system

Abstract

The system according to this embodiment aims to propose an optimal holiday plan based on the individual needs of each family. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit receives family information. The analysis unit analyzes the information entered by the reception unit. The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The proposal unit proposes the plan generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to optimize a family's holiday plan according to individual needs.

[0005] The system according to an embodiment aims to propose an optimal holiday plan based on the individual needs of a family.

Means for Solving the Problems

[0006] The system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a proposal unit. The reception unit inputs family information. The analysis unit analyzes the information input by the reception unit. The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The proposal unit proposes the plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose an optimal holiday plan based on the individual needs of the family. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI Family Planner according to an embodiment of the present invention is a system that personalizedly suggests how families should spend their holidays. This system proposes an optimal holiday plan based on information such as family composition, age, hobbies, budget, and weather. The AI ​​Family Planner learns the preferences and interests of each family member and also takes into account local event information, crowd forecasts, and reviews. Furthermore, it provides a detailed schedule, packing list, and reservation guidance. This service targets nuclear families with working parents and children ranging from preschoolers to junior high school students, and is offered to people who do not have the time to plan their holidays. For example, the AI ​​Family Planner takes into account information such as family composition, age, hobbies, budget, and weather. Next, the AI ​​analyzes this information and generates an optimal holiday plan. The generated plan learns the preferences and interests of each family member and also takes into account local event information, crowd forecasts, and reviews. Furthermore, it provides a detailed schedule, packing list, and reservation guidance. As a method of utilizing the generating AI, natural language processing is used to understand preferences and requests through dialogue with the family, and pattern recognition is used to learn the family's preferences from past choices and evaluations. Using image recognition, the system analyzes age and facial expressions from family photos, and predicts weather, crowd levels, and satisfaction levels using a predictive model. Furthermore, it generates suggestions combining text, images, and audio using multimodal AI. This allows the AI ​​family planner to personalize and suggest how families can spend their holidays.

[0029] The AI ​​family planner according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a suggestion unit. The reception unit inputs family information. Family information includes, but is not limited to, family structure, age, hobbies, budget, and weather. The reception unit stores the family information entered by the user in a database, for example. The reception unit can also improve input efficiency by referring to past input history. For example, the reception unit can automatically display the family structure and hobbies previously entered by the user, saving the user the trouble of re-entering the information. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data mining, statistical analysis, and machine learning algorithms. The analysis unit can learn the preferences and interests of each family member. For example, the analysis unit learns the preferences and interests of family members based on survey results and past behavioral history. The analysis unit can also consider local event information, congestion predictions, and word-of-mouth ratings. For example, the analysis unit collects local event information based on event calendars, local news, and social media posts. The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The generation unit provides, for example, a detailed schedule, a packing list, and reservation information. The generation unit may also include a prediction unit that predicts weather and crowd levels. For example, the generation unit predicts weather and crowd levels based on weather data and real-time pedestrian flow data. The suggestion unit proposes the plan generated by the generation unit. The suggestion unit may include a feedback unit that receives user feedback. The suggestion unit may also estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit will make suggestions in a calmer way. This allows the AI ​​family planner according to the embodiment to personalize and suggest how families should spend their holidays.

[0030] The reception desk inputs family information. This information includes, but is not limited to, family structure, ages, hobbies, budget, and weather. The reception desk stores the family information entered by the user in a database. Specifically, the user enters family information through a dedicated interface, and this information is stored in a secure database. The database uses encryption technology to protect the information and prevent unauthorized access from external sources. The reception desk can also improve input efficiency by referring to past input history. For example, it can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. This allows the user to enter information quickly and easily. Furthermore, the reception desk can use voice input and image recognition technology to allow the user to input information by voice or upload photos to automatically recognize the family structure. This further improves user convenience. The reception desk also has a function to analyze the information entered by the user in real time and prompt the user to check and correct the input content. For example, if there is an error in the input content or incomplete information has been entered, the reception desk will provide the user with appropriate feedback to support accurate information entry. This allows the reception desk to enable users to input family information accurately and efficiently, improving the overall accuracy and reliability of the system.

[0031] The analysis department analyzes the information entered by the reception department. The analysis department uses data mining, statistical analysis, and machine learning algorithms to analyze the information. Specifically, it uses data mining techniques to extract useful patterns and trends from family information and statistical analysis to reveal data distribution and correlations. Furthermore, it can use machine learning algorithms to learn the preferences and interests of each family member. For example, the analysis department learns the preferences and interests of family members based on survey results and past behavioral history. This allows the analysis department to accurately understand the individual needs and preferences of each member and provide personalized suggestions. The analysis department can also consider local event information, crowd predictions, and user reviews. For example, it collects local event information based on event calendars, local news, and social media posts. This allows the analysis department to grasp the latest event information and local trends and reflect them in family holiday plans. Furthermore, the analysis department uses crowd prediction algorithms to predict the crowding levels of specific locations and events, and adjust plans to ensure families have a comfortable experience. By considering user reviews, the analysis department can select locations and activities that have received high ratings from users. This allows the analysis unit to provide a plan that best suits the family's needs and improve satisfaction.

[0032] The generation unit generates holiday plans based on information analyzed by the analysis unit. For example, the generation unit provides detailed schedules, packing lists, and reservation information. Specifically, based on data provided by the analysis unit, the generation unit creates a detailed schedule tailored to the family's needs and preferences. The schedule includes places to visit, activity times, transportation, and dining locations. The packing list includes necessary and recommended items to help users prepare smoothly. Furthermore, the generation unit provides reservation information for activities and restaurants that require reservations. The generation unit can also include a forecasting unit that predicts weather and crowd levels. For example, the generation unit predicts weather and crowd levels based on meteorological data and real-time pedestrian flow data. This allows the generation unit to flexibly adjust the plan according to weather changes and crowd levels, ensuring the family's comfort. For instance, if rain is expected, it might suggest indoor activities and provide alternatives to avoid crowded areas. This allows the generation unit to optimize the family's holiday plan and improve satisfaction.

[0033] The proposal unit proposes the plan generated by the generation unit. The proposal unit includes, for example, a feedback unit that receives user feedback. Specifically, the proposal unit presents the generated plan to the user and allows the user to provide feedback on the plan. The user inputs opinions and requests regarding the plan content and proposed activities, and the proposal unit collects and analyzes this feedback. The proposal unit can also estimate the user's emotions and adjust the way the proposal is expressed based on the estimated emotions. For example, if the user is nervous, the proposal unit will make suggestions using calm language. Specifically, the proposal unit estimates the user's emotions using natural language processing technology based on the user's input and past feedback. If the user is relaxed, it will make suggestions using casual language; if the user is nervous, it will use polite and calm language. In this way, the proposal unit can make suggestions that are considerate of the user's emotions and improve user satisfaction. Furthermore, the proposal unit can revise the plan based on user feedback and make more appropriate suggestions. For example, if the user provides negative feedback on a particular activity, the proposal unit will replace that activity with another one. Furthermore, the proposal department can continuously learn from user feedback and improve the accuracy of future proposals. This allows the proposal department to provide users with the best possible plans, making family holidays more fulfilling.

[0034] The analysis unit can learn the preferences and interests of each family member. For example, the analysis unit learns the preferences and interests of family members based on survey results and past behavioral history. The analysis unit can also learn the preferences and interests of family members using social media analysis. For example, the analysis unit learns preferences and interests based on event information shared and posts "liked" by family members on social media. This makes it possible to make suggestions based on the preferences and interests of family members. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input family members' behavioral history data into AI and have the AI ​​perform the learning of preferences and interests.

[0035] The analysis unit can consider local event information, congestion forecasts, and user reviews. For example, the analysis unit collects local event information based on event calendars, local news, and social media posts. The analysis unit can perform congestion forecasts using historical data analysis and real-time sensor data. For example, the analysis unit predicts congestion levels based on past congestion patterns and traffic information. The analysis unit can also collect user reviews based on reviews on review sites and comments on social media. For example, the analysis unit analyzes reviews on review sites and calculates user reviews. This makes it possible to make suggestions that take into account local event information, congestion forecasts, and user reviews. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input local event information and congestion forecast data into AI and have the AI ​​perform the analysis.

[0036] The generation unit can provide detailed schedules, packing lists, and reservation guidance. For example, the generation unit can create a detailed schedule including activities, travel times, and rest times for each time slot. The generation unit can provide packing lists including items needed for the trip, seasonal items, and equipment needed for specific activities. The generation unit can also provide reservation guidance, including restaurant reservations, ticket purchases, and accommodation reservations. For example, the generation unit can automatically make restaurant reservations for the user's choice and provide reservation confirmation. This improves user convenience by providing detailed schedules, packing lists, and reservation guidance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user data into AI and have the AI ​​generate detailed schedules and packing lists.

[0037] The generation unit may include a prediction unit that predicts weather and congestion levels. For example, the generation unit predicts the weather based on meteorological data. The generation unit can also predict congestion levels based on real-time pedestrian flow data. For example, the generation unit predicts congestion levels based on past congestion patterns and traffic information. The generation unit can also adjust the plan content based on the weather and congestion level prediction results. For example, the generation unit suggests indoor activities when the weather is bad and suggests plans to avoid congestion when congestion is expected. This allows for the provision of more appropriate plans by predicting weather and congestion levels. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input meteorological data and pedestrian flow data into AI and have the AI ​​perform weather and congestion level predictions.

[0038] The proposal unit may include a feedback unit that receives user feedback. The proposal unit collects user feedback, for example, through surveys, review collection, or direct exchange of opinions. The proposal unit can improve the accuracy of its proposals based on user feedback. For example, the proposal unit makes proposals based on plans that users have previously given high ratings to. The proposal unit can also make proposals based on plans that users have previously avoided. The proposal unit can also adjust the content of its proposals based on feedback provided by users. For example, the proposal unit prioritizes suggesting activities and events that users prefer. In this way, the accuracy of the proposals can be improved by receiving user feedback. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input user feedback data into AI and have the AI ​​adjust the content of its proposals.

[0039] The reception desk can improve input efficiency by referring to past input history when entering family information. For example, the reception desk can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. The reception desk can also make optimal suggestions based on the budget and weather preferences that the user has entered in the past. The reception desk can also prioritize suggesting frequently visited places and events based on the information the user has entered in the past. In this way, input efficiency can be improved by referring to past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past input history data into AI and have the AI ​​perform the input efficiency improvements.

[0040] The reception desk can customize input fields based on the user's current living situation and areas of interest when entering family information. For example, if the user has started a new hobby, the reception desk can add input fields related to that hobby. If the user has recently moved, the reception desk can also include information related to the new area in the input fields. If the user is interested in a particular event, the reception desk can also add information related to that event in the input fields. This allows for the collection of more relevant information by customizing input fields based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's living situation data into AI and have the AI ​​perform the customization of input fields.

[0041] The reception desk can prioritize inputting highly relevant information when a user enters family information, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can prioritize inputting event information related to that region. If the user is traveling, the reception desk can also prioritize inputting information about their travel destination. If the user frequently visits a specific region, the reception desk can also prioritize inputting information related to that region. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into an AI and have the AI ​​prioritize inputting highly relevant information.

[0042] The reception desk can analyze the user's social media activity and input relevant information when family information is entered. For example, the reception desk can input relevant information based on event information shared by the user on social media. The reception desk can also input relevant information based on information about accounts the user follows on social media. The reception desk can also input relevant information based on posts the user "likes" on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​input the relevant information.

[0043] The analysis unit can improve the accuracy of its analysis by referring to the past behavioral history of family members during the analysis process. For example, the analysis unit can analyze events of interest based on the history of events that family members have participated in in the past. The analysis unit can also analyze relevant information based on the history of places that family members have visited in the past. The analysis unit can also analyze activities of interest based on the history of activities that family members have performed in the past. In this way, the accuracy of the analysis can be improved by referring to the past behavioral history of family members. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the behavioral history data of family members into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0044] The analysis unit can perform analysis while considering the attribute information of family members. For example, the analysis unit can analyze appropriate activities by considering the age of family members. The analysis unit can also analyze events of interest by considering the hobbies of family members. The analysis unit can also analyze appropriate plans by considering the health status of family members. In this way, by considering the attribute information of family members, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input family member attribute information data into AI and have the AI ​​perform the analysis.

[0045] The analysis unit can perform analysis while considering the geographical distribution of families. For example, the analysis unit can perform analysis based on event information in the area where the family lives. The analysis unit can also perform analysis based on information about areas that families frequently visit. The analysis unit can also perform analysis based on information about areas where families are planning to travel. By considering the geographical distribution of families, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of families into AI and have the AI ​​perform the analysis.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can perform analysis by referring to the latest tourist guidebooks. The analysis unit can also analyze information related to family interests by referring to academic papers. The analysis unit can also analyze the latest event information by referring to online databases. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature and data into the AI ​​and have the AI ​​perform the task of improving the accuracy of the analysis.

[0047] The generation unit can generate the optimal plan by referring to past plan generation history during the generation process. For example, the generation unit can generate a similar plan based on plans that the user has preferred in the past. The generation unit can also generate a different plan based on plans that the user has avoided in the past. The generation unit can also generate the optimal plan based on plans that the user has given high ratings to in the past. In this way, the optimal plan can be generated by referring to past plan generation history. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past plan generation history data into AI and have the AI ​​perform the generation of the optimal plan.

[0048] The generation unit can customize the plan content based on the family's current living situation during generation. For example, if a family member has taken up a new hobby, the generation unit can generate a plan related to that hobby. If a family member has recently moved, the generation unit can also generate a plan related to their new area. If a family member is interested in a particular event, the generation unit can also generate a plan related to that event. This allows for the provision of more appropriate plans by customizing the plan content based on the family's current living situation. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input family living situation data into AI and have the AI ​​perform the plan customization.

[0049] The generation unit can generate an optimal plan by considering the family's geographical location information during the generation process. For example, the generation unit can generate a plan based on event information in the area where the family lives. The generation unit can also generate a plan based on information about areas the family frequently visits. The generation unit can also generate a plan based on information about areas the family is planning to travel to. This allows for the provision of more appropriate plans by considering the family's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the family's geographical location data into an AI and have the AI ​​generate the optimal plan.

[0050] The generation unit can adjust the plan content by referring to relevant event information and customer reviews during the generation process. For example, the generation unit can adjust the plan based on the latest event information. The generation unit can also adjust the plan based on events with high customer reviews. The generation unit can also adjust the plan based on events that families have previously given high ratings to. This allows the generation unit to provide more appropriate plans by referring to relevant event information and customer reviews. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input event information and customer review data into the AI ​​and have the AI ​​perform the adjustment of the plan content.

[0051] The proposal unit can improve the accuracy of its proposals by referring to past feedback from the family. For example, the proposal unit can make proposals based on plans that the family has previously given high ratings to. The proposal unit can also make proposals based on plans that the family has previously avoided. The proposal unit can also adjust the content of its proposals based on feedback that the family has previously provided. In this way, the accuracy of the proposals can be improved by referring to past feedback from the family. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input family feedback data into AI and have the AI ​​adjust the content of the proposals.

[0052] The suggestion unit can make suggestions while considering family attribute information. For example, the suggestion unit can suggest appropriate activities considering the age of family members. The suggestion unit can also suggest events of interest considering the hobbies of family members. The suggestion unit can also suggest appropriate plans considering the health status of family members. In this way, more appropriate suggestions can be provided by considering family attribute information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input family attribute information data into AI and have the AI ​​adjust the suggested content.

[0053] The suggestion unit can make optimal suggestions by considering the family's geographical location information. For example, the suggestion unit can make suggestions based on event information in the area where the family lives. It can also make suggestions based on information about areas the family frequently visits. It can also make suggestions based on information about areas the family is planning to travel to. By considering the family's geographical location information, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the family's geographical location data into AI and have the AI ​​generate optimal suggestions.

[0054] The proposal department can adjust the content of its proposals by referring to relevant market data and trends. For example, it can suggest popular events based on the latest market data. It can also suggest activities that families might be interested in based on trend information. It can also combine market data and trend information to suggest the optimal plan. This allows it to provide more appropriate suggestions by referring to relevant market data and trends. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input market data and trend information into AI and have the AI ​​adjust the content of the suggestions.

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

[0056] The AI ​​Family Planner can also include a Health Management Department. This department can monitor the health status of family members and propose health-conscious plans. For example, it can record the exercise levels and dietary habits of family members and suggest healthy activities. It can also consider allergy information and suggest allergy-friendly restaurants and events. Furthermore, it can monitor the stress levels of family members and suggest relaxing activities. This allows for the provision of plans that take the family's health into consideration.

[0057] The AI ​​Family Planner can also include an Education Department. This department can monitor the learning progress of family members and suggest educational activities. For example, it can suggest visits to museums and science centers tailored to the child's grade level and interests. It can also suggest workshops and seminars that the whole family can enjoy. Furthermore, the Education Department can record the learning progress of family members and provide helpful learning resources. This allows for the provision of plans that take into account the family's learning situation.

[0058] The AI ​​Family Planner can also include an Entertainment Department. This department can suggest entertainment options tailored to the preferences of each family member. For example, it can suggest tickets to movies and plays that the whole family can enjoy. It can also suggest games and activities that the whole family can enjoy. Furthermore, it can suggest concerts and live events based on the musical preferences of each family member. This allows the planner to provide entertainment options that meet the family's specific needs.

[0059] The AI ​​Family Planner can also be equipped with a traffic management function. This function can optimize family travel and propose efficient travel plans. For example, it can consider public transport schedules and suggest the best travel route. It can also suggest plans that optimize the fuel efficiency and driving time of the family's vehicles. Furthermore, it can suggest routes that avoid congestion based on real-time traffic information. This makes family travel more efficient and provides less stressful travel plans.

[0060] The AI ​​Family Planner can also be equipped with a communication function. This function can facilitate communication among family members and support smooth information sharing. For example, it can provide a chat room that all family members can participate in, facilitating the exchange of ideas about the plan. It can also provide a shared calendar for family members to support scheduling. Furthermore, it can offer a feature that allows family members to share their location in real time. This facilitates smoother communication among family members and enables the creation of better plans.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk enters family information. This information includes family structure, ages, hobbies, budget, weather, etc. The reception desk can also save the family information entered by the user in a database and improve input efficiency by referring to past input history. For example, it can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses data mining, statistical analysis, and machine learning algorithms to analyze the information and learn the preferences and interests of each family member. Furthermore, it can also consider local event information, congestion predictions, and word-of-mouth ratings. For example, it can collect local event information based on event calendars, local news, and social media posts. Step 3: The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The generation unit provides a detailed schedule, packing list, and reservation information. Furthermore, the generation unit may also include a prediction unit that predicts weather and crowd levels, based on weather data and real-time pedestrian flow data. Step 4: The proposal unit proposes the plan generated by the generation unit. The proposal unit may also include a feedback unit that receives user feedback, estimates the user's emotions, and adjusts the expression of the proposal based on the estimated user emotions. For example, if the user is nervous, the proposal may be presented in a calmer tone.

[0063] (Example of form 2) An AI Family Planner according to an embodiment of the present invention is a system that personalizedly suggests how families should spend their holidays. This system proposes an optimal holiday plan based on information such as family composition, age, hobbies, budget, and weather. The AI ​​Family Planner learns the preferences and interests of each family member and also takes into account local event information, crowd forecasts, and reviews. Furthermore, it provides a detailed schedule, packing list, and reservation guidance. This service targets nuclear families with working parents and children ranging from preschoolers to junior high school students, and is offered to people who do not have the time to plan their holidays. For example, the AI ​​Family Planner takes into account information such as family composition, age, hobbies, budget, and weather. Next, the AI ​​analyzes this information and generates an optimal holiday plan. The generated plan learns the preferences and interests of each family member and also takes into account local event information, crowd forecasts, and reviews. Furthermore, it provides a detailed schedule, packing list, and reservation guidance. As a method of utilizing the generating AI, natural language processing is used to understand preferences and requests through dialogue with the family, and pattern recognition is used to learn the family's preferences from past choices and evaluations. Using image recognition, the system analyzes age and facial expressions from family photos, and predicts weather, crowd levels, and satisfaction levels using a predictive model. Furthermore, it generates suggestions combining text, images, and audio using multimodal AI. This allows the AI ​​family planner to personalize and suggest how families can spend their holidays.

[0064] The AI ​​family planner according to this embodiment comprises a reception unit, an analysis unit, a generation unit, and a suggestion unit. The reception unit inputs family information. Family information includes, but is not limited to, family structure, age, hobbies, budget, and weather. The reception unit stores the family information entered by the user in a database, for example. The reception unit can also improve input efficiency by referring to past input history. For example, the reception unit can automatically display the family structure and hobbies previously entered by the user, saving the user the trouble of re-entering the information. The analysis unit analyzes the information entered by the reception unit. The analysis unit analyzes the information using, for example, data mining, statistical analysis, and machine learning algorithms. The analysis unit can learn the preferences and interests of each family member. For example, the analysis unit learns the preferences and interests of family members based on survey results and past behavioral history. The analysis unit can also consider local event information, congestion predictions, and word-of-mouth ratings. For example, the analysis unit collects local event information based on event calendars, local news, and social media posts. The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The generation unit provides, for example, a detailed schedule, a packing list, and reservation information. The generation unit may also include a prediction unit that predicts weather and crowd levels. For example, the generation unit predicts weather and crowd levels based on weather data and real-time pedestrian flow data. The suggestion unit proposes the plan generated by the generation unit. The suggestion unit may include a feedback unit that receives user feedback. The suggestion unit may also estimate the user's emotions and adjust the way the suggestion is expressed based on the estimated user emotions. For example, if the user is stressed, the suggestion unit will make suggestions in a calmer way. This allows the AI ​​family planner according to the embodiment to personalize and suggest how families should spend their holidays.

[0065] The reception desk inputs family information. This information includes, but is not limited to, family structure, ages, hobbies, budget, and weather. The reception desk stores the family information entered by the user in a database. Specifically, the user enters family information through a dedicated interface, and this information is stored in a secure database. The database uses encryption technology to protect the information and prevent unauthorized access from external sources. The reception desk can also improve input efficiency by referring to past input history. For example, it can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. This allows the user to enter information quickly and easily. Furthermore, the reception desk can use voice input and image recognition technology to allow the user to input information by voice or upload photos to automatically recognize the family structure. This further improves user convenience. The reception desk also has a function to analyze the information entered by the user in real time and prompt the user to check and correct the input content. For example, if there is an error in the input content or incomplete information has been entered, the reception desk will provide the user with appropriate feedback to support accurate information entry. This allows the reception desk to enable users to input family information accurately and efficiently, improving the overall accuracy and reliability of the system.

[0066] The analysis department analyzes the information entered by the reception department. The analysis department uses data mining, statistical analysis, and machine learning algorithms to analyze the information. Specifically, it uses data mining techniques to extract useful patterns and trends from family information and statistical analysis to reveal data distribution and correlations. Furthermore, it can use machine learning algorithms to learn the preferences and interests of each family member. For example, the analysis department learns the preferences and interests of family members based on survey results and past behavioral history. This allows the analysis department to accurately understand the individual needs and preferences of each member and provide personalized suggestions. The analysis department can also consider local event information, crowd predictions, and user reviews. For example, it collects local event information based on event calendars, local news, and social media posts. This allows the analysis department to grasp the latest event information and local trends and reflect them in family holiday plans. Furthermore, the analysis department uses crowd prediction algorithms to predict the crowding levels of specific locations and events, and adjust plans to ensure families have a comfortable experience. By considering user reviews, the analysis department can select locations and activities that have received high ratings from users. This allows the analysis unit to provide a plan that best suits the family's needs and improve satisfaction.

[0067] The generation unit generates holiday plans based on information analyzed by the analysis unit. For example, the generation unit provides detailed schedules, packing lists, and reservation information. Specifically, based on data provided by the analysis unit, the generation unit creates a detailed schedule tailored to the family's needs and preferences. The schedule includes places to visit, activity times, transportation, and dining locations. The packing list includes necessary and recommended items to help users prepare smoothly. Furthermore, the generation unit provides reservation information for activities and restaurants that require reservations. The generation unit can also include a forecasting unit that predicts weather and crowd levels. For example, the generation unit predicts weather and crowd levels based on meteorological data and real-time pedestrian flow data. This allows the generation unit to flexibly adjust the plan according to weather changes and crowd levels, ensuring the family's comfort. For instance, if rain is expected, it might suggest indoor activities and provide alternatives to avoid crowded areas. This allows the generation unit to optimize the family's holiday plan and improve satisfaction.

[0068] The proposal unit proposes the plan generated by the generation unit. The proposal unit includes, for example, a feedback unit that receives user feedback. Specifically, the proposal unit presents the generated plan to the user and allows the user to provide feedback on the plan. The user inputs opinions and requests regarding the plan content and proposed activities, and the proposal unit collects and analyzes this feedback. The proposal unit can also estimate the user's emotions and adjust the way the proposal is expressed based on the estimated emotions. For example, if the user is nervous, the proposal unit will make suggestions using calm language. Specifically, the proposal unit estimates the user's emotions using natural language processing technology based on the user's input and past feedback. If the user is relaxed, it will make suggestions using casual language; if the user is nervous, it will use polite and calm language. In this way, the proposal unit can make suggestions that are considerate of the user's emotions and improve user satisfaction. Furthermore, the proposal unit can revise the plan based on user feedback and make more appropriate suggestions. For example, if the user provides negative feedback on a particular activity, the proposal unit will replace that activity with another one. Furthermore, the proposal department can continuously learn from user feedback and improve the accuracy of future proposals. This allows the proposal department to provide users with the best possible plans, making family holidays more fulfilling.

[0069] The analysis unit can learn the preferences and interests of each family member. For example, the analysis unit learns the preferences and interests of family members based on survey results and past behavioral history. The analysis unit can also learn the preferences and interests of family members using social media analysis. For example, the analysis unit learns preferences and interests based on event information shared and posts "liked" by family members on social media. This makes it possible to make suggestions based on the preferences and interests of family members. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input family members' behavioral history data into AI and have the AI ​​perform the learning of preferences and interests.

[0070] The analysis unit can consider local event information, congestion forecasts, and user reviews. For example, the analysis unit collects local event information based on event calendars, local news, and social media posts. The analysis unit can perform congestion forecasts using historical data analysis and real-time sensor data. For example, the analysis unit predicts congestion levels based on past congestion patterns and traffic information. The analysis unit can also collect user reviews based on reviews on review sites and comments on social media. For example, the analysis unit analyzes reviews on review sites and calculates user reviews. This makes it possible to make suggestions that take into account local event information, congestion forecasts, and user reviews. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input local event information and congestion forecast data into AI and have the AI ​​perform the analysis.

[0071] The generation unit can provide detailed schedules, packing lists, and reservation guidance. For example, the generation unit can create a detailed schedule including activities, travel times, and rest times for each time slot. The generation unit can provide packing lists including items needed for the trip, seasonal items, and equipment needed for specific activities. The generation unit can also provide reservation guidance, including restaurant reservations, ticket purchases, and accommodation reservations. For example, the generation unit can automatically make restaurant reservations for the user's choice and provide reservation confirmation. This improves user convenience by providing detailed schedules, packing lists, and reservation guidance. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user data into AI and have the AI ​​generate detailed schedules and packing lists.

[0072] The generation unit may include a prediction unit that predicts weather and congestion levels. For example, the generation unit predicts the weather based on meteorological data. The generation unit can also predict congestion levels based on real-time pedestrian flow data. For example, the generation unit predicts congestion levels based on past congestion patterns and traffic information. The generation unit can also adjust the plan content based on the weather and congestion level prediction results. For example, the generation unit suggests indoor activities when the weather is bad and suggests plans to avoid congestion when congestion is expected. This allows for the provision of more appropriate plans by predicting weather and congestion levels. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input meteorological data and pedestrian flow data into AI and have the AI ​​perform weather and congestion level predictions.

[0073] The proposal unit may include a feedback unit that receives user feedback. The proposal unit collects user feedback, for example, through surveys, review collection, or direct exchange of opinions. The proposal unit can improve the accuracy of its proposals based on user feedback. For example, the proposal unit makes proposals based on plans that users have previously given high ratings to. The proposal unit can also make proposals based on plans that users have previously avoided. The proposal unit can also adjust the content of its proposals based on feedback provided by users. For example, the proposal unit prioritizes suggesting activities and events that users prefer. In this way, the accuracy of the proposals can be improved by receiving user feedback. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input user feedback data into AI and have the AI ​​adjust the content of its proposals.

[0074] The reception desk can estimate the user's emotions and adjust the timing of family information input based on the estimated emotions. For example, if the user is stressed, the reception desk can simplify the input and request only the minimum necessary information. If the user is relaxed, the reception desk can encourage them to enter more detailed information, allowing for greater customization. If the user is in a hurry, the reception desk can prioritize voice input and collect information quickly. This reduces the user's burden by adjusting the input timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can improve input efficiency by referring to past input history when entering family information. For example, the reception desk can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. The reception desk can also make optimal suggestions based on the budget and weather preferences that the user has entered in the past. The reception desk can also prioritize suggesting frequently visited places and events based on the information the user has entered in the past. In this way, input efficiency can be improved by referring to past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input past input history data into AI and have the AI ​​perform the input efficiency improvements.

[0076] The reception desk can customize input fields based on the user's current living situation and areas of interest when entering family information. For example, if the user has started a new hobby, the reception desk can add input fields related to that hobby. If the user has recently moved, the reception desk can also include information related to the new area in the input fields. If the user is interested in a particular event, the reception desk can also add information related to that event in the input fields. This allows for the collection of more relevant information by customizing input fields based on the user's living situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's living situation data into AI and have the AI ​​perform the customization of input fields.

[0077] The reception desk can estimate the user's emotions and prioritize the family information to be entered based on the estimated emotions. For example, if the user is tired, the reception desk may prioritize entering only the most important information. If the user is relaxed, the reception desk may also encourage them to enter more detailed information. If the user is in a hurry, the reception desk may also prompt them to enter only the minimum necessary information. This enables efficient information gathering by prioritizing the information to be entered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion-based priority determination.

[0078] The reception desk can prioritize inputting highly relevant information when a user enters family information, taking into account the user's geographical location. For example, if the user lives in a specific region, the reception desk can prioritize inputting event information related to that region. If the user is traveling, the reception desk can also prioritize inputting information about their travel destination. If the user frequently visits a specific region, the reception desk can also prioritize inputting information related to that region. In this way, by considering the user's geographical location, highly relevant information can be prioritized. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into an AI and have the AI ​​prioritize inputting highly relevant information.

[0079] The reception desk can analyze the user's social media activity and input relevant information when family information is entered. For example, the reception desk can input relevant information based on event information shared by the user on social media. The reception desk can also input relevant information based on information about accounts the user follows on social media. The reception desk can also input relevant information based on posts the user "likes" on social media. This allows for the efficient collection of relevant information by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into AI and have the AI ​​input the relevant information.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide more information. If the user is in a hurry, the analysis unit can also perform a simplified analysis and provide results quickly. If the user is stressed, the analysis unit can also display the analysis results in a visually easy-to-understand manner. This allows for more appropriate analysis results to be provided by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method.

[0081] The analysis unit can improve the accuracy of its analysis by referring to the past behavioral history of family members during the analysis process. For example, the analysis unit can analyze events of interest based on the history of events that family members have participated in in the past. The analysis unit can also analyze relevant information based on the history of places that family members have visited in the past. The analysis unit can also analyze activities of interest based on the history of activities that family members have performed in the past. In this way, the accuracy of the analysis can be improved by referring to the past behavioral history of family members. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the behavioral history data of family members into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0082] The analysis unit can perform analysis while considering the attribute information of family members. For example, the analysis unit can analyze appropriate activities by considering the age of family members. The analysis unit can also analyze events of interest by considering the hobbies of family members. The analysis unit can also analyze appropriate plans by considering the health status of family members. In this way, by considering the attribute information of family members, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input family member attribute information data into AI and have the AI ​​perform the analysis.

[0083] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, it is possible to provide results that are easier to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0084] The analysis unit can perform analysis while considering the geographical distribution of families. For example, the analysis unit can perform analysis based on event information in the area where the family lives. The analysis unit can also perform analysis based on information about areas that families frequently visit. The analysis unit can also perform analysis based on information about areas where families are planning to travel. By considering the geographical distribution of families, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input geographical distribution data of families into AI and have the AI ​​perform the analysis.

[0085] The analysis unit can improve the accuracy of its analysis by referring to relevant literature and data during the analysis process. For example, the analysis unit can perform analysis by referring to the latest tourist guidebooks. The analysis unit can also analyze information related to family interests by referring to academic papers. The analysis unit can also analyze the latest event information by referring to online databases. In this way, the accuracy of the analysis can be improved by referring to relevant literature and data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant literature and data into the AI ​​and have the AI ​​perform the task of improving the accuracy of the analysis.

[0086] The generation unit can estimate the user's emotions and adjust the content of the generated plan based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a relaxed plan. If the user is in a hurry, the generation unit can also generate an efficient plan. If the user is excited, the generation unit can also generate an active plan. This allows for the provision of a more appropriate plan by adjusting the content of the plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the plan.

[0087] The generation unit can generate the optimal plan by referring to past plan generation history during the generation process. For example, the generation unit can generate a similar plan based on plans that the user has preferred in the past. The generation unit can also generate a different plan based on plans that the user has avoided in the past. The generation unit can also generate the optimal plan based on plans that the user has given high ratings to in the past. In this way, the optimal plan can be generated by referring to past plan generation history. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past plan generation history data into AI and have the AI ​​perform the generation of the optimal plan.

[0088] The generation unit can customize the plan content based on the family's current living situation during generation. For example, if a family member has taken up a new hobby, the generation unit can generate a plan related to that hobby. If a family member has recently moved, the generation unit can also generate a plan related to their new area. If a family member is interested in a particular event, the generation unit can also generate a plan related to that event. This allows for the provision of more appropriate plans by customizing the plan content based on the family's current living situation. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input family living situation data into AI and have the AI ​​perform the plan customization.

[0089] The generation unit can estimate the user's emotions and determine the priority of the plans to generate based on the estimated emotions. For example, if the user is tired, the generation unit will prioritize relaxing plans. If the user is relaxed, the generation unit may also prioritize active plans. If the user is in a hurry, the generation unit may also prioritize efficient plans. This allows for the provision of more appropriate plans by prioritizing plans according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generation AI and have the generation AI determine the priority of plans.

[0090] The generation unit can generate an optimal plan by considering the family's geographical location information during the generation process. For example, the generation unit can generate a plan based on event information in the area where the family lives. The generation unit can also generate a plan based on information about areas the family frequently visits. The generation unit can also generate a plan based on information about areas the family is planning to travel to. This allows for the provision of more appropriate plans by considering the family's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the family's geographical location data into an AI and have the AI ​​generate the optimal plan.

[0091] The generation unit can adjust the plan content by referring to relevant event information and customer reviews during the generation process. For example, the generation unit can adjust the plan based on the latest event information. The generation unit can also adjust the plan based on events with high customer reviews. The generation unit can also adjust the plan based on events that families have previously given high ratings to. This allows the generation unit to provide more appropriate plans by referring to relevant event information and customer reviews. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input event information and customer review data into the AI ​​and have the AI ​​perform the adjustment of the plan content.

[0092] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can present suggestions in a calm manner. If the user is relaxed, the suggestion unit can present suggestions in a cheerful manner. If the user is in a hurry, the suggestion unit can present suggestions in a concise manner. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented.

[0093] The proposal unit can improve the accuracy of its proposals by referring to past feedback from the family. For example, the proposal unit can make proposals based on plans that the family has previously given high ratings to. The proposal unit can also make proposals based on plans that the family has previously avoided. The proposal unit can also adjust the content of its proposals based on feedback that the family has previously provided. In this way, the accuracy of the proposals can be improved by referring to past feedback from the family. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input family feedback data into AI and have the AI ​​adjust the content of the proposals.

[0094] The suggestion unit can make suggestions while considering family attribute information. For example, the suggestion unit can suggest appropriate activities considering the age of family members. The suggestion unit can also suggest events of interest considering the hobbies of family members. The suggestion unit can also suggest appropriate plans considering the health status of family members. In this way, more appropriate suggestions can be provided by considering family attribute information. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input family attribute information data into AI and have the AI ​​adjust the suggested content.

[0095] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is tired, the suggestion unit will prioritize relaxing plans. If the user is relaxed, the suggestion unit may also prioritize active plans. If the user is in a hurry, the suggestion unit may also prioritize efficient plans. This allows for the provision of more appropriate suggestions by prioritizing suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI determine the priority of suggestions.

[0096] The suggestion unit can make optimal suggestions by considering the family's geographical location information. For example, the suggestion unit can make suggestions based on event information in the area where the family lives. It can also make suggestions based on information about areas the family frequently visits. It can also make suggestions based on information about areas the family is planning to travel to. By considering the family's geographical location information, it can provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input the family's geographical location data into AI and have the AI ​​generate optimal suggestions.

[0097] The proposal department can adjust the content of its proposals by referring to relevant market data and trends. For example, it can suggest popular events based on the latest market data. It can also suggest activities that families might be interested in based on trend information. It can also combine market data and trend information to suggest the optimal plan. This allows it to provide more appropriate suggestions by referring to relevant market data and trends. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input market data and trend information into AI and have the AI ​​adjust the content of the suggestions.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The AI ​​Family Planner can also include a Health Management Department. This department can monitor the health status of family members and propose health-conscious plans. For example, it can record the exercise levels and dietary habits of family members and suggest healthy activities. It can also consider allergy information and suggest allergy-friendly restaurants and events. Furthermore, it can monitor the stress levels of family members and suggest relaxing activities. This allows for the provision of plans that take the family's health into consideration.

[0100] The AI ​​Family Planner can also include an Education Department. This department can monitor the learning progress of family members and suggest educational activities. For example, it can suggest visits to museums and science centers tailored to the child's grade level and interests. It can also suggest workshops and seminars that the whole family can enjoy. Furthermore, the Education Department can record the learning progress of family members and provide helpful learning resources. This allows for the provision of plans that take into account the family's learning situation.

[0101] The AI ​​Family Planner can also include an Entertainment Department. This department can suggest entertainment options tailored to the preferences of each family member. For example, it can suggest tickets to movies and plays that the whole family can enjoy. It can also suggest games and activities that the whole family can enjoy. Furthermore, it can suggest concerts and live events based on the musical preferences of each family member. This allows the planner to provide entertainment options that meet the family's specific needs.

[0102] The AI ​​Family Planner can also be equipped with a traffic management function. This function can optimize family travel and propose efficient travel plans. For example, it can consider public transport schedules and suggest the best travel route. It can also suggest plans that optimize the fuel efficiency and driving time of the family's vehicles. Furthermore, it can suggest routes that avoid congestion based on real-time traffic information. This makes family travel more efficient and provides less stressful travel plans.

[0103] The AI ​​Family Planner can also be equipped with a communication function. This function can facilitate communication among family members and support smooth information sharing. For example, it can provide a chat room that all family members can participate in, facilitating the exchange of ideas about the plan. It can also provide a shared calendar for family members to support scheduling. Furthermore, it can offer a feature that allows family members to share their location in real time. This facilitates smoother communication among family members and enables the creation of better plans.

[0104] The AI ​​Family Planner can further utilize its emotion estimation function to suggest plans based on the emotions of family members. For example, if a family member is feeling stressed, the suggestion function will suggest relaxing activities. If a family member is feeling agitated, it can suggest active activities. Furthermore, if a family member is feeling tired, it can suggest a plan that prioritizes rest. This allows the planner to provide a plan that is tailored to the emotions of each family member.

[0105] The AI ​​Family Planner can further support communication based on the emotions of family members by using its emotion estimation function. For example, if a family member is feeling stressed, the communication function can send a message in a calm tone. If a family member is relaxed, the communication function can also send a message in a cheerful tone. Furthermore, if a family member is in a hurry, the communication function can send a concise message. This supports communication that is appropriate to the emotions of each family member.

[0106] The AI ​​Family Planner can further utilize its emotion estimation function to collect feedback based on the emotions of family members. For example, the feedback function can request detailed feedback if a family member is satisfied. It can also request concise feedback if a family member is dissatisfied. Furthermore, it can request positive feedback if a family member is agitated. This allows the AI ​​to collect feedback tailored to the emotions of family members and improve the accuracy of its suggestions.

[0107] The AI ​​Family Planner can further utilize emotion estimation capabilities to adjust schedules based on the emotions of family members. For example, if a family member is feeling stressed, the scheduling function can suggest a more relaxed schedule. If a family member is relaxed, it can suggest an active schedule. Furthermore, if a family member is in a hurry, it can suggest an efficient schedule. This allows for schedule adjustments tailored to the emotions of family members, providing a more appropriate plan.

[0108] The AI ​​Family Planner can also use emotion estimation to provide packing lists based on the emotions of family members. For example, if a family member is feeling stressed, the packing list will add items that help them relax. If a family member is excited, the packing list can also add items needed for active activities. Furthermore, if a family member is tired, the packing list can add items that prioritize rest. This allows the planner to provide packing lists that are tailored to the emotions of each family member.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk enters family information. This information includes family structure, ages, hobbies, budget, weather, etc. The reception desk can also save the family information entered by the user in a database and improve input efficiency by referring to past input history. For example, it can automatically display the family structure and hobbies that the user has entered in the past, saving the user the trouble of re-entering the information. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis unit uses data mining, statistical analysis, and machine learning algorithms to analyze the information and learn the preferences and interests of each family member. Furthermore, it can also consider local event information, congestion predictions, and word-of-mouth ratings. For example, it can collect local event information based on event calendars, local news, and social media posts. Step 3: The generation unit generates a holiday plan based on the information analyzed by the analysis unit. The generation unit provides a detailed schedule, packing list, and reservation information. Furthermore, the generation unit may also include a prediction unit that predicts weather and crowd levels, based on weather data and real-time pedestrian flow data. Step 4: The proposal unit proposes the plan generated by the generation unit. The proposal unit may also include a feedback unit that receives user feedback, estimates the user's emotions, and adjusts the expression of the proposal based on the estimated user emotions. For example, if the user is nervous, the proposal may be presented in a calmer tone.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and stores the family information entered by the user in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining and machine learning algorithms. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a detailed schedule and a packing list. The proposal unit is implemented by the control unit 46A of the smart device 14 and presents the generated plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and stores the family information entered by the user in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining and machine learning algorithms. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a detailed schedule and a list of items to bring. The proposal unit is implemented by the control unit 46A of the smart glasses 214 and presents the generated plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and stores the family information entered by the user in the database 24. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining and machine learning algorithms. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates a detailed schedule and a list of items to bring. The proposal unit is implemented by the control unit 46A of the headset terminal 314 and presents the generated plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, generation unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and stores the family information entered by the user in the database 24. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the information using data mining and machine learning algorithms. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates a detailed schedule and a list of belongings. The proposal unit is implemented by, for example, the control unit 46A of the robot 414 and presents the generated plan to the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] 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.

[0174] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) The reception area where family information is entered, An analysis unit analyzes the information input by the reception unit, A generation unit that generates a holiday plan based on the information analyzed by the analysis unit, The system comprises a proposal unit that proposes the plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Learn the preferences and interests of each family member. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We take into account local event information, crowd predictions, and customer reviews. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is We provide a detailed schedule, packing list, and booking information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is It is equipped with a forecasting unit that predicts weather and congestion levels. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, It includes a feedback unit for receiving user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of family information input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering family information, refer to past input history to improve input efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering family information, the input fields are customized based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the family information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering family information, the system prioritizes inputting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When entering family information, the system analyzes the user's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by referring to the past behavioral history of family members. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the attribute information of family members will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the geographical distribution of families will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature and data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts the content of the plan generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system references past plan generation history to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, the plan's contents are customized based on the family's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the plans generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the system considers the family's geographical location to generate the optimal plan. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the plan content is adjusted by referring to relevant event information and customer reviews. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, refer to past feedback from family members to improve the accuracy of the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, take into account the family's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we take into account the geographical location information of the family to provide the most suitable suggestion. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making a proposal, we adjust the content of the proposal by referring to relevant market data and trends. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area where family information is entered, An analysis unit analyzes the information input by the reception unit, A generation unit that generates a holiday plan based on the information analyzed by the analysis unit, The system comprises a proposal unit that proposes the plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned analysis unit, Learn the preferences and interests of each family member. The system according to feature 1.

3. The aforementioned analysis unit, We take into account local event information, crowd predictions, and customer reviews. The system according to feature 1.

4. The generating unit is We provide a detailed schedule, packing list, and booking information. The system according to feature 1.

5. The generating unit is It is equipped with a forecasting unit that predicts weather and congestion levels. The system according to feature 1.

6. The aforementioned proposal section is, It includes a feedback unit for receiving user feedback. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of family information input based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When entering family information, refer to past input history to improve input efficiency. The system according to feature 1.

9. The aforementioned reception unit is When entering family information, the input fields are customized based on the user's current lifestyle and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the family information to be entered based on those estimated emotions. The system according to feature 1.

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