system

Abstract

The system according to this embodiment aims to share and standardize information related to the operation of condominium management associations and to solve problems efficiently. [Solution] The system according to the embodiment comprises a collection unit, an anonymization unit, a learning unit, and a provision unit. The collection unit collects information relating to the operation of a condominium management association. The anonymization unit anonymizes the information collected by the collection unit. The learning unit learns the information anonymized by the anonymization unit. The provision unit provides answers and solutions to questions and problems based on the information learned by the learning unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, information related to the operation of condominium management associations has not been shared and standardized, and there is a problem that it takes time and effort to solve problems.

[0005] The system according to the embodiment aims to share and standardize information related to the operation of condominium management associations and solve problems efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an anonymization unit, a learning unit, and a provision unit. The collection unit collects information related to the operation of a condominium management association. The anonymization unit anonymizes the information collected by the collection unit. The learning unit learns the information anonymized by the anonymization unit. The provision unit provides answers and solutions to questions and problems based on the information learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can share and standardize information related to the operation of a condominium management association, enabling efficient problem solving. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages 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 condominium management association operation support system according to an embodiment of the present invention is a system that trains a large-scale language model (LLM) with various information related to the operation of a condominium management association in an anonymized form, and provides optimal answers and countermeasures when questions or problems arise. The condominium management association operation support system collects information such as operational know-how and trouble response cases managed by each condominium management association and anonymizes it. Next, the anonymized information is trained on the LLM. The LLM analyzes this information and provides optimal answers and countermeasures when questions or problems arise. It also learns from past success and failure cases and utilizes this to solve specific problems. For example, if a condominium management association faces a particular problem, it inputs information about that problem into the LLM. The LLM provides optimal answers and countermeasures based on similar past cases. This allows the management association to solve problems quickly and effectively. Furthermore, the LLM analyzes past success and failure cases and proposes the best course of action. For example, if a similar problem occurred in the past, it can propose the best course of action for a new problem based on the solution to that problem. This allows the management association to operate efficiently by utilizing past experience. This system allows for the accumulation and sharing of knowledge and experience regarding the operation of condominium management associations nationwide, supporting the formation of high-quality housing and local communities. At the same time, individual privacy is protected, ensuring safe use. This enables the condominium management association support system to efficiently collect, anonymize, learn from, and provide information related to the operation of condominium management associations.

[0029] The condominium management association operation support system according to the embodiment comprises a collection unit, an anonymization unit, a learning unit, and a provision unit. The collection unit collects information related to the operation of condominium management associations. For example, the collection unit collects operational know-how and trouble-solving cases from each condominium management association. The collection unit can collect information through, for example, questionnaires and interviews. The collection unit can also extract information from databases. For example, the collection unit extracts necessary information from a database related to the operation of management associations. The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. For example, the anonymization unit removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit masks specific data so that individuals cannot be identified. The learning unit learns the information anonymized by the anonymization unit. For example, the learning unit learns the anonymized information using LLM. For example, the learning unit analyzes the information using machine learning algorithms and learns the optimal answers and countermeasures. The provision unit provides answers and solutions to questions and problems based on information learned by the learning unit. For example, the provision unit uses AI to provide the optimal answers and solutions. For example, the provision unit provides the optimal answers and solutions to questions and problems entered by the user based on past success and failure cases. As a result, the condominium management association operation support system according to the embodiment can efficiently collect, anonymize, learn, and provide information related to the operation of the condominium management association.

[0030] The Information Collection Department collects information related to the operation of condominium management associations. For example, the Department collects operational know-how and examples of trouble-solving from each condominium management association. Specifically, the Department can collect information through questionnaires and interviews. Questionnaires are conducted using online forms or paper media to gather specific experiences and opinions on operations from members of the management associations. Interviews are conducted directly with representatives and members of the management associations to collect detailed operational know-how and specific examples of trouble-solving. The Department can also extract information from databases. For example, the Department can extract necessary information from databases related to the operation of management associations. These databases include past meeting minutes, accounting reports, and records of trouble-solving, and this information is systematically organized and collected. Furthermore, the Department can also collect publicly available information on the internet and expert opinions. For example, it collects useful information from specialized websites and forums related to condominium management and incorporates it into the system. This allows the Department to collect a wide range of data from diverse sources and provide comprehensive information on the operation of condominium management associations. The collected information is stored in a central database and made accessible to other departments. This allows the data collection unit to efficiently and effectively collect information, thereby improving the overall performance of the system.

[0031] The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. Specifically, it removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data to prevent the identification of individuals. Specifically, this can be done by redacting parts of names or addresses, or by converting certain numerical data into ranges. Furthermore, the anonymization unit can also perform data simulation. Simulation is a technique that prevents the identification of individuals by replacing actual data with randomly generated data. For example, it can replace actual names with randomly generated names, or convert addresses into fictitious addresses. This allows the anonymization unit to securely protect the collected information and prevent the leakage of personal information. Furthermore, the anonymization unit can evaluate the quality of the anonymized data and perform re-anonymization as needed. For example, if there is still a risk that the anonymized data could identify an individual, additional anonymization processing can be applied. This allows the anonymization unit to safely and effectively anonymize the collected information and improve the reliability of the entire system.

[0032] The learning unit learns from information anonymized by the anonymization unit. For example, the learning unit learns from anonymized information using an LLM (Large-Scale Language Model). Specifically, it analyzes the information using machine learning algorithms and learns optimal answers and solutions. An LLM learns from large amounts of text data and possesses natural language processing capabilities. The learning unit inputs anonymized information into the LLM and analyzes operational know-how and troubleshooting case studies. For example, the LLM analyzes collected meeting minutes and troubleshooting records to extract common patterns and success stories. The LLM can also compare the operational methods of different management associations and identify the optimal method. Furthermore, the learning unit continuously learns new information to improve the model's accuracy. For example, newly collected information is regularly input into the LLM to update the model. This allows the learning unit to always provide highly accurate answers and solutions based on the latest information. Additionally, the learning unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the learning unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0033] The service provider provides answers and solutions to questions and problems based on information learned by the learning unit. For example, the service provider uses AI to provide optimal answers and solutions. Specifically, it provides optimal answers and solutions to user-inputted questions and problems based on past success and failure cases. For example, if a user asks, "How can I resolve noise problems in my apartment building?", the service provider will propose specific solutions based on past cases of noise problem resolution. The service provider uses LLM to analyze the user's question and generate the optimal answer. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the answers. For example, users can evaluate the provided answers, and the answers are revised based on that evaluation. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide users with quick and reliable answers through website chatbots, email, SMS, etc. This allows the service provider to provide users with quick and reliable answers and solutions, supporting the operation of apartment management associations. Furthermore, the service provider can provide customized information according to user needs. For example, the system proposes optimal solutions and countermeasures based on the operational status and type of problems of a particular condominium management association. This allows the service provider to offer users more specific and practical information, enabling efficient support for the operation of condominium management associations.

[0034] The data collection unit can collect operational know-how and trouble-solving case studies from each condominium management association. For example, the data collection unit can collect operational know-how and trouble-solving case studies from each condominium management association. The data collection unit can collect information through, for example, questionnaires and interviews. The data collection unit can also extract information from databases. For example, the data collection unit can extract necessary information from databases related to the operation of management associations. This improves the richness of the information by collecting operational know-how and trouble-solving case studies from each condominium management association. Some or all of the above-mentioned processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the results of questionnaires and interviews into AI and have the AI ​​perform the information collection.

[0035] The anonymization unit can remove personal information from the collected information. For example, the anonymization unit can remove personal information from the collected information. For example, the anonymization unit can remove personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data so that individuals cannot be identified. This enhances privacy protection by removing personal information. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the collected information into AI and have the AI ​​perform the removal of personal information.

[0036] The learning unit can learn optimal answers and solutions based on anonymized information. For example, the learning unit learns anonymized information using LLM. For example, the learning unit analyzes the information using machine learning algorithms and learns optimal answers and solutions. As a result, the accuracy of optimal answers and solutions is improved by learning based on anonymized information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input anonymized information into a generative AI and have the generative AI perform the learning of optimal answers and solutions.

[0037] The service provider can provide the best answers and solutions when questions or problems arise. For example, the service provider can use AI to provide the best answers and solutions. For example, the service provider can provide the best answers and solutions to questions or problems entered by the user based on past success and failure cases. This enables quick and effective problem solving by providing the best answers and solutions when questions or problems arise. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input questions or problems entered by the user into the AI ​​and have the AI ​​provide the best answers and solutions.

[0038] The service provider can analyze past success and failure cases and propose the best course of action. For example, the service provider can use AI to analyze past success and failure cases and propose the best course of action. For example, the service provider can use past success and failure cases as a basis for solving specific problems. This makes it possible to propose the best course of action by analyzing past success and failure cases. Some or all of the above-described processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input past success and failure cases into AI and have the AI ​​propose the best course of action.

[0039] The data collection unit can analyze the past information provision history of each condominium management association and select the optimal data collection method. For example, the data collection unit can conduct regular data collection for management associations that have frequently provided information in the past. For example, the data collection unit can collect information from management associations that have provided little information in the past using a simple questionnaire format. For example, the data collection unit can focus on collecting information on specific topics based on past data provision history. By analyzing past data provision history, the optimal data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data provision history into AI and have the AI ​​select the optimal data collection method.

[0040] The data collection unit can filter information based on the current operational status and areas of interest of the management association. For example, the data collection unit can prioritize collecting information related to projects currently underway by the management association. For example, the data collection unit can collect only relevant information based on the management association's areas of interest. For example, the data collection unit can filter and collect necessary information according to the management association's operational status. This allows for efficient collection of necessary information by filtering information based on the management association's operational status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the management association's operational status and areas of interest into AI and have the AI ​​perform the information filtering.

[0041] The data collection unit can prioritize the collection of highly relevant information based on the geographical location information of the management association. For example, the data collection unit prioritizes the collection of regional information related to the location of the management association. For example, the data collection unit collects information by referencing information from other geographically close management associations. For example, the data collection unit prioritizes the collection of highly relevant information based on the characteristics of the region. This makes it possible to collect information that is tailored to regional characteristics by prioritizing the collection of highly relevant information based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the management association into AI and have the AI ​​perform the collection of highly relevant information.

[0042] The data collection unit can analyze the management association's social media activities and collect relevant information during the information gathering process. For example, the data collection unit can collect information that the management association posts on social media. For example, the data collection unit can analyze the management association's activities on social media and collect relevant information. For example, the data collection unit can analyze the interests of the management association's followers on social media and collect relevant information. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the management association's social media activity data into AI and have the AI ​​collect relevant information.

[0043] The anonymization unit can adjust the level of detail of anonymization based on the importance of the information during the anonymization process. For example, the anonymization unit applies a detailed anonymization method to information of high importance. For example, the anonymization unit applies a simpler anonymization method to information of low importance. For example, the anonymization unit adjusts the level of detail of anonymization in stages according to the importance of the information. This makes it possible to perform appropriate anonymization by adjusting the level of detail of anonymization according to the importance of the information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without using AI. For example, the anonymization unit can input information importance data into AI and have the AI ​​perform the adjustment of the level of detail of anonymization.

[0044] The anonymization unit can apply different anonymization algorithms depending on the category of information during the anonymization process. For example, the anonymization unit applies a strong anonymization algorithm to information containing personal information. For example, the anonymization unit applies a simpler anonymization algorithm to general operational information. The anonymization unit selects and applies the most suitable anonymization algorithm depending on the category of information. This enables efficient anonymization by applying the most suitable anonymization algorithm according to the category of information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information category data into AI and have the AI ​​apply the most suitable anonymization algorithm.

[0045] The anonymization unit can determine the priority of anonymization based on the information submission date during the anonymization process. For example, the anonymization unit may prioritize anonymizing recently submitted information. For example, the anonymization unit may postpone anonymizing older information. For example, the anonymization unit may adjust the anonymization priority in stages according to the submission date. This allows for the prioritization of the most recent information by determining the anonymization priority based on the information submission date. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information submission date data into AI and have the AI ​​perform the determination of the anonymization priority.

[0046] The anonymization unit can adjust the order of anonymization based on the relevance of the information during the anonymization process. For example, the anonymization unit may prioritize anonymizing highly relevant information. For example, the anonymization unit may postpone anonymizing less relevant information. For example, the anonymization unit may adjust the order of anonymization in stages according to the relevance of the information. This allows for prioritizing the anonymization of highly relevant information by adjusting the order of anonymization based on the relevance of the information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information relevance data into AI and have AI perform the adjustment of the anonymization order.

[0047] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0048] The learning unit can apply different learning methods to different categories of information during the learning process. For example, the learning unit might apply a powerful learning method to data related to personal information. For example, it might apply a simpler learning method to general operational information. The learning unit can select and apply the most suitable learning method according to the category of information. This enables efficient learning by applying the most suitable learning method according to the category of information. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information category data into the AI ​​and have the AI ​​apply the most suitable learning method.

[0049] The learning unit can weight the training data based on when the information was submitted during training. For example, the learning unit may prioritize recently submitted information during training. For example, the learning unit may reduce the weight of older information during training. For example, the learning unit may adjust the weighting of the training data in stages according to the submission date. This makes it possible to prioritize the latest information by weighting the training data based on when the information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the information submission date data into the AI ​​and have the AI ​​perform the weighting of the training data.

[0050] The learning unit can perform learning by referring to relevant market data during the learning process. For example, the learning unit can select the optimal learning method based on the relevant market data. For example, the learning unit can adjust the parameters of the learning algorithm by referring to the relevant market data. For example, the learning unit can improve the accuracy of the learning algorithm based on the relevant market data. Thus, the accuracy of the learning algorithm is improved by referring to the relevant market data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input relevant market data into AI and have AI perform the learning.

[0051] The information provider can adjust the level of detail in the answers and solutions based on the importance of the problem at the time of delivery. For example, the provider can provide detailed answers and solutions for high-importance problems. For example, the provider can provide concise answers and solutions for low-importance problems. For example, the provider can adjust the level of detail in the answers and solutions in stages according to the importance of the problem. This makes it possible to provide appropriate information by adjusting the level of detail in the answers and solutions according to the importance of the problem. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input problem importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answers and solutions.

[0052] The information provider can apply different information provision algorithms depending on the category of the problem at the time of provision. For example, the information provider can provide expert answers and solutions for technical problems. For example, the information provider can provide simple answers and solutions for general operational problems. The information provider can select and apply the most suitable information provision algorithm depending on the category of the problem. This enables efficient information provision by applying the most suitable information provision algorithm according to the category of the problem. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input problem category data into AI and have the AI ​​execute the application of the most suitable information provision algorithm.

[0053] The service provider can determine the priority of answers and solutions based on the submission date of the problem at the time of provision. For example, the service provider may prioritize providing answers and solutions to recently submitted problems. For example, the service provider may postpone providing answers and solutions to older problems. For example, the service provider may adjust the priority of answers and solutions in stages according to the submission date. This allows for a quick response to the latest problems by determining the priority of answers and solutions based on the submission date of the problem. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input problem submission date data into AI and have the AI ​​perform the determination of the priority of answers and solutions.

[0054] The service provider can adjust the order of answers and solutions based on the relevance of the problems when providing them. For example, the service provider can prioritize providing answers and solutions for highly relevant problems. For example, it can postpone providing answers and solutions for less relevant problems. For example, the service provider can adjust the order of answers and solutions in stages according to the relevance of the problems. This allows for priority addressing of highly relevant problems by adjusting the order of answers and solutions based on the relevance of the problems. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input problem relevance data into AI and have AI perform the adjustment of the order of answers and solutions.

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

[0056] The condominium management association support system can also be equipped with a prediction unit. This unit can predict future troubles and problems based on collected information and learned data. For example, it can analyze past trouble data to predict the probability of troubles occurring in specific seasons or events. It can also analyze the management association's operational status and resident trends to predict future operational challenges. Furthermore, it can refer to data from other condominium management associations to predict the likelihood of similar troubles occurring. This allows management associations to take preventative measures and avoid troubles from occurring in the first place.

[0057] The condominium management association support system can also include a notification function. Based on collected information and learned data, the notification function can notify the management association and residents of important information. For example, the notification function can quickly notify relevant parties when a problem occurs. It can also notify residents of regular maintenance schedules and important meeting schedules. Furthermore, the notification function can inform residents of important changes and new rules regarding the management association's operations. This allows the management association and residents to always be informed of the latest information and respond quickly.

[0058] The condominium management association support system can also include an evaluation unit. This unit can assess the effectiveness of the provided solutions and countermeasures and collect feedback. For example, after the management association implements the provided solutions and countermeasures, the evaluation unit can collect the results and evaluate their effectiveness. Furthermore, the evaluation unit can collect feedback from residents and identify areas for improvement in the provided solutions and countermeasures. Additionally, the evaluation unit can improve the accuracy of the provided solutions and countermeasures based on past evaluation data. This ensures that the condominium management association support system can always provide the most optimal solutions and countermeasures.

[0059] The condominium management association support system can also include a customization section. This customization section allows for the system's functions and the information provided to be tailored to the specific needs and requests of each management association. For example, the customization section can prioritize providing information on topics important to a particular management association. Furthermore, the customization section can adjust system settings according to the management association's operational policies and the characteristics of its residents. In addition, the customization section can add and improve system functions based on feedback from the management associations. This allows the condominium management association support system to provide services optimized for the needs of each management association.

[0060] The condominium management association support system can also be equipped with a statistics department. This department can perform statistical analysis based on collected information and learned data. For example, it can analyze the frequency of problems and the time it takes to resolve them, thereby evaluating the operational efficiency of the management association. Furthermore, it can compile resident satisfaction and opinions, identifying areas for improvement in the management association's operational policies. In addition, the statistics department can conduct comparative analyses with other condominium management associations to derive best practices. This allows management associations to make data-driven decisions and improve the quality of their operations.

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

[0062] Step 1: The collection department collects information related to the operation of condominium management associations. For example, the collection department collects operational know-how and examples of trouble-solving from each condominium management association. The collection department can collect information through questionnaires and interviews. The collection department can also extract information from databases. For example, the collection department extracts necessary information from databases related to the operation of management associations. Step 2: The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. The anonymization unit removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data to prevent the identification of individuals. Step 3: The learning unit learns from the anonymized information by the anonymization unit. The learning unit learns from the anonymized information using, for example, LLM. The learning unit analyzes the information using machine learning algorithms and learns the optimal answers and solutions. Step 4: The provisioning unit provides answers and solutions to questions and problems based on the information learned by the learning unit. The provisioning unit uses AI to provide the optimal answers and solutions. The provisioning unit provides the optimal answers and solutions to questions and problems entered by the user based on past success and failure cases.

[0063] (Example of form 2) The condominium management association operation support system according to an embodiment of the present invention is a system that trains a large-scale language model (LLM) with various information related to the operation of a condominium management association in an anonymized form, and provides optimal answers and countermeasures when questions or problems arise. The condominium management association operation support system collects information such as operational know-how and trouble response cases managed by each condominium management association and anonymizes it. Next, the anonymized information is trained on the LLM. The LLM analyzes this information and provides optimal answers and countermeasures when questions or problems arise. It also learns from past success and failure cases and utilizes this to solve specific problems. For example, if a condominium management association faces a particular problem, it inputs information about that problem into the LLM. The LLM provides optimal answers and countermeasures based on similar past cases. This allows the management association to solve problems quickly and effectively. Furthermore, the LLM analyzes past success and failure cases and proposes the best course of action. For example, if a similar problem occurred in the past, it can propose the best course of action for a new problem based on the solution to that problem. This allows the management association to operate efficiently by utilizing past experience. This system allows for the accumulation and sharing of knowledge and experience regarding the operation of condominium management associations nationwide, supporting the formation of high-quality housing and local communities. At the same time, individual privacy is protected, ensuring safe use. This enables the condominium management association support system to efficiently collect, anonymize, learn from, and provide information related to the operation of condominium management associations.

[0064] The condominium management association operation support system according to the embodiment comprises a collection unit, an anonymization unit, a learning unit, and a provision unit. The collection unit collects information related to the operation of condominium management associations. For example, the collection unit collects operational know-how and trouble-solving cases from each condominium management association. The collection unit can collect information through, for example, questionnaires and interviews. The collection unit can also extract information from databases. For example, the collection unit extracts necessary information from a database related to the operation of management associations. The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. For example, the anonymization unit removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit masks specific data so that individuals cannot be identified. The learning unit learns the information anonymized by the anonymization unit. For example, the learning unit learns the anonymized information using LLM. For example, the learning unit analyzes the information using machine learning algorithms and learns the optimal answers and countermeasures. The provision unit provides answers and solutions to questions and problems based on information learned by the learning unit. For example, the provision unit uses AI to provide the optimal answers and solutions. For example, the provision unit provides the optimal answers and solutions to questions and problems entered by the user based on past success and failure cases. As a result, the condominium management association operation support system according to the embodiment can efficiently collect, anonymize, learn, and provide information related to the operation of the condominium management association.

[0065] The Information Collection Department collects information related to the operation of condominium management associations. For example, the Department collects operational know-how and examples of trouble-solving from each condominium management association. Specifically, the Department can collect information through questionnaires and interviews. Questionnaires are conducted using online forms or paper media to gather specific experiences and opinions on operations from members of the management associations. Interviews are conducted directly with representatives and members of the management associations to collect detailed operational know-how and specific examples of trouble-solving. The Department can also extract information from databases. For example, the Department can extract necessary information from databases related to the operation of management associations. These databases include past meeting minutes, accounting reports, and records of trouble-solving, and this information is systematically organized and collected. Furthermore, the Department can also collect publicly available information on the internet and expert opinions. For example, it collects useful information from specialized websites and forums related to condominium management and incorporates it into the system. This allows the Department to collect a wide range of data from diverse sources and provide comprehensive information on the operation of condominium management associations. The collected information is stored in a central database and made accessible to other departments. This allows the data collection unit to efficiently and effectively collect information, thereby improving the overall performance of the system.

[0066] The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. Specifically, it removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data to prevent the identification of individuals. Specifically, this can be done by redacting parts of names or addresses, or by converting certain numerical data into ranges. Furthermore, the anonymization unit can also perform data simulation. Simulation is a technique that prevents the identification of individuals by replacing actual data with randomly generated data. For example, it can replace actual names with randomly generated names, or convert addresses into fictitious addresses. This allows the anonymization unit to securely protect the collected information and prevent the leakage of personal information. Furthermore, the anonymization unit can evaluate the quality of the anonymized data and perform re-anonymization as needed. For example, if there is still a risk that the anonymized data could identify an individual, additional anonymization processing can be applied. This allows the anonymization unit to safely and effectively anonymize the collected information and improve the reliability of the entire system.

[0067] The learning unit learns from information anonymized by the anonymization unit. For example, the learning unit learns from anonymized information using an LLM (Large-Scale Language Model). Specifically, it analyzes the information using machine learning algorithms and learns optimal answers and solutions. An LLM learns from large amounts of text data and possesses natural language processing capabilities. The learning unit inputs anonymized information into the LLM and analyzes operational know-how and troubleshooting case studies. For example, the LLM analyzes collected meeting minutes and troubleshooting records to extract common patterns and success stories. The LLM can also compare the operational methods of different management associations and identify the optimal method. Furthermore, the learning unit continuously learns new information to improve the model's accuracy. For example, newly collected information is regularly input into the LLM to update the model. This allows the learning unit to always provide highly accurate answers and solutions based on the latest information. Additionally, the learning unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the learning unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0068] The service provider provides answers and solutions to questions and problems based on information learned by the learning unit. For example, the service provider uses AI to provide optimal answers and solutions. Specifically, it provides optimal answers and solutions to user-inputted questions and problems based on past success and failure cases. For example, if a user asks, "How can I resolve noise problems in my apartment building?", the service provider will propose specific solutions based on past cases of noise problem resolution. The service provider uses LLM to analyze the user's question and generate the optimal answer. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the answers. For example, users can evaluate the provided answers, and the answers are revised based on that evaluation. The service provider can also reliably transmit information using multiple communication methods. For example, it can provide users with quick and reliable answers through website chatbots, email, SMS, etc. This allows the service provider to provide users with quick and reliable answers and solutions, supporting the operation of apartment management associations. Furthermore, the service provider can provide customized information according to user needs. For example, the system proposes optimal solutions and countermeasures based on the operational status and type of problems of a particular condominium management association. This allows the service provider to offer users more specific and practical information, enabling efficient support for the operation of condominium management associations.

[0069] The data collection unit can collect operational know-how and trouble-solving case studies from each condominium management association. For example, the data collection unit can collect operational know-how and trouble-solving case studies from each condominium management association. The data collection unit can collect information through, for example, questionnaires and interviews. The data collection unit can also extract information from databases. For example, the data collection unit can extract necessary information from databases related to the operation of management associations. This improves the richness of the information by collecting operational know-how and trouble-solving case studies from each condominium management association. Some or all of the above-mentioned processes in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the results of questionnaires and interviews into AI and have the AI ​​perform the information collection.

[0070] The anonymization unit can remove personal information from the collected information. For example, the anonymization unit can remove personal information from the collected information. For example, the anonymization unit can remove personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data so that individuals cannot be identified. This enhances privacy protection by removing personal information. Some or all of the above processing in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input the collected information into AI and have the AI ​​perform the removal of personal information.

[0071] The learning unit can learn optimal answers and solutions based on anonymized information. For example, the learning unit learns anonymized information using LLM. For example, the learning unit analyzes the information using machine learning algorithms and learns optimal answers and solutions. As a result, the accuracy of optimal answers and solutions is improved by learning based on anonymized information. Some or all of the above processing in the learning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the learning unit can input anonymized information into a generative AI and have the generative AI perform the learning of optimal answers and solutions.

[0072] The service provider can provide the best answers and solutions when questions or problems arise. For example, the service provider can use AI to provide the best answers and solutions. For example, the service provider can provide the best answers and solutions to questions or problems entered by the user based on past success and failure cases. This enables quick and effective problem solving by providing the best answers and solutions when questions or problems arise. Some or all of the above processing in the service provider may be performed using AI, or not using AI. For example, the service provider can input questions or problems entered by the user into the AI ​​and have the AI ​​provide the best answers and solutions.

[0073] The service provider can analyze past success and failure cases and propose the best course of action. For example, the service provider can use AI to analyze past success and failure cases and propose the best course of action. For example, the service provider can use past success and failure cases as a basis for solving specific problems. This makes it possible to propose the best course of action by analyzing past success and failure cases. Some or all of the above-described processes in the service provider may be performed using AI, or not using AI. For example, the service provider can input past success and failure cases into AI and have the AI ​​propose the best course of action.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of information collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit increases the frequency of information collection to collect more detailed information. For example, if the user is in a hurry, the data collection unit collects information quickly to enable immediate response. In this way, the user's burden is reduced by adjusting the timing of information collection according to the user's 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, for example, or without AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of information collection.

[0075] The data collection unit can analyze the past information provision history of each condominium management association and select the optimal data collection method. For example, the data collection unit can conduct regular data collection for management associations that have frequently provided information in the past. For example, the data collection unit can collect information from management associations that have provided little information in the past using a simple questionnaire format. For example, the data collection unit can focus on collecting information on specific topics based on past data provision history. By analyzing past data provision history, the optimal data collection method can be selected. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data provision history into AI and have the AI ​​select the optimal data collection method.

[0076] The data collection unit can filter information based on the current operational status and areas of interest of the management association. For example, the data collection unit can prioritize collecting information related to projects currently underway by the management association. For example, the data collection unit can collect only relevant information based on the management association's areas of interest. For example, the data collection unit can filter and collect necessary information according to the management association's operational status. This allows for efficient collection of necessary information by filtering information based on the management association's operational status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on the management association's operational status and areas of interest into AI and have the AI ​​perform the information filtering.

[0077] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed information. For example, if the user is in a hurry, the data collection unit will prioritize collecting information that requires immediate attention. In this way, important information can be collected preferentially by prioritizing information according to the user's 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, for example, or not using AI. For example, the data collection unit can input user emotion data into an AI and have the AI ​​perform the determination of information prioritization.

[0078] The data collection unit can prioritize the collection of highly relevant information based on the geographical location information of the management association. For example, the data collection unit prioritizes the collection of regional information related to the location of the management association. For example, the data collection unit collects information by referencing information from other geographically close management associations. For example, the data collection unit prioritizes the collection of highly relevant information based on the characteristics of the region. This makes it possible to collect information that is tailored to regional characteristics by prioritizing the collection of highly relevant information based on geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical location information of the management association into AI and have the AI ​​perform the collection of highly relevant information.

[0079] The data collection unit can analyze the management association's social media activities and collect relevant information during the information gathering process. For example, the data collection unit can collect information that the management association posts on social media. For example, the data collection unit can analyze the management association's activities on social media and collect relevant information. For example, the data collection unit can analyze the interests of the management association's followers on social media and collect relevant information. This allows for the efficient collection of relevant information by analyzing social media activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the management association's social media activity data into AI and have the AI ​​collect relevant information.

[0080] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on the estimated emotions. For example, if the user is stressed, the anonymization unit applies a simple anonymization method and processes the data quickly. For example, if the user is relaxed, the anonymization unit applies a detailed anonymization method to improve the accuracy of the information. For example, if the user is in a hurry, the anonymization unit performs anonymization quickly and provides the information immediately. This allows for quick and appropriate anonymization by adjusting the anonymization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the anonymization unit may be performed using AI or not using AI. For example, the anonymization unit can input user emotion data into an AI and have the AI ​​adjust the anonymization method.

[0081] The anonymization unit can adjust the level of detail of anonymization based on the importance of the information during the anonymization process. For example, the anonymization unit applies a detailed anonymization method to information of high importance. For example, the anonymization unit applies a simpler anonymization method to information of low importance. For example, the anonymization unit adjusts the level of detail of anonymization in stages according to the importance of the information. This makes it possible to perform appropriate anonymization by adjusting the level of detail of anonymization according to the importance of the information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without using AI. For example, the anonymization unit can input information importance data into AI and have the AI ​​perform the adjustment of the level of detail of anonymization.

[0082] The anonymization unit can apply different anonymization algorithms depending on the category of information during the anonymization process. For example, the anonymization unit applies a strong anonymization algorithm to information containing personal information. For example, the anonymization unit applies a simpler anonymization algorithm to general operational information. The anonymization unit selects and applies the most suitable anonymization algorithm depending on the category of information. This enables efficient anonymization by applying the most suitable anonymization algorithm according to the category of information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information category data into AI and have the AI ​​apply the most suitable anonymization algorithm.

[0083] The anonymization unit can estimate the user's emotions and determine the priority of anonymization based on the estimated emotions. For example, if the user is stressed, the anonymization unit will prioritize anonymizing information of high importance. For example, if the user is relaxed, the anonymization unit will prioritize anonymizing detailed information. For example, if the user is in a hurry, the anonymization unit will prioritize anonymizing information that requires immediate attention. In this way, important information can be anonymized preferentially by determining the priority of anonymization according to the user's 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 anonymization unit may be performed using AI, for example, or not using AI. For example, the anonymization unit can input user emotion data into AI and have the AI ​​perform the determination of anonymization priorities.

[0084] The anonymization unit can determine the priority of anonymization based on the information submission date during the anonymization process. For example, the anonymization unit may prioritize anonymizing recently submitted information. For example, the anonymization unit may postpone anonymizing older information. For example, the anonymization unit may adjust the anonymization priority in stages according to the submission date. This allows for the prioritization of the most recent information by determining the anonymization priority based on the information submission date. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information submission date data into AI and have the AI ​​perform the determination of the anonymization priority.

[0085] The anonymization unit can adjust the order of anonymization based on the relevance of the information during the anonymization process. For example, the anonymization unit may prioritize anonymizing highly relevant information. For example, the anonymization unit may postpone anonymizing less relevant information. For example, the anonymization unit may adjust the order of anonymization in stages according to the relevance of the information. This allows for prioritizing the anonymization of highly relevant information by adjusting the order of anonymization based on the relevance of the information. Some or all of the above-described processes in the anonymization unit may be performed using AI, for example, or without AI. For example, the anonymization unit can input information relevance data into AI and have AI perform the adjustment of the anonymization order.

[0086] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize learning data of high importance. For example, if the user is relaxed, the learning unit will prioritize learning detailed data. For example, if the user is in a hurry, the learning unit will prioritize learning data that requires a quick response. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the selection of training data.

[0087] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit analyzes past learning data and adjusts the parameters of the learning algorithm. For example, the learning unit improves the accuracy of the learning algorithm by referring to past learning data. In this way, the accuracy of the learning algorithm is improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have AI perform the optimization of the learning algorithm.

[0088] The learning unit can apply different learning methods to different categories of information during the learning process. For example, the learning unit might apply a powerful learning method to data related to personal information. For example, it might apply a simpler learning method to general operational information. The learning unit can select and apply the most suitable learning method according to the category of information. This enables efficient learning by applying the most suitable learning method according to the category of information. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input information category data into the AI ​​and have the AI ​​apply the most suitable learning method.

[0089] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will reduce the learning frequency to alleviate the user's burden. For example, if the user is relaxed, the learning unit will increase the learning frequency and learn more detailed information. For example, if the user is in a hurry, the learning unit will prioritize learning information that requires a quick response. This reduces the user's burden by adjusting the learning frequency according to the user's 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​adjust the learning frequency.

[0090] The learning unit can weight the training data based on when the information was submitted during training. For example, the learning unit may prioritize recently submitted information during training. For example, the learning unit may reduce the weight of older information during training. For example, the learning unit may adjust the weighting of the training data in stages according to the submission date. This makes it possible to prioritize the latest information by weighting the training data based on when the information was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input the information submission date data into the AI ​​and have the AI ​​perform the weighting of the training data.

[0091] The learning unit can perform learning by referring to relevant market data during the learning process. For example, the learning unit can select the optimal learning method based on the relevant market data. For example, the learning unit can adjust the parameters of the learning algorithm by referring to the relevant market data. For example, the learning unit can improve the accuracy of the learning algorithm based on the relevant market data. Thus, the accuracy of the learning algorithm is improved by referring to the relevant market data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input relevant market data into AI and have AI perform the learning.

[0092] The service provider can estimate the user's emotions and adjust the way answers and solutions are presented based on the estimated emotions. For example, if the user is stressed, the service provider will provide concise and easy-to-understand explanations. If the user is relaxed, the service provider will provide detailed explanations. If the user is in a hurry, the service provider will provide concise information that requires immediate attention. By adjusting the way answers and solutions are presented according to the user's emotions, it becomes possible to provide information that is easy for the user to understand. 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 service provider may be performed using AI, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​adjust the way answers and solutions are presented.

[0093] The information provider can adjust the level of detail in the answers and solutions based on the importance of the problem at the time of delivery. For example, the provider can provide detailed answers and solutions for high-importance problems. For example, the provider can provide concise answers and solutions for low-importance problems. For example, the provider can adjust the level of detail in the answers and solutions in stages according to the importance of the problem. This makes it possible to provide appropriate information by adjusting the level of detail in the answers and solutions according to the importance of the problem. Some or all of the above processing in the information provider may be performed using AI, for example, or without using AI. For example, the information provider can input problem importance data into AI and have the AI ​​perform the adjustment of the level of detail in the answers and solutions.

[0094] The information provider can apply different information provision algorithms depending on the category of the problem at the time of provision. For example, the information provider can provide expert answers and solutions for technical problems. For example, the information provider can provide simple answers and solutions for general operational problems. The information provider can select and apply the most suitable information provision algorithm depending on the category of the problem. This enables efficient information provision by applying the most suitable information provision algorithm according to the category of the problem. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input problem category data into AI and have the AI ​​execute the application of the most suitable information provision algorithm.

[0095] The service provider can estimate the user's emotions and prioritize answers and solutions based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority answers and solutions. For example, if the user is relaxed, the service provider will prioritize providing detailed answers and solutions. For example, if the user is in a hurry, the service provider will prioritize providing answers and solutions that require a quick response. In this way, by prioritizing answers and solutions according to the user's emotions, important information can be provided preferentially. 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 service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into AI and have the AI ​​determine the priority of answers and solutions.

[0096] The service provider can determine the priority of answers and solutions based on the submission date of the problem at the time of provision. For example, the service provider may prioritize providing answers and solutions to recently submitted problems. For example, the service provider may postpone providing answers and solutions to older problems. For example, the service provider may adjust the priority of answers and solutions in stages according to the submission date. This allows for a quick response to the latest problems by determining the priority of answers and solutions based on the submission date of the problem. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider may input problem submission date data into AI and have the AI ​​perform the determination of the priority of answers and solutions.

[0097] The service provider can adjust the order of answers and solutions based on the relevance of the problems when providing them. For example, the service provider can prioritize providing answers and solutions for highly relevant problems. For example, it can postpone providing answers and solutions for less relevant problems. For example, the service provider can adjust the order of answers and solutions in stages according to the relevance of the problems. This allows for priority addressing of highly relevant problems by adjusting the order of answers and solutions based on the relevance of the problems. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input problem relevance data into AI and have AI perform the adjustment of the order of answers and solutions.

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

[0099] The condominium management association support system can also be equipped with a prediction unit. This unit can predict future troubles and problems based on collected information and learned data. For example, it can analyze past trouble data to predict the probability of troubles occurring in specific seasons or events. It can also analyze the management association's operational status and resident trends to predict future operational challenges. Furthermore, it can refer to data from other condominium management associations to predict the likelihood of similar troubles occurring. This allows management associations to take preventative measures and avoid troubles from occurring in the first place.

[0100] The condominium management association support system can also include a notification function. Based on collected information and learned data, the notification function can notify the management association and residents of important information. For example, the notification function can quickly notify relevant parties when a problem occurs. It can also notify residents of regular maintenance schedules and important meeting schedules. Furthermore, the notification function can inform residents of important changes and new rules regarding the management association's operations. This allows the management association and residents to always be informed of the latest information and respond quickly.

[0101] The condominium management association support system can also include an evaluation unit. This unit can assess the effectiveness of the provided solutions and countermeasures and collect feedback. For example, after the management association implements the provided solutions and countermeasures, the evaluation unit can collect the results and evaluate their effectiveness. Furthermore, the evaluation unit can collect feedback from residents and identify areas for improvement in the provided solutions and countermeasures. Additionally, the evaluation unit can improve the accuracy of the provided solutions and countermeasures based on past evaluation data. This ensures that the condominium management association support system can always provide the most optimal solutions and countermeasures.

[0102] The condominium management association support system can also include a customization section. This customization section allows for the system's functions and the information provided to be tailored to the specific needs and requests of each management association. For example, the customization section can prioritize providing information on topics important to a particular management association. Furthermore, the customization section can adjust system settings according to the management association's operational policies and the characteristics of its residents. In addition, the customization section can add and improve system functions based on feedback from the management associations. This allows the condominium management association support system to provide services optimized for the needs of each management association.

[0103] The condominium management association support system can also be equipped with a statistics department. This department can perform statistical analysis based on collected information and learned data. For example, it can analyze the frequency of problems and the time it takes to resolve them, thereby evaluating the operational efficiency of the management association. Furthermore, it can compile resident satisfaction and opinions, identifying areas for improvement in the management association's operational policies. In addition, the statistics department can conduct comparative analyses with other condominium management associations to derive best practices. This allows management associations to make data-driven decisions and improve the quality of their operations.

[0104] The condominium management association support system has a data collection unit that estimates the user's emotions and adjusts the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of information collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit increases the frequency of information collection to collect more detailed information. For example, if the user is in a hurry, the data collection unit collects information quickly to enable immediate response. In this way, the system reduces the user's burden by adjusting the timing of information collection according to the user's 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, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of information collection.

[0105] The condominium management association support system can estimate the user's emotions and adjust the way answers and solutions are presented based on the estimated emotions. For example, if the user is stressed, the system provides concise and easy-to-understand explanations. If the user is relaxed, the system provides detailed explanations. If the user is in a hurry, the system provides concise information requiring immediate attention. By adjusting the way answers and solutions are presented according to the user's emotions, the system can provide information that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into AI and have the AI ​​adjust the way answers and solutions are presented.

[0106] The condominium management association support system has a learning unit that estimates the user's emotions and selects training data based on the estimated emotions. For example, if the user is stressed, the learning unit prioritizes learning data of high importance. For example, if the user is relaxed, the learning unit prioritizes learning detailed data. For example, if the user is in a hurry, the learning unit prioritizes learning data that requires a quick response. This enables efficient learning by selecting training data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into AI and have the AI ​​perform the selection of training data.

[0107] The condominium management association support system has an anonymization unit that estimates the user's emotions and adjusts the anonymization method based on the estimated emotions. For example, if the user is stressed, the anonymization unit applies a simple anonymization method and processes the data quickly. For example, if the user is relaxed, the anonymization unit applies a detailed anonymization method to improve the accuracy of the information. For example, if the user is in a hurry, the anonymization unit performs anonymization quickly and provides information immediately. This allows for quick and appropriate anonymization by adjusting the anonymization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 anonymization unit may be performed using AI or not. For example, the anonymization unit can input user emotion data into an AI and have the AI ​​adjust the anonymization method.

[0108] The condominium management association support system can estimate the user's emotions and prioritize answers and solutions based on those emotions. For example, if the user is stressed, the system will prioritize providing high-priority answers and solutions. If the user is relaxed, the system will prioritize providing detailed answers and solutions. If the user is in a hurry, the system will prioritize providing answers and solutions that require immediate attention. This allows the system to prioritize important information by determining the priority of answers and solutions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the system may be performed using AI or not. For example, the system can input user emotion data into an AI and have the AI ​​determine the priority of answers and solutions.

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

[0110] Step 1: The collection department collects information related to the operation of condominium management associations. For example, the collection department collects operational know-how and examples of trouble-solving from each condominium management association. The collection department can collect information through questionnaires and interviews. The collection department can also extract information from databases. For example, the collection department extracts necessary information from databases related to the operation of management associations. Step 2: The anonymization unit anonymizes the information collected by the collection unit. For example, the anonymization unit removes personal information from the collected information. The anonymization unit removes personal information such as names, addresses, and contact information. The anonymization unit can also perform data masking. For example, the anonymization unit can mask certain data to prevent the identification of individuals. Step 3: The learning unit learns from the anonymized information by the anonymization unit. The learning unit learns from the anonymized information using, for example, LLM. The learning unit analyzes the information using machine learning algorithms and learns the optimal answers and solutions. Step 4: The provisioning unit provides answers and solutions to questions and problems based on the information learned by the learning unit. The provisioning unit uses AI to provide the optimal answers and solutions. The provisioning unit provides the optimal answers and solutions to questions and problems entered by the user based on past success and failure cases.

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

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

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

[0114] Each of the multiple elements described above, including the collection unit, anonymization unit, learning unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects operational know-how and trouble-shooting cases from each condominium management association. The anonymization unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes personal information from the collected information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the anonymized information using LLM. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the optimal answers and solutions to questions and problems entered by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0130] Each of the multiple elements described above, including the collection unit, anonymization unit, learning unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects operational know-how and trouble-shooting cases from each condominium management association. The anonymization unit is implemented by the identification processing unit 290 of the data processing unit 12 and removes personal information from the collected information. The learning unit is implemented by the identification processing unit 290 of the data processing unit 12 and learns the anonymized information using LLM. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the optimal answer or solution to the question or problem entered by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0146] Each of the multiple elements described above, including the collection unit, anonymization unit, learning unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects operational know-how and trouble-shooting cases from each condominium management association. The anonymization unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes personal information from the collected information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the anonymized information using LLM. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the optimal answer or solution to the question or problem entered by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0163] Each of the multiple elements described above, including the collection unit, anonymization unit, learning unit, and provision unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects operational know-how and troubleshooting cases from each condominium management association. The anonymization unit is implemented by the specific processing unit 290 of the data processing unit 12 and removes personal information from the collected information. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the anonymized information using LLM. The provision unit is implemented by the control unit 46A of the robot 414 and provides the optimal answer or solution to questions and problems entered by the user. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

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

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

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

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

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

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

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

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

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

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

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

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

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

[0182] (Note 1) A collection department that collects information regarding the operation of the condominium management association, An anonymization unit that anonymizes the information collected by the collection unit, A learning unit that learns the anonymized information by the anonymization unit, The system includes a providing unit that provides answers and solutions to questions and problems based on the information learned by the learning unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect operational know-how and examples of how to handle problems from each condominium management association. The system described in Appendix 1, characterized by the features described herein. (Note 3) The anonymization unit is, Delete personal information from collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, Learn the optimal answers and solutions based on anonymized information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, We provide the best answers and solutions when questions or problems arise. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We analyze past success and failure cases and propose the best course of action. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We will analyze the past information provision history of each condominium management association and select the most optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filter it based on the current operational status and areas of interest of the management association. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information based on the geographical location of the management association. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze the management association's social media activities and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The anonymization unit is, During anonymization, the level of detail of the anonymization is adjusted based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The anonymization unit is, When anonymizing, different anonymization algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The anonymization unit is, When anonymizing data, the priority of anonymization is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The anonymization unit is, During anonymization, the order of anonymization is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned learning unit, During learning, different learning methods are applied to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned learning unit, During training, the training data is weighted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, During learning, the system references relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way answers and solutions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing the solution, adjust the level of detail in the answer and solution based on the importance of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the solution, a different provisioning algorithm will be applied depending on the category of the problem. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of answers and solutions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing the information, we will prioritize the answers and solutions based on when the problems were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the solutions, adjust the order of the answers and solutions based on the relevance of the problems. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A collection department that collects information regarding the operation of the condominium management association, An anonymization unit that anonymizes the information collected by the collection unit, A learning unit that learns the anonymized information by the anonymization unit, The system includes a providing unit that provides answers and solutions to questions and problems based on the information learned by the learning unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect operational know-how and examples of how to handle problems from each condominium management association. The system according to feature 1.

3. The anonymization unit is, Delete personal information from collected data. The system according to feature 1.

4. The aforementioned learning unit, Learn the optimal answers and solutions based on anonymized information. The system according to feature 1.

5. The aforementioned supply unit is, We provide the best answers and solutions when questions or problems arise. The system according to feature 1.

6. The aforementioned supply unit is, We analyze past success and failure cases and propose the best course of action. The system according to feature 1.

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

8. The aforementioned collection unit is We will analyze the past information provision history of each condominium management association and select the most optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filter it based on the current operational status and areas of interest of the management association. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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