system

Abstract

We provide the system. [Solution] A terminal that collects user lifestyle pattern data, An information processing device that analyzes the data and evaluates the user's environmental impact, A generative model that generates environmentally friendly behavioral options, Information and communication means for notifying the user of the options, A data analysis method that evaluates environmental impacts and generates proposals based on citizens' behavioral data, A means of presenting options that suggest energy consumption and the use of public transportation, A system that includes this.

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 modern society, it is difficult to clearly understand and practice a method to minimize the impact on the environment in the lives of individual consumers. As a result, there are problems such as the progress of global warming and environmental destruction. In addition, there is a lack of systems that present environmentally considerate action options, and it is also a problem that it is difficult for individuals to be aware of the impact of their actions on the environment.

Means for Solving the Problems

[0005] This invention provides a means for evaluating the impact of user behavior on the environment by collecting user lifestyle pattern data via a terminal and analyzing this data through a server. Furthermore, it provides a system that proposes practical and effective actions to reduce environmental impact by automatically generating environmentally friendly behavioral options using a generative model and notifying the user via communication means.

[0006] A "user" refers to an individual or group that uses the system and is the target of suggestions from the system based on their lifestyle patterns.

[0007] "Lifestyle pattern data" refers to data on users' daily activities, location information, electrical appliances they use, and means of transportation, and is used to assess environmental impacts.

[0008] A "terminal" refers to a device used to collect lifestyle pattern data from users and communicate with the system, such as a smartphone or personal computer.

[0009] A "server" is a computer system that analyzes collected lifestyle pattern data and evaluates the impact of user behavior on the environment.

[0010] A "generative model" refers to an algorithm or AI model used to analyze users' lifestyle data and generate alternative actions based on environmental considerations.

[0011] "Communication means" refers to means including interfaces or network technologies for sending and receiving data between a terminal and a server and notifying the user of action options.

[0012] "Environmental impact" refers to the results that show, using numerical values ​​or evaluation scores, the burden that specific user actions have on the natural environment, resource consumption, greenhouse gas emissions, etc.

[0013] "Feedback" refers to data that records user behavior and uses that information to inform future suggestions. [Brief explanation of the drawing]

[0014] [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. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0029] 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.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] The 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.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention, as an "environmentally conscious behavior support system," collects behavioral data from users in their daily lives and proposes environmentally conscious behaviors. This system is configured through the cooperation of a terminal and a server.

[0036] First, with the user's consent, the device continuously collects lifestyle pattern data such as location information, appliances used, and daily schedules. The collected data is securely transmitted from the device to a server.

[0037] Next, the server analyzes this data and evaluates the impact of user behavior on the environment. At this stage, it refers to an external environmental database to calculate carbon dioxide emissions, energy consumption, and other factors. Based on the analysis results, the server uses a generative model to automatically generate appropriate environmentally conscious behavioral options for the user.

[0038] For example, the server can detect if a user uses a car for commuting and suggest using public transport or cycling to encourage a reduction in environmental impact. It can also provide advice on seasonal energy-saving methods at home.

[0039] The generated suggestions are sent to the terminal via communication means and notified to the user. The user can receive these suggestions and see the specific benefits that each option brings to the environment.

[0040] Furthermore, if the user follows the suggested options, their actions are recorded by the device and sent to the server as feedback data. This feedback is used by the system to make future suggestions more tailored to the user's individual circumstances.

[0041] This system enables users to naturally consider the environment in their daily lives. Its aim is to contribute to the preservation of the global environment by promoting sustainable behavior.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The device obtains user consent and collects location information, data on appliances being used, and schedule information in real time. This information is temporarily stored locally.

[0045] Step 2:

[0046] The device sends the collected data to the server via a secure communication method. After transmission, a backup of the data may be saved locally.

[0047] Step 3:

[0048] The server analyzes the received data and applies a model to predict user behavior patterns. This allows for the identification of energy consumption and transportation usage.

[0049] Step 4:

[0050] The server references an external environmental database to evaluate the environmental impact (such as CO2 emissions and energy consumption) related to user actions.

[0051] Step 5:

[0052] The server uses a generative model to generate environmentally conscious behavioral options based on the user's lifestyle patterns. For example, using public transportation instead of a private car.

[0053] Step 6:

[0054] The server generates options and sends them to the terminal via a communication method. Each option may also include explanations of its economic benefits and environmental advantages.

[0055] Step 7:

[0056] The device notifies the user of options and presents action suggestions in a way that is easy for the user to understand.

[0057] Step 8:

[0058] The device checks whether the user followed the suggested options and records the result. This is recorded even in cases where there is no significant change in the action taken.

[0059] Step 9:

[0060] The device sends recorded behavioral data to the server, which then uses it to improve the system's accuracy for future suggestions.

[0061] In this way, a cycle is formed that supports users' environmentally conscious behavior.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] In modern society, awareness of environmental issues is increasing, and the challenge is how individuals can concretely take environmentally conscious actions in their daily lives. However, previous systems lacked specific and actionable suggestions based on individual lifestyle patterns, making it difficult for users to naturally incorporate sustainable behaviors.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] In this invention, the server includes information gathering means, data transmission means, data analysis means, action suggestion generation means, and user notification means. This enables users to naturally incorporate sustainable behaviors into their daily lives by automatically generating individual environmentally conscious options based on the user's lifestyle patterns and presenting specific benefits.

[0067] "Information gathering means" refers to a device or method for continuously acquiring user location information, home appliance usage status, and lifestyle pattern data such as schedules.

[0068] "Data transmission means" refers to a method or device for encrypting collected data and transmitting it to a server using a secure communication protocol.

[0069] "Data analysis means" refers to a program or device that evaluates the environmental impact of a user, such as carbon dioxide emissions, based on data received on a server.

[0070] A "behavioral suggestion generation means" is a device or program that utilizes a generation AI model to automatically generate environmentally conscious options for user behavior.

[0071] "User notification means" refers to a method or device for notifying a user terminal of action suggestions generated via a communication network.

[0072] "Feedback processing means" refers to a device or method that records the user's selection and its results, and updates the generated AI model in order to reflect these in future suggestions.

[0073] "Information display means" refers to a device or method for visually displaying to users the economic and health benefits in addition to the reduction of environmental impact.

[0074] This invention is a system that supports environmentally conscious behavior in users' daily lives. The system's main components are a terminal that collects and analyzes information, and a server that generates action suggestions based on that information.

[0075] First, the user installs a dedicated application on their device (e.g., a smartphone). This application uses the device's sensors to collect lifestyle pattern data such as location information, appliance usage data, and schedules. For example, it uses GPS to understand the user's movement and record what modes of transportation they are using.

[0076] Next, the terminal encrypts the collected data and sends it to the server using a secure communication method such as the HTTPS protocol. This process ensures data security. The server uses a programming environment such as Python to analyze the received data. The analysis includes algorithms for calculating carbon dioxide emissions and also accesses external environmental databases.

[0077] Based on the analysis results, the server automatically generates environmentally conscious behavioral suggestions using a generative AI model. The generative AI model compares the user's past behavioral data and generates suggestions that help improve their behavior. For example, based on data that "the user frequently uses a car," the server might suggest that "using public transportation reduces energy consumption."

[0078] The generated suggestions are sent to the terminal via the communication network and notified to the user. By receiving this, the user can understand how they can make concrete contributions to the environment. The notification may include specific benefits, such as, "By following this suggestion, you can reduce CO2 emissions by 20%." By inputting a prompt such as, "Suggest ways to improve commuting choices and reduce energy consumption" as an example, the generation AI model will build appropriate suggestions.

[0079] Thus, this system aims to naturally encourage users to adopt sustainable lifestyles and contribute to the preservation of the global environment.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users install a dedicated application on their device and authorize the collection of location information, appliance usage data, and schedule data. The system takes data from various sensors as input and generates lifestyle pattern data as output. This data is collected in real time from the device's built-in sensors and connected smart appliances.

[0083] Step 2:

[0084] The device encrypts the collected lifestyle pattern data and sends it to the server using the HTTPS protocol. It receives collected lifestyle pattern data as input and generates secure communication data as output. Data encryption is performed using modern encryption algorithms, and the data is ready for transmission.

[0085] Step 3:

[0086] The server stores the received data in a database and performs data analysis in a Python environment. It takes the received data as input and generates analysis results as output. The server also accesses an external environmental database and runs a model to calculate carbon dioxide emissions and energy consumption. This analysis process also includes data cleaning and formatting.

[0087] Step 4:

[0088] The server generates action suggestions using a generative AI model based on the analysis results. It receives analysis results as input and creates specific action suggestions for the user as output. The generative AI model constructs optimized suggestions based on the user's behavioral patterns. These suggestions include feasible actions and expected effects.

[0089] Step 5:

[0090] The server sends the generated action suggestions to the terminal via the communication network. It receives the generated suggestions as input and generates notification data for the user's terminal as output. The user receives the notification on their terminal and can learn about specific actions to take. The notification may include specific figures or the benefits of the action.

[0091] Step 6:

[0092] When a user selects and performs a suggested action, the results are recorded on the device. The device receives the user's action selection as input and generates feedback data as output. This data, which records the actual action and its results, is sent to the server to be used in future suggestions.

[0093] Step 7:

[0094] The server receives feedback data and updates the generating AI model. It receives feedback data as input and generates improved suggestions as output. The updated model is trained to better adapt to the user's individual behavior patterns, enabling more accurate suggestions.

[0095] (Application Example 1)

[0096] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0097] Modern urban dwellers are often insensitive to the environmental impact of their daily choices. As a result, they frequently continue environmentally harmful behaviors unconsciously. Furthermore, a lack of environmentally friendly options and a failure to understand the specific effects of those choices are significant challenges. Moreover, with the growing concept of smart cities, there is a need to build systems that enable citizens to participate in and contribute to environmental protection.

[0098] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0099] In this invention, the information processing device includes means for collecting user lifestyle pattern data, means for analyzing said data and evaluating environmental impact, and data analysis means for evaluating environmental impact based on citizen behavior data and generating proposals. This makes it possible to concretely present the impact of citizens' daily behavior on the environment and provide options with less environmental impact.

[0100] A "device" is an electronic device used to collect data on a user's lifestyle patterns.

[0101] An "information processing device" is a central device in a system that analyzes collected data to evaluate the impact of user behavior on the environment.

[0102] A "generative model" is an algorithm that automatically generates environmentally friendly behavioral options.

[0103] "Information and communication means" refers to communication technologies used to notify users of generated action options.

[0104] "Data analysis methods" refer to analytical methods used to evaluate environmental impacts and generate proposals based on collected behavioral data.

[0105] A "method of presenting options" refers to a method of suggesting to users, based on analysis results, how to improve energy efficiency or utilize public transportation.

[0106] To realize this invention, the first step is to collect data on the user's daily lifestyle patterns using a device. This data includes location information, data on household appliances used, and daily schedules. This data is obtained only with the user's consent. The data collected by the device is securely transmitted to a server.

[0107] The server analyzes the received lifestyle pattern data using specialized information processing equipment and evaluates its impact on the environment. Here, the server references an external environmental database to calculate carbon dioxide emissions and energy consumption. Based on these calculations, it uses data analysis tools to pass the analysis results to a generating AI model. The generating AI model automatically generates appropriate environmentally conscious behavioral options for the user.

[0108] For example, if a user commutes by private car, the system can detect this and suggest using public transport or a bicycle. It can also advise on energy-saving methods at home based on the season and weather. The generated suggestions are sent from the server to the user's device via communication, and the user is notified. The notification includes specific figures and benefits to raise environmental awareness.

[0109] For example, if a user living in Tokyo commutes by car every day, the system calculates the CO2 emissions and suggests using public transportation as an environmentally friendly option. Furthermore, it provides specific advice on reducing electricity consumption, such as shortening the time spent using home appliances at night.

[0110] An example of a prompt to input into the generative model is: "Based on the user's travel data, suggest more environmentally friendly modes of transportation. Please provide options considering the current mode of transport and energy consumption."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The device collects lifestyle pattern data such as the user's location, data from household appliances used, and daily schedules. The input is the user's behavioral data, and the output is the collected dataset. To collect this data, the device utilizes GPS sensors and feedback from smart devices.

[0114] Step 2:

[0115] The device securely transmits collected lifestyle pattern data to the server. The input is the dataset collected in step 1, and the output is data packets for the server to receive. This process uses communication protocols to encrypt the data and protect privacy.

[0116] Step 3:

[0117] The server analyzes the received data using an information processing device. The input is user lifestyle pattern data, and the output is an assessment of the environmental impact. The server refers to an external environmental database, calculates CO2 emissions and energy consumption, and determines the extent to which user behavior affects the environment.

[0118] Step 4:

[0119] The server generates proposals using a generative AI model based on the analysis results. The input is the environmental impact assessment results, and the output is a list of environmentally conscious behavioral options. For example, the generative model automatically generates options such as "using public transport" and "traveling by bicycle," and specifically shows how environmentally friendly each option is and to what extent.

[0120] Step 5:

[0121] The server notifies the terminal of the generated action options using information and communication means. The input is a list of options, and the output is a notification message to the user. The notification is made using the application's push notification function, and the user receives the suggestions on their terminal.

[0122] Step 6:

[0123] The user reviews the suggestions received from the server and selects an action based on them. The input is the suggestion message, and the output is the user's selected action data. The user compares the suggested options and decides on an action, considering their environmental or economic benefits.

[0124] Step 7:

[0125] The terminal sends the user's selected actions back to the server as data, which is then used as feedback data. The input is the user's action data, and the output is the feedback data recorded on the server. Based on this feedback, the server uses it to generate more suitable suggestions for future interactions.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention is an "environmentally conscious behavior support system" designed to transform users' daily behaviors into more environmentally conscious ones, and aims to improve the user experience in particular by incorporating emotion recognition technology. The system consists of a terminal, a server, a generative model, communication means, and an emotion engine.

[0128] First, with the user's permission, the device collects lifestyle pattern data, including location information, the electronic devices being used, and schedules. Furthermore, an emotion engine on the device analyzes the user's emotional state via the camera and microphone, and tags the data in real time.

[0129] The acquired data is sent to the server using a secure communication method. The server evaluates the lifestyle pattern data and the user's current emotional state, and generates optimal behavioral options using a generative model. Here, the user's emotional state plays a crucial role, and the suggestions are adjusted to be relatable to the user's feelings.

[0130] For example, for users experiencing stress, we would prioritize suggesting relaxing activities or easily implemented environmentally conscious actions. Specifically, this could involve suggesting, "How about relaxing at a nearby library on your way home from work today, and then walking home?"

[0131] The generated suggestions are sent to the device, and a notification is triggered. The user can review these and choose the option that best suits them. Furthermore, if there is a change in the user's feelings regarding their choice, the device feeds the new data back to the server. This feedback is reflected in future suggestions, strengthening the system's ability to continuously provide the best options for the user.

[0132] In this way, users can lead a sustainable lifestyle without undue burden and become more aware of the environmental impact of their actions. This invention will greatly contribute to supporting next-generation environmentally conscious lifestyles.

[0133] The following describes the processing flow.

[0134] Step 1:

[0135] The device collects lifestyle pattern data after confirming the user's consent. This includes location information, device usage information, schedule, etc., and is constantly updated.

[0136] Step 2:

[0137] The device's emotion engine analyzes the user's emotional state in real time. This involves analyzing facial expressions and voice tone using the camera and microphone to identify what emotions the user is experiencing.

[0138] Step 3:

[0139] The device sends lifestyle pattern data and emotional data together to the server. Communication is conducted securely, and privacy is ensured.

[0140] Step 4:

[0141] The server analyzes the received data and performs an evaluation that takes into account both the user's behavior patterns and emotional state. This allows it to determine which action is optimal for the user's current emotional state.

[0142] Step 5:

[0143] The server's generation model creates actionable options that reduce environmental impact while also considering user emotions. For example, if a user is tired, it will recommend actions that require minimal effort.

[0144] Step 6:

[0145] The server generates options, which are then adjusted to create a proposal that includes emotional and economic benefits, and this proposal is sent to the terminal via communication.

[0146] Step 7:

[0147] The device notifies the user of suggestions. The user interface is designed to be easy for the user to understand and to help them choose an action.

[0148] Step 8:

[0149] The user selects an action based on the suggestions and takes action. During this process, the device records which suggestion was chosen.

[0150] Step 9:

[0151] The device feeds back the selected action and subsequent emotional changes to the server. The server uses this information to improve the accuracy of its next suggestions.

[0152] In this way, a process is established to support users in adopting the most environmentally conscious lifestyle.

[0153] (Example 2)

[0154] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0155] In modern society, achieving sustainable lifestyles that consider the environmental impact of user behavior while also addressing individual emotional states is a challenging task. Conventional environmentally conscious systems often fail to adequately consider user emotional states, resulting in uniform suggestions and frequently lacking user satisfaction. To enable users to continue sustainable behaviors without undue burden, more personalized suggestions and feedback loops are necessary.

[0156] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0157] In this invention, the server includes information terminal means for collecting user location information, power consumption data, and schedule information; emotion analysis means for evaluating emotional states based on facial expressions and voice using a camera and microphone; and data analysis means for integrating and analyzing the user's lifestyle patterns and emotional states. This enables the server to provide behavioral options appropriate to the user's emotional state, allowing the user to maintain a comfortable and environmentally conscious lifestyle.

[0158] "Information terminal means" refers to a device or system for collecting user location information, power consumption data, and schedule information.

[0159] "Emotion analysis means" refers to technology that uses cameras and microphones to evaluate a user's emotional state from their facial expressions and voice.

[0160] "Data analysis means" refers to technology that has the function of integrating and analyzing data on users' lifestyle patterns and emotional states that has been collected.

[0161] A "generative model means" is an algorithm or model for generating optimal action options based on the user's emotional state.

[0162] "Communication means" refers to data transmission technology or protocols used to notify the user of generated action options.

[0163] A "data feedback mechanism" is a system or process that transmits changes in the user's emotions based on their behavioral choices to a server and reflects these changes in future suggestions.

[0164] A "user interface" is the system component that includes display screens and input methods that present environmental impact reduction, economic benefits, and health benefits when suggesting actions to the user.

[0165] The present invention is an environmentally conscious behavior support system for improving healthcare and environmental awareness, and specifically provides personalized suggestions based on the user's emotional state and lifestyle data. This system consists of an information terminal, an emotion analysis means, a data analysis means, a generative model means, a communication means, and a data feedback means.

[0166] Information terminals include smartphones and wearable devices, through which user location information, power consumption data, and schedule information are collected. This data is acquired in real time using sensors on each terminal.

[0167] The emotion analysis method utilizes facial recognition and voice analysis technologies. These technologies include detecting facial expressions using image processing libraries and analyzing voice tone using voice recognition libraries. This makes it possible to clearly register the user's emotional state, such as stress levels and relaxation levels.

[0168] The data analysis method uses programming languages ​​such as Python and R on the server to comprehensively evaluate lifestyle data and emotional data received from users.

[0169] The generative modeling method utilizes a generative AI model, employing OpenAI® and other common machine learning frameworks to create user-specific action options. An example of a prompt is, "Please suggest an appropriate environmentally conscious action if the user is experiencing stress."

[0170] The communication method involves the bidirectional exchange of data between the server and the information terminal via the internet. The data is protected using encryption technologies such as AES and HTTPS.

[0171] The data feedback mechanism collects feedback data on the user's chosen actions and incorporates this information into subsequent suggestions. This allows the system to continuously optimize suggestions to meet the user's needs.

[0172] For example, if the system detects that a user is feeling stressed on their way home, it will notify their smartphone with a suggestion such as, "How about taking a 10-minute walk in a nearby park?" This suggestion takes the user's emotional state into consideration and encourages environmentally friendly behavior within reasonable limits.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The device acquires location information using a GPS sensor with the user's permission. It also collects power consumption data from smart devices and retrieves schedule information from scheduling applications. This information is stored in an internal database as data that constitutes the user's lifestyle pattern. Inputs include the user's current location, power consumption status, and schedule, which are then integrated and output as lifestyle data.

[0176] Step 2:

[0177] The emotion analysis system on the device activates a facial recognition system using the camera and a voice analysis system using the microphone. This evaluates the user's emotional state from their facial expressions and voice, tagging it as a state such as "relaxed" or "stressed." At this stage, the input is real-time video and audio data, and the output is emotional state tags. Specifically, it detects the user's smiles and angry expressions and quantifies those emotions.

[0178] Step 3:

[0179] The server receives lifestyle data and emotional state tags sent from the terminal. Using a data analysis tool, Python is used to integrate this data and evaluate the user's overall daily behavioral patterns and current emotional state. The input data consists of lifestyle data and emotional state, and the analysis outputs the user's lifestyle context. Specific actions include comparing past similar emotional states and behavioral patterns to plan recommended future actions.

[0180] Step 4:

[0181] The generative model on the server uses an AI model (e.g., a machine learning algorithm) based on the received data to generate the most suitable action options for the user at that moment. The prompt used is "Please suggest environmentally conscious actions suitable for a user who wants to relax." The input is the user's life context, and the output is a list of suggested actions. A specific action involves combining past successful action patterns to refine the suggestions.

[0182] Step 5:

[0183] The server sends the generated suggestions to the device. The device notifies the user of the received suggestions. For example, it might display "Try taking a 20-minute walk in a nearby quiet park" on the smartphone screen. The input is the generated action options, and the output is the message as a notification to the user. The specific action is to attract the user's attention by utilizing the device's notification function.

[0184] Step 6:

[0185] The user reviews the suggested action and chooses whether to perform it. The device collects new emotional data as a result of the choice using an emotional analysis tool and sends it back to the server. The input is the user's response to the suggestion, and the output is the updated emotional data. The specific action is to analyze the newly obtained emotional changes and store them as feedback data for future suggestions.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In modern society, individual lifestyles have a significant impact on the environment. To live a more sustainable life, it is necessary to propose environmentally conscious behaviors that are feasible and emotionally acceptable to users. Furthermore, these proposals must be flexible and adaptable to the user's emotional state, taking into account their physical and mental health as well as their economic interests.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes a device for understanding the user's lifestyle habits, a data processing unit for analyzing the habit information obtained from the device and calculating the user's impact on the environment, a generation model for generating environmentally friendly activity suggestions, a notification unit for communicating the activity suggestions to the user, and an emotion recognition function for evaluating the user's emotional state and making suggestions based on that emotional state. This makes it possible for the user to accept and implement sustainable lifestyle activities without difficulty, reduce their impact on the environment, and enjoy both physical and mental health and economic benefits.

[0191] A "device for understanding users' lifestyle habits" is a terminal used to collect information about users' daily activities, the devices they use, and their location.

[0192] A "data processing unit" is an information processing unit that analyzes collected lifestyle information and calculates the user's influence on their environment based on that analysis.

[0193] A "generative model" is an algorithm or system that generates optimal, environmentally friendly activity suggestions for users based on acquired data.

[0194] The "notification section" is a means of communicating the generated activity plan to the user, and typically uses digital communication methods.

[0195] The "emotion recognition function" is a feature that evaluates the user's emotional state and adjusts the suggested content based on that evaluation, collecting data via the camera and microphone.

[0196] The "environmentally conscious behavior support system" of the present invention is designed to help users live a more environmentally friendly life. This system collects and analyzes data related to the user's daily life to propose optimal action plans. Specifically, it is configured as follows:

[0197] First, the device collects data such as location information, the electronic devices used, and schedules using smartphones or smart glasses to understand the user's lifestyle. This device is equipped with Python's OpenCV and TENSORFLOW®, and uses a camera and microphone to evaluate the user's emotional state in real time.

[0198] The collected data is sent to the server using HTTPS, a secure communication protocol. The server's data processing unit calculates the user's environmental impact and passes the data to a generative AI model. This generative model uses algorithms to generate environmentally friendly action plans.

[0199] The generated action suggestions are sent as push notifications to the user's device from the notification unit. The content of the notifications is adjusted based on the user's emotional state, prioritizing relaxing activities and economical options.

[0200] For example, if a user is feeling stressed at work, the system might suggest, "Why not take a walk in the park during your next break and use a shared bicycle on your way back, if possible?" This suggestion takes the user's lifestyle into consideration and supports sustainable choices.

[0201] An example of a prompt for this generating AI model would be: "List the environmentally friendly options you can recommend when a user is commuting while under high stress."

[0202] In this way, the system can help users achieve a sustainable lifestyle without undue burden, while minimizing its impact on the environment.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The device uses a smartphone or smart glasses to collect data on the user's location, schedule, and the devices they are using. This input data records the user's basic lifestyle habits. The device's camera and microphone are used to analyze the user's emotional state in real time using Python's OpenCV and TensorFlow.

[0206] Step 2:

[0207] The device transmits collected lifestyle data and emotional state data to the server via HTTPS, a secure communication protocol. The output at this stage is a dataset that is stored on the server as basic data for analysis.

[0208] Step 3:

[0209] The server's data processing unit analyzes the received lifestyle and emotional state data to calculate the user's impact on the environment. This analysis uses an algorithm related to environmental impact, and the output is a numerical representation of the environmental impact.

[0210] Step 4:

[0211] The server sends a prompt to the generative AI model based on the previous analysis results, generating the most optimal and environmentally friendly action for the user. The generative AI model processes the prompt and prepares multiple executable alternatives. This output is generated as specific action options.

[0212] Step 5:

[0213] The server sends the generated action suggestions to the user's terminal via the notification unit. The terminal receives this and presents it to the user as a push notification. This notification takes into account prioritization based on the user's emotional state and provides clear information for the user to choose from.

[0214] Step 6:

[0215] After the user selects and performs a suggested action, the selection is recorded on the device and sent to the server as feedback. This allows the system to learn the user's selection patterns and further optimize future suggestions. The feedback is stored on the server as behavioral history and changes in emotion.

[0216] 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.

[0217] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0218] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0219] [Second Embodiment]

[0220] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0221] 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.

[0222] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0223] 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.

[0224] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0225] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0226] 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.

[0227] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0228] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0229] The 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.

[0230] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0231] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0232] This invention, as an "environmentally conscious behavior support system," collects behavioral data from users in their daily lives and proposes environmentally conscious behaviors. This system is configured through the cooperation of a terminal and a server.

[0233] First, with the user's consent, the device continuously collects lifestyle pattern data such as location information, appliances used, and daily schedules. The collected data is securely transmitted from the device to a server.

[0234] Next, the server analyzes this data and evaluates the impact of user behavior on the environment. At this stage, it refers to an external environmental database to calculate carbon dioxide emissions, energy consumption, and other factors. Based on the analysis results, the server uses a generative model to automatically generate appropriate environmentally conscious behavioral options for the user.

[0235] For example, the server can detect if a user uses a car for commuting and suggest using public transport or cycling to encourage a reduction in environmental impact. It can also provide advice on seasonal energy-saving methods at home.

[0236] The generated suggestions are sent to the terminal via communication means and notified to the user. The user can receive these suggestions and see the specific benefits that each option brings to the environment.

[0237] Furthermore, if the user follows the suggested options, their actions are recorded by the device and sent to the server as feedback data. This feedback is used by the system to make future suggestions more tailored to the user's individual circumstances.

[0238] This system enables users to naturally consider the environment in their daily lives. Its aim is to contribute to the preservation of the global environment by promoting sustainable behavior.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The device obtains user consent and collects location information, data on appliances being used, and schedule information in real time. This information is temporarily stored locally.

[0242] Step 2:

[0243] The device sends the collected data to the server via a secure communication method. After transmission, a backup of the data may be saved locally.

[0244] Step 3:

[0245] The server analyzes the received data and applies a model to predict user behavior patterns. This allows for the identification of energy consumption and transportation usage.

[0246] Step 4:

[0247] The server references an external environmental database to evaluate the environmental impact (such as CO2 emissions and energy consumption) related to user actions.

[0248] Step 5:

[0249] The server uses a generative model to generate environmentally conscious behavioral options based on the user's lifestyle patterns. For example, using public transportation instead of a private car.

[0250] Step 6:

[0251] The server generates options and sends them to the terminal via a communication method. Each option may also include explanations of its economic benefits and environmental advantages.

[0252] Step 7:

[0253] The device notifies the user of options and presents action suggestions in a way that is easy for the user to understand.

[0254] Step 8:

[0255] The device checks whether the user followed the suggested options and records the result. This is recorded even in cases where there is no significant change in the action taken.

[0256] Step 9:

[0257] The device sends recorded behavioral data to the server, which then uses it to improve the system's accuracy for future suggestions.

[0258] In this way, a cycle is formed that supports users' environmentally conscious behavior.

[0259] (Example 1)

[0260] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0261] In modern society, awareness of environmental issues is increasing, and the challenge is how individuals can concretely take environmentally conscious actions in their daily lives. However, previous systems lacked specific and actionable suggestions based on individual lifestyle patterns, making it difficult for users to naturally incorporate sustainable behaviors.

[0262] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0263] In this invention, the server includes information gathering means, data transmission means, data analysis means, action suggestion generation means, and user notification means. This enables users to naturally incorporate sustainable behaviors into their daily lives by automatically generating individual environmentally conscious options based on the user's lifestyle patterns and presenting specific benefits.

[0264] "Information gathering means" refers to a device or method for continuously acquiring user location information, home appliance usage status, and lifestyle pattern data such as schedules.

[0265] "Data transmission means" refers to a method or device for encrypting collected data and transmitting it to a server using a secure communication protocol.

[0266] "Data analysis means" refers to a program or device that evaluates the environmental impact of a user, such as carbon dioxide emissions, based on data received on a server.

[0267] A "behavioral suggestion generation means" is a device or program that utilizes a generation AI model to automatically generate environmentally conscious options for user behavior.

[0268] "User notification means" refers to a method or device for notifying a user terminal of action suggestions generated via a communication network.

[0269] "Feedback processing means" refers to a device or method that records the user's selection and its results, and updates the generated AI model in order to reflect these in future suggestions.

[0270] "Information display means" refers to a device or method for visually displaying to users the economic and health benefits in addition to the reduction of environmental impact.

[0271] This invention is a system that supports environmentally conscious behavior in users' daily lives. The system's main components are a terminal that collects and analyzes information, and a server that generates action suggestions based on that information.

[0272] First, the user installs a dedicated application on their device (e.g., a smartphone). This application uses the device's sensors to collect lifestyle pattern data such as location information, appliance usage data, and schedules. For example, it uses GPS to understand the user's movement and record what modes of transportation they are using.

[0273] Next, the terminal encrypts the collected data and sends it to the server using a secure communication method such as the HTTPS protocol. This process ensures data security. The server uses a programming environment such as Python to analyze the received data. The analysis includes algorithms for calculating carbon dioxide emissions and also accesses external environmental databases.

[0274] Based on the analysis results, the server automatically generates environmentally conscious behavioral suggestions using a generative AI model. The generative AI model compares the user's past behavioral data and generates suggestions that help improve their behavior. For example, based on data that "the user frequently uses a car," the server might suggest that "using public transportation reduces energy consumption."

[0275] The generated suggestions are sent to the terminal via the communication network and notified to the user. By receiving this, the user can understand how they can make concrete contributions to the environment. The notification may include specific benefits, such as, "By following this suggestion, you can reduce CO2 emissions by 20%." By inputting a prompt such as, "Suggest ways to improve commuting choices and reduce energy consumption" as an example, the generation AI model will build appropriate suggestions.

[0276] Thus, this system aims to naturally encourage users to adopt sustainable lifestyles and contribute to the preservation of the global environment.

[0277] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0278] Step 1:

[0279] The user installs a dedicated application on the terminal and permits the collection of location information, the usage status of household appliances, and schedule data. As input, it captures data from various sensors and, as output, generates lifestyle pattern data. This data is collected in real-time from the built-in sensors of the terminal and connected smart appliances.

[0280] Step 2:

[0281] The terminal encrypts the collected lifestyle pattern data and transmits it to the server using the HTTPS protocol. As input, it receives the collected lifestyle pattern data and, as output, generates secure communication data. The data encryption is performed using modern encryption algorithms, and it is ready for transmission.

[0282] Step 3:

[0283] The server stores the received data in a database and performs data analysis in a Python environment. As input, it receives the received data and, as output, generates analysis results. The server also accesses an external environmental database and executes a model to calculate carbon dioxide emissions and energy consumption. In this analysis process, data cleaning and shaping are also performed.

[0284] Step 4:

[0285] The server generates action proposals using a generated AI model based on the analysis results. As input, it receives the analysis results and, as output, creates specific action proposals for the user. The generated AI model constructs optimized proposals based on the user's behavior patterns. These proposals include executable actions and expected effects.

[0286] Step 5:

[0287] The server sends the generated action suggestions to the terminal via the communication network. It receives the generated suggestions as input and generates notification data for the user's terminal as output. The user receives the notification on their terminal and can learn about specific actions to take. The notification may include specific figures or the benefits of the action.

[0288] Step 6:

[0289] When a user selects and performs a suggested action, the results are recorded on the device. The device receives the user's action selection as input and generates feedback data as output. This data, which records the actual action and its results, is sent to the server to be used in future suggestions.

[0290] Step 7:

[0291] The server receives feedback data and updates the generating AI model. It receives feedback data as input and generates improved suggestions as output. The updated model is trained to better adapt to the user's individual behavior patterns, enabling more accurate suggestions.

[0292] (Application Example 1)

[0293] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0294] Modern urban dwellers are often insensitive to the environmental impact of their daily choices. As a result, they frequently continue environmentally harmful behaviors unconsciously. Furthermore, a lack of environmentally friendly options and a failure to understand the specific effects of those choices are significant challenges. Moreover, with the growing concept of smart cities, there is a need to build systems that enable citizens to participate in and contribute to environmental protection.

[0295] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0296] In this invention, the information processing device includes means for collecting user lifestyle pattern data, means for analyzing said data and evaluating environmental impact, and data analysis means for evaluating environmental impact based on citizen behavior data and generating proposals. This makes it possible to concretely present the impact of citizens' daily behavior on the environment and provide options with less environmental impact.

[0297] A "device" is an electronic device used to collect data on a user's lifestyle patterns.

[0298] An "information processing device" is a central device in a system that analyzes collected data to evaluate the impact of user behavior on the environment.

[0299] A "generative model" is an algorithm that automatically generates environmentally friendly behavioral options.

[0300] "Information and communication means" refers to communication technologies used to notify users of generated action options.

[0301] "Data analysis methods" refer to analytical methods used to evaluate environmental impacts and generate proposals based on collected behavioral data.

[0302] A "method of presenting options" refers to a method of suggesting to users, based on analysis results, how to improve energy efficiency or utilize public transportation.

[0303] To realize this invention, the first step is to collect data on the user's daily lifestyle patterns using a device. This data includes location information, data on household appliances used, and daily schedules. This data is obtained only with the user's consent. The data collected by the device is securely transmitted to a server.

[0304] At the server, the received lifestyle pattern data is analyzed by a specialized information processing device to evaluate the impact on the environment. Here, the server refers to an external environmental database to calculate carbon dioxide emissions and energy consumption. Based on these calculations, the analysis results are passed to a generated AI model using data analysis means. The generated AI model is responsible for automatically generating appropriate environmentally conscious action options for the user.

[0305] For example, when it detects that a user commutes by private car, it can propose using public transportation or bicycles. It is also possible to advise on energy-saving methods at home according to seasons and weather. The generated proposals are sent from the server to the terminal via information communication means and notified to the user. Specific numerical values and merits are also presented in the notification to enhance environmental awareness.

[0306] As a specific example, if a user living in Tokyo commutes by car every day, this system calculates the CO2 emissions and proposes using public transportation as an environmentally friendly option. Furthermore, as specific advice to reduce electricity consumption, it provides energy-saving methods such as shortening the night-time home appliance usage time.

[0307] An example of a prompt sentence input to the generation model is: "Based on the user's movement data, please propose more environmentally friendly means of movement. Please inform me of the options considering the current means of transportation and energy consumption."

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The device collects lifestyle pattern data such as the user's location, data from household appliances used, and daily schedules. The input is the user's behavioral data, and the output is the collected dataset. To collect this data, the device utilizes GPS sensors and feedback from smart devices.

[0311] Step 2:

[0312] The device securely transmits collected lifestyle pattern data to the server. The input is the dataset collected in step 1, and the output is data packets for the server to receive. This process uses communication protocols to encrypt the data and protect privacy.

[0313] Step 3:

[0314] The server analyzes the received data using an information processing device. The input is user lifestyle pattern data, and the output is an assessment of the environmental impact. The server refers to an external environmental database, calculates CO2 emissions and energy consumption, and determines the extent to which user behavior affects the environment.

[0315] Step 4:

[0316] The server generates proposals using a generative AI model based on the analysis results. The input is the environmental impact assessment results, and the output is a list of environmentally conscious behavioral options. For example, the generative model automatically generates options such as "using public transport" and "traveling by bicycle," and specifically shows how environmentally friendly each option is and to what extent.

[0317] Step 5:

[0318] The server notifies the terminal of the generated action options using information and communication means. The input is a list of options, and the output is a notification message to the user. The notification is made using the application's push notification function, and the user receives the suggestions on their terminal.

[0319] Step 6:

[0320] The user reviews the suggestions received from the server and selects an action based on them. The input is the suggestion message, and the output is the user's selected action data. The user compares the suggested options and decides on an action, considering their environmental or economic benefits.

[0321] Step 7:

[0322] The terminal sends the user's selected actions back to the server as data, which is then used as feedback data. The input is the user's action data, and the output is the feedback data recorded on the server. Based on this feedback, the server uses it to generate more suitable suggestions for future interactions.

[0323] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0324] This invention is an "environmentally conscious behavior support system" designed to transform users' daily behaviors into more environmentally conscious ones, and aims to improve the user experience in particular by incorporating emotion recognition technology. The system consists of a terminal, a server, a generative model, communication means, and an emotion engine.

[0325] First, with the user's permission, the device collects lifestyle pattern data, including location information, the electronic devices being used, and schedules. Furthermore, an emotion engine on the device analyzes the user's emotional state via the camera and microphone, and tags the data in real time.

[0326] The acquired data is sent to the server using a secure communication method. The server evaluates the lifestyle pattern data and the user's current emotional state, and generates optimal behavioral options using a generative model. Here, the user's emotional state plays a crucial role, and the suggestions are adjusted to be relatable to the user's feelings.

[0327] For example, for users experiencing stress, we would prioritize suggesting relaxing activities or easily implemented environmentally conscious actions. Specifically, this could involve suggesting, "How about relaxing at a nearby library on your way home from work today, and then walking home?"

[0328] The generated suggestions are sent to the device, and a notification is triggered. The user can review these and choose the option that best suits them. Furthermore, if there is a change in the user's feelings regarding their choice, the device feeds the new data back to the server. This feedback is reflected in future suggestions, strengthening the system's ability to continuously provide the best options for the user.

[0329] In this way, users can lead a sustainable lifestyle without undue burden and become more aware of the environmental impact of their actions. This invention will greatly contribute to supporting next-generation environmentally conscious lifestyles.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The device collects lifestyle pattern data after confirming the user's consent. This includes location information, device usage information, schedule, etc., and is constantly updated.

[0333] Step 2:

[0334] The device's emotion engine analyzes the user's emotional state in real time. This involves analyzing facial expressions and voice tone using the camera and microphone to identify what emotions the user is experiencing.

[0335] Step 3:

[0336] The device sends lifestyle pattern data and emotional data together to the server. Communication is conducted securely, and privacy is ensured.

[0337] Step 4:

[0338] The server analyzes the received data and performs an evaluation that takes into account both the user's behavior patterns and emotional state. This allows it to determine which action is optimal for the user's current emotional state.

[0339] Step 5:

[0340] The server's generation model creates actionable options that reduce environmental impact while also considering user emotions. For example, if a user is tired, it will recommend actions that require minimal effort.

[0341] Step 6:

[0342] The server generates options, which are then adjusted to create a proposal that includes emotional and economic benefits, and this proposal is sent to the terminal via communication.

[0343] Step 7:

[0344] The device notifies the user of suggestions. The user interface is designed to be easy for the user to understand and to help them choose an action.

[0345] Step 8:

[0346] The user selects an action based on the suggestions and takes action. During this process, the device records which suggestion was chosen.

[0347] Step 9:

[0348] The device feeds back the selected action and subsequent emotional changes to the server. The server uses this information to improve the accuracy of its next suggestions.

[0349] In this way, a process is established to support users in adopting the most environmentally conscious lifestyle.

[0350] (Example 2)

[0351] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0352] In modern society, achieving sustainable lifestyles that consider the environmental impact of user behavior while also addressing individual emotional states is a challenging task. Conventional environmentally conscious systems often fail to adequately consider user emotional states, resulting in uniform suggestions and frequently lacking user satisfaction. To enable users to continue sustainable behaviors without undue burden, more personalized suggestions and feedback loops are necessary.

[0353] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0354] In this invention, the server includes information terminal means for collecting user location information, power consumption data, and schedule information; emotion analysis means for evaluating emotional states based on facial expressions and voice using a camera and microphone; and data analysis means for integrating and analyzing the user's lifestyle patterns and emotional states. This enables the server to provide behavioral options appropriate to the user's emotional state, allowing the user to maintain a comfortable and environmentally conscious lifestyle.

[0355] "Information terminal means" refers to a device or system for collecting user location information, power consumption data, and schedule information.

[0356] "Emotion analysis means" refers to technology that uses cameras and microphones to evaluate a user's emotional state from their facial expressions and voice.

[0357] "Data analysis means" refers to technology that has the function of integrating and analyzing data on users' lifestyle patterns and emotional states that has been collected.

[0358] A "generative model means" is an algorithm or model for generating optimal action options based on the user's emotional state.

[0359] "Communication means" refers to data transmission technology or protocols used to notify the user of generated action options.

[0360] A "data feedback mechanism" is a system or process that transmits changes in the user's emotions based on their behavioral choices to a server and reflects these changes in future suggestions.

[0361] A "user interface" is the system component that includes display screens and input methods that present environmental impact reduction, economic benefits, and health benefits when suggesting actions to the user.

[0362] The present invention is an environmentally conscious behavior support system for improving healthcare and environmental awareness, and specifically provides personalized suggestions based on the user's emotional state and lifestyle data. This system consists of an information terminal, an emotion analysis means, a data analysis means, a generative model means, a communication means, and a data feedback means.

[0363] Information terminals include smartphones and wearable devices, through which user location information, power consumption data, and schedule information are collected. This data is acquired in real time using sensors on each terminal.

[0364] The emotion analysis method utilizes facial recognition and voice analysis technologies. These technologies include detecting facial expressions using image processing libraries and analyzing voice tone using voice recognition libraries. This makes it possible to clearly register the user's emotional state, such as stress levels and relaxation levels.

[0365] The data analysis method uses programming languages ​​such as Python and R on the server to comprehensively evaluate lifestyle data and emotional data received from users.

[0366] The generative model employs a generative AI model, utilizing OpenAI and other common machine learning frameworks to create user-specific action options. An example of a prompt is, "Please suggest environmentally conscious actions appropriate for a user experiencing stress."

[0367] The communication method involves the bidirectional exchange of data between the server and the information terminal via the internet. The data is protected using encryption technologies such as AES and HTTPS.

[0368] The data feedback mechanism collects feedback data on the user's chosen actions and incorporates this information into subsequent suggestions. This allows the system to continuously optimize suggestions to meet the user's needs.

[0369] For example, if the system detects that a user is feeling stressed on their way home, it will notify their smartphone with a suggestion such as, "How about taking a 10-minute walk in a nearby park?" This suggestion takes the user's emotional state into consideration and encourages environmentally friendly behavior within reasonable limits.

[0370] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0371] Step 1:

[0372] The device acquires location information using a GPS sensor with the user's permission. It also collects power consumption data from smart devices and retrieves schedule information from scheduling applications. This information is stored in an internal database as data that constitutes the user's lifestyle pattern. Inputs include the user's current location, power consumption status, and schedule, which are then integrated and output as lifestyle data.

[0373] Step 2:

[0374] The emotion analysis system on the device activates a facial recognition system using the camera and a voice analysis system using the microphone. This evaluates the user's emotional state from their facial expressions and voice, tagging it as a state such as "relaxed" or "stressed." At this stage, the input is real-time video and audio data, and the output is emotional state tags. Specifically, it detects the user's smiles and angry expressions and quantifies those emotions.

[0375] Step 3:

[0376] The server receives lifestyle data and emotional state tags sent from the terminal. Using a data analysis tool, Python is used to integrate this data and evaluate the user's overall daily behavioral patterns and current emotional state. The input data consists of lifestyle data and emotional state, and the analysis outputs the user's lifestyle context. Specific actions include comparing past similar emotional states and behavioral patterns to plan recommended future actions.

[0377] Step 4:

[0378] The generative model on the server uses an AI model (e.g., a machine learning algorithm) based on the received data to generate the most suitable action options for the user at that moment. The prompt used is "Please suggest environmentally conscious actions suitable for a user who wants to relax." The input is the user's life context, and the output is a list of suggested actions. A specific action involves combining past successful action patterns to refine the suggestions.

[0379] Step 5:

[0380] The server sends the generated suggestions to the device. The device notifies the user of the received suggestions. For example, it might display "Try taking a 20-minute walk in a nearby quiet park" on the smartphone screen. The input is the generated action options, and the output is the message as a notification to the user. The specific action is to attract the user's attention by utilizing the device's notification function.

[0381] Step 6:

[0382] The user reviews the suggested action and chooses whether to perform it. The device collects new emotional data as a result of the choice using an emotional analysis tool and sends it back to the server. The input is the user's response to the suggestion, and the output is the updated emotional data. The specific action is to analyze the newly obtained emotional changes and store them as feedback data for future suggestions.

[0383] (Application Example 2)

[0384] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0385] In modern society, individual lifestyles have a significant impact on the environment. To live a more sustainable life, it is necessary to propose environmentally conscious behaviors that are feasible and emotionally acceptable to users. Furthermore, these proposals must be flexible and adaptable to the user's emotional state, taking into account their physical and mental health as well as their economic interests.

[0386] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0387] In this invention, the server includes a device for understanding the user's lifestyle habits, a data processing unit for analyzing the habit information obtained from the device and calculating the user's impact on the environment, a generation model for generating environmentally friendly activity suggestions, a notification unit for communicating the activity suggestions to the user, and an emotion recognition function for evaluating the user's emotional state and making suggestions based on that emotional state. This makes it possible for the user to accept and implement sustainable lifestyle activities without difficulty, reduce their impact on the environment, and enjoy both physical and mental health and economic benefits.

[0388] A "device for understanding users' lifestyle habits" is a terminal used to collect information about users' daily activities, the devices they use, and their location.

[0389] A "data processing unit" is an information processing unit that analyzes collected lifestyle information and calculates the user's influence on their environment based on that analysis.

[0390] A "generative model" is an algorithm or system that generates optimal, environmentally friendly activity suggestions for users based on acquired data.

[0391] The "notification section" is a means of communicating the generated activity plan to the user, and typically uses digital communication methods.

[0392] The "emotion recognition function" is a feature that evaluates the user's emotional state and adjusts the suggested content based on that evaluation, collecting data via the camera and microphone.

[0393] The "environmentally conscious behavior support system" of the present invention is designed to help users live a more environmentally friendly life. This system collects and analyzes data related to the user's daily life to propose optimal action plans. Specifically, it is configured as follows:

[0394] First, the device collects data such as location information, the electronic devices used, and schedules using smartphones or smart glasses to understand the user's lifestyle. This device is equipped with Python's OpenCV and TensorFlow, and uses a camera and microphone to evaluate the user's emotional state in real time.

[0395] The collected data is sent to the server using HTTPS, a secure communication protocol. The server's data processing unit calculates the user's environmental impact and passes the data to a generative AI model. This generative model uses algorithms to generate environmentally friendly action plans.

[0396] The generated action suggestions are sent as push notifications to the user's device from the notification unit. The content of the notifications is adjusted based on the user's emotional state, prioritizing relaxing activities and economical options.

[0397] For example, if a user is feeling stressed at work, the system might suggest, "Why not take a walk in the park during your next break and use a shared bicycle on your way back, if possible?" This suggestion takes the user's lifestyle into consideration and supports sustainable choices.

[0398] An example of a prompt for this generating AI model would be: "List the environmentally friendly options you can recommend when a user is commuting while under high stress."

[0399] In this way, the system can help users achieve a sustainable lifestyle without undue burden, while minimizing its impact on the environment.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The device uses a smartphone or smart glasses to collect data on the user's location, schedule, and the devices they are using. This input data records the user's basic lifestyle habits. The device's camera and microphone are used to analyze the user's emotional state in real time using Python's OpenCV and TensorFlow.

[0403] Step 2:

[0404] The device transmits collected lifestyle data and emotional state data to the server via HTTPS, a secure communication protocol. The output at this stage is a dataset that is stored on the server as basic data for analysis.

[0405] Step 3:

[0406] The server's data processing unit analyzes the received lifestyle and emotional state data to calculate the user's impact on the environment. This analysis uses an algorithm related to environmental impact, and the output is a numerical representation of the environmental impact.

[0407] Step 4:

[0408] The server sends a prompt to the generative AI model based on the previous analysis results, generating the most optimal and environmentally friendly action for the user. The generative AI model processes the prompt and prepares multiple executable alternatives. This output is generated as specific action options.

[0409] Step 5:

[0410] The server sends the generated action suggestions to the user's terminal via the notification unit. The terminal receives this and presents it to the user as a push notification. This notification takes into account prioritization based on the user's emotional state and provides clear information for the user to choose from.

[0411] Step 6:

[0412] After the user selects and performs a suggested action, the selection is recorded on the device and sent to the server as feedback. This allows the system to learn the user's selection patterns and further optimize future suggestions. The feedback is stored on the server as behavioral history and changes in emotion.

[0413] 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.

[0414] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0415] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0416] [Third Embodiment]

[0417] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0418] 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.

[0419] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0420] 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.

[0421] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0422] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0423] 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.

[0424] 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.

[0425] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0426] The 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.

[0427] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0428] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0429] This invention, as an "environmentally conscious behavior support system," collects behavioral data from users in their daily lives and proposes environmentally conscious behaviors. This system is configured through the cooperation of a terminal and a server.

[0430] First, with the user's consent, the device continuously collects lifestyle pattern data such as location information, appliances used, and daily schedules. The collected data is securely transmitted from the device to a server.

[0431] Next, the server analyzes this data and evaluates the impact of user behavior on the environment. At this stage, it refers to an external environmental database to calculate carbon dioxide emissions, energy consumption, and other factors. Based on the analysis results, the server uses a generative model to automatically generate appropriate environmentally conscious behavioral options for the user.

[0432] For example, the server can detect if a user uses a car for commuting and suggest using public transport or cycling to encourage a reduction in environmental impact. It can also provide advice on seasonal energy-saving methods at home.

[0433] The generated suggestions are sent to the terminal via communication means and notified to the user. The user can receive these suggestions and see the specific benefits that each option brings to the environment.

[0434] Furthermore, if the user follows the suggested options, their actions are recorded by the device and sent to the server as feedback data. This feedback is used by the system to make future suggestions more tailored to the user's individual circumstances.

[0435] This system enables users to naturally consider the environment in their daily lives. Its aim is to contribute to the preservation of the global environment by promoting sustainable behavior.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The device obtains user consent and collects location information, data on appliances being used, and schedule information in real time. This information is temporarily stored locally.

[0439] Step 2:

[0440] The device sends the collected data to the server via a secure communication method. After transmission, a backup of the data may be saved locally.

[0441] Step 3:

[0442] The server analyzes the received data and applies a model to predict user behavior patterns. This allows for the identification of energy consumption and transportation usage.

[0443] Step 4:

[0444] The server references an external environmental database to evaluate the environmental impact (such as CO2 emissions and energy consumption) related to user actions.

[0445] Step 5:

[0446] The server uses a generative model to generate environmentally conscious behavioral options based on the user's lifestyle patterns. For example, using public transportation instead of a private car.

[0447] Step 6:

[0448] The server generates options and sends them to the terminal via a communication method. Each option may also include explanations of its economic benefits and environmental advantages.

[0449] Step 7:

[0450] The device notifies the user of options and presents action suggestions in a way that is easy for the user to understand.

[0451] Step 8:

[0452] The device checks whether the user followed the suggested options and records the result. This is recorded even in cases where there is no significant change in the action taken.

[0453] Step 9:

[0454] The device sends recorded behavioral data to the server, which then uses it to improve the system's accuracy for future suggestions.

[0455] In this way, a cycle is formed that supports users' environmentally conscious behavior.

[0456] (Example 1)

[0457] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0458] In modern society, awareness of environmental issues is increasing, and the challenge is how individuals can concretely take environmentally conscious actions in their daily lives. However, previous systems lacked specific and actionable suggestions based on individual lifestyle patterns, making it difficult for users to naturally incorporate sustainable behaviors.

[0459] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0460] In this invention, the server includes information gathering means, data transmission means, data analysis means, action suggestion generation means, and user notification means. This enables users to naturally incorporate sustainable behaviors into their daily lives by automatically generating individual environmentally conscious options based on the user's lifestyle patterns and presenting specific benefits.

[0461] "Information gathering means" refers to a device or method for continuously acquiring user location information, home appliance usage status, and lifestyle pattern data such as schedules.

[0462] "Data transmission means" refers to a method or device for encrypting collected data and transmitting it to a server using a secure communication protocol.

[0463] "Data analysis means" refers to a program or device that evaluates the environmental impact of a user, such as carbon dioxide emissions, based on data received on a server.

[0464] A "behavioral suggestion generation means" is a device or program that utilizes a generation AI model to automatically generate environmentally conscious options for user behavior.

[0465] "User notification means" refers to a method or device for notifying a user terminal of action suggestions generated via a communication network.

[0466] "Feedback processing means" refers to a device or method that records the user's selection and its results, and updates the generated AI model in order to reflect these in future suggestions.

[0467] "Information display means" refers to a device or method for visually displaying to users the economic and health benefits in addition to the reduction of environmental impact.

[0468] This invention is a system that supports environmentally conscious behavior in users' daily lives. The system's main components are a terminal that collects and analyzes information, and a server that generates action suggestions based on that information.

[0469] First, the user installs a dedicated application on their device (e.g., a smartphone). This application uses the device's sensors to collect lifestyle pattern data such as location information, appliance usage data, and schedules. For example, it uses GPS to understand the user's movement and record what modes of transportation they are using.

[0470] Next, the terminal encrypts the collected data and sends it to the server using a secure communication method such as the HTTPS protocol. This process ensures data security. The server uses a programming environment such as Python to analyze the received data. The analysis includes algorithms for calculating carbon dioxide emissions and also accesses external environmental databases.

[0471] Based on the analysis results, the server automatically generates environmentally conscious behavioral suggestions using a generative AI model. The generative AI model compares the user's past behavioral data and generates suggestions that help improve their behavior. For example, based on data that "the user frequently uses a car," the server might suggest that "using public transportation reduces energy consumption."

[0472] The generated suggestions are sent to the terminal via the communication network and notified to the user. By receiving this, the user can understand how they can make concrete contributions to the environment. The notification may include specific benefits, such as, "By following this suggestion, you can reduce CO2 emissions by 20%." By inputting a prompt such as, "Suggest ways to improve commuting choices and reduce energy consumption" as an example, the generation AI model will build appropriate suggestions.

[0473] Thus, this system aims to naturally encourage users to adopt sustainable lifestyles and contribute to the preservation of the global environment.

[0474] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0475] Step 1:

[0476] Users install a dedicated application on their device and authorize the collection of location information, appliance usage data, and schedule data. The system takes data from various sensors as input and generates lifestyle pattern data as output. This data is collected in real time from the device's built-in sensors and connected smart appliances.

[0477] Step 2:

[0478] The device encrypts the collected lifestyle pattern data and sends it to the server using the HTTPS protocol. It receives collected lifestyle pattern data as input and generates secure communication data as output. Data encryption is performed using modern encryption algorithms, and the data is ready for transmission.

[0479] Step 3:

[0480] The server stores the received data in a database and performs data analysis in a Python environment. It takes the received data as input and generates analysis results as output. The server also accesses an external environmental database and runs a model to calculate carbon dioxide emissions and energy consumption. This analysis process also includes data cleaning and formatting.

[0481] Step 4:

[0482] The server generates action suggestions using a generative AI model based on the analysis results. It receives analysis results as input and creates specific action suggestions for the user as output. The generative AI model constructs optimized suggestions based on the user's behavioral patterns. These suggestions include feasible actions and expected effects.

[0483] Step 5:

[0484] The server sends the generated action suggestions to the terminal via the communication network. It receives the generated suggestions as input and generates notification data for the user's terminal as output. The user receives the notification on their terminal and can learn about specific actions to take. The notification may include specific figures or the benefits of the action.

[0485] Step 6:

[0486] When a user selects and performs a suggested action, the results are recorded on the device. The device receives the user's action selection as input and generates feedback data as output. This data, which records the actual action and its results, is sent to the server to be used in future suggestions.

[0487] Step 7:

[0488] The server receives feedback data and updates the generating AI model. It receives feedback data as input and generates improved suggestions as output. The updated model is trained to better adapt to the user's individual behavior patterns, enabling more accurate suggestions.

[0489] (Application Example 1)

[0490] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0491] Modern urban dwellers are often insensitive to the environmental impact of their daily choices. As a result, they frequently continue environmentally harmful behaviors unconsciously. Furthermore, a lack of environmentally friendly options and a failure to understand the specific effects of those choices are significant challenges. Moreover, with the growing concept of smart cities, there is a need to build systems that enable citizens to participate in and contribute to environmental protection.

[0492] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0493] In this invention, the information processing device includes means for collecting user lifestyle pattern data, means for analyzing said data and evaluating environmental impact, and data analysis means for evaluating environmental impact based on citizen behavior data and generating proposals. This makes it possible to concretely present the impact of citizens' daily behavior on the environment and provide options with less environmental impact.

[0494] A "device" is an electronic device used to collect data on a user's lifestyle patterns.

[0495] An "information processing device" is a central device in a system that analyzes collected data to evaluate the impact of user behavior on the environment.

[0496] A "generative model" is an algorithm that automatically generates environmentally friendly behavioral options.

[0497] "Information and communication means" refers to communication technologies used to notify users of generated action options.

[0498] "Data analysis methods" refer to analytical methods used to evaluate environmental impacts and generate proposals based on collected behavioral data.

[0499] A "method of presenting options" refers to a method of suggesting to users, based on analysis results, how to improve energy efficiency or utilize public transportation.

[0500] To realize this invention, the first step is to collect data on the user's daily lifestyle patterns using a device. This data includes location information, data on household appliances used, and daily schedules. This data is obtained only with the user's consent. The data collected by the device is securely transmitted to a server.

[0501] The server analyzes the received lifestyle pattern data using specialized information processing equipment and evaluates its impact on the environment. Here, the server references an external environmental database to calculate carbon dioxide emissions and energy consumption. Based on these calculations, it uses data analysis tools to pass the analysis results to a generating AI model. The generating AI model automatically generates appropriate environmentally conscious behavioral options for the user.

[0502] For example, if a user commutes by private car, the system can detect this and suggest using public transport or a bicycle. It can also advise on energy-saving methods at home based on the season and weather. The generated suggestions are sent from the server to the user's device via communication, and the user is notified. The notification includes specific figures and benefits to raise environmental awareness.

[0503] For example, if a user living in Tokyo commutes by car every day, the system calculates the CO2 emissions and suggests using public transportation as an environmentally friendly option. Furthermore, it provides specific advice on reducing electricity consumption, such as shortening the time spent using home appliances at night.

[0504] An example of a prompt to input into the generative model is: "Based on the user's travel data, suggest more environmentally friendly modes of transportation. Please provide options considering the current mode of transport and energy consumption."

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The device collects lifestyle pattern data such as the user's location, data from household appliances used, and daily schedules. The input is the user's behavioral data, and the output is the collected dataset. To collect this data, the device utilizes GPS sensors and feedback from smart devices.

[0508] Step 2:

[0509] The device securely transmits collected lifestyle pattern data to the server. The input is the dataset collected in step 1, and the output is data packets for the server to receive. This process uses communication protocols to encrypt the data and protect privacy.

[0510] Step 3:

[0511] The server analyzes the received data using an information processing device. The input is user lifestyle pattern data, and the output is an assessment of the environmental impact. The server refers to an external environmental database, calculates CO2 emissions and energy consumption, and determines the extent to which user behavior affects the environment.

[0512] Step 4:

[0513] The server generates proposals using a generative AI model based on the analysis results. The input is the environmental impact assessment results, and the output is a list of environmentally conscious behavioral options. For example, the generative model automatically generates options such as "using public transport" and "traveling by bicycle," and specifically shows how environmentally friendly each option is and to what extent.

[0514] Step 5:

[0515] The server notifies the terminal of the generated action options using information and communication means. The input is a list of options, and the output is a notification message to the user. The notification is made using the application's push notification function, and the user receives the suggestions on their terminal.

[0516] Step 6:

[0517] The user reviews the suggestions received from the server and selects an action based on them. The input is the suggestion message, and the output is the user's selected action data. The user compares the suggested options and decides on an action, considering their environmental or economic benefits.

[0518] Step 7:

[0519] The terminal sends the user's selected actions back to the server as data, which is then used as feedback data. The input is the user's action data, and the output is the feedback data recorded on the server. Based on this feedback, the server uses it to generate more suitable suggestions for future interactions.

[0520] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0521] This invention is an "environmentally conscious behavior support system" designed to transform users' daily behaviors into more environmentally conscious ones, and aims to improve the user experience in particular by incorporating emotion recognition technology. The system consists of a terminal, a server, a generative model, communication means, and an emotion engine.

[0522] First, with the user's permission, the device collects lifestyle pattern data, including location information, the electronic devices being used, and schedules. Furthermore, an emotion engine on the device analyzes the user's emotional state via the camera and microphone, and tags the data in real time.

[0523] The acquired data is sent to the server using a secure communication method. The server evaluates the lifestyle pattern data and the user's current emotional state, and generates optimal behavioral options using a generative model. Here, the user's emotional state plays a crucial role, and the suggestions are adjusted to be relatable to the user's feelings.

[0524] For example, for users experiencing stress, we would prioritize suggesting relaxing activities or easily implemented environmentally conscious actions. Specifically, this could involve suggesting, "How about relaxing at a nearby library on your way home from work today, and then walking home?"

[0525] The generated suggestions are sent to the device, and a notification is triggered. The user can review these and choose the option that best suits them. Furthermore, if there is a change in the user's feelings regarding their choice, the device feeds the new data back to the server. This feedback is reflected in future suggestions, strengthening the system's ability to continuously provide the best options for the user.

[0526] In this way, users can lead a sustainable lifestyle without undue burden and become more aware of the environmental impact of their actions. This invention will greatly contribute to supporting next-generation environmentally conscious lifestyles.

[0527] The following describes the processing flow.

[0528] Step 1:

[0529] The device collects lifestyle pattern data after confirming the user's consent. This includes location information, device usage information, schedule, etc., and is constantly updated.

[0530] Step 2:

[0531] The device's emotion engine analyzes the user's emotional state in real time. This involves analyzing facial expressions and voice tone using the camera and microphone to identify what emotions the user is experiencing.

[0532] Step 3:

[0533] The device sends lifestyle pattern data and emotional data together to the server. Communication is conducted securely, and privacy is ensured.

[0534] Step 4:

[0535] The server analyzes the received data and performs an evaluation that takes into account both the user's behavior patterns and emotional state. This allows it to determine which action is optimal for the user's current emotional state.

[0536] Step 5:

[0537] The server's generation model creates actionable options that reduce environmental impact while also considering user emotions. For example, if a user is tired, it will recommend actions that require minimal effort.

[0538] Step 6:

[0539] The server generates options, which are then adjusted to create a proposal that includes emotional and economic benefits, and this proposal is sent to the terminal via communication.

[0540] Step 7:

[0541] The device notifies the user of suggestions. The user interface is designed to be easy for the user to understand and to help them choose an action.

[0542] Step 8:

[0543] The user selects an action based on the suggestions and takes action. During this process, the device records which suggestion was chosen.

[0544] Step 9:

[0545] The device feeds back the selected action and subsequent emotional changes to the server. The server uses this information to improve the accuracy of its next suggestions.

[0546] In this way, a process is established to support users in adopting the most environmentally conscious lifestyle.

[0547] (Example 2)

[0548] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0549] In modern society, achieving sustainable lifestyles that consider the environmental impact of user behavior while also addressing individual emotional states is a challenging task. Conventional environmentally conscious systems often fail to adequately consider user emotional states, resulting in uniform suggestions and frequently lacking user satisfaction. To enable users to continue sustainable behaviors without undue burden, more personalized suggestions and feedback loops are necessary.

[0550] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0551] In this invention, the server includes information terminal means for collecting user location information, power consumption data, and schedule information; emotion analysis means for evaluating emotional states based on facial expressions and voice using a camera and microphone; and data analysis means for integrating and analyzing the user's lifestyle patterns and emotional states. This enables the server to provide behavioral options appropriate to the user's emotional state, allowing the user to maintain a comfortable and environmentally conscious lifestyle.

[0552] "Information terminal means" refers to a device or system for collecting user location information, power consumption data, and schedule information.

[0553] "Emotion analysis means" refers to technology that uses cameras and microphones to evaluate a user's emotional state from their facial expressions and voice.

[0554] "Data analysis means" refers to technology that has the function of integrating and analyzing data on users' lifestyle patterns and emotional states that has been collected.

[0555] A "generative model means" is an algorithm or model for generating optimal action options based on the user's emotional state.

[0556] "Communication means" refers to data transmission technology or protocols used to notify the user of generated action options.

[0557] A "data feedback mechanism" is a system or process that transmits changes in the user's emotions based on their behavioral choices to a server and reflects these changes in future suggestions.

[0558] A "user interface" is the system component that includes display screens and input methods that present environmental impact reduction, economic benefits, and health benefits when suggesting actions to the user.

[0559] The present invention is an environmentally conscious behavior support system for improving healthcare and environmental awareness, and specifically provides personalized suggestions based on the user's emotional state and lifestyle data. This system consists of an information terminal, an emotion analysis means, a data analysis means, a generative model means, a communication means, and a data feedback means.

[0560] Information terminals include smartphones and wearable devices, through which user location information, power consumption data, and schedule information are collected. This data is acquired in real time using sensors on each terminal.

[0561] The emotion analysis method utilizes facial recognition and voice analysis technologies. These technologies include detecting facial expressions using image processing libraries and analyzing voice tone using voice recognition libraries. This makes it possible to clearly register the user's emotional state, such as stress levels and relaxation levels.

[0562] The data analysis method uses programming languages ​​such as Python and R on the server to comprehensively evaluate lifestyle data and emotional data received from users.

[0563] The generative model employs a generative AI model, utilizing OpenAI and other common machine learning frameworks to create user-specific action options. An example of a prompt is, "Please suggest environmentally conscious actions appropriate for a user experiencing stress."

[0564] The communication method involves the bidirectional exchange of data between the server and the information terminal via the internet. The data is protected using encryption technologies such as AES and HTTPS.

[0565] The data feedback mechanism collects feedback data on the user's chosen actions and incorporates this information into subsequent suggestions. This allows the system to continuously optimize suggestions to meet the user's needs.

[0566] For example, if the system detects that a user is feeling stressed on their way home, it will notify their smartphone with a suggestion such as, "How about taking a 10-minute walk in a nearby park?" This suggestion takes the user's emotional state into consideration and encourages environmentally friendly behavior within reasonable limits.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The device acquires location information using a GPS sensor with the user's permission. It also collects power consumption data from smart devices and retrieves schedule information from scheduling applications. This information is stored in an internal database as data that constitutes the user's lifestyle pattern. Inputs include the user's current location, power consumption status, and schedule, which are then integrated and output as lifestyle data.

[0570] Step 2:

[0571] The emotion analysis system on the device activates a facial recognition system using the camera and a voice analysis system using the microphone. This evaluates the user's emotional state from their facial expressions and voice, tagging it as a state such as "relaxed" or "stressed." At this stage, the input is real-time video and audio data, and the output is emotional state tags. Specifically, it detects the user's smiles and angry expressions and quantifies those emotions.

[0572] Step 3:

[0573] The server receives lifestyle data and emotional state tags sent from the terminal. Using a data analysis tool, Python is used to integrate this data and evaluate the user's overall daily behavioral patterns and current emotional state. The input data consists of lifestyle data and emotional state, and the analysis outputs the user's lifestyle context. Specific actions include comparing past similar emotional states and behavioral patterns to plan recommended future actions.

[0574] Step 4:

[0575] The generative model on the server uses an AI model (e.g., a machine learning algorithm) based on the received data to generate the most suitable action options for the user at that moment. The prompt used is "Please suggest environmentally conscious actions suitable for a user who wants to relax." The input is the user's life context, and the output is a list of suggested actions. A specific action involves combining past successful action patterns to refine the suggestions.

[0576] Step 5:

[0577] The server sends the generated suggestions to the device. The device notifies the user of the received suggestions. For example, it might display "Try taking a 20-minute walk in a nearby quiet park" on the smartphone screen. The input is the generated action options, and the output is the message as a notification to the user. The specific action is to attract the user's attention by utilizing the device's notification function.

[0578] Step 6:

[0579] The user reviews the suggested action and chooses whether to perform it. The device collects new emotional data as a result of the choice using an emotional analysis tool and sends it back to the server. The input is the user's response to the suggestion, and the output is the updated emotional data. The specific action is to analyze the newly obtained emotional changes and store them as feedback data for future suggestions.

[0580] (Application Example 2)

[0581] Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0582] In modern society, individual lifestyles have a significant impact on the environment. To live a more sustainable life, it is necessary to propose environmentally conscious behaviors that are feasible and emotionally acceptable to users. Furthermore, these proposals must be flexible and adaptable to the user's emotional state, taking into account their physical and mental health as well as their economic interests.

[0583] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0584] In this invention, the server includes a device for understanding the user's lifestyle habits, a data processing unit for analyzing the habit information obtained from the device and calculating the user's impact on the environment, a generation model for generating environmentally friendly activity suggestions, a notification unit for communicating the activity suggestions to the user, and an emotion recognition function for evaluating the user's emotional state and making suggestions based on that emotional state. This makes it possible for the user to accept and implement sustainable lifestyle activities without difficulty, reduce their impact on the environment, and enjoy both physical and mental health and economic benefits.

[0585] A "device for understanding users' lifestyle habits" is a terminal used to collect information about users' daily activities, the devices they use, and their location.

[0586] A "data processing unit" is an information processing unit that analyzes collected lifestyle information and calculates the user's influence on their environment based on that analysis.

[0587] A "generative model" is an algorithm or system that generates optimal, environmentally friendly activity suggestions for users based on acquired data.

[0588] The "notification section" is a means of communicating the generated activity plan to the user, and typically uses digital communication methods.

[0589] The "emotion recognition function" is a feature that evaluates the user's emotional state and adjusts the suggested content based on that evaluation, collecting data via the camera and microphone.

[0590] The "environmentally conscious behavior support system" of the present invention is designed to help users live a more environmentally friendly life. This system collects and analyzes data related to the user's daily life to propose optimal action plans. Specifically, it is configured as follows:

[0591] First, the device collects data such as location information, the electronic devices used, and schedules using smartphones or smart glasses to understand the user's lifestyle. This device is equipped with Python's OpenCV and TensorFlow, and uses a camera and microphone to evaluate the user's emotional state in real time.

[0592] The collected data is sent to the server using HTTPS, a secure communication protocol. The server's data processing unit calculates the user's environmental impact and passes the data to a generative AI model. This generative model uses algorithms to generate environmentally friendly action plans.

[0593] The generated action suggestions are sent as push notifications to the user's device from the notification unit. The content of the notifications is adjusted based on the user's emotional state, prioritizing relaxing activities and economical options.

[0594] For example, if a user is feeling stressed at work, the system might suggest, "Why not take a walk in the park during your next break and use a shared bicycle on your way back, if possible?" This suggestion takes the user's lifestyle into consideration and supports sustainable choices.

[0595] An example of a prompt for this generating AI model would be: "List the environmentally friendly options you can recommend when a user is commuting while under high stress."

[0596] In this way, the system can help users achieve a sustainable lifestyle without undue burden, while minimizing its impact on the environment.

[0597] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0598] Step 1:

[0599] The device uses a smartphone or smart glasses to collect data on the user's location, schedule, and the devices they are using. This input data records the user's basic lifestyle habits. The device's camera and microphone are used to analyze the user's emotional state in real time using Python's OpenCV and TensorFlow.

[0600] Step 2:

[0601] The device transmits collected lifestyle data and emotional state data to the server via HTTPS, a secure communication protocol. The output at this stage is a dataset that is stored on the server as basic data for analysis.

[0602] Step 3:

[0603] The server's data processing unit analyzes the received lifestyle and emotional state data to calculate the user's impact on the environment. This analysis uses an algorithm related to environmental impact, and the output is a numerical representation of the environmental impact.

[0604] Step 4:

[0605] The server sends a prompt to the generative AI model based on the previous analysis results, generating the most optimal and environmentally friendly action for the user. The generative AI model processes the prompt and prepares multiple executable alternatives. This output is generated as specific action options.

[0606] Step 5:

[0607] The server sends the generated action suggestions to the user's terminal via the notification unit. The terminal receives this and presents it to the user as a push notification. This notification takes into account prioritization based on the user's emotional state and provides clear information for the user to choose from.

[0608] Step 6:

[0609] After the user selects and performs a suggested action, the selection is recorded on the device and sent to the server as feedback. This allows the system to learn the user's selection patterns and further optimize future suggestions. The feedback is stored on the server as behavioral history and changes in emotion.

[0610] 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.

[0611] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0612] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0613] [Fourth Embodiment]

[0614] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0615] 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.

[0616] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0617] 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.

[0618] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0619] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0620] 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.

[0621] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0622] 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.

[0623] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0624] The 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.

[0625] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0626] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] This invention, as an "environmentally conscious behavior support system," collects behavioral data from users in their daily lives and proposes environmentally conscious behaviors. This system is configured through the cooperation of a terminal and a server.

[0628] First, with the user's consent, the device continuously collects lifestyle pattern data such as location information, appliances used, and daily schedules. The collected data is securely transmitted from the device to a server.

[0629] Next, the server analyzes this data and evaluates the impact of user behavior on the environment. At this stage, it refers to an external environmental database to calculate carbon dioxide emissions, energy consumption, and other factors. Based on the analysis results, the server uses a generative model to automatically generate appropriate environmentally conscious behavioral options for the user.

[0630] For example, the server can detect if a user uses a car for commuting and suggest using public transport or cycling to encourage a reduction in environmental impact. It can also provide advice on seasonal energy-saving methods at home.

[0631] The generated suggestions are sent to the terminal via communication means and notified to the user. The user can receive these suggestions and see the specific benefits that each option brings to the environment.

[0632] Furthermore, if the user follows the suggested options, their actions are recorded by the device and sent to the server as feedback data. This feedback is used by the system to make future suggestions more tailored to the user's individual circumstances.

[0633] This system enables users to naturally consider the environment in their daily lives. Its aim is to contribute to the preservation of the global environment by promoting sustainable behavior.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] The device obtains user consent and collects location information, data on appliances being used, and schedule information in real time. This information is temporarily stored locally.

[0637] Step 2:

[0638] The device sends the collected data to the server via a secure communication method. After transmission, a backup of the data may be saved locally.

[0639] Step 3:

[0640] The server analyzes the received data and applies a model to predict user behavior patterns. This allows for the identification of energy consumption and transportation usage.

[0641] Step 4:

[0642] The server references an external environmental database to evaluate the environmental impact (such as CO2 emissions and energy consumption) related to user actions.

[0643] Step 5:

[0644] The server uses a generative model to generate environmentally conscious behavioral options based on the user's lifestyle patterns. For example, using public transportation instead of a private car.

[0645] Step 6:

[0646] The server generates options and sends them to the terminal via a communication method. Each option may also include explanations of its economic benefits and environmental advantages.

[0647] Step 7:

[0648] The device notifies the user of options and presents action suggestions in a way that is easy for the user to understand.

[0649] Step 8:

[0650] The device checks whether the user followed the suggested options and records the result. This is recorded even in cases where there is no significant change in the action taken.

[0651] Step 9:

[0652] The device sends recorded behavioral data to the server, which then uses it to improve the system's accuracy for future suggestions.

[0653] In this way, a cycle is formed that supports users' environmentally conscious behavior.

[0654] (Example 1)

[0655] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] In modern society, awareness of environmental issues is increasing, and the challenge is how individuals can concretely take environmentally conscious actions in their daily lives. However, previous systems lacked specific and actionable suggestions based on individual lifestyle patterns, making it difficult for users to naturally incorporate sustainable behaviors.

[0657] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0658] In this invention, the server includes information gathering means, data transmission means, data analysis means, action suggestion generation means, and user notification means. This enables users to naturally incorporate sustainable behaviors into their daily lives by automatically generating individual environmentally conscious options based on the user's lifestyle patterns and presenting specific benefits.

[0659] "Information gathering means" refers to a device or method for continuously acquiring user location information, home appliance usage status, and lifestyle pattern data such as schedules.

[0660] "Data transmission means" refers to a method or device for encrypting collected data and transmitting it to a server using a secure communication protocol.

[0661] "Data analysis means" refers to a program or device that evaluates the environmental impact of a user, such as carbon dioxide emissions, based on data received on a server.

[0662] A "behavioral suggestion generation means" is a device or program that utilizes a generation AI model to automatically generate environmentally conscious options for user behavior.

[0663] "User notification means" refers to a method or device for notifying a user terminal of action suggestions generated via a communication network.

[0664] "Feedback processing means" refers to a device or method that records the user's selection and its results, and updates the generated AI model in order to reflect these in future suggestions.

[0665] "Information display means" refers to a device or method for visually displaying to users the economic and health benefits in addition to the reduction of environmental impact.

[0666] This invention is a system that supports environmentally conscious behavior in users' daily lives. The system's main components are a terminal that collects and analyzes information, and a server that generates action suggestions based on that information.

[0667] First, the user installs a dedicated application on their device (e.g., a smartphone). This application uses the device's sensors to collect lifestyle pattern data such as location information, appliance usage data, and schedules. For example, it uses GPS to understand the user's movement and record what modes of transportation they are using.

[0668] Next, the terminal encrypts the collected data and sends it to the server using a secure communication method such as the HTTPS protocol. This process ensures data security. The server uses a programming environment such as Python to analyze the received data. The analysis includes algorithms for calculating carbon dioxide emissions and also accesses external environmental databases.

[0669] Based on the analysis results, the server automatically generates environmentally conscious behavioral suggestions using a generative AI model. The generative AI model compares the user's past behavioral data and generates suggestions that help improve their behavior. For example, based on data that "the user frequently uses a car," the server might suggest that "using public transportation reduces energy consumption."

[0670] The generated suggestions are sent to the terminal via the communication network and notified to the user. By receiving this, the user can understand how they can make concrete contributions to the environment. The notification may include specific benefits, such as, "By following this suggestion, you can reduce CO2 emissions by 20%." By inputting a prompt such as, "Suggest ways to improve commuting choices and reduce energy consumption" as an example, the generation AI model will build appropriate suggestions.

[0671] Thus, this system aims to naturally encourage users to adopt sustainable lifestyles and contribute to the preservation of the global environment.

[0672] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0673] Step 1:

[0674] Users install a dedicated application on their device and authorize the collection of location information, appliance usage data, and schedule data. The system takes data from various sensors as input and generates lifestyle pattern data as output. This data is collected in real time from the device's built-in sensors and connected smart appliances.

[0675] Step 2:

[0676] The device encrypts the collected lifestyle pattern data and sends it to the server using the HTTPS protocol. It receives collected lifestyle pattern data as input and generates secure communication data as output. Data encryption is performed using modern encryption algorithms, and the data is ready for transmission.

[0677] Step 3:

[0678] The server stores the received data in a database and performs data analysis in a Python environment. It takes the received data as input and generates analysis results as output. The server also accesses an external environmental database and runs a model to calculate carbon dioxide emissions and energy consumption. This analysis process also includes data cleaning and formatting.

[0679] Step 4:

[0680] The server generates action suggestions using a generative AI model based on the analysis results. It receives analysis results as input and creates specific action suggestions for the user as output. The generative AI model constructs optimized suggestions based on the user's behavioral patterns. These suggestions include feasible actions and expected effects.

[0681] Step 5:

[0682] The server sends the generated action suggestions to the terminal via the communication network. It receives the generated suggestions as input and generates notification data for the user's terminal as output. The user receives the notification on their terminal and can learn about specific actions to take. The notification may include specific figures or the benefits of the action.

[0683] Step 6:

[0684] When a user selects and performs a suggested action, the results are recorded on the device. The device receives the user's action selection as input and generates feedback data as output. This data, which records the actual action and its results, is sent to the server to be used in future suggestions.

[0685] Step 7:

[0686] The server receives feedback data and updates the generating AI model. It receives feedback data as input and generates improved suggestions as output. The updated model is trained to better adapt to the user's individual behavior patterns, enabling more accurate suggestions.

[0687] (Application Example 1)

[0688] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] Modern urban dwellers are often insensitive to the environmental impact of their daily choices. As a result, they frequently continue environmentally harmful behaviors unconsciously. Furthermore, a lack of environmentally friendly options and a failure to understand the specific effects of those choices are significant challenges. Moreover, with the growing concept of smart cities, there is a need to build systems that enable citizens to participate in and contribute to environmental protection.

[0690] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0691] In this invention, the information processing device includes means for collecting user lifestyle pattern data, means for analyzing said data and evaluating environmental impact, and data analysis means for evaluating environmental impact based on citizen behavior data and generating proposals. This makes it possible to concretely present the impact of citizens' daily behavior on the environment and provide options with less environmental impact.

[0692] A "device" is an electronic device used to collect data on a user's lifestyle patterns.

[0693] An "information processing device" is a central device in a system that analyzes collected data to evaluate the impact of user behavior on the environment.

[0694] A "generative model" is an algorithm that automatically generates environmentally friendly behavioral options.

[0695] "Information and communication means" refers to communication technologies used to notify users of generated action options.

[0696] "Data analysis methods" refer to analytical methods used to evaluate environmental impacts and generate proposals based on collected behavioral data.

[0697] A "method of presenting options" refers to a method of suggesting to users, based on analysis results, how to improve energy efficiency or utilize public transportation.

[0698] To realize this invention, the first step is to collect data on the user's daily lifestyle patterns using a device. This data includes location information, data on household appliances used, and daily schedules. This data is obtained only with the user's consent. The data collected by the device is securely transmitted to a server.

[0699] The server analyzes the received lifestyle pattern data using specialized information processing equipment and evaluates its impact on the environment. Here, the server references an external environmental database to calculate carbon dioxide emissions and energy consumption. Based on these calculations, it uses data analysis tools to pass the analysis results to a generating AI model. The generating AI model automatically generates appropriate environmentally conscious behavioral options for the user.

[0700] For example, if a user commutes by private car, the system can detect this and suggest using public transport or a bicycle. It can also advise on energy-saving methods at home based on the season and weather. The generated suggestions are sent from the server to the user's device via communication, and the user is notified. The notification includes specific figures and benefits to raise environmental awareness.

[0701] For example, if a user living in Tokyo commutes by car every day, the system calculates the CO2 emissions and suggests using public transportation as an environmentally friendly option. Furthermore, it provides specific advice on reducing electricity consumption, such as shortening the time spent using home appliances at night.

[0702] An example of a prompt to input into the generative model is: "Based on the user's travel data, suggest more environmentally friendly modes of transportation. Please provide options considering the current mode of transport and energy consumption."

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The device collects lifestyle pattern data such as the user's location, data from household appliances used, and daily schedules. The input is the user's behavioral data, and the output is the collected dataset. To collect this data, the device utilizes GPS sensors and feedback from smart devices.

[0706] Step 2:

[0707] The device securely transmits collected lifestyle pattern data to the server. The input is the dataset collected in step 1, and the output is data packets for the server to receive. This process uses communication protocols to encrypt the data and protect privacy.

[0708] Step 3:

[0709] The server analyzes the received data using an information processing device. The input is user lifestyle pattern data, and the output is an assessment of the environmental impact. The server refers to an external environmental database, calculates CO2 emissions and energy consumption, and determines the extent to which user behavior affects the environment.

[0710] Step 4:

[0711] The server generates proposals using a generative AI model based on the analysis results. The input is the environmental impact assessment results, and the output is a list of environmentally conscious behavioral options. For example, the generative model automatically generates options such as "using public transport" and "traveling by bicycle," and specifically shows how environmentally friendly each option is and to what extent.

[0712] Step 5:

[0713] The server notifies the terminal of the generated action options using information and communication means. The input is a list of options, and the output is a notification message to the user. The notification is made using the application's push notification function, and the user receives the suggestions on their terminal.

[0714] Step 6:

[0715] The user reviews the suggestions received from the server and selects an action based on them. The input is the suggestion message, and the output is the user's selected action data. The user compares the suggested options and decides on an action, considering their environmental or economic benefits.

[0716] Step 7:

[0717] The terminal sends the user's selected actions back to the server as data, which is then used as feedback data. The input is the user's action data, and the output is the feedback data recorded on the server. Based on this feedback, the server uses it to generate more suitable suggestions for future interactions.

[0718] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0719] This invention is an "environmentally conscious behavior support system" designed to transform users' daily behaviors into more environmentally conscious ones, and aims to improve the user experience in particular by incorporating emotion recognition technology. The system consists of a terminal, a server, a generative model, communication means, and an emotion engine.

[0720] First, with the user's permission, the device collects lifestyle pattern data, including location information, the electronic devices being used, and schedules. Furthermore, an emotion engine on the device analyzes the user's emotional state via the camera and microphone, and tags the data in real time.

[0721] The acquired data is sent to the server using a secure communication method. The server evaluates the lifestyle pattern data and the user's current emotional state, and generates optimal behavioral options using a generative model. Here, the user's emotional state plays a crucial role, and the suggestions are adjusted to be relatable to the user's feelings.

[0722] For example, for users experiencing stress, we would prioritize suggesting relaxing activities or easily implemented environmentally conscious actions. Specifically, this could involve suggesting, "How about relaxing at a nearby library on your way home from work today, and then walking home?"

[0723] The generated suggestions are sent to the device, and a notification is triggered. The user can review these and choose the option that best suits them. Furthermore, if there is a change in the user's feelings regarding their choice, the device feeds the new data back to the server. This feedback is reflected in future suggestions, strengthening the system's ability to continuously provide the best options for the user.

[0724] In this way, users can lead a sustainable lifestyle without undue burden and become more aware of the environmental impact of their actions. This invention will greatly contribute to supporting next-generation environmentally conscious lifestyles.

[0725] The following describes the processing flow.

[0726] Step 1:

[0727] The device collects lifestyle pattern data after confirming the user's consent. This includes location information, device usage information, schedule, etc., and is constantly updated.

[0728] Step 2:

[0729] The device's emotion engine analyzes the user's emotional state in real time. This involves analyzing facial expressions and voice tone using the camera and microphone to identify what emotions the user is experiencing.

[0730] Step 3:

[0731] The device sends lifestyle pattern data and emotional data together to the server. Communication is conducted securely, and privacy is ensured.

[0732] Step 4:

[0733] The server analyzes the received data and performs an evaluation that takes into account both the user's behavior patterns and emotional state. This allows it to determine which action is optimal for the user's current emotional state.

[0734] Step 5:

[0735] The server's generation model creates actionable options that reduce environmental impact while also considering user emotions. For example, if a user is tired, it will recommend actions that require minimal effort.

[0736] Step 6:

[0737] The server generates options, which are then adjusted to create a proposal that includes emotional and economic benefits, and this proposal is sent to the terminal via communication.

[0738] Step 7:

[0739] The device notifies the user of suggestions. The user interface is designed to be easy for the user to understand and to help them choose an action.

[0740] Step 8:

[0741] The user selects an action based on the suggestions and takes action. During this process, the device records which suggestion was chosen.

[0742] Step 9:

[0743] The device feeds back the selected action and subsequent emotional changes to the server. The server uses this information to improve the accuracy of its next suggestions.

[0744] In this way, a process is established to support users in adopting the most environmentally conscious lifestyle.

[0745] (Example 2)

[0746] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0747] In modern society, achieving sustainable lifestyles that consider the environmental impact of user behavior while also addressing individual emotional states is a challenging task. Conventional environmentally conscious systems often fail to adequately consider user emotional states, resulting in uniform suggestions and frequently lacking user satisfaction. To enable users to continue sustainable behaviors without undue burden, more personalized suggestions and feedback loops are necessary.

[0748] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0749] In this invention, the server includes information terminal means for collecting user location information, power consumption data, and schedule information; emotion analysis means for evaluating emotional states based on facial expressions and voice using a camera and microphone; and data analysis means for integrating and analyzing the user's lifestyle patterns and emotional states. This enables the server to provide behavioral options appropriate to the user's emotional state, allowing the user to maintain a comfortable and environmentally conscious lifestyle.

[0750] "Information terminal means" refers to a device or system for collecting user location information, power consumption data, and schedule information.

[0751] "Emotion analysis means" refers to technology that uses cameras and microphones to evaluate a user's emotional state from their facial expressions and voice.

[0752] "Data analysis means" refers to technology that has the function of integrating and analyzing data on users' lifestyle patterns and emotional states that has been collected.

[0753] A "generative model means" is an algorithm or model for generating optimal action options based on the user's emotional state.

[0754] "Communication means" refers to data transmission technology or protocols used to notify the user of generated action options.

[0755] A "data feedback mechanism" is a system or process that transmits changes in the user's emotions based on their behavioral choices to a server and reflects these changes in future suggestions.

[0756] A "user interface" is the system component that includes display screens and input methods that present environmental impact reduction, economic benefits, and health benefits when suggesting actions to the user.

[0757] The present invention is an environmentally conscious behavior support system for improving healthcare and environmental awareness, and specifically provides personalized suggestions based on the user's emotional state and lifestyle data. This system consists of an information terminal, an emotion analysis means, a data analysis means, a generative model means, a communication means, and a data feedback means.

[0758] Information terminals include smartphones and wearable devices, through which user location information, power consumption data, and schedule information are collected. This data is acquired in real time using sensors on each terminal.

[0759] The emotion analysis method utilizes facial recognition and voice analysis technologies. These technologies include detecting facial expressions using image processing libraries and analyzing voice tone using voice recognition libraries. This makes it possible to clearly register the user's emotional state, such as stress levels and relaxation levels.

[0760] The data analysis method uses programming languages ​​such as Python and R on the server to comprehensively evaluate lifestyle data and emotional data received from users.

[0761] The generative model employs a generative AI model, utilizing OpenAI and other common machine learning frameworks to create user-specific action options. An example of a prompt is, "Please suggest environmentally conscious actions appropriate for a user experiencing stress."

[0762] The communication method involves the bidirectional exchange of data between the server and the information terminal via the internet. The data is protected using encryption technologies such as AES and HTTPS.

[0763] The data feedback mechanism collects feedback data on the user's chosen actions and incorporates this information into subsequent suggestions. This allows the system to continuously optimize suggestions to meet the user's needs.

[0764] For example, if the system detects that a user is feeling stressed on their way home, it will notify their smartphone with a suggestion such as, "How about taking a 10-minute walk in a nearby park?" This suggestion takes the user's emotional state into consideration and encourages environmentally friendly behavior within reasonable limits.

[0765] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0766] Step 1:

[0767] The device acquires location information using a GPS sensor with the user's permission. It also collects power consumption data from smart devices and retrieves schedule information from scheduling applications. This information is stored in an internal database as data that constitutes the user's lifestyle pattern. Inputs include the user's current location, power consumption status, and schedule, which are then integrated and output as lifestyle data.

[0768] Step 2:

[0769] The emotion analysis system on the device activates a facial recognition system using the camera and a voice analysis system using the microphone. This evaluates the user's emotional state from their facial expressions and voice, tagging it as a state such as "relaxed" or "stressed." At this stage, the input is real-time video and audio data, and the output is emotional state tags. Specifically, it detects the user's smiles and angry expressions and quantifies those emotions.

[0770] Step 3:

[0771] The server receives lifestyle data and emotional state tags sent from the terminal. Using a data analysis tool, Python is used to integrate this data and evaluate the user's overall daily behavioral patterns and current emotional state. The input data consists of lifestyle data and emotional state, and the analysis outputs the user's lifestyle context. Specific actions include comparing past similar emotional states and behavioral patterns to plan recommended future actions.

[0772] Step 4:

[0773] The generative model on the server uses an AI model (e.g., a machine learning algorithm) based on the received data to generate the most suitable action options for the user at that moment. The prompt used is "Please suggest environmentally conscious actions suitable for a user who wants to relax." The input is the user's life context, and the output is a list of suggested actions. A specific action involves combining past successful action patterns to refine the suggestions.

[0774] Step 5:

[0775] The server sends the generated suggestions to the device. The device notifies the user of the received suggestions. For example, it might display "Try taking a 20-minute walk in a nearby quiet park" on the smartphone screen. The input is the generated action options, and the output is the message as a notification to the user. The specific action is to attract the user's attention by utilizing the device's notification function.

[0776] Step 6:

[0777] The user reviews the suggested action and chooses whether to perform it. The device collects new emotional data as a result of the choice using an emotional analysis tool and sends it back to the server. The input is the user's response to the suggestion, and the output is the updated emotional data. The specific action is to analyze the newly obtained emotional changes and store them as feedback data for future suggestions.

[0778] (Application Example 2)

[0779] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0780] In modern society, individual lifestyles have a significant impact on the environment. To live a more sustainable life, it is necessary to propose environmentally conscious behaviors that are feasible and emotionally acceptable to users. Furthermore, these proposals must be flexible and adaptable to the user's emotional state, taking into account their physical and mental health as well as their economic interests.

[0781] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0782] In this invention, the server includes a device for understanding the user's lifestyle habits, a data processing unit for analyzing the habit information obtained from the device and calculating the user's impact on the environment, a generation model for generating environmentally friendly activity suggestions, a notification unit for communicating the activity suggestions to the user, and an emotion recognition function for evaluating the user's emotional state and making suggestions based on that emotional state. This makes it possible for the user to accept and implement sustainable lifestyle activities without difficulty, reduce their impact on the environment, and enjoy both physical and mental health and economic benefits.

[0783] A "device for understanding users' lifestyle habits" is a terminal used to collect information about users' daily activities, the devices they use, and their location.

[0784] A "data processing unit" is an information processing unit that analyzes collected lifestyle information and calculates the user's influence on their environment based on that analysis.

[0785] A "generative model" is an algorithm or system that generates optimal, environmentally friendly activity suggestions for users based on acquired data.

[0786] The "notification section" is a means of communicating the generated activity plan to the user, and typically uses digital communication methods.

[0787] The "emotion recognition function" is a feature that evaluates the user's emotional state and adjusts the suggested content based on that evaluation, collecting data via the camera and microphone.

[0788] The "environmentally conscious behavior support system" of the present invention is designed to help users live a more environmentally friendly life. This system collects and analyzes data related to the user's daily life to propose optimal action plans. Specifically, it is configured as follows:

[0789] First, the device collects data such as location information, the electronic devices used, and schedules using smartphones or smart glasses to understand the user's lifestyle. This device is equipped with Python's OpenCV and TensorFlow, and uses a camera and microphone to evaluate the user's emotional state in real time.

[0790] The collected data is sent to the server using HTTPS, a secure communication protocol. The server's data processing unit calculates the user's environmental impact and passes the data to a generative AI model. This generative model uses algorithms to generate environmentally friendly action plans.

[0791] The generated action suggestions are sent as push notifications to the user's device from the notification unit. The content of the notifications is adjusted based on the user's emotional state, prioritizing relaxing activities and economical options.

[0792] For example, if a user is feeling stressed at work, the system might suggest, "Why not take a walk in the park during your next break and use a shared bicycle on your way back, if possible?" This suggestion takes the user's lifestyle into consideration and supports sustainable choices.

[0793] An example of a prompt for this generating AI model would be: "List the environmentally friendly options you can recommend when a user is commuting while under high stress."

[0794] In this way, the system can help users achieve a sustainable lifestyle without undue burden, while minimizing its impact on the environment.

[0795] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0796] Step 1:

[0797] The device uses a smartphone or smart glasses to collect data on the user's location, schedule, and the devices they are using. This input data records the user's basic lifestyle habits. The device's camera and microphone are used to analyze the user's emotional state in real time using Python's OpenCV and TensorFlow.

[0798] Step 2:

[0799] The device transmits collected lifestyle data and emotional state data to the server via HTTPS, a secure communication protocol. The output at this stage is a dataset that is stored on the server as basic data for analysis.

[0800] Step 3:

[0801] The server's data processing unit analyzes the received lifestyle and emotional state data to calculate the user's impact on the environment. This analysis uses an algorithm related to environmental impact, and the output is a numerical representation of the environmental impact.

[0802] Step 4:

[0803] The server sends a prompt to the generative AI model based on the previous analysis results, generating the most optimal and environmentally friendly action for the user. The generative AI model processes the prompt and prepares multiple executable alternatives. This output is generated as specific action options.

[0804] Step 5:

[0805] The server sends the generated action suggestions to the user's terminal via the notification unit. The terminal receives this and presents it to the user as a push notification. This notification takes into account prioritization based on the user's emotional state and provides clear information for the user to choose from.

[0806] Step 6:

[0807] After the user selects and performs a suggested action, the selection is recorded on the device and sent to the server as feedback. This allows the system to learn the user's selection patterns and further optimize future suggestions. The feedback is stored on the server as behavioral history and changes in emotion.

[0808] 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.

[0809] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0810] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0811] 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.

[0812] Figure 9 shows an 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.

[0813] 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.

[0814] 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.

[0815] 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, motorcycles, etc., 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, for example, based 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.

[0816] 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."

[0817] 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.

[0818] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0819] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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 the like 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.

[0828] 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.

[0829] The following is further disclosed regarding the embodiments described above.

[0830] (Claim 1)

[0831] A terminal that collects user lifestyle pattern data,

[0832] A server that analyzes the data and evaluates the user's environmental impact,

[0833] A generative model that generates environmentally friendly behavioral options,

[0834] A means of communication for notifying the user of the options,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, comprising means for updating data to reflect user behavior as feedback.

[0838] (Claim 3)

[0839] The system according to claim 1, comprising a user interface that, in notifying the user, presents not only the reduction of environmental impact but also the economic and health benefits.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of continuously collecting information such as the user's location information, home appliance usage, and lifestyle pattern data including schedules,

[0843] A data transmission means that encrypts the data and sends it to a server using a secure communication protocol,

[0844] A data analysis means for analyzing the data and evaluating environmental impacts, including carbon dioxide emissions,

[0845] A means for generating behavioral suggestions that automatically generates environmentally conscious options for user behavior using a generative AI model,

[0846] A user notification means that notifies the user terminal of the selection via a communication network,

[0847] A system that includes this.

[0848] (Claim 2)

[0849] The system according to claim 1, further comprising a feedback processing means for recording the user's selection and its results, and updating the generated AI model to reflect these in the next suggestion.

[0850] (Claim 3)

[0851] The system according to claim 1, further comprising information display means for visually displaying economic benefits and health benefits in addition to the reduction of environmental impact when notifying users.

[0852] "Application Example 1"

[0853] (Claim 1)

[0854] A terminal that collects user lifestyle pattern data,

[0855] An information processing device that analyzes the data and evaluates the user's environmental impact,

[0856] A generative model that generates environmentally friendly behavioral options,

[0857] Information and communication means for notifying the user of the options,

[0858] A data analysis method that evaluates environmental impacts and generates proposals based on citizens' behavioral data,

[0859] A means of presenting options that suggest energy consumption and the use of public transportation,

[0860] A system that includes this.

[0861] (Claim 2)

[0862] The system according to claim 1, comprising means for updating data to reflect user behavior as feedback.

[0863] (Claim 3)

[0864] The system according to claim 1, comprising a user display that, in notifications to the user, presents not only the reduction of environmental impact but also the economic and health benefits, with the aim of raising citizens' environmental awareness.

[0865] "Example 2 of combining an emotion engine"

[0866] (Claim 1)

[0867] An information terminal means for collecting user location information, power consumption data, and schedule information,

[0868] An emotion analysis means that uses a camera and microphone to evaluate emotional states based on facial expressions and voice,

[0869] A data analysis method that integrates and analyzes the user's lifestyle patterns and emotional state,

[0870] A generative model means for generating behavioral options appropriate to the user's emotional state,

[0871] A means of communication for notifying the user of the options,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising a data feedback means that sends emotional feedback based on the user's selected actions to a server and reflects this in future suggestions.

[0875] (Claim 3)

[0876] The system according to claim 1, comprising a user interface for simultaneously presenting environmental impact reduction, economic and health benefits when suggesting actions to the user.

[0877] "Application example 2 when combining with an emotional engine"

[0878] (Claim 1)

[0879] A device that understands the user's lifestyle habits,

[0880] A data processing unit analyzes information about habits obtained from the device to calculate the user's influence on the environment,

[0881] A generative model for generating environmentally friendly activity plans,

[0882] A notification unit that communicates the proposed activity to the user,

[0883] An emotion recognition function that evaluates the user's emotional state and makes suggestions based on that emotional state,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising a data update unit for reflecting user activity feedback.

[0887] (Claim 3)

[0888] The system according to claim 1, comprising a user interface that includes not only the reduction of environmental impact but also economic and health benefits in notifications to users. [Explanation of symbols]

[0889] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal that collects user lifestyle pattern data, An information processing device that analyzes the data and evaluates the user's environmental impact, A generative model that generates environmentally friendly behavioral options, Information and communication means for notifying the user of the options, A data analysis method that evaluates environmental impacts and generates proposals based on citizens' behavioral data, A means of presenting options that suggest energy consumption and the use of public transportation, A system that includes this.

2. The system according to claim 1, further comprising a data update means for reflecting user behavior as feedback.

3. The system according to claim 1, comprising a user display that, in notifications to the user, presents not only the reduction of environmental impact but also the economic and health benefits, with the aim of raising citizens' environmental awareness.

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