system

Abstract

The system according to this embodiment aims to understand local needs and propose events that transcend generations. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects local voices and online feedback. The analysis unit analyzes the data collected by the collection unit to understand local needs and trends. The proposal unit generates cross-generational event proposals and formulates implementation plans based on the analysis results obtained by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , ,

[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, including the 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 that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to accurately grasp the needs of the region and propose events that transcended generations.

[0005] The system according to the embodiment aims to grasp the needs of the region and propose events that transcend generations.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects local voices and online feedback. The analysis unit analyzes the data collected by the data collection unit to understand local needs and trends. Based on the analysis results obtained by the analysis unit, the proposal unit generates cross-generational event proposals and formulates an implementation plan. [Effects of the Invention]

[0007] The system according to this embodiment can understand local needs and propose events that transcend generations. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a system that uses an AI agent to analyze local needs and propose intergenerational events in order to solve the stagnation of community activities caused by the aging of the local population and lack of cooperation with young people. This system collects local voices and online feedback, and a generating AI analyzes the collected data to grasp local needs and trends. Next, the generating AI generates event proposals that are attractive to all generations and formulates an implementation plan. This mechanism deepens connections with the community and revitalizes community activities. For example, the system digitizes opinions and requests from residents and visualizes local issues. For example, it collects information such as what kind of events residents want to participate in and what problems they have. This makes it possible to grasp local needs. Next, the generating AI analyzes the collected data. The generating AI analyzes local needs and trends based on local voices and online feedback. For example, it can identify events that are easy for the elderly to participate in and activities that young people are interested in. This makes it possible to generate event proposals that are tailored to local needs. Based on the generated event proposals, an implementation plan is formulated. The generating AI generates event proposals that are attractive to all generations based on local needs and formulates a concrete implementation plan. For example, we propose events that can be enjoyed by participants of all generations, such as family-friendly cultural festivals and IT courses for seniors. This will deepen community ties and revitalize community activities. For instance, in a family-friendly cultural festival, the joint operation by seniors and young people will promote intergenerational exchange. In an IT course for seniors, young people can act as instructors, teaching digital technology to seniors and bridging the digital divide in the community. Furthermore, the generation AI will analyze resident participation data in real time to optimize events. For example, it can flexibly adjust the content and schedule of events based on participant feedback. This will allow us to provide events that are more accessible to a wider range of residents. Looking ahead, we envision strengthening collaboration with local communities and government agencies and expanding to other regions. For example, introducing a similar system in other regions could promote the revitalization of the entire area.Furthermore, as a long-term vision, it is possible to aim for the creation of a trusting network and the realization of a sustainable society in which people actively participate. In this way, by utilizing AI agents, it is possible to listen to the voices of the community, propose events tailored to local needs, and deepen connections with the community. This can resolve the stagnation of community activities caused by the aging population and lack of collaboration with young people, and revitalize the entire region.

[0029] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects local voices and online feedback. The data collection unit collects residents' opinions and requests, for example, through questionnaires. The data collection unit can also collect residents' opinions and requests through interviews. Furthermore, the data collection unit can also collect residents' opinions and requests through workshops. For example, the data collection unit conducts online questionnaires to collect residents' opinions and requests. The data collection unit can also collect specific opinions and requests from residents through interviews. The data collection unit can also hold workshops to provide residents with an opportunity to express their opinions directly. The analysis unit analyzes the data collected by the data collection unit to understand local needs and trends. The analysis unit analyzes the collected data, for example, using data mining techniques. Furthermore, the analysis unit can analyze the collected data using text analysis techniques. Furthermore, the analysis unit can analyze the collected data using statistical analysis techniques. For example, the analysis unit uses data mining techniques to identify local needs. The analysis unit can also analyze residents' opinions and requests using text analysis technology. The analysis unit can also grasp local trends using statistical analysis technology. The proposal unit generates intergenerational event proposals and develops implementation plans based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a family-oriented cultural festival. It can also propose IT courses for the elderly. Furthermore, the proposal unit can propose workshops for young people. For example, the proposal unit proposes a family-oriented cultural festival and develops a concrete implementation plan. The proposal unit can also propose IT courses for the elderly and develop a concrete implementation plan. The proposal unit can also propose workshops for young people and develop a concrete implementation plan. In this way, the system according to the embodiment deepens local connections and revitalizes community activities by collecting and analyzing local voices and online feedback and generating intergenerational event proposals.

[0030] The data collection department collects local voices and online feedback. For example, the department collects residents' opinions and requests through surveys. It can also collect residents' opinions and requests through interviews. Furthermore, it can collect residents' opinions and requests through workshops. For instance, the department conducts online surveys to collect residents' opinions and requests. It can also collect specific opinions and requests from residents through interviews. The department can also hold workshops to provide residents with an opportunity to express their opinions directly. By combining these methods, the department can collect a wide range of diverse opinions and requests from residents. For example, online surveys allow residents to easily submit their opinions from home, enabling the collection of many opinions in a short period. Interviews allow for a deeper exploration of each resident's specific opinions and requests, providing more detailed information. Workshops are expected to generate new ideas and solutions through the exchange of opinions among residents. The data collection department centrally manages this data and prepares it for provision to the analysis department. During the data collection process, measures are taken to ensure privacy protection and data accuracy. For example, during surveys and interviews, explanations regarding the handling of personal information are provided, and data is collected only after obtaining consent. Furthermore, the collected data is anonymized and encrypted and stored securely. This allows the data collection department to collect data efficiently and effectively while gaining the trust of residents.

[0031] The analysis department analyzes the data collected by the data collection department to understand local needs and trends. For example, the analysis department uses data mining techniques to analyze the collected data. It can also analyze the collected data using text analysis techniques. Furthermore, it can analyze the collected data using statistical analysis techniques. For instance, the analysis department uses data mining techniques to identify local needs. It can also analyze residents' opinions and requests using text analysis techniques. It can also grasp local trends using statistical analysis techniques. The analysis department utilizes these techniques to extract useful information from the collected data and clarify local needs and trends. By using data mining techniques, patterns and relationships can be found in large amounts of data, allowing for the identification of local needs. By using text analysis techniques, residents' opinions and requests can be analyzed using natural language processing techniques to extract frequently occurring keywords and themes. By using statistical analysis techniques, data distribution and trends can be understood, clarifying local trends. Based on these analysis results, the analysis department understands local needs and trends and provides them to the proposal department. The analysis department also visualizes the analysis results and presents them clearly using graphs and charts. This allows the proposal department to make concrete proposals based on the analysis results. Furthermore, the analysis department regularly updates the analysis results and provides the latest information, enabling them to respond quickly to changes in local needs and trends. This allows the analysis department to accurately grasp local needs and trends and support the activities of the proposal department.

[0032] The proposal department generates intergenerational event ideas and develops implementation plans based on the analysis results obtained by the analysis department. For example, the proposal department might propose a family-oriented cultural festival. It could also propose an IT course for senior citizens. Furthermore, it could propose a workshop for young people. For example, the proposal department might propose a family-oriented cultural festival and develop a concrete implementation plan. It could also propose an IT course for senior citizens and develop a concrete implementation plan. It could also propose a workshop for young people and develop a concrete implementation plan. The proposal department then develops detailed implementation plans to bring these event ideas to life. These plans include the event's purpose and content, date, time, location, target participants and recruitment methods, budget, and necessary resources. The proposal department considers local needs and trends while designing the event's content and format, creating a program that participants can enjoy while learning. For example, a family-oriented cultural festival might include workshops and games for parents and children to enjoy together, as well as exhibits and performances showcasing local traditional culture. The IT courses for seniors will include programs teaching beginners how to use computers and smartphones, as well as safe internet usage. Workshops for young people will offer programs aimed at creative activities and skill development, promoting interaction among young people. It is also important for the proposal department to collaborate with local stakeholders and organizations to realize these event proposals. The proposal department will coordinate with stakeholders and build cooperative systems to ensure the smooth operation of the events. In this way, the proposal department can concretize event proposals that meet local needs, develop implementation plans, deepen local connections, and revitalize community activities.

[0033] The data collection unit can collect residents' opinions and requests. For example, the data collection unit can collect residents' opinions and requests through questionnaires. The data collection unit can also collect residents' specific opinions and requests through interviews. The data collection unit can also hold workshops to provide residents with an opportunity to express their opinions directly. In this way, by collecting residents' opinions and requests, the needs of the community can be understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the questionnaire results into a generating AI and have the generating AI perform an analysis of residents' opinions and requests.

[0034] The analysis unit can analyze local needs and trends based on local voices and online feedback. For example, the analysis unit can analyze the collected data using data mining techniques. The analysis unit can also analyze the collected data using text analysis techniques. The analysis unit can also analyze the collected data using statistical analysis techniques. This allows for the understanding of local needs and trends by analyzing data based on local voices and online feedback. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of local needs and trends.

[0035] The proposal department can generate events that can be enjoyed by participants of all generations, such as family-oriented cultural festivals or IT courses for seniors. For example, the proposal department can propose a family-oriented cultural festival. It can also propose an IT course for seniors. It can also propose a workshop for young people. By generating events that can be enjoyed by participants of all generations, it is possible to deepen community ties. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can have a generation AI perform the generation of event proposals.

[0036] The proposal department can analyze resident participation data in real time and flexibly adjust the event content and schedule. For example, the proposal department can adjust the event content based on participant feedback. The proposal department can also adjust the event schedule based on participant feedback. The proposal department can flexibly adjust the event content and schedule based on participant feedback. This allows for the provision of events that are more accessible to a wider range of residents by analyzing resident participation data in real time and flexibly adjusting the event content and schedule. Some or all of the above-described processes in the proposal department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal department can input participation data into a generative AI and have the generative AI perform adjustments to the event content and schedule.

[0037] The collection unit can analyze residents' past opinion submission history and select the optimal collection method. For example, the collection unit can select the optimal collection method based on the frequency of opinions previously submitted by residents. The collection unit can also analyze the content of opinions previously submitted by residents and prepare relevant questions. The collection unit can also prioritize suggesting collection methods previously used by residents (online, in-person, etc.). This allows the collection unit to select the optimal collection method by analyzing residents' past opinion submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input past opinion submission history into a generating AI and have the generating AI select the optimal collection method.

[0038] The data collection unit can filter opinions and requests based on residents' current living situations and areas of interest. For example, the data collection unit can collect relevant opinions and requests based on residents' living situations (work, family, etc.). The data collection unit can also collect relevant opinions and requests based on residents' areas of interest (hobbies, interests, etc.). The data collection unit can also determine the priority of opinions and requests to be collected based on residents' living situations and areas of interest. This allows for the collection of more relevant opinions and requests by filtering based on residents' current living situations and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on residents' living situations and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The collection unit can prioritize collecting highly relevant opinions and requests by considering the geographical location information of residents. For example, the collection unit can prioritize collecting opinions related to local issues based on the geographical location information of residents. The collection unit can also prioritize collecting opinions related to nearby events based on the geographical location information of residents. The collection unit can also prioritize collecting opinions related to local needs based on the geographical location information of residents. In this way, by considering the geographical location information of residents, it is possible to prioritize collecting opinions related to local issues. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the geographical location information of residents into a generating AI and have the generating AI perform the collection of highly relevant opinions.

[0040] The collection unit can analyze residents' social media activity and collect relevant opinions when gathering opinions and requests. For example, the collection unit can analyze residents' social media activity and collect relevant opinions. Based on residents' social media activity, the collection unit can also collect opinions on topics of high interest. Based on residents' social media activity, the collection unit can also collect opinions related to local issues. In this way, relevant opinions can be collected by analyzing residents' social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input residents' social media activity data into a generating AI and have the generating AI perform the collection of relevant opinions.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions and requests during the analysis. For example, the analysis unit can perform a detailed analysis on opinions and requests of high importance. The analysis unit can also perform a concise analysis on opinions and requests of low importance. The analysis unit can also determine the priority of the analysis according to importance. This allows for a more detailed analysis of more important opinions and requests by adjusting the level of detail based on the importance of the opinions and requests. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of opinions and requests into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of opinions and requests during analysis. For example, the analysis unit can apply a specific analysis algorithm to opinions and requests directed at the elderly. The analysis unit can also apply a different analysis algorithm to opinions and requests directed at young people. The analysis unit can also apply yet another analysis algorithm to opinions and requests directed at families. By applying different analysis algorithms depending on the category of opinions and requests, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input opinion and request category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0043] The analysis unit can determine the priority of analysis based on when opinions and requests were submitted. For example, the analysis unit will prioritize the analysis of recently submitted opinions and requests. The analysis unit can also determine the priority of analysis based on the submission date. The analysis unit can also adjust the level of detail of the analysis according to the submission date. This allows for a faster response by determining the priority of analysis based on when opinions and requests were submitted. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission date data of opinions and requests into a generation AI and have the generation AI perform the determination of the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of opinions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant opinions and requests. The analysis unit can also adjust the order of analysis based on relevance. The analysis unit can also adjust the level of detail of the analysis according to the relevance. This allows for prioritizing the analysis of more relevant opinions and requests by adjusting the order of analysis based on the relevance of opinions and requests. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the relevance data of opinions and requests into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0045] The proposal unit can adjust the level of detail of a proposal based on the importance of the event. For example, the proposal unit can provide detailed proposals for high-importance events, and concise proposals for low-importance events. The proposal unit can also prioritize proposals according to their importance. This allows for more detailed proposals to be provided for more important events by adjusting the level of detail based on the importance of the event. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input event importance data into a generative AI and have the generative AI adjust the level of detail of the proposal.

[0046] The suggestion unit can apply different suggestion algorithms depending on the event category when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to events for the elderly. The suggestion unit can also apply a different suggestion algorithm to events for young people. The suggestion unit can also apply yet another suggestion algorithm to events for families. By applying different suggestion algorithms depending on the event category, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input event category data into a generative AI and have the generative AI perform the application of the suggestion algorithm.

[0047] The proposal department can determine the priority of proposals based on the timing of the events when submitting them. For example, the proposal department will prioritize proposals for events that will take place in the near future. The proposal department can also determine the priority of proposals based on the timing of the events. The proposal department can also adjust the level of detail of proposals according to the timing of the events. This allows for a quicker response by determining the priority of proposals based on the timing of the events. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input event timing data into a generative AI and have the generative AI perform the determination of proposal priorities.

[0048] The proposal unit can adjust the order of proposals based on the relevance of events when making a proposal. For example, the proposal unit will prioritize proposing events that are highly relevant. The proposal unit can also adjust the order of proposals based on relevance. The proposal unit can also adjust the level of detail of proposals according to relevance. This allows the proposal unit to prioritize proposing more relevant events by adjusting the order of proposals based on the relevance of events. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input event relevance data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The data collection department can adjust its collection methods when gathering residents' opinions and requests, taking into account their health data. For example, if a resident is in good health, the department can conduct face-to-face interviews. If a resident's health is unstable, the department can conduct online questionnaires. Furthermore, based on residents' health data, the department can prioritize collecting opinions and requests related to health. This allows for the collection of opinions and requests from a larger number of residents by selecting appropriate collection methods according to their health status.

[0051] The analytics department can analyze local needs and trends based on local voices and online feedback, taking into account residents' purchasing history. For example, it can identify popular products and services in the area based on residents' purchasing history. It can also grasp local consumption trends based on residents' purchasing history. Furthermore, it can generate event proposals that match local needs based on residents' purchasing history. By considering residents' purchasing history, it can provide more accurate analytical results.

[0052] The proposal department, based on the analysis results obtained by the analysis department, can consider residents' hobbies and interests when generating event proposals that transcend generations. For example, the proposal department can propose sports events based on residents' hobbies. It can also propose cultural events based on residents' interests. Furthermore, the proposal department can propose workshops and seminars based on residents' hobbies and interests. By generating event proposals that match residents' hobbies and interests, it becomes possible to provide events that are more accessible to a wider range of residents.

[0053] The proposal department can analyze resident participation data in real time and flexibly adjust event content and schedules, taking residents' transportation methods into consideration when making suggestions. For example, based on residents' transportation methods, the proposal department can suggest events in locations easily accessible by public transport. It can also suggest events in locations with ample parking facilities. Furthermore, it can suggest events within walking distance based on residents' transportation methods. By generating event proposals tailored to residents' transportation methods, it is possible to provide events that are more accessible to a wider range of residents.

[0054] The collection department can adjust the timing of collecting residents' opinions and requests, taking into account their daily routines. For example, the collection department can conduct surveys during the time residents are returning home from work. They can also conduct interviews during times when residents are relaxing on their days off. Furthermore, the collection department can adjust the timing of collecting opinions and requests based on residents' daily routines. This allows for the collection of opinions and requests from a wider range of residents by selecting appropriate collection timings that align with their lifestyles.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The collection team gathers local voices and online feedback. For example, the collection team collects residents' opinions and requests through surveys, interviews, and workshops. Online surveys can be conducted to collect residents' opinions and requests. Specific opinions and requests can be collected through interviews, and workshops can be held to provide residents with an opportunity to express their opinions directly. Step 2: The analysis unit analyzes the data collected by the collection unit to understand local needs and trends. For example, the analysis unit uses data mining techniques, text analysis techniques, and statistical analysis techniques to analyze the collected data. By using data mining techniques, local needs can be identified; by using text analysis techniques, residents' opinions and requests can be analyzed; and by using statistical analysis techniques, local trends can be understood. Step 3: Based on the analysis results obtained by the analysis department, the proposal department generates intergenerational event proposals and develops implementation plans. For example, the proposal department proposes a family-oriented cultural festival, IT courses for the elderly, and workshops for young people, and develops concrete implementation plans. This will deepen community ties and revitalize community activities.

[0057] (Example of form 2) The system according to an embodiment of the present invention is a system that uses an AI agent to analyze local needs and propose intergenerational events in order to solve the stagnation of community activities caused by the aging of the local population and lack of cooperation with young people. This system collects local voices and online feedback, and a generating AI analyzes the collected data to grasp local needs and trends. Next, the generating AI generates event proposals that are attractive to all generations and formulates an implementation plan. This mechanism deepens connections with the community and revitalizes community activities. For example, the system digitizes opinions and requests from residents and visualizes local issues. For example, it collects information such as what kind of events residents want to participate in and what problems they have. This makes it possible to grasp local needs. Next, the generating AI analyzes the collected data. The generating AI analyzes local needs and trends based on local voices and online feedback. For example, it can identify events that are easy for the elderly to participate in and activities that young people are interested in. This makes it possible to generate event proposals that are tailored to local needs. Based on the generated event proposals, an implementation plan is formulated. The generating AI generates event proposals that are attractive to all generations based on local needs and formulates a concrete implementation plan. For example, we propose events that can be enjoyed by participants of all generations, such as family-friendly cultural festivals and IT courses for seniors. This will deepen community ties and revitalize community activities. For instance, in a family-friendly cultural festival, the joint operation by seniors and young people will promote intergenerational exchange. In an IT course for seniors, young people can act as instructors, teaching digital technology to seniors and bridging the digital divide in the community. Furthermore, the generation AI will analyze resident participation data in real time to optimize events. For example, it can flexibly adjust the content and schedule of events based on participant feedback. This will allow us to provide events that are more accessible to a wider range of residents. Looking ahead, we envision strengthening collaboration with local communities and government agencies and expanding to other regions. For example, introducing a similar system in other regions could promote the revitalization of the entire area.Furthermore, as a long-term vision, it is possible to aim for the creation of a trusting network and the realization of a sustainable society in which people actively participate. In this way, by utilizing AI agents, it is possible to listen to the voices of the community, propose events tailored to local needs, and deepen connections with the community. This can resolve the stagnation of community activities caused by the aging population and lack of collaboration with young people, and revitalize the entire region.

[0058] The system according to this embodiment comprises a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects local voices and online feedback. The data collection unit collects residents' opinions and requests, for example, through questionnaires. The data collection unit can also collect residents' opinions and requests through interviews. Furthermore, the data collection unit can also collect residents' opinions and requests through workshops. For example, the data collection unit conducts online questionnaires to collect residents' opinions and requests. The data collection unit can also collect specific opinions and requests from residents through interviews. The data collection unit can also hold workshops to provide residents with an opportunity to express their opinions directly. The analysis unit analyzes the data collected by the data collection unit to understand local needs and trends. The analysis unit analyzes the collected data, for example, using data mining techniques. Furthermore, the analysis unit can analyze the collected data using text analysis techniques. Furthermore, the analysis unit can analyze the collected data using statistical analysis techniques. For example, the analysis unit uses data mining techniques to identify local needs. The analysis unit can also analyze residents' opinions and requests using text analysis technology. The analysis unit can also grasp local trends using statistical analysis technology. The proposal unit generates intergenerational event proposals and develops implementation plans based on the analysis results obtained by the analysis unit. For example, the proposal unit proposes a family-oriented cultural festival. It can also propose IT courses for the elderly. Furthermore, the proposal unit can propose workshops for young people. For example, the proposal unit proposes a family-oriented cultural festival and develops a concrete implementation plan. The proposal unit can also propose IT courses for the elderly and develop a concrete implementation plan. The proposal unit can also propose workshops for young people and develop a concrete implementation plan. In this way, the system according to the embodiment deepens local connections and revitalizes community activities by collecting and analyzing local voices and online feedback and generating intergenerational event proposals.

[0059] The data collection department collects local voices and online feedback. For example, the department collects residents' opinions and requests through surveys. It can also collect residents' opinions and requests through interviews. Furthermore, it can collect residents' opinions and requests through workshops. For instance, the department conducts online surveys to collect residents' opinions and requests. It can also collect specific opinions and requests from residents through interviews. The department can also hold workshops to provide residents with an opportunity to express their opinions directly. By combining these methods, the department can collect a wide range of diverse opinions and requests from residents. For example, online surveys allow residents to easily submit their opinions from home, enabling the collection of many opinions in a short period. Interviews allow for a deeper exploration of each resident's specific opinions and requests, providing more detailed information. Workshops are expected to generate new ideas and solutions through the exchange of opinions among residents. The data collection department centrally manages this data and prepares it for provision to the analysis department. During the data collection process, measures are taken to ensure privacy protection and data accuracy. For example, during surveys and interviews, explanations regarding the handling of personal information are provided, and data is collected only after obtaining consent. Furthermore, the collected data is anonymized and encrypted and stored securely. This allows the data collection department to collect data efficiently and effectively while gaining the trust of residents.

[0060] The analysis department analyzes the data collected by the data collection department to understand local needs and trends. For example, the analysis department uses data mining techniques to analyze the collected data. It can also analyze the collected data using text analysis techniques. Furthermore, it can analyze the collected data using statistical analysis techniques. For instance, the analysis department uses data mining techniques to identify local needs. It can also analyze residents' opinions and requests using text analysis techniques. It can also grasp local trends using statistical analysis techniques. The analysis department utilizes these techniques to extract useful information from the collected data and clarify local needs and trends. By using data mining techniques, patterns and relationships can be found in large amounts of data, allowing for the identification of local needs. By using text analysis techniques, residents' opinions and requests can be analyzed using natural language processing techniques to extract frequently occurring keywords and themes. By using statistical analysis techniques, data distribution and trends can be understood, clarifying local trends. Based on these analysis results, the analysis department understands local needs and trends and provides them to the proposal department. The analysis department also visualizes the analysis results and presents them clearly using graphs and charts. This allows the proposal department to make concrete proposals based on the analysis results. Furthermore, the analysis department regularly updates the analysis results and provides the latest information, enabling them to respond quickly to changes in local needs and trends. This allows the analysis department to accurately grasp local needs and trends and support the activities of the proposal department.

[0061] The proposal department generates intergenerational event ideas and develops implementation plans based on the analysis results obtained by the analysis department. For example, the proposal department might propose a family-oriented cultural festival. It could also propose an IT course for senior citizens. Furthermore, it could propose a workshop for young people. For example, the proposal department might propose a family-oriented cultural festival and develop a concrete implementation plan. It could also propose an IT course for senior citizens and develop a concrete implementation plan. It could also propose a workshop for young people and develop a concrete implementation plan. The proposal department then develops detailed implementation plans to bring these event ideas to life. These plans include the event's purpose and content, date, time, location, target participants and recruitment methods, budget, and necessary resources. The proposal department considers local needs and trends while designing the event's content and format, creating a program that participants can enjoy while learning. For example, a family-oriented cultural festival might include workshops and games for parents and children to enjoy together, as well as exhibits and performances showcasing local traditional culture. The IT courses for seniors will include programs teaching beginners how to use computers and smartphones, as well as safe internet usage. Workshops for young people will offer programs aimed at creative activities and skill development, promoting interaction among young people. It is also important for the proposal department to collaborate with local stakeholders and organizations to realize these event proposals. The proposal department will coordinate with stakeholders and build cooperative systems to ensure the smooth operation of the events. In this way, the proposal department can concretize event proposals that meet local needs, develop implementation plans, deepen local connections, and revitalize community activities.

[0062] The data collection unit can collect residents' opinions and requests. For example, the data collection unit can collect residents' opinions and requests through questionnaires. The data collection unit can also collect residents' specific opinions and requests through interviews. The data collection unit can also hold workshops to provide residents with an opportunity to express their opinions directly. In this way, by collecting residents' opinions and requests, the needs of the community can be understood. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the questionnaire results into a generating AI and have the generating AI perform an analysis of residents' opinions and requests.

[0063] The analysis unit can analyze local needs and trends based on local voices and online feedback. For example, the analysis unit can analyze the collected data using data mining techniques. The analysis unit can also analyze the collected data using text analysis techniques. The analysis unit can also analyze the collected data using statistical analysis techniques. This allows for the understanding of local needs and trends by analyzing data based on local voices and online feedback. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may be performed without generative AI. For example, the analysis unit can input the collected data into a generative AI and have the generative AI perform the analysis of local needs and trends.

[0064] The proposal department can generate events that can be enjoyed by participants of all generations, such as family-oriented cultural festivals or IT courses for seniors. For example, the proposal department can propose a family-oriented cultural festival. It can also propose an IT course for seniors. It can also propose a workshop for young people. By generating events that can be enjoyed by participants of all generations, it is possible to deepen community ties. Some or all of the above processing in the proposal department may be performed using a generation AI, or it may be performed without a generation AI. For example, the proposal department can have a generation AI perform the generation of event proposals.

[0065] The proposal department can analyze resident participation data in real time and flexibly adjust the event content and schedule. For example, the proposal department can adjust the event content based on participant feedback. The proposal department can also adjust the event schedule based on participant feedback. The proposal department can flexibly adjust the event content and schedule based on participant feedback. This allows for the provision of events that are more accessible to a wider range of residents by analyzing resident participation data in real time and flexibly adjusting the event content and schedule. Some or all of the above-described processes in the proposal department may be performed using a generative AI, or they may not be performed using a generative AI. For example, the proposal department can input participation data into a generative AI and have the generative AI perform adjustments to the event content and schedule.

[0066] The data collection unit can estimate residents' emotions and adjust the timing of collecting opinions and requests based on the estimated emotions. For example, if residents are stressed, the data collection unit will collect opinions and requests during relaxed times. If residents are agitated, the data collection unit can collect opinions and requests immediately and respond quickly. If residents are calm, the data collection unit can take more time to collect detailed opinions and requests. By adjusting the timing of collecting opinions and requests based on residents' emotions, opinions and requests can be collected at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input residents' emotion data into a generative AI and have the generative AI adjust the timing of collecting opinions and requests.

[0067] The collection unit can analyze residents' past opinion submission history and select the optimal collection method. For example, the collection unit can select the optimal collection method based on the frequency of opinions previously submitted by residents. The collection unit can also analyze the content of opinions previously submitted by residents and prepare relevant questions. The collection unit can also prioritize suggesting collection methods previously used by residents (online, in-person, etc.). This allows the collection unit to select the optimal collection method by analyzing residents' past opinion submission history. Some or all of the above processes in the collection unit may be performed using AI or not. For example, the collection unit can input past opinion submission history into a generating AI and have the generating AI select the optimal collection method.

[0068] The data collection unit can filter opinions and requests based on residents' current living situations and areas of interest. For example, the data collection unit can collect relevant opinions and requests based on residents' living situations (work, family, etc.). The data collection unit can also collect relevant opinions and requests based on residents' areas of interest (hobbies, interests, etc.). The data collection unit can also determine the priority of opinions and requests to be collected based on residents' living situations and areas of interest. This allows for the collection of more relevant opinions and requests by filtering based on residents' current living situations and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data on residents' living situations and areas of interest into a generating AI and have the generating AI perform the filtering.

[0069] The data collection unit can estimate residents' emotions and determine the priority of opinions and requests to collect based on the estimated emotions. For example, if a resident is dissatisfied, the data collection unit will prioritize collecting those opinions and requests. If a resident is agitated, the data collection unit can also quickly collect those opinions and requests. If a resident is relaxed, the data collection unit can also collect detailed opinions and requests. This allows for the priority collection of more important opinions and requests by determining the priority of opinions and requests based on residents' emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input residents' emotion data into a generative AI and have the generative AI determine the priority of opinions and requests.

[0070] The collection unit can prioritize collecting highly relevant opinions and requests by considering the geographical location information of residents. For example, the collection unit can prioritize collecting opinions related to local issues based on the geographical location information of residents. The collection unit can also prioritize collecting opinions related to nearby events based on the geographical location information of residents. The collection unit can also prioritize collecting opinions related to local needs based on the geographical location information of residents. In this way, by considering the geographical location information of residents, it is possible to prioritize collecting opinions related to local issues. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the geographical location information of residents into a generating AI and have the generating AI perform the collection of highly relevant opinions.

[0071] The collection unit can analyze residents' social media activity and collect relevant opinions when gathering opinions and requests. For example, the collection unit can analyze residents' social media activity and collect relevant opinions. Based on residents' social media activity, the collection unit can also collect opinions on topics of high interest. Based on residents' social media activity, the collection unit can also collect opinions related to local issues. In this way, relevant opinions can be collected by analyzing residents' social media activity. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input residents' social media activity data into a generating AI and have the generating AI perform the collection of relevant opinions.

[0072] The analysis unit can estimate the emotions of residents and adjust the presentation of the analysis based on the estimated emotions. For example, if a resident is relaxed, the analysis unit can provide detailed analysis results. If a resident is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. If a resident is excited, the analysis unit can also provide visually stimulating analysis results. In this way, by adjusting the presentation of the analysis based on the emotions of residents, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input resident emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the opinions and requests during the analysis. For example, the analysis unit can perform a detailed analysis on opinions and requests of high importance. The analysis unit can also perform a concise analysis on opinions and requests of low importance. The analysis unit can also determine the priority of the analysis according to importance. This allows for a more detailed analysis of more important opinions and requests by adjusting the level of detail based on the importance of the opinions and requests. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the importance data of opinions and requests into a generation AI and have the generation AI perform the adjustment of the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the category of opinions and requests during analysis. For example, the analysis unit can apply a specific analysis algorithm to opinions and requests directed at the elderly. The analysis unit can also apply a different analysis algorithm to opinions and requests directed at young people. The analysis unit can also apply yet another analysis algorithm to opinions and requests directed at families. By applying different analysis algorithms depending on the category of opinions and requests, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input opinion and request category data into a generative AI and have the generative AI execute the application of the analysis algorithm.

[0075] The analysis unit can estimate the emotions of residents and adjust the length of the analysis based on the estimated emotions. For example, if a resident is in a hurry, the analysis unit can provide a short, concise analysis result. If a resident is relaxed, the analysis unit can also provide a detailed analysis result. If a resident is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the emotions of residents, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using or without a generative AI. For example, the analysis unit can input resident emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0076] The analysis unit can determine the priority of analysis based on when opinions and requests were submitted. For example, the analysis unit will prioritize the analysis of recently submitted opinions and requests. The analysis unit can also determine the priority of analysis based on the submission date. The analysis unit can also adjust the level of detail of the analysis according to the submission date. This allows for a faster response by determining the priority of analysis based on when opinions and requests were submitted. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the submission date data of opinions and requests into a generation AI and have the generation AI perform the determination of the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of opinions and requests during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant opinions and requests. The analysis unit can also adjust the order of analysis based on relevance. The analysis unit can also adjust the level of detail of the analysis according to the relevance. This allows for prioritizing the analysis of more relevant opinions and requests by adjusting the order of analysis based on the relevance of opinions and requests. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the analysis unit can input the relevance data of opinions and requests into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0078] The suggestion unit can estimate the residents' emotions and adjust the way the suggestion is presented based on those emotions. For example, if a resident is relaxed, the suggestion unit can provide a detailed suggestion. If a resident is in a hurry, the suggestion unit can provide a concise suggestion that gets straight to the point. If a resident is excited, the suggestion unit can provide a visually stimulating suggestion. By adjusting the way the suggestion is presented based on the residents' emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input residents' emotion data into a generative AI and have the generative AI adjust the way the suggestion is presented.

[0079] The proposal unit can adjust the level of detail of a proposal based on the importance of the event. For example, the proposal unit can provide detailed proposals for high-importance events, and concise proposals for low-importance events. The proposal unit can also prioritize proposals according to their importance. This allows for more detailed proposals to be provided for more important events by adjusting the level of detail based on the importance of the event. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input event importance data into a generative AI and have the generative AI adjust the level of detail of the proposal.

[0080] The suggestion unit can apply different suggestion algorithms depending on the event category when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to events for the elderly. The suggestion unit can also apply a different suggestion algorithm to events for young people. The suggestion unit can also apply yet another suggestion algorithm to events for families. By applying different suggestion algorithms depending on the event category, it is possible to provide more appropriate suggestions. Some or all of the above processing in the suggestion unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the suggestion unit can input event category data into a generative AI and have the generative AI perform the application of the suggestion algorithm.

[0081] The suggestion unit can estimate the residents' emotions and adjust the length of the suggestion based on the estimated emotions. For example, if a resident is in a hurry, the suggestion unit can provide a short, to-the-point suggestion. If a resident is relaxed, the suggestion unit can also provide a detailed suggestion. If a resident is excited, the suggestion unit can also provide a visually stimulating suggestion. By adjusting the length of the suggestion based on the resident's emotions, it is possible to provide a more appropriate suggestion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using or without a generative AI. For example, the suggestion unit can input resident emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0082] The proposal department can determine the priority of proposals based on the timing of the events when submitting them. For example, the proposal department will prioritize proposals for events that will take place in the near future. The proposal department can also determine the priority of proposals based on the timing of the events. The proposal department can also adjust the level of detail of proposals according to the timing of the events. This allows for a quicker response by determining the priority of proposals based on the timing of the events. Some or all of the above processing in the proposal department may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal department can input event timing data into a generative AI and have the generative AI perform the determination of proposal priorities.

[0083] The proposal unit can adjust the order of proposals based on the relevance of events when making a proposal. For example, the proposal unit will prioritize proposing events that are highly relevant. The proposal unit can also adjust the order of proposals based on relevance. The proposal unit can also adjust the level of detail of proposals according to relevance. This allows the proposal unit to prioritize proposing more relevant events by adjusting the order of proposals based on the relevance of events. Some or all of the above processing in the proposal unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the proposal unit can input event relevance data into a generative AI and have the generative AI perform the adjustment of the order of proposals.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The data collection department can adjust its collection methods when gathering residents' opinions and requests, taking into account their health data. For example, if a resident is in good health, the department can conduct face-to-face interviews. If a resident's health is unstable, the department can conduct online questionnaires. Furthermore, based on residents' health data, the department can prioritize collecting opinions and requests related to health. This allows for the collection of opinions and requests from a larger number of residents by selecting appropriate collection methods according to their health status.

[0086] The analytics department can analyze local needs and trends based on local voices and online feedback, taking into account residents' purchasing history. For example, it can identify popular products and services in the area based on residents' purchasing history. It can also grasp local consumption trends based on residents' purchasing history. Furthermore, it can generate event proposals that match local needs based on residents' purchasing history. By considering residents' purchasing history, it can provide more accurate analytical results.

[0087] The proposal department, based on the analysis results obtained by the analysis department, can consider residents' hobbies and interests when generating event proposals that transcend generations. For example, the proposal department can propose sports events based on residents' hobbies. It can also propose cultural events based on residents' interests. Furthermore, the proposal department can propose workshops and seminars based on residents' hobbies and interests. By generating event proposals that match residents' hobbies and interests, it becomes possible to provide events that are more accessible to a wider range of residents.

[0088] The proposal department can analyze resident participation data in real time and flexibly adjust event content and schedules, taking residents' transportation methods into consideration when making suggestions. For example, based on residents' transportation methods, the proposal department can suggest events in locations easily accessible by public transport. It can also suggest events in locations with ample parking facilities. Furthermore, it can suggest events within walking distance based on residents' transportation methods. By generating event proposals tailored to residents' transportation methods, it is possible to provide events that are more accessible to a wider range of residents.

[0089] The collection unit can estimate residents' emotions and adjust the method of collecting opinions and requests based on those estimates. For example, if residents are feeling stressed, the collection unit can collect opinions and requests in a relaxed environment. If residents are agitated, the collection unit can also collect opinions and requests quickly. Furthermore, if residents are calm, the collection unit can take more time to collect detailed opinions and requests. By adjusting the collection method based on residents' emotions, opinions and requests can be collected at a more appropriate time.

[0090] The analysis unit can estimate the residents' emotions and adjust the presentation of the analysis based on those estimated emotions. For example, if the residents are relaxed, the analysis unit can provide detailed analysis results. If the residents are in a hurry, the analysis unit can provide concise analysis results that get straight to the point. Furthermore, if the residents are excited, the analysis unit can provide visually stimulating analysis results. By adjusting the presentation of the analysis based on the residents' emotions, more appropriate analysis results can be provided.

[0091] The proposal team can estimate residents' emotions and adjust the way proposals are presented based on those estimated emotions. For example, if residents are relaxed, the proposal team can provide detailed proposals. If residents are in a hurry, the proposal team can provide concise proposals that get straight to the point. Furthermore, if residents are excited, the proposal team can provide visually stimulating proposals. By adjusting the way proposals are presented based on residents' emotions, more appropriate proposals can be provided.

[0092] The collection unit can estimate residents' emotions and determine the priority of opinions and requests to collect based on those estimated emotions. For example, if residents are dissatisfied, the collection unit can prioritize collecting those opinions and requests. If residents are agitated, the collection unit can also quickly collect those opinions and requests. Furthermore, if residents are relaxed, the collection unit can collect detailed opinions and requests. By prioritizing opinions and requests based on residents' emotions, the collection unit can prioritize collecting more important opinions and requests.

[0093] The proposal system can estimate residents' emotions and adjust the length of the proposal based on those estimates. For example, if residents are in a hurry, the system can provide a short, concise proposal. If residents are relaxed, it can provide a more detailed proposal. Furthermore, if residents are excited, it can provide a visually stimulating proposal. By adjusting the length of the proposal based on residents' emotions, the system can provide more appropriate suggestions.

[0094] The collection department can adjust the timing of collecting residents' opinions and requests, taking into account their daily routines. For example, the collection department can conduct surveys during the time residents are returning home from work. They can also conduct interviews during times when residents are relaxing on their days off. Furthermore, the collection department can adjust the timing of collecting opinions and requests based on residents' daily routines. This allows for the collection of opinions and requests from a wider range of residents by selecting appropriate collection timings that align with their lifestyles.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The collection team gathers local voices and online feedback. For example, the collection team collects residents' opinions and requests through surveys, interviews, and workshops. Online surveys can be conducted to collect residents' opinions and requests. Specific opinions and requests can be collected through interviews, and workshops can be held to provide residents with an opportunity to express their opinions directly. Step 2: The analysis unit analyzes the data collected by the collection unit to understand local needs and trends. For example, the analysis unit uses data mining techniques, text analysis techniques, and statistical analysis techniques to analyze the collected data. By using data mining techniques, local needs can be identified; by using text analysis techniques, residents' opinions and requests can be analyzed; and by using statistical analysis techniques, local trends can be understood. Step 3: Based on the analysis results obtained by the analysis department, the proposal department generates intergenerational event proposals and develops implementation plans. For example, the proposal department proposes a family-oriented cultural festival, IT courses for the elderly, and workshops for young people, and develops concrete implementation plans. This will deepen community ties and revitalize community activities.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects residents' opinions and requests using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to understand local needs and trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates cross-generational event proposals based on the analysis results and formulates an implementation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect residents' opinions and requests, and the control unit 46A collects the data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to understand local needs and trends. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which generates cross-generational event proposals based on the analysis results and formulates an implementation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects residents' opinions and requests using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A collects the data. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected data to grasp local needs and trends. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and generates cross-generational event proposals based on the analysis results and formulates an implementation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the collection unit, analysis 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 collection unit uses the camera 42 and microphone 238 of the robot 414 to collect residents' opinions and requests, and the control unit 46A collects the data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the collected data to understand local needs and trends. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates cross-generational event proposals based on the analysis results and formulates an implementation plan. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The collection department gathers local voices and online feedback, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand local needs and trends, The system comprises a proposal unit that generates cross-generational event proposals and formulates an execution plan based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect residents' opinions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on local voices and online feedback, we analyze local needs and trends. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We create events that can be enjoyed by participants of all generations, such as family-friendly cultural festivals and IT courses for seniors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, By analyzing resident participation data in real time, the event content and schedule can be flexibly adjusted. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the sentiments of residents and adjust the timing of collecting their opinions and requests based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We will analyze residents' past opinion submission history and select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting opinions and requests, filtering is performed based on residents' current living situations and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the sentiments of the residents and determine the priority of the opinions and requests to be collected based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting opinions and requests, we will prioritize collecting opinions that are highly relevant, taking into account the geographical location of residents. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting opinions and requests, we analyze residents' social media activity and gather relevant opinions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the residents' emotions and adjust the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the level of detail of the analysis is adjusted based on the importance of the opinions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of opinions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the residents' sentiments and adjusts the length of the analysis based on the estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when opinions and requests were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the order of analysis will be adjusted based on the relevance of opinions and requests. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We estimate the residents' feelings and adjust the way the proposal is expressed based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the event. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the event category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Estimate the residents' sentiments and adjust the length of the proposal based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of the events. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the events. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 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 collection department gathers local voices and online feedback, The data collected by the aforementioned collection unit is analyzed by an analysis unit to understand local needs and trends, The system comprises a proposal unit that generates cross-generational event proposals and formulates an execution plan based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect residents' opinions and requests. The system according to feature 1.

3. The aforementioned analysis unit, Based on local voices and online feedback, we analyze local needs and trends. The system according to feature 1.

4. The aforementioned proposal section is, We create events that can be enjoyed by participants of all generations, such as family-friendly cultural festivals and IT courses for seniors. The system according to feature 1.

5. The aforementioned proposal section is, By analyzing resident participation data in real time, the event content and schedule can be flexibly adjusted. The system according to feature 1.

6. The aforementioned collection unit is We estimate the sentiments of residents and adjust the timing of collecting their opinions and requests based on those estimated sentiments. The system according to feature 1.

7. The aforementioned collection unit is We will analyze residents' past opinion submission history and select the most suitable collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting opinions and requests, filtering is performed based on residents' current living situations and areas of interest. The system according to feature 1.

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