision method can be suggested based on the user's past preferred provision method. The provision interface can also be improved based on the user's past feedback. Furthermore, adjustments can be made to avoid provision methods that the user has previously dissatisfied with. In this way, by reflecting past feedback, the provision method can be customized and the optimal date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can customize the provision method using a generation AI model that inputs past feedback data and outputs the optimal provision method.

[0053] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, learning history, data type, etc. The learning unit analyzes past learning data using a generative AI to optimize the learning algorithm. For example, the learning unit selects an optimal algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust algorithm parameters. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can optimize the learning algorithm using a generative AI model that inputs past learning data and outputs an optimal algorithm.

[0054] The learning unit can update the learning data during learning by reflecting user feedback. For example, the learning unit updates the learning data during learning by reflecting user feedback. Feedback includes, for example, survey results and user evaluations, but is not limited to these examples. The learning unit uses a generative AI to analyze user feedback and update the learning data. For example, the learning unit adds learning data based on user feedback. The learning unit can also analyze user feedback and modify the learning data. Furthermore, the accuracy of the learning data can be improved by reflecting user feedback. In this way, the accuracy of the learning data is improved by reflecting feedback. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can update the learning data using a generative AI model that inputs feedback data and outputs optimal learning data.

[0055] During learning, the learning unit can analyze the user's past dating history and weight the learning data. For example, during learning, the learning unit can analyze the user's past dating history and weight the learning data. The past dating history can include, but is not limited to, date, time, location, and activity details. The learning unit uses a generative AI to analyze the user's past dating history and weight the learning data. For example, important data can be weighted based on the user's past dating history. The learning unit can also analyze the user's past dating history to determine the priority of the learning data. Furthermore, the learning unit can adjust the weighting of the learning data by referring to the user's past dating history. In this way, the learning data can be appropriately weighted by analyzing the past dating history. Some or all of the above-described processing in the learning unit is performed using a generative AI. For example, the learning unit can weight the learning data using a generative AI model that inputs the past dating history and outputs optimal weighting.

[0056] The learning unit can select training data taking into account the user's geographical location information during learning. For example, the learning unit selects training data taking into account the user's geographical location information during learning. Geographical location information includes, but is not limited to, GPS data, location information services, etc. The learning unit uses a generative AI to analyze the user's geographical location information and select training data. For example, if the user is in an urban area, the learning unit can prioritize learning data related to date plans within the city. Also, if the user is in a suburban area, the learning unit can prioritize learning data related to date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the learning unit can prioritize learning data related to tourist spots. This allows optimal training data to be selected for the user by taking into account the geographical location information. Some or all of the above-described processing in the learning unit is performed using a generative AI. For example, the learning unit can select training data using a generative AI model that inputs geographical location information and outputs optimal training data.

[0057] The learning unit can analyze the user's social media activity during learning and incorporate related data into the learning. For example, the learning unit can analyze the user's social media activity during learning and incorporate related data into the learning. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The learning unit uses a generative AI to analyze the user's social media activity and incorporate related data into the learning. For example, the learning unit can learn data about the locations the user checked in to on social media. The learning unit can also analyze the content of the user's social media posts to learn related data. Furthermore, the learning unit can also learn related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data related to the user can be incorporated into the learning. Some or all of the above-described processing in the learning unit can be performed using a generative AI. For example, the learning unit can incorporate social media activity data into the learning using a generative AI model that inputs social media activity data and outputs optimal learning data.

[0058] The learning unit can adjust the learning algorithm during learning by reflecting the user's past feedback. For example, the learning unit adjusts the learning algorithm during learning by reflecting the user's past feedback. Feedback includes, but is not limited to, survey results and user evaluations. The learning unit uses a generative AI to analyze the user's feedback and adjust the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on the user's past feedback. The learning algorithm can also be improved by analyzing the user's past feedback. Furthermore, the accuracy of the learning algorithm can be improved by reflecting the user's past feedback. In this way, the accuracy of the learning algorithm is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can adjust the learning algorithm using a generative AI model that inputs feedback data and outputs an optimal algorithm.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The generator not only generates date plans based on the user's preferences and budget, but can also improve the accuracy of the plans based on the user's past dating history and feedback. For example, it generates plans that provide similar experiences by taking into account places the user has visited and activities they have participated in in the past. It can also propose plans that are more satisfying by incorporating user feedback and elements of plans that were well-received in the past. Furthermore, it can extract specific patterns from the user's dating history and prioritize the proposal of plans that the user tends to prefer. This makes it possible to provide more personalized date plans by utilizing the user's past dating history and feedback.

[0061] The providing unit not only provides the generated date plan to the user, but also customizes the method of providing based on the user's current situation. For example, if the user is out, the providing unit provides the date plan using a smartphone notification. If the user is at home, the providing unit can provide a detailed plan via email or website. Furthermore, if the user often checks date plans during a specific time period, the providing unit can provide a plan tailored to that time period. This allows the optimal method of providing the date plan to be selected according to the user's situation, making it more convenient to use the date plan.

[0062] The reception unit not only accepts input of the user's preferences and budget, but can also analyze the user's social media activity and suggest related input items. For example, it can suggest relevant input items for date plans based on the places the user has checked in and the content of their posts on social media. It can also suggest popular date spots and activities based on the activities of the user's friends. It can also analyze the user's interests on social media and provide customization options based on those interests. This makes it possible to suggest more relevant date plans by utilizing the user's social media activity.

[0063] The providing unit not only provides the generated date plan to the user, but can also select the optimal method of provision by referring to the user's past usage history. For example, if the user has preferred to be provided by email in the past, the providing unit can preferentially select email. Also, if the user has preferred app notifications, the providing unit can preferentially select that method. Furthermore, if the user's past usage history indicates that it is effective to provide the plan during a specific time period, the providing unit can provide the plan according to that time period. In this way, by referring to the past usage history, the optimal method of provision can be selected to the user, making the use of date plans more convenient.

[0064] The learning unit not only learns the user's feedback and reflects it in the next proposal, but can also select learning data taking into account the user's geographical location information. For example, if the user is in an urban area, the learning unit can prioritize learning data related to date plans within the city. Also, if the user is in the suburbs, it can prioritize learning data related to date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, it can prioritize learning data related to tourist spots. In this way, by taking geographical location information into consideration, it is possible to select the most suitable learning data for the user and propose more relevant date plans.

[0065] The learning unit not only learns the user's feedback and reflects it in the next proposal, but can also analyze the user's social media activity and incorporate related data into the learning. For example, it can learn data about the places the user has checked in on social media. It can also analyze the content of the user's social media posts to learn related data. It can also learn related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data related to the user can be incorporated into the learning and more personalized date plans can be proposed.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit receives input of the user's preferences or budget. The user's preferences include, but are not limited to, for example, preferences for food, activities, and locations. The budget includes, but is not limited to, for example, in 1,000 yen increments or 10,000 yen increments. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a date plan. The generation AI can use natural language generation models such as GPT-4 or Gemini. The generation unit uses the generation AI to present new ideas and recommended locations. For example, the generation AI can learn from past date plans and user feedback to generate the optimal plan. Step 3: The providing unit provides the date plan generated by the generating unit to the user. The providing method may include, but is not limited to, email, app notification, website display, etc. Step 4: The learning unit uses the generation AI to learn the date plan generated by the generation unit and reflect the learned information in the next proposal. Learning methods include, but are not limited to, machine learning algorithms and feedback integration methods.

[0068] (Example 2) A date plan suggestion system according to an embodiment of the present invention proposes an ideal date plan, taking into account a user's preferences and budget. The date plan suggestion system accepts input from the user regarding preferences and budget, and a generation AI analyzes this information to present new ideas and recommended locations. The generation AI learns from past date plans and user feedback to generate an optimal plan. For example, if a user inputs "a place where I can enjoy nature within a budget of 5,000 yen," the generation AI will suggest places where you can enjoy nature. The generation AI generates a plan to provide a special experience by taking into account the user's preferences and past dating history. For example, it suggests new locations and activities based on the user's past visits and favorite activities. This allows users to discover new locations and activities and makes it easier to create date plans. Furthermore, the date plan suggestion system allows couples to share special moments and build deeper bonds. For example, the plans proposed by the generation AI include activities that can be enjoyed together and romantic locations, helping couples deepen their relationship. Users can also customize the plans proposed by the generation AI to create date plans tailored to their preferences. As a result, the date plan suggestion system is a groundbreaking solution for providing couples with a special experience and making it easy to create date plans. As a result, the date plan suggestion system can suggest ideal date plans based on the user's preferences and budget, providing a special experience. For example, the system can enable users to discover new places and activities and share special time. It can also help couples deepen their relationships.

[0069] A date plan suggestion system according to an embodiment includes a reception unit, a generation unit, a provision unit, and a learning unit. The reception unit accepts input of a user's preferences or budget. The user's preferences include, but are not limited to, for example, preferences for food, activities, and locations. The budget includes, but is not limited to, for example, in units of 1,000 yen or 10,000 yen. The generation unit uses a generation AI to analyze the information accepted by the reception unit and generate a date plan. The generation AI may use a natural language generation model such as GPT-4 or Gemini. The generation unit presents new ideas and recommended locations using the generation AI. For example, the generation AI may learn from past date plans and user feedback to generate an optimal plan. The provision unit provides the date plan generated by the generation unit to the user. Methods of providing the date plan include, but are not limited to, for example, email, app notification, and website display. The learning unit uses the generation AI to learn from the date plan generated by the generation unit and reflect the learning in the next proposal. The learning method may include, but is not limited to, a machine learning algorithm, a feedback capturing method, etc. As a result, the date plan suggestion system according to the embodiment can suggest an ideal date plan based on the user's preferences and budget, and provide a special experience.

[0070] The generation unit can present new ideas and recommended places using a generation AI. The generation unit presents new ideas and recommended places using, for example, a generation AI. The generation AI can use a natural language generation model such as GPT-4 or Gemini. The generation unit learns from past date plans and user feedback to generate an optimal plan. For example, the generation AI extracts new ideas from past data and presents them to the user. The generation AI can also select recommended places taking into account the user's preferences and past dating history. In this way, new ideas and recommended places can be effectively presented using the generation AI. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can present ideas and places using a generation AI model that inputs the user's preferences and budget and outputs new ideas and recommended places.

[0071] The generation unit can learn the user's past dating history and feedback and generate an appropriate plan. For example, the generation unit can learn the user's past dating history and feedback and generate an appropriate plan. Past dating history includes, but is not limited to, the date, time, location, and activity details. Feedback includes, but is not limited to, survey results and user ratings. The generation unit uses a generation AI to learn the past dating history and feedback and generate an optimal plan. For example, the generation AI can analyze the user's preferences and tendencies based on the past dating history and generate an appropriate plan. The generation AI can also incorporate user feedback and reflect it in its next proposal. This allows for the generation of a more appropriate date plan by taking the user's past dating history and feedback into consideration. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can generate a plan using a generation AI model that inputs the user's past dating history and feedback and outputs an appropriate plan.

[0072] The providing unit can provide the generated date plan to the user. For example, the providing unit provides the date plan generated by the generating unit to the user. The providing method includes, but is not limited to, email, app notification, website display, etc. The providing unit can also select the optimal providing method for the user using a generating AI. For example, the providing unit selects the optimal providing method based on the user's past usage history. The providing unit can also incorporate user feedback and improve the providing method. This allows the user to easily use the date plan by providing the generated date plan to the user. Some or all of the above-mentioned processing in the providing unit is performed using a generating AI. For example, the providing unit can provide the plan using a generating AI model that inputs the date plan generated by the generating unit and outputs the optimal providing method.

[0073] The learning unit can learn user feedback using the generation AI and reflect it in the next proposal. The learning unit, for example, uses the generation AI to learn user feedback and reflect it in the next proposal. Feedback includes, for example, survey results and user ratings, but is not limited to these examples. The learning unit uses the generation AI to incorporate user feedback and reflect it in the next proposal. For example, the generation AI can improve the accuracy of the next proposal based on the user feedback. The generation AI can also analyze the content of the feedback and identify areas for improvement in the proposal. In this way, by learning user feedback, the accuracy of the next proposal can be improved. Some or all of the above-mentioned processing in the learning unit is performed using the generation AI. For example, the learning unit can learn the feedback using a generative AI model that inputs user feedback and outputs data to be reflected in the next proposal.

[0074] The provision unit may provide a function that allows a user to customize a proposed plan. For example, the provision unit may provide a function that allows a user to customize a proposed plan. Customization may include, but is not limited to, changing the schedule, adding or deleting activities, and the like. The provision unit may also support customization to suit the user's preferences using a generative AI. For example, the provision unit may suggest optimal customization options based on the user's past customization history. The provision unit may also incorporate user feedback and improve the customization function. This allows the user to customize the proposed plan to suit their preferences. Some or all of the above-described processing in the provision unit is performed using a generative AI. For example, the provision unit may customize the plan using a generative AI model that inputs the proposed plan and outputs customization options.

[0075] The reception unit can estimate the user's emotions and adjust the input method for preferences and budget based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the input method for preferences and budget based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. The reception unit estimates the user's emotions and adjusts the input method using a generation AI. For example, if the user is stressed, the reception unit provides a simple interface and minimizes input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit prioritizes voice input, allowing the user to quickly input preferences and budget. This allows for a more comfortable input experience by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit can adjust the input method using a generative AI model that takes the user's emotional data as input and outputs the optimal input method.

[0076] The reception unit can analyze the user's past input history and propose an optimal input format. The reception unit, for example, analyzes the user's past input history and proposes an optimal input format. The input history includes, for example, past input data, input date and time, etc., but is not limited to these examples. The reception unit uses a generation AI to analyze the past input history and propose an optimal input format. For example, preferences and budgets frequently entered by the user in the past can be automatically displayed as candidates. The reception unit can also prioritize proposals based on input methods (voice, text, etc.) used by the user in the past. Furthermore, preferences and budgets to be used in specific time periods can be predicted and proposed based on the user's past input history. In this way, by analyzing the past input history, the optimal input format can be provided to the user. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can propose an input format using a generation AI model that inputs the past input history and outputs an optimal input format.

[0077] The reception unit can dynamically change input items based on the user's current situation. The reception unit dynamically changes input items based on, for example, the user's current situation (e.g., weather or time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The reception unit uses a generation AI to analyze the user's current situation and dynamically change the input items. For example, when it rains, input items for indoor date plans can be displayed preferentially. Also, at night, input items for places with enjoyable night views can be displayed preferentially. Furthermore, on holidays, input items for long date plans can be displayed preferentially. In this way, by changing the input items according to the current situation, more appropriate date plans can be proposed. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can dynamically change input items using a generation AI model that inputs current situation data and outputs optimal input items.

[0078] The reception unit can select the optimal input means depending on the user's input method. The reception unit selects the optimal input means depending on, for example, the user's input method (voice, text, image, etc.). Input methods include, but are not limited to, voice input, text input, and image input. The reception unit uses a generative AI to analyze the user's input method and select the optimal input means. For example, if the user selects voice input, the reception unit uses voice recognition technology to input preferences and budget. Also, if the user selects text input, the reception unit can provide an interface that supports keyboard input. Furthermore, if the user selects image input, the reception unit can also use image analysis technology to input preferences and budget. This improves input convenience by selecting the optimal means depending on the user's input method. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can select the input means using a generative AI model that receives the user's input method data as input and outputs the optimal input means.

[0079] The reception unit can estimate the user's emotions and prioritize input items based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and prioritizes input items based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. The reception unit estimates the user's emotions and prioritizes input items using a generation AI. For example, if the user is excited, the reception unit can prioritize displaying input items related to activities. Also, if the user is relaxed, the reception unit can prioritize displaying input items related to budgets. Furthermore, if the user is stressed, the reception unit can prioritize displaying simple input items. This allows for a more appropriate input experience by prioritizing input items according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit is performed using the generation AI. For example, the reception unit can determine the priority of input items using a generative AI model that receives user emotion data as input and outputs the optimal priority of input items.

[0080] The reception unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. For example, the reception unit prioritizes displaying highly relevant input items by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. The reception unit uses a generation AI to analyze the user's geographical location information and prioritize displaying highly relevant input items. For example, if the user is in an urban area, the reception unit can prioritize displaying input items for date plans within the city. Also, if the user is in a suburban area, the reception unit can prioritize displaying input items for date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the reception unit can prioritize displaying input items for date plans that include tourist spots. This allows the user to be provided with highly relevant input items by taking into account the geographical location information. Some or all of the above-described processing in the reception unit is performed using a generation AI. For example, the reception unit can prioritize displaying input items by using a generation AI model that inputs geographical location information and outputs highly relevant input items.

[0081] The reception unit can analyze the user's social media activity and suggest related input items. The reception unit, for example, analyzes the user's social media activity and suggests related input items. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The reception unit uses a generative AI to analyze the user's social media activity and suggest related input items. For example, based on the location where the user checked in on social media, the reception unit can suggest related input items for a date plan. The reception unit can also analyze the content of the user's social media posts and suggest input items for related activities. Furthermore, the reception unit can suggest related input items for a date plan based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to suggest related input items for the user. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can suggest input items using a generative AI model that inputs social media activity data and outputs related input items.

[0082] The reception unit can customize the input method by reflecting the user's past feedback. The reception unit, for example, customizes the input method by reflecting the user's past feedback. Feedback includes, for example, survey results and user evaluations, but is not limited to these examples. The reception unit uses a generative AI to analyze the user's past feedback and customize the input method. For example, the reception unit may preferentially suggest input methods that the user has previously preferred. The reception unit can also improve the input interface based on the user's past feedback. Furthermore, the reception unit can also adjust the input interface to avoid input methods that the user has previously dissatisfied with. In this way, by reflecting past feedback, the optimal input method can be provided to the user. Some or all of the above-described processing in the reception unit is performed using a generative AI. For example, the reception unit can customize the input method using a generative AI model that inputs past feedback data and outputs the optimal input method.

[0083] The generation unit can estimate the user's emotions and adjust the presentation of the date plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the presentation of the date plan based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. The generation unit uses a generation AI to estimate the user's emotions and adjust the presentation of the date plan. For example, if the user is relaxed, the generation unit can generate a date plan that proceeds at a leisurely pace. If the user is in a hurry, the generation unit can generate a date plan that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a date plan that adds visually stimulating effects. This allows for a more appropriate plan to be provided by adjusting the presentation of the date plan based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit is performed using the generation AI. For example, the generation unit can adjust the expression method using a generative AI model that takes the user's emotional data as input and outputs the optimal way to express a date plan.

[0084] The generation unit can adjust the level of detail of the date plan based on the user's preferences and budget when generating the date plan. For example, the generation unit adjusts the level of detail of the plan based on the user's preferences and budget when generating the date plan. Preferences include, but are not limited to, food preferences, activity preferences, and location preferences. Budgets include, but are not limited to, increments of 1,000 yen and 10,000 yen. The generation unit uses a generation AI to analyze the user's preferences and budget and adjust the level of detail of the plan. For example, if the user's budget is limited, the generation unit can generate a cost-effective date plan. It can also generate a date plan that includes detailed activity descriptions based on the user's preferences. Furthermore, if the user has a larger budget, the generation unit can generate a luxurious date plan. By adjusting the level of detail of the plan based on the user's preferences and budget, a more appropriate date plan can be provided. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can adjust the level of detail of the plan using a generation AI model that inputs the user's preferences and budget and outputs the level of detail of the plan.

[0085] When generating a date plan, the generation unit can improve the accuracy of the plan by referring to the user's past dating history. For example, when generating a date plan, the generation unit can improve the accuracy of the plan by referring to the user's past dating history. Past dating history includes, but is not limited to, dates, locations, and activity details. The generation unit uses a generation AI to analyze the user's past dating history and improve the accuracy of the plan. For example, a new date plan can be generated based on places the user has visited in the past. A date plan including preferred activities can also be generated from the user's past dating history. Furthermore, the generation unit can analyze the user's past dating history and generate an optimal date plan. By referring to the past dating history, the accuracy of the plan can be improved. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can generate a plan using a generation AI model that inputs the user's past dating history and outputs data to improve the accuracy of the plan.

[0086] The generation unit can customize a date plan based on the user's current situation when generating the plan. For example, when generating a date plan, the generation unit customizes the plan based on the user's current situation (e.g., weather and time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The generation unit uses a generation AI to analyze the user's current situation and customize the plan. For example, when it rains, the generation unit generates a date plan that can be enjoyed indoors. Also, at night, the generation unit can generate a date plan that allows you to enjoy the night view. Furthermore, on holidays, the generation unit can generate a date plan that can be enjoyed for a long time. In this way, by customizing the plan according to the current situation, a more appropriate date plan can be provided. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can customize the plan using a generation AI model that inputs current situation data and outputs an optimal plan.

[0087] The generation unit can estimate the user's emotions and adjust the length of the date plan based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the date plan based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. The generation unit estimates the user's emotions and adjusts the length of the date plan using a generation AI. For example, if the user is in a hurry, the generation unit can generate a date plan that can be enjoyed in a short amount of time. Also, if the user is relaxed, the generation unit can generate a date plan that can be enjoyed for a long time. Furthermore, if the user is excited, the generation unit can generate a date plan with many activities. This allows for adjusting the length of the date plan according to the user's emotions, thereby providing a more appropriate plan. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the generation unit is performed using the generation AI. For example, the generation unit can adjust the length of the plan using a generation AI model that takes the user's emotional data as input and outputs the optimal length of the date plan.

[0088] When generating a date plan, the generation unit can propose an optimal plan by taking into account the user's geographical location information. For example, when generating a date plan, the generation unit proposes an optimal plan by taking into account the user's geographical location information. Examples of geographical location information include, but are not limited to, GPS data and location information services. The generation unit uses a generation AI to analyze the user's geographical location information and propose an optimal plan. For example, if the user is in an urban area, the generation unit can propose a date plan within the city. Also, if the user is in the suburbs, the generation unit can propose a date plan that allows users to enjoy nature. Furthermore, if the user is in a tourist destination, the generation unit can propose a date plan that includes tourist spots. This allows the user to be provided with an optimal date plan by taking into account the geographical location information. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can propose a plan using a generation AI model that inputs geographical location information and outputs an optimal plan.

[0089] The generation unit can analyze the user's social media activity and suggest related plans when generating a date plan. For example, the generation unit can analyze the user's social media activity and suggest related plans when generating a date plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The generation unit can analyze the user's social media activity and suggest related plans using a generation AI. For example, the generation unit can suggest related date plans based on the locations the user has checked in to on social media. The generation unit can also analyze the content of the user's social media posts and suggest related activities. Furthermore, the generation unit can suggest related date plans based on the activities of the user's friends on social media. In this way, by analyzing social media activity, it is possible to provide relevant date plans to the user. Some or all of the above-mentioned processing in the generation unit is performed using a generation AI. For example, the generation unit can suggest plans using a generation AI model that inputs social media activity data and outputs related plans.

[0090] The generation unit can customize a date plan by reflecting the user's past feedback when generating the plan. For example, the generation unit customizes the plan by reflecting the user's past feedback when generating the date plan. Feedback includes, but is not limited to, survey results and user ratings. The generation unit uses a generation AI to analyze the user's past feedback and customize the plan. For example, a new plan can be generated based on date plans that the user liked in the past. The content of the plan can also be improved based on the user's past feedback. Furthermore, adjustments can be made to avoid plans that the user was dissatisfied with in the past. By reflecting past feedback, the optimal date plan can be provided to the user. Some or all of the above-described processing in the generation unit is performed using a generation AI. For example, the generation unit can customize the plan using a generation AI model that inputs past feedback data and outputs an optimal plan.

[0091] The providing unit can estimate the user's emotions and adjust the method of providing the date plan based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the method of providing the date plan based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, and text analysis, but is not limited to these examples. The providing unit uses a generation AI to estimate the user's emotions and adjust the method of providing the date plan. For example, if the user is relaxed, the providing unit can provide the date plan at a leisurely pace. If the user is in a hurry, the providing unit can provide a concise date plan. Furthermore, if the user is excited, the providing unit can provide the date plan with a visually stimulating effect. This allows for adjusting the method of providing the date plan based on the user's emotions, thereby providing a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the providing unit is performed using the generation AI. For example, the providing unit can adjust the providing method using a generative AI model that takes user emotion data as input and outputs the optimal providing method.

[0092] The provision unit can select the optimal provision method by referring to the user's past usage history when providing a date plan. For example, when providing a date plan, the provision unit selects the optimal provision method by referring to the user's past usage history. The usage history includes, for example, past usage data, usage dates and times, etc., but is not limited to these examples. The provision unit uses a generation AI to analyze the user's past usage history and select the optimal provision method. For example, the provision unit prioritizes the selection of a provision method that the user has preferred in the past. The provision unit can also suggest the optimal provision method based on the user's past usage history. Furthermore, the user's past usage history can be analyzed to select the most efficient provision method. In this way, the optimal provision method for the user can be selected by referring to the past usage history. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can select the provision method using a generation AI model that inputs past usage history and outputs the optimal provision method.

[0093] The provision unit can customize the content to be provided based on the user's current situation when providing a date plan. For example, when providing a date plan, the provision unit customizes the content to be provided based on the user's current situation (e.g., weather and time of day). The current situation includes, but is not limited to, weather information, time of day, and location information. The provision unit uses a generation AI to analyze the user's current situation and customize the content to be provided. For example, when it rains, the provision unit provides a date plan that can be enjoyed indoors. Also, at night, the provision unit can provide a date plan that allows for enjoying a night view. Furthermore, on holidays, the provision unit can provide a date plan that can be enjoyed for a long time. In this way, by customizing the content to be provided based on the current situation, a more appropriate date plan can be provided. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can customize the content to be provided using a generation AI model that inputs current situation data and outputs optimal content to be provided.

[0094] The provision unit can improve the provision method by reflecting user feedback when providing a date plan. For example, when providing a date plan, the provision unit improves the provision method by reflecting user feedback. Feedback includes, but is not limited to, survey results and user ratings. The provision unit uses a generation AI to analyze user feedback and improve the provision method. For example, the provision unit can suggest a new provision method based on a provision method that the user has previously preferred. The provision interface can also be improved based on the user's past feedback. Furthermore, it can be adjusted to avoid a provision method that the user has previously dissatisfied with. In this way, by reflecting feedback, the provision method can be improved and the optimal date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can improve the provision method using a generation AI model that inputs feedback data and outputs an optimal provision method.

[0095] The providing unit can estimate the user's emotions and adjust the order in which the date plans are provided based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the order in which the date plans are provided based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, text analysis, etc., but is not limited to these examples. The providing unit uses a generation AI to estimate the user's emotions and adjust the order in which the plans are provided. For example, if the user is relaxed, the providing unit can provide the date plans in a leisurely order. Also, if the user is in a hurry, the providing unit can provide important information first. Furthermore, if the user is excited, the providing unit can provide the date plans in a visually stimulating order. This allows for adjusting the order in which the plans are provided based on the user's emotions, thereby providing a more appropriate date plan. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the providing unit is performed using the generation AI. For example, the providing unit can adjust the providing order using a generative AI model that takes user emotion data as input and outputs the optimal providing order.

[0096] The providing unit can select the optimal provision method by taking into consideration the user's geographical location information when providing a date plan. For example, when providing a date plan, the providing unit selects the optimal provision method by taking into consideration the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information services, etc. The providing unit uses a generation AI to analyze the user's geographical location information and select the optimal provision method. For example, if the user is in an urban area, the providing unit can prioritize providing date plans within the city. Also, if the user is in a suburban area, the providing unit can prioritize providing date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the providing unit can prioritize providing date plans that include tourist spots. In this way, the optimal provision method for the user can be selected by taking into consideration the geographical location information. Some or all of the above-described processing in the providing unit is performed using a generation AI. For example, the providing unit can select the provision method using a generation AI model that inputs geographical location information and outputs the optimal provision method.

[0097] The provision unit can analyze the user's social media activity and provide a relevant plan when providing a date plan. For example, when providing a date plan, the provision unit can analyze the user's social media activity and provide a relevant plan. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The provision unit uses a generative AI to analyze the user's social media activity and provide a relevant plan. For example, the provision unit can provide a relevant date plan based on the location where the user checked in on social media. The provision unit can also analyze the content of the user's social media posts and provide relevant activities. Furthermore, the provision unit can provide a relevant date plan based on the activity of the user's friends on social media. In this way, by analyzing social media activity, a relevant date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generative AI. For example, the provision unit can provide a plan using a generative AI model that inputs social media activity data and outputs a relevant plan.

[0098] The provision unit can customize the provision method by reflecting the user's past feedback when providing a date plan. For example, when providing a date plan, the provision unit customizes the provision method by reflecting the user's past feedback. Feedback includes, but is not limited to, survey results and user ratings. The provision unit uses a generation AI to analyze the user's past feedback and customize the provision method. For example, a new provision method can be suggested based on the user's past preferred provision method. The provision interface can also be improved based on the user's past feedback. Furthermore, adjustments can be made to avoid provision methods that the user has previously dissatisfied with. In this way, by reflecting past feedback, the provision method can be customized and the optimal date plan can be provided to the user. Some or all of the above-mentioned processing in the provision unit is performed using a generation AI. For example, the provision unit can customize the provision method using a generation AI model that inputs past feedback data and outputs the optimal provision method.

[0099] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and selects training data based on the estimated user emotions. Emotion estimation includes, but is not limited to, facial expression recognition, voice analysis, and text analysis. The learning unit estimates the user's emotions and selects training data using a generation AI. For example, if the user is relaxed, the learning unit may select training data by prioritizing feedback in a relaxed state. Also, if the user is excited, the learning unit may select training data by prioritizing feedback in an excited state. Furthermore, if the user is stressed, the learning unit may select training data by prioritizing feedback for reducing stress. This allows for more appropriate learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the learning unit is performed using the generation AI. For example, the learning unit can select learning data using a generative AI model that takes user emotional data as input and outputs optimal learning data.

[0100] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. Past learning data includes, but is not limited to, learning history, data type, etc. The learning unit analyzes past learning data using a generative AI to optimize the learning algorithm. For example, the learning unit selects an optimal algorithm based on the past learning data. The learning unit can also analyze past learning data and adjust algorithm parameters. Furthermore, the learning unit can improve the accuracy of the learning algorithm by referring to the past learning data. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can optimize the learning algorithm using a generative AI model that inputs past learning data and outputs an optimal algorithm.

[0101] The learning unit can update the learning data during learning by reflecting user feedback. For example, the learning unit updates the learning data during learning by reflecting user feedback. Feedback includes, for example, survey results and user evaluations, but is not limited to these examples. The learning unit uses a generative AI to analyze user feedback and update the learning data. For example, the learning unit adds learning data based on user feedback. The learning unit can also analyze user feedback and modify the learning data. Furthermore, the accuracy of the learning data can be improved by reflecting user feedback. In this way, the accuracy of the learning data is improved by reflecting feedback. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can update the learning data using a generative AI model that inputs feedback data and outputs optimal learning data.

[0102] During learning, the learning unit can analyze the user's past dating history and weight the learning data. For example, during learning, the learning unit can analyze the user's past dating history and weight the learning data. The past dating history can include, but is not limited to, date, time, location, and activity details. The learning unit uses a generative AI to analyze the user's past dating history and weight the learning data. For example, important data can be weighted based on the user's past dating history. The learning unit can also analyze the user's past dating history to determine the priority of the learning data. Furthermore, the learning unit can adjust the weighting of the learning data by referring to the user's past dating history. In this way, the learning data can be appropriately weighted by analyzing the past dating history. Some or all of the above-described processing in the learning unit is performed using a generative AI. For example, the learning unit can weight the learning data using a generative AI model that inputs the past dating history and outputs optimal weighting.

[0103] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated user emotions. The learning unit, for example, estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. Emotion estimation includes, for example, facial expression recognition, voice analysis, and text analysis, but is not limited to these examples. The learning unit estimates the user's emotions and adjusts the learning frequency using a generation AI. For example, if the user is relaxed, the learning unit can set the learning frequency low. Also, if the user is excited, the learning unit can set the learning frequency high. Furthermore, if the user is stressed, the learning unit can adjust the learning frequency to reduce stress. In this way, more appropriate learning can be achieved by adjusting the learning frequency according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the learning unit is performed using the generation AI. For example, the learning unit can adjust the learning frequency using a generative AI model that takes user emotional data as input and outputs the optimal learning frequency.

[0104] The learning unit can select training data taking into account the user's geographical location information during learning. For example, the learning unit selects training data taking into account the user's geographical location information during learning. Geographical location information includes, but is not limited to, GPS data, location information services, etc. The learning unit uses a generative AI to analyze the user's geographical location information and select training data. For example, if the user is in an urban area, the learning unit can prioritize learning data related to date plans within the city. Also, if the user is in a suburban area, the learning unit can prioritize learning data related to date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, the learning unit can prioritize learning data related to tourist spots. This allows optimal training data to be selected for the user by taking into account the geographical location information. Some or all of the above-described processing in the learning unit is performed using a generative AI. For example, the learning unit can select training data using a generative AI model that inputs geographical location information and outputs optimal training data.

[0105] The learning unit can analyze the user's social media activity during learning and incorporate related data into the learning. For example, the learning unit can analyze the user's social media activity during learning and incorporate related data into the learning. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. The learning unit uses a generative AI to analyze the user's social media activity and incorporate related data into the learning. For example, the learning unit can learn data about the locations the user checked in to on social media. The learning unit can also analyze the content of the user's social media posts to learn related data. Furthermore, the learning unit can also learn related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data related to the user can be incorporated into the learning. Some or all of the above-described processing in the learning unit can be performed using a generative AI. For example, the learning unit can incorporate social media activity data into the learning using a generative AI model that inputs social media activity data and outputs optimal learning data.

[0106] The learning unit can adjust the learning algorithm during learning by reflecting the user's past feedback. For example, the learning unit adjusts the learning algorithm during learning by reflecting the user's past feedback. Feedback includes, but is not limited to, survey results and user evaluations. The learning unit uses a generative AI to analyze the user's feedback and adjust the learning algorithm. For example, the learning unit adjusts the parameters of the learning algorithm based on the user's past feedback. The learning algorithm can also be improved by analyzing the user's past feedback. Furthermore, the accuracy of the learning algorithm can be improved by reflecting the user's past feedback. In this way, the accuracy of the learning algorithm is improved by reflecting the past feedback. Some or all of the above-mentioned processing in the learning unit is performed using a generative AI. For example, the learning unit can adjust the learning algorithm using a generative AI model that inputs feedback data and outputs an optimal algorithm. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives input of the user's preferences and budget. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a date plan using a generation AI. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated date plan to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns from the generated date plan and reflects it in the next proposal. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives input of the user's preferences and budget. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a date plan using a generation AI. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated date plan to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns from the generated date plan and reflects it in the next proposal. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset-type terminal 314 and receives input of the user's preferences and budget. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a date plan using a generation AI. The provision unit is realized by the speaker 240 of the headset-type terminal 314 and provides the generated date plan to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns from the generated date plan and reflects it in the next proposal. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, provision unit, and learning unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives input of the user's preferences and budget. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a date plan using a generation AI. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated date plan to the user. The learning unit is realized by the specific processing unit 290 of the data processing device 12 and learns from the generated date plan and reflects it in the next proposal.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit not only accepts input of the user's preferences and budget, but can also dynamically change the input interface taking into account the user's current mood and physical condition. For example, if the user is tired, the reception unit provides a simple and intuitive interface to minimize input effort. If the user is relaxed, the reception unit provides detailed customization options, allowing the user to fine-tune date plans to suit their preferences. Furthermore, if the user is in a hurry, the reception unit can quickly complete input using voice input or pre-saved templates. This provides flexible input methods according to the user's situation, resulting in a more comfortable user experience.

[0109] The generator not only generates date plans based on the user's preferences and budget, but can also improve the accuracy of the plans based on the user's past dating history and feedback. For example, it generates plans that provide similar experiences by taking into account places the user has visited and activities they have participated in in the past. It can also propose plans that are more satisfying by incorporating user feedback and elements of plans that were well-received in the past. Furthermore, it can extract specific patterns from the user's dating history and prioritize the proposal of plans that the user tends to prefer. This makes it possible to provide more personalized date plans by utilizing the user's past dating history and feedback.

[0110] The providing unit not only provides the generated date plan to the user, but also customizes the method of providing based on the user's current situation. For example, if the user is out, the providing unit provides the date plan using a smartphone notification. If the user is at home, the providing unit can provide a detailed plan via email or website. Furthermore, if the user often checks date plans during a specific time period, the providing unit can provide a plan tailored to that time period. This allows the optimal method of providing the date plan to be selected according to the user's situation, making it more convenient to use the date plan.

[0111] The learning unit not only learns user feedback and reflects it in the next proposal, but can also estimate the user's emotions and select training data based on the estimated emotions. For example, if the user expresses positive emotions about a date plan, elements of that plan will be emphasized and incorporated into the training data. Conversely, if the user expresses negative emotions, the training data can be adjusted to avoid those elements. Furthermore, by tracking changes in the user's emotions and analyzing long-term trends, more accurate date plan proposals can be made. This allows the system to select training data based on the user's emotions and achieve more personalized proposals.

[0112] The reception unit not only accepts input of the user's preferences and budget, but can also analyze the user's social media activity and suggest related input items. For example, it can suggest relevant input items for date plans based on the places the user has checked in and the content of their posts on social media. It can also suggest popular date spots and activities based on the activities of the user's friends. It can also analyze the user's interests on social media and provide customization options based on those interests. This makes it possible to suggest more relevant date plans by utilizing the user's social media activity.

[0113] When generating a date plan, the generation unit can estimate the user's emotions and adjust the way the plan is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a date plan that proceeds at a leisurely pace. If the user is excited, the generation unit can also generate a date plan that adds visually stimulating effects. Furthermore, if the user is feeling stressed, the generation unit can also generate a date plan that focuses on relaxing activities. In this way, the way the date plan is presented can be adjusted according to the user's emotions, making it possible to provide a more appropriate plan.

[0114] The providing unit not only provides the generated date plan to the user, but can also select the optimal method of provision by referring to the user's past usage history. For example, if the user has preferred to be provided by email in the past, the providing unit can preferentially select email. Also, if the user has preferred app notifications, the providing unit can preferentially select that method. Furthermore, if the user's past usage history indicates that it is effective to provide the plan during a specific time period, the providing unit can provide the plan according to that time period. In this way, by referring to the past usage history, the optimal method of provision can be selected to the user, making the use of date plans more convenient.

[0115] The learning unit not only learns the user's feedback and reflects it in the next proposal, but can also select learning data taking into account the user's geographical location information. For example, if the user is in an urban area, the learning unit can prioritize learning data related to date plans within the city. Also, if the user is in the suburbs, it can prioritize learning data related to date plans that allow users to enjoy nature. Furthermore, if the user is in a tourist destination, it can prioritize learning data related to tourist spots. In this way, by taking geographical location information into consideration, it is possible to select the most suitable learning data for the user and propose more relevant date plans.

[0116] The providing unit not only provides the generated date plan to the user, but also estimates the user's emotions and adjusts the method of providing the plan based on the estimated emotions. For example, if the user is relaxed, the providing unit can provide the date plan at a leisurely pace. If the user is in a hurry, the providing unit can provide a concise date plan. Furthermore, if the user is excited, the providing unit can provide the date plan by adding visually stimulating effects. In this way, the providing unit can adjust the method of providing the plan according to the user's emotions and provide a more appropriate date plan.

[0117] The learning unit not only learns the user's feedback and reflects it in the next proposal, but can also analyze the user's social media activity and incorporate related data into the learning. For example, it can learn data about the places the user has checked in on social media. It can also analyze the content of the user's social media posts to learn related data. It can also learn related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activity, data related to the user can be incorporated into the learning and more personalized date plans can be proposed.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit receives input of the user's preferences or budget. The user's preferences include, but are not limited to, for example, preferences for food, activities, and locations. The budget includes, but is not limited to, for example, in 1,000 yen increments or 10,000 yen increments. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a date plan. The generation AI can use natural language generation models such as GPT-4 or Gemini. The generation unit uses the generation AI to present new ideas and recommended locations. For example, the generation AI can learn from past date plans and user feedback to generate the optimal plan. Step 3: The providing unit provides the date plan generated by the generating unit to the user. The providing method may include, but is not limited to, email, app notification, website display, etc. Step 4: The learning unit uses the generation AI to learn the date plan generated by the generation unit and reflect the learned information in the next proposal. Learning methods include, but are not limited to, machine learning algorithms and feedback integration methods.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of a user's preferences or budget; a generation unit that analyzes the information received by the reception unit and generates a date plan; a providing unit that provides the date plan generated by the generating unit; A learning unit is provided to learn the date plan generated by the generation unit and reflect the learned date plan in the next proposal. A system characterized by:

2. The generation unit Generative AI provides new ideas and recommended places 2. The system of claim 1.

3. The generation unit It learns from users' past dating history and feedback to generate appropriate plans.

2. The system of claim 1.

4. The providing unit Providing users with generated date plans 2. The system of claim 1.

5. The learning unit Generative AI learns from user feedback and incorporates it into future proposals 2. The system of claim 1.

6. The providing unit Provide users with the ability to customize the proposed plan 2. The system of claim 1.

7. The reception unit Inferring user emotions and adjusting the way preferences and budgets are entered based on the inferred user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past input history and suggests the optimal input format 2. The system of claim 1.

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