System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting operational status and weather conditions of each communication device; generative model means for estimating efficient power consumption based on the operational status and weather conditions; and means for controlling power consumption of a communication device and means for performing power transfer to a renewable energy source based on an estimation result of the generative model means.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern communications infrastructure, especially with the spread of 5G, power consumption by communications devices is rapidly increasing. This has raised concerns about rising energy costs and environmental impact. Furthermore, while the use of renewable energy is increasing, its effective management and optimization of power supply are difficult challenges. Conventional systems lack means to efficiently solve these problems. The present invention addresses these challenges by providing a system that improves the efficiency of power consumption by communications devices and optimizes the use of renewable energy. [Means for solving the problem]

[0005] The present invention includes a means for collecting the operating status and weather conditions of each communication device, thereby enabling accurate data to be obtained in real time. It also employs a generative model means for estimating efficient power consumption based on the operating status and weather conditions. Furthermore, it includes a means for controlling the power consumption of the communication device and switching the power source to a renewable energy source based on the estimation results of the generative model means. Additionally, it incorporates a means for updating the generative model means based on the power supply and demand situation and a means for correcting the generative model means through feedback, thereby achieving continuous optimization of the system. This makes it possible to increase the energy efficiency of communication devices and reduce environmental impact.

[0006] A "communication device" is a device that communicates data over a wireless network and manages specific operating conditions and power consumption.

[0007] "Operational status" refers to information about the current operating state and performance of a communication device, including, for example, communication volume and operating time.

[0008] "Weather conditions" refers to environmental data obtained from outside, such as weather, temperature, solar radiation, and wind speed, and indicates factors that affect the energy consumption of communications devices.

[0009] "Generative model means" refers to an AI-based algorithm and its implementation device that uses collected data to estimate optimal power consumption and power switching timing.

[0010] "Power consumption" refers to the amount of power consumed by a communication device when it is operating, and is a key indicator for which efficiency is required.

[0011] "Renewable energy sources" refers to devices that generate electricity using natural energy, such as solar power generation and wind power generation, and their supply systems.

[0012] "Electricity supply and demand situation" refers to information on the current balance between electricity supply and demand, and energy management is optimized based on this information.

[0013] "Feedback" refers to the information and process used to evaluate the results of a system's operations and reflect them in subsequent operations.

[0014] "Power source switching" refers to the operation of changing the power supply source used by a communication device, and includes, for example, switching from renewable energy to commercial power. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. The following describes specific embodiments of the present invention.

[0037] System Overview

[0038] The system of the present invention collects information on the operating status and weather conditions of each communication device, and then uses a generative AI model to calculate the optimal power consumption and power switching timing. The specific components and their operation are as follows:

[0039] 1. Data Collection Methods

[0040] The server collects operational status data from each communication device (terminal) in real time, including communication volume, operating time, temperature, etc.

[0041] The server obtains weather conditions from external weather data providers and identifies relevant weather data based on the location information of the communication device.

[0042] 2. Generative AI Model Means

[0043] The server inputs the collected operational status data and weather data into a generative AI model, which uses past data and experience to predict the optimal power consumption and timing for renewable energy use for each communication device.

[0044] 3. Power management measures

[0045] The server analyzes the prediction results of the generative AI model and, based on that, sends instructions to each communication device to control power consumption.

[0046] Based on instructions received from the server, the terminal (communication device) adjusts power consumption in real time and switches power to renewable energy sources as needed.

[0047] 4. Feedback methods

[0048] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model based on the collected feedback, improving the system's prediction accuracy.

[0049] Specific examples

[0050] For example, consider a situation where communication device A needs to handle more data traffic than its normal utilization rate in the afternoon. In this case, the server operates as follows:

[0051] 1. The server collects real-time operational status data and related weather data sent from communication device A.

[0052] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and timing for renewable energy use for communication device A between 2:00 PM and 4:00 PM.

[0053] 3. Based on the prediction results of the generative AI model, the server sends an instruction to communication device A to "reduce current power consumption by 30% and switch to solar power generation from 2:30 pm."

[0054] 4. Communication device A adjusts its power consumption in real time according to instructions from the server and switches to renewable energy at the specified time.

[0055] 5. Users monitor the system's performance and provide real-time feedback as needed, which the server uses to update the generative AI model and further improve the accuracy of future predictions.

[0056] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reduced environmental impact.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] The server acquires operational status data from each communication device (terminal) in real time. Specifically, it periodically collects data such as the amount of communication traffic, operating time, and temperature sent from the communication device.

[0060] Step 2:

[0061] The server retrieves weather data from external weather data providers, including data on current weather, temperature, solar radiation, wind speed, etc., and further identifies weather conditions associated with each communication device in association with the communication device's location information.

[0062] Step 3:

[0063] The server preprocesses the operational status data collected in step 1 and the weather data acquired in step 2, preparing them for input into the generative AI model. This preprocessing includes imputing missing values ​​and standardizing the data.

[0064] Step 4:

[0065] The server inputs the preprocessed data into a generative AI model that predicts the optimal power consumption and renewable energy usage timing for each communication device. The generative AI model makes these predictions based on past data and experience.

[0066] Step 5:

[0067] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions for each communication device, such as "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0068] Step 6:

[0069] The server transmits the generated operation instructions to each communication device, and upon receiving the instructions, the communication device adjusts its power consumption in real time based on the instructions.

[0070] Step 7:

[0071] The terminal (communication device) will switch its power source to renewable energy sources as needed, for example, by following instructions to prioritize the use of electricity from solar or wind power.

[0072] Step 8:

[0073] Users can monitor the system's operation and check performance data, such as availability, power consumption, and renewable energy usage.

[0074] Step 9:

[0075] If the user determines that the system's behavior or predictions are inappropriate or that there is room for improvement, the user provides feedback to the server, including specific problems and suggestions for improvement.

[0076] Step 10:

[0077] The server collects user feedback and performance data, and continuously learns and updates the generative AI model based on that data, thereby improving the accuracy of the next prediction.

[0078] Example 1

[0079] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0080] Currently, there is a demand for optimizing the power consumption of communication devices and efficiently using renewable energy. However, existing systems have difficulty managing power consumption while fully considering the operating status of communication devices and weather conditions. In addition, there is a lack of a mechanism to incorporate user feedback and update the AI ​​model, which makes it difficult to improve prediction accuracy.

[0081] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0082] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for the communication device to adjust power consumption in real time based on the generative AI model, and means for updating the generative AI model based on feedback from users. This allows for efficient management of power consumption of the communication devices and optimal utilization of renewable energy.

[0083] A "communication device" is an electronic device for transmitting and receiving data.

[0084] "Operational status" is data indicating the current operating state and performance indicators of a communication device.

[0085] "Weather conditions" refers to environmental data such as the weather and temperature of the area where the communication device is installed.

[0086] A "generative model implementation" is an algorithm and software for estimating optimal power consumption based on collected data.

[0087] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0088] "Renewable energy sources" are sustainable energy sources obtained from nature, such as solar power and wind power.

[0089] A "generative AI model" is an artificial intelligence model that predicts optimal power consumption for communication devices based on past data and experience.

[0090] "Real time" refers to a time period in which data processing and responses occur almost immediately.

[0091] "Feedback" refers to opinions and suggestions for corrections from users regarding the results of system operations.

[0092] "Update" means improving current models and systems based on new data and feedback.

[0093] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. An embodiment of the present invention will now be described in detail.

[0094] This system consists of a server, terminals (communications devices), and users. The server collects information on the operating status of each communications device and external weather conditions in real time, and calculates the optimal amount of electricity consumption and the timing of renewable energy usage based on this information. This is done using a generative AI model. Specifically, the generative AI model, built using TensorFlow, is trained and inference processed on AWS SageMaker.

[0095] Data collection

[0096] The server collects operational status data such as communication volume, operating time, and temperature from each communication device. The server also obtains weather data provided in JSON format from an external weather data provider and identifies relevant weather data based on the location information of the communication device. This data collection is performed using an automated process using AWS Lambda, and the collected data is stored in Amazon S3.

[0097] Using generative AI models

[0098] The server inputs the collected operational status data and weather data into a generative AI model. This generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on past data and experience. To implement the model, TensorFlow is used to build it, and AWS SageMaker is used to train and infer the model.

[0099] Generating Power Management Instructions

[0100] Based on the prediction results of the generative AI model, the server generates specific instructions for the communication device to control power consumption, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." These instructions are sent to the communication device using the MQTT protocol.

[0101] Power consumption regulation

[0102] The communication device adjusts power consumption in real time based on instructions from the server, and switches power to renewable energy sources at the set time. This is done using a general-purpose computer as an edge computing device, and transmits control instructions via Azure IoT Hub.

[0103] Feedback and Model Updates

[0104] Users monitor the system's operation status and provide feedback in real time. Using a dedicated monitoring application, they send the necessary feedback to the server based on the system's operation results. The server collects the user feedback using Google Cloud Pub / Sub and analyzes it using BigQuery. The generative AI model is then retrained based on the analysis results to improve the accuracy of the next prediction.

[0105] Specific examples

[0106] Here is an example prompt:

[0107] "Please obtain the current traffic volume and weather data of communication device A in real time."

[0108] "Based on the collected data, please predict the optimal power consumption and renewable energy usage timing between 2:00 PM and 4:00 PM."

[0109] "Based on the predictions of the generative AI model, please instruct communication device A to reduce power consumption by 30% and switch to solar power generation starting at 2:30 PM."

[0110] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0111] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0112] Step 1: Data collection

[0113] The server collects operational status data of each communication device and external weather data in real time. The inputs are operational status data (communication volume, operating time, temperature, etc.) sent from the communication device and weather information obtained from a weather data provider. The server automates this data collection using AWS Lambda and stores the collected data in Amazon S3. The output is well-formatted operational status data and weather data.

[0114] Step 2: Data Shaping

[0115] The server converts the collected operational status data and weather data into a format that can be input to the generative AI model. The input at this time is the raw data collected in step 1. Data reformatting involves imputing missing values, normalizing values, and encoding categorical data. The output is a dataset in a format that is compatible with the generative AI model.

[0116] Step 3: Input to the generative AI model

[0117] The server inputs the formatted dataset into the generative AI model to predict optimal power consumption and the timing of renewable energy usage. The input at this time is the dataset formatted in step 2. The output is the predicted optimal power consumption and timing of energy usage for each communication device. The server runs the generative AI model built with TensorFlow on AWS SageMaker.

[0118] Step 4: Generate Power Management Directives

[0119] The server generates specific power management instructions based on the prediction results of the generative AI model. The input at this time is the prediction results obtained in step 3. The server analyzes the prediction results and generates specific instructions for each communication device, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." The output is the power management instructions sent to the terminal.

[0120] Step 5: Sending instructions

[0121] The server sends the generated power management instructions to each communication device. The input at this time is the power management instruction generated in step 4. The server uses the MQTT protocol to send the power management instructions to the communication devices in real time. The output is the power management instruction received by the communication devices.

[0122] Step 6: Adjusting power consumption

[0123] The terminal (communication device) adjusts its power consumption in real time based on instructions received from the server. The input at this time is the power management instruction received in step 5. The terminal switches its power source to a renewable energy source (e.g., solar power generation) at the set time. The output is the adjusted power consumption state.

[0124] Step 7: Submit your feedback

[0125] The user monitors the system's operation status and provides real-time feedback to the server. The input is the user's observations and evaluations. The user checks the system's operation results using a dedicated monitoring application and enters the necessary feedback. The output is feedback data sent to the server.

[0126] Step 8: Update the generative AI model

[0127] The server receives feedback data from users and updates the generative AI model. The input is the feedback data collected in step 7. The server collects the feedback using Google Cloud Pub / Sub and analyzes it with BigQuery. The server retrains the generative AI model based on the analysis results to improve prediction accuracy. The output is an updated generative AI model.

[0128] (Application example 1)

[0129] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0130] In recent years, energy issues and increasing environmental impacts have become social challenges. In particular, physical stores operate a variety of electrical devices, and efficient management of their energy consumption is essential for achieving sustainable operations. However, conventional systems have difficulty optimizing power consumption in real time or appropriately controlling the timing of renewable energy usage. Furthermore, energy management methods within physical stores based on weather conditions and operating status are immature, making it difficult to effectively improve energy efficiency. New methods are needed to solve these problems and achieve energy conservation and sustainability in physical stores.

[0131] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0132] In this invention, the server includes means for collecting the operating status and weather conditions of each electronic device, a generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for controlling the power consumption of the electronic devices based on the estimation results of the generative model means and means for switching power to a renewable energy source, and means for optimizing the energy consumption and usage timing of various devices using in-store operating status data and external weather data. This enables real-time optimization of energy consumption and effective use of renewable energy in physical stores.

[0133] "Electronic equipment" is a general term for all devices and equipment operating within a store, including, for example, lighting, heating and cooling, POS systems, and security cameras.

[0134] "Operating status" refers to data that indicates how frequently and at what output an electronic device is operating, and specifically includes operating time, power consumption, frequency of use, and the like.

[0135] "Weather conditions" refers to data related to the weather in the external environment, and specifically includes temperature, humidity, wind speed, rainfall, and the like.

[0136] "Means for collection" refers to systems and methods for collecting data on the operating status of electronic devices and weather conditions using various sensors and data acquisition devices.

[0137] "Generative modeling tools" refer to machine learning models and AI systems that use collected data to predict optimal electricity consumption and the timing of renewable energy use.

[0138] "Estimation results" refers to the predicted data and analysis results obtained by the generative model means, and specifically includes energy consumption and the scheduled time of use of renewable energy.

[0139] "Renewable energy sources" refers to sustainable energy sources such as solar, wind, and hydroelectric power.

[0140] "Means for performing power source switching" refers to a control device or system for automatically switching between renewable energy sources and conventional power sources as needed.

[0141] "Optimization measures" refers to the means and methods for efficiently adjusting the operation of each electronic device and minimizing energy consumption based on collected data and estimation results.

[0142] In order to carry out the present invention, an embodiment including the following system configuration and operation procedure is adopted.

[0143] System Configuration

[0144] 1. Server:

[0145] Data collection method: The server collects real-time operational status data of each electronic device in the physical store (lighting, heating and cooling, POS system, security camera), obtains weather conditions from an external weather data provider, and identifies relevant weather data based on the store's location information.

[0146] Generative model means: The server inputs the collected operational status data and weather data into a generative AI model, which is then used to predict optimal power consumption and the timing of renewable energy use.

[0147] Power management: The server analyzes the results of the generative AI model's estimations and sends instructions to each electronic device to control its energy consumption. It also switches the power source to renewable energy sources as needed.

[0148] 2. Terminals (electronic devices):

[0149] Based on instructions received from the server, each electronic device adjusts its power consumption in real time and switches to renewable energy at the specified times.

[0150] 3. User:

[0151] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model to improve prediction accuracy.

[0152] Processing Description

[0153] 1. Data collection methods:

[0154] The server uses sensors and data collection devices to collect data on the operating status of each electronic device in the physical store as well as external weather data. For example, the store's lighting has an illuminance sensor, the heating and cooling has a temperature sensor, the POS system has transaction data, and the security camera has operating time data.

[0155] 2. Generative modeling methods:

[0156] The server inputs the collected operational status data and weather data into a generative AI model, which predicts optimal energy usage and the timing of renewable energy use based on past data and experience. The generative AI model uses deep learning platforms such as Tensorflow and PyTorch.

[0157] 3. Power management measures:

[0158] The server sends instructions to each electronic device to adjust its energy consumption based on the predictions of the generative AI model, and also sends control signals to indicate when it needs to switch to renewable energy sources, allowing the electronic device to consume power efficiently and maximize the use of sustainable energy.

[0159] Specific examples

[0160] Consider a situation where the electronics in a brick-and-mortar store exceed their normal utilization rate one afternoon. In this case, the server operates as follows:

[0161] 1. The server collects real-time operating status data sent from electronic devices (e.g., lighting 75%, heating / cooling 60%, POS system 7%, security camera 3%) and related weather data (e.g., temperature 30°C, humidity 60%).

[0162] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and renewable energy usage timing for electronic devices between 2:00 PM and 4:00 PM.

[0163] 3. Based on the prediction results of the generative AI model, the server sends instructions to each electronic device to "adjust lighting consumption to 50%, heating and cooling consumption to 40%, and switch to solar power generation from 2:30 pm."

[0164] 4. Electronic devices adjust their power consumption in real time according to instructions from the server and switch to renewable energy at the specified times.

[0165] Example prompt for a generative AI model:

[0166] tokyo_store_lighting=75, hvac=60, pos_system=7, security_camera=3, temperature=30, humidity=60

[0167] In this way, the system of the present invention effectively manages energy consumption in physical stores and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reducing environmental impact.

[0168] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0169] Step 1:

[0170] The server collects operational status data from electronic devices in the physical store. Specifically, the server obtains real-time operational status data from various sensors and data acquisition devices on lighting, heating and cooling, POS systems, security cameras, etc. This operational status data includes each device's operating time, power consumption, frequency of use, etc. The input data is the operating status of each device, and the output is storage of this data in a database.

[0171] Step 2:

[0172] The server obtains weather condition data from an external weather data provider. Using the store's location information, the server obtains current weather data (e.g., temperature, humidity, wind speed, and rainfall) in real time from the weather provider's API. The input data is location information, and the output is weather data. This weather data is also stored in the database, just like the operating status data.

[0173] Step 3:

[0174] The server inputs the collected operational status data and weather data into the generative AI model. The generative AI model predicts optimal energy consumption and the timing for using renewable energy based on past data and experience. The input data are operational status data and weather data, and the output is predicted data for the estimated optimal energy consumption and the timing for using renewable energy.

[0175] Step 4:

[0176] The server analyzes the estimation results of the generative AI model and sends instructions to the electronic devices to adjust their power consumption. For example, it sends specific instructions such as "Adjust lighting consumption to 50% and heating / cooling consumption to 40% between 2:00 PM and 4:00 PM, and switch to solar power generation from 2:30 PM." The input data is the estimation results of the generative AI model, and the output is a control signal to the electronic devices.

[0177] Step 5:

[0178] The terminal (electronic device) adjusts its power consumption in real time based on instructions received from the server. Specifically, the electronic device controls power consumption according to instructions from the server and switches to renewable energy at the specified time. The input data is a control signal from the server, and the output is the adjusted power consumption and the switch to renewable energy.

[0179] Step 6:

[0180] Users monitor the system's operation and provide real-time feedback. The user's feedback is sent to the server, which then updates the generative AI model based on this feedback to further improve the accuracy of the next prediction. The input data is user feedback, and the output is the updated generative AI model.

[0181] Through these steps, the system of the present invention effectively manages energy consumption in physical stores and optimizes the use of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0182] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0183] The present invention is a system for improving the efficiency of power consumption in communication devices and achieving optimal utilization of renewable energy. Furthermore, by combining this system with an emotion engine that recognizes user emotions, it enables optimal energy management based on the user's emotions. Specific embodiments of the present invention are described below.

[0184] System Overview

[0185] The system of the present invention consists of the following main components:

[0186] 1. Data Collection Methods

[0187] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[0188] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[0189] 2. Generative AI Model Means

[0190] The server inputs the collected operational status data and weather data into a generative AI model, which then predicts optimal power consumption and the timing of renewable energy use.

[0191] 3. Power management measures

[0192] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[0193] 4. Emotion Engine

[0194] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0195] 5. Feedback channels

[0196] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[0197] Specific examples

[0198] For example, consider a situation where communication device A must process more data than its normal utilization rate in the afternoon of a certain day. The processing flow in this case is as follows:

[0199] 1. Collecting operational status data

[0200] The server acquires real-time operational status data transmitted from communication device A. The operational status data includes communication volume, operating time, and temperature.

[0201] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of communication device A.

[0202] 2. Using generative AI models

[0203] The server inputs the operational status data, weather data, and user emotion data obtained from the emotion engine into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy use.

[0204] 3. Sending operation instructions

[0205] The server analyzes the prediction results obtained from the generative AI model and sends instructions to communication device A to "reduce power consumption by 30% between 2:00 p.m. and 4:00 p.m. and switch to solar power generation."

[0206] The terminal (communication device A) adjusts power consumption in real time based on instructions from the server and switches to renewable energy at the specified time.

[0207] 4. Providing Feedback

[0208] The user monitors the operation results of communication device A and their own emotional data, for example, to check whether power consumption has been reduced as predicted and whether renewable energy has been used appropriately.

[0209] Users can provide feedback to the server as needed, which the server uses to update the generative AI model and improve the accuracy of the next prediction.

[0210] In this way, the system of the present invention, which combines an emotion engine, can improve the efficiency of power consumption in communication devices and optimize the use of renewable energy. Furthermore, by taking into account the user's emotion data, it becomes possible to manage energy in a way that takes into account the user's comfort and stress level.

[0211] The processing flow will be explained below.

[0212] Step 1:

[0213] The server collects operational status data from each communication device (terminal) in real time. Specifically, it periodically acquires data such as communication volume, operating time, and temperature, and centralizes the collected data.

[0214] Step 2:

[0215] The server retrieves the latest weather conditions from external weather data providers, including information on weather, temperature, solar radiation, wind speed, etc. It then matches the location information of the communication devices to identify the weather data relevant to each device.

[0216] Step 3:

[0217] The server collects emotional data through an emotion engine that recognizes the user's emotions. The emotional data is acquired in real time from the user's facial expressions and voice. The emotion engine quantifies the emotional data and indicates the user's stress level and comfort level.

[0218] Step 4:

[0219] The server inputs the collected operational status data, weather data, and emotion data into the generative AI model, performing preprocessing on the data, such as normalizing it and filling in missing values.

[0220] Step 5:

[0221] The generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on the given data. The generative AI model derives efficient energy management from past data and learning results.

[0222] Step 6:

[0223] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions based on them, such as "reduce electricity consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0224] Step 7:

[0225] The server then sends operational instructions based on the analysis results to each communication device, which then adjusts their power consumption in real time based on these instructions.

[0226] Step 8:

[0227] The terminal (communication device) can switch its power source to a renewable energy source as needed, for example, by setting it to prioritize the use of electricity from solar power or wind power.

[0228] Step 9:

[0229] The user monitors the results of the system's operation and also checks their own emotional data to assess how comfortable or stressed they feel.

[0230] Step 10:

[0231] Users provide feedback on the system's behavior as needed. The server uses this feedback to continuously learn and update the generative AI model, improving prediction accuracy.

[0232] Example 2

[0233] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0234] The power consumption of communication devices is increasing year by year, necessitating efficient power management. However, conventional methods do not adequately optimize the timing of renewable energy usage or power consumption. Furthermore, power management does not take into account the user's comfort and stress level. Therefore, a system that reduces power waste and realizes optimal use of renewable energy is needed.

[0235] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0236] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, a generative artificial intelligence model means for estimating efficient power consumption based on the operating status, weather conditions, and user emotion data, means for controlling the power consumption of the communication device based on the estimation result of the generative artificial intelligence model means and means for switching power to a renewable energy source, emotion recognition means for generating user emotion data, and means for updating the generative artificial intelligence model means based on performance data and emotion data. This makes it possible to improve the efficiency of power consumption of the communication device, optimally utilize renewable energy, and enable energy management that takes user comfort and stress levels into consideration.

[0237] A "communication device" is a device that can be connected to a network and send and receive data. Examples include smartphones, tablets, and personal computers.

[0238] "Operational status data" refers to data that indicates information related to the operation of a communication device. Specifically, this includes information such as communication volume, operating time, and temperature.

[0239] "Weather conditions" refers to data that indicates information about the weather at a particular location, such as temperature, humidity, wind speed, and air pressure.

[0240] A "generative AI model" is a machine learning model that makes predictions and optimizations based on collected data. It refers to a model built using artificial intelligence technology.

[0241] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0242] "Renewable energy sources" refers to energy sources that provide environmentally friendly energy, such as solar, wind, and hydropower.

[0243] "Emotion recognition means" refers to a technical means for analyzing a user's emotions and generating emotion data. For example, it includes technology that analyzes a user's facial expressions and voice using a camera or microphone.

[0244] "Performance data" refers to data that records the past operation results of the system. Specifically, it includes information such as power consumption and renewable energy usage.

[0245] "Feedback data" is data that records user evaluations and opinions on the behavior of the system. It is used to improve and optimize the system.

[0246] "Generative AI model means" refers to a technical means for estimating efficient power consumption using a generative AI model, utilizing machine learning algorithms and computational resources.

[0247] System Program Overview

[0248] This invention is a system that improves the efficiency of power consumption in communication devices and realizes optimal utilization of renewable energy. Furthermore, by combining it with an emotion engine that recognizes user emotions, optimal energy management based on the user's emotions is possible. Specific embodiments of the invention are described below.

[0249] Hardware and software used

[0250] Server: Collects data, runs generative AI models, sends instructions to control power consumption, generates emotion data, and processes feedback data.

[0251] Software used: TensorFlow, PyTorch, API communication module

[0252] Terminal (communication device): Sends operational status data of the communication device to the server and executes power consumption control instructions.

[0253] Example: Smartphone, tablet, PC

[0254] Users: Monitor the system's behavior and provide feedback.

[0255] Hardware used: Camera, microphone

[0256] Program processing

[0257] Data collection

[0258] The server obtains real-time operational status data from each communication device (terminal), including communication volume, operating time, temperature, etc. The server also obtains weather data such as temperature, humidity, wind speed, and air pressure from weather data providers via API, and identifies relevant weather data based on the location information of the communication device.

[0259] Predictions from generative AI models

[0260] The server inputs collected operational status data, weather data, and user sentiment data into a generative AI model built with TensorFlow and PyTorch to predict optimal power consumption and the timing of renewable energy use.

[0261] Controlling Power Consumption

[0262] The server analyzes the prediction results of the generative AI model and sends specific operational instructions to each communication device. The terminal (communication device) adjusts its power consumption in real time based on instructions from the server and switches to renewable energy at the specified timing.

[0263] Use of emotion engine

[0264] The server recognizes the user's emotions and generates emotional data based on them. This data, which indicates the user's stress level and comfort level, is collected using a camera and microphone. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0265] Providing feedback

[0266] The user monitors the system's operating status and their own emotional data. They check the system's operating results (for example, the reduction in power consumption of communication devices or the use of renewable energy) and provide feedback to the server as needed. The server then updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[0267] Specific examples

[0268] For example, suppose that one afternoon, communication device A needs to handle more data traffic than its normal utilization rate. In this case, the process flow is as follows:

[0269] 1. Collecting operational status data

[0270] The server acquires real-time operating status data (communication volume, operating time, temperature) sent from communication device A. The server also acquires weather conditions (temperature, humidity, wind speed) via API and identifies related weather data based on the location information of communication device A.

[0271] 2. Using generative AI models

[0272] The server inputs operational status data, weather data, and user emotion data into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy usage.

[0273] 3. Sending operation instructions

[0274] The server analyzes the prediction results obtained from the generative AI model and sends an instruction to communication device A to "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation." Communication device A adjusts its power consumption based on this instruction and switches to renewable energy at the specified time.

[0275] 4. Providing Feedback

[0276] The user monitors the operation results of communication device A and their own emotional data. For example, they check whether power consumption was reduced as predicted or whether renewable energy was used appropriately, and provide feedback to the server. The server then updates the generative AI model based on this information to improve the accuracy of the next prediction.

[0277] Prompt Sentence Examples

[0278] "Communication device A is expected to operate at a higher than usual rate between 2:00 and 4:00 PM. Please predict the optimal power consumption and timing for using renewable energy during this time. Current operation status data includes communication volume, operation time, and temperature, while weather data includes temperature, humidity, and wind speed. Also, please take into account user emotion data."

[0279] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0280] Step 1: Collect data

[0281] The server obtains real-time operational status data from each communication device (terminal). The operational status data includes communication volume, operating time, temperature, etc. The server also obtains current weather conditions (temperature, humidity, wind speed, air pressure, etc.) from a weather data provider via an API and identifies relevant weather data based on the location information of the communication device. In this step, the operational status data sent from each communication device and the weather data obtained from the weather data provider are input, and the server outputs a database that manages all of this data collectively.

[0282] Specific behavior:

[0283] The server uses an API to obtain real-time data on communication volume, operating time, and temperature from the communication device.

[0284] The server calls the weather data provider's API to obtain temperature, humidity, wind speed, and air pressure, and identifies data that matches the location information of the communication device.

[0285] Step 2: Generative AI model prediction

[0286] The server inputs the operational status data, weather data, and user emotion data collected in step 1 into a generative AI model. The generative AI model is executed using a machine learning framework (e.g., TensorFlow or PyTorch) on the server. This model predicts the optimal amount of electricity consumption and the timing to use renewable energy. In this step, the operational status data, weather data, and user emotion data are input, and the predicted results of the optimal amount of electricity consumption and the timing to use renewable energy are output.

[0287] Specific behavior:

[0288] The server runs the generative AI model using a machine learning framework and inputs operational status data, weather data, and emotion data.

[0289] The output of the generative AI model is a specific prediction result, such as "reduce electricity consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0290] Step 3: Sending operation instructions

[0291] The server analyzes the prediction results of the generative AI model obtained in step 2 and sends specific operation instructions to each communication device. In this step, the prediction results from the generative AI model are input, and specific operation instructions to the communication device are output.

[0292] Specific behavior:

[0293] The server analyzes the prediction results obtained from the generative AI model and creates optimal power consumption adjustment instructions for each communication device.

[0294] The server sends specific instructions to communication device A via an API, such as "Reduce power consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0295] Step 4: Adjusting power consumption

[0296] The terminal (communication device) adjusts power consumption in real time based on instructions from the server. In this step, the operation instructions from the server are the input, and the adjusted power consumption and renewable energy usage results are the output.

[0297] Specific behavior:

[0298] The communication device A receives and executes the instruction to reduce power consumption from the server.

[0299] Communication device A suspends certain processes and adjusts its operating mode to reduce power consumption.

[0300] Switch to renewable energy sources at designated times.

[0301] Step 5: Generate sentiment data and provide feedback

[0302] The user monitors the system's operation status and provides feedback to the server. The server then identifies the user's emotions through emotion recognition means and generates emotion data. In step 5, the system's operation results and the user's emotion data are input, and the feedback data and an updated generative AI model are output.

[0303] Specific behavior:

[0304] The server uses a camera and microphone to acquire the user's emotional data (stress level and comfort level), and generates data using an emotion engine.

[0305] Users can check the progress of power consumption reductions and renewable energy usage on the dashboard.

[0306] If desired, the user sends feedback to the server.

[0307] The server updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[0308] (Application example 2)

[0309] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] The present invention relates to a system that improves the efficiency of power consumption of communication devices and optimizes the use of renewable energy. In particular, the present invention aims to simultaneously achieve user comfort and efficient power consumption by enabling energy management that takes user emotions into account. Conventional systems simply manage power consumption based on the operating status of communication devices and weather conditions, and are unable to manage energy that takes user emotions and stress levels into account. This makes it difficult to improve power consumption efficiency without compromising user comfort.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0312] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for acquiring user emotion data and inputting it to the generative model means, means for controlling the power consumption of the communication device based on the estimation result of the generative model means and means for switching power to a renewable energy source, means for optimizing the operating environment of the communication device based on the user emotion data, and means for feeding back the operation results of the system and the user emotion data. This enables energy management that takes user emotion into consideration, thereby making it possible to efficiently consume power in the communication device while maintaining user comfort.

[0313] The "operating status" indicates the operating state of the communication device, and includes information such as communication volume, operating time, and temperature.

[0314] "Weather conditions" refers to weather information, such as temperature, precipitation, and wind speed, associated with the location information of the communication device, obtained from a weather data provider.

[0315] "Generative model means" refers to an AI model used to predict optimal power consumption for communications devices and the timing of renewable energy use based on collected operational status data and weather data.

[0316] "Emotion data" is data generated based on the user's emotions, and indicates the stress level and comfort the user feels toward the system.

[0317] The "means for controlling power consumption" is a means for adjusting the amount of power consumed by the communication device based on the prediction results obtained from the generative model means.

[0318] "Renewable energy sources" refers to power sources that use energy obtained from the natural environment, such as solar power and wind power.

[0319] "Means for performing power supply switching" refers to means for switching the power supply from a conventional power source to a renewable energy source.

[0320] "Means for optimizing the operating environment" refers to means for adjusting the communication device and its surrounding environment (e.g., temperature, lighting, music, etc.) based on the user's emotional data, to create a comfortable environment for the user.

[0321] The "feedback means" is a means for providing the system's operational results and user emotion data to the server, and automatically updating and correcting the generative model means based on this.

[0322] The present invention provides a system for improving the efficiency of power consumption in communication devices and optimizing the use of renewable energy. The system also recognizes a user's emotions and manages energy based on those emotions, thereby managing power consumption while maintaining user comfort. Specific embodiments of the present invention are described below.

[0323] System Configuration

[0324] This system is broadly composed of the following components:

[0325] 1. Data Collection Methods

[0326] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[0327] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[0328] 2. Generative Modeling Methods

[0329] The server inputs the collected operational status data, weather data, and emotional data obtained from users into a generative AI model, which then predicts the optimal power consumption for the communications device and the timing of renewable energy usage.

[0330] 3. Power management measures

[0331] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[0332] 4. Emotion Engine

[0333] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0334] 5. Feedback channels

[0335] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[0336] Hardware and software used

[0337] The following hardware and software are used to implement the system:

[0338] Hardware: Smartphones, temperature sensors, humidity sensors, emotion recognition cameras, etc.

[0339] Software: Python, weather data API, energy management system, generative AI model

[0340] Data processing and calculation

[0341] The server centrally manages data collected from communication devices and various sensors and processes it in real time. The generative AI model uses this data to predict optimal power consumption and timing for using renewable energy. The server analyzes the results and generates specific instructions for each communication device. This series of processes includes the following data processing and calculations:

[0342] Data collection and pre-processing: Collect data from each communication device and weather data provider and convert it into the required format.

[0343] Use of generative AI models: Input data into generative AI models to get predicted power consumption and energy usage timing.

[0344] Instruction generation and transmission: Based on the prediction results, the server generates and transmits appropriate operation instructions to each communication device.

[0345] Specific examples

[0346] A cafe is introducing this system to optimize the in-store environment while reducing power consumption. When a customer enters the store, the system connects with the customer's smartphone and automatically optimizes the temperature, humidity, and lighting. It also adjusts the volume and selection of background music to help customers relax. The store's power consumption will be switched to renewable energy, making for an environmentally friendly operation.

[0347] Prompt Sentence Examples

[0348] "Please collect temperature, humidity, and customer sentiment data from within the store, and predict the optimal energy consumption and timing for using renewable energy based on the weather forecast for the day. Also, please output specific operating instructions to provide a comfortable environment for customers."

[0349] As described above, the system of the present invention can improve the efficiency of power consumption of communication devices while taking into consideration the user's emotions, and can realize the effective use of renewable energy.

[0350] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0351] Step 1:

[0352] The server collects operational status data from each communication device (terminal). This operational status data includes communication volume, operating time, temperature, etc. This makes it possible to understand the terminal's power consumption and operating status. The input is operational status data from the communication device, and the output is the server receiving and saving this data.

[0353] Step 2:

[0354] The server obtains weather conditions from a weather data provider, including temperature, precipitation, wind speed, etc., based on the location information of the communication device. The input is the location information of the communication device and weather data from the weather data provider, and the output is the identified weather data, which allows the influence of the external environment to be taken into account.

[0355] Step 3:

[0356] The server collects the user's emotional data, including the user's stress level and comfort level. The data is collected using emotion-recognition cameras and sensors and analyzed through an emotion engine. The input is information obtained from the user's facial expressions and behavior, and the output is analyzed emotional data.

[0357] Step 4:

[0358] The server inputs the collected operational status data, weather data, and emotion data into a generative AI model. Based on this data, the generative AI model predicts optimal power consumption and the timing of renewable energy usage. The input is the collected data, and the output is the predicted power consumption and timing of energy usage.

[0359] Step 5:

[0360] The server generates and transmits operational instructions to control the power consumption of the communications device based on the prediction results of the generative AI model. The terminal receives instructions from the server, adjusts power consumption in real time, and switches to renewable energy sources as needed. The input is the prediction result of the generative AI model, and the output is specific operational instructions for the communications device.

[0361] Step 6:

[0362] The user monitors the system's operation status and checks performance data and emotion data. For example, they check whether power consumption has been reduced as predicted or whether renewable energy has been used appropriately. The inputs are the system's operation results and emotion data, and the output is the monitoring results of these data.

[0363] Step 7:

[0364] Users can provide feedback to the server as needed. The server updates the generative AI model based on this feedback to improve the accuracy of the next prediction. The input is the user's feedback, and the output is the updated generative AI model.

[0365] Through the above steps, the server, terminal, and user work together to make the power consumption of the communication device more efficient, thereby optimizing the use of renewable energy while maintaining user comfort.

[0366] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0367] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0368] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0369] [Second embodiment]

[0370] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0371] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0372] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0373] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0374] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0375] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0376] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0377] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0378] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0379] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0380] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0381] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0382] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. The following describes specific embodiments of the present invention.

[0383] System Overview

[0384] The system of the present invention collects information on the operating status and weather conditions of each communication device, and then uses a generative AI model to calculate the optimal power consumption and power switching timing. The specific components and their operation are as follows:

[0385] 1. Data Collection Methods

[0386] The server collects operational status data from each communication device (terminal) in real time, including communication volume, operating time, temperature, etc.

[0387] The server obtains weather conditions from external weather data providers and identifies relevant weather data based on the location information of the communication device.

[0388] 2. Generative AI Model Means

[0389] The server inputs the collected operational status data and weather data into a generative AI model, which uses past data and experience to predict the optimal power consumption and timing for renewable energy use for each communication device.

[0390] 3. Power management measures

[0391] The server analyzes the prediction results of the generative AI model and, based on that, sends instructions to each communication device to control power consumption.

[0392] Based on instructions received from the server, the terminal (communication device) adjusts power consumption in real time and switches power to renewable energy sources as needed.

[0393] 4. Feedback methods

[0394] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model based on the collected feedback, improving the system's prediction accuracy.

[0395] Specific examples

[0396] For example, consider a situation where communication device A needs to handle more data traffic than its normal utilization rate in the afternoon. In this case, the server operates as follows:

[0397] 1. The server collects real-time operational status data and related weather data sent from communication device A.

[0398] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and timing for renewable energy use for communication device A between 2:00 PM and 4:00 PM.

[0399] 3. Based on the prediction results of the generative AI model, the server sends an instruction to communication device A to "reduce current power consumption by 30% and switch to solar power generation from 2:30 pm."

[0400] 4. Communication device A adjusts its power consumption in real time according to instructions from the server and switches to renewable energy at the specified time.

[0401] 5. Users monitor the system's performance and provide real-time feedback as needed, which the server uses to update the generative AI model and further improve the accuracy of future predictions.

[0402] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reduced environmental impact.

[0403] The processing flow will be explained below.

[0404] Step 1:

[0405] The server acquires operational status data from each communication device (terminal) in real time. Specifically, it periodically collects data such as the amount of communication traffic, operating time, and temperature sent from the communication device.

[0406] Step 2:

[0407] The server retrieves weather data from external weather data providers, including data on current weather, temperature, solar radiation, wind speed, etc., and further identifies weather conditions associated with each communication device in association with the communication device's location information.

[0408] Step 3:

[0409] The server preprocesses the operational status data collected in step 1 and the weather data acquired in step 2, preparing them for input into the generative AI model. This preprocessing includes imputing missing values ​​and standardizing the data.

[0410] Step 4:

[0411] The server inputs the preprocessed data into a generative AI model that predicts the optimal power consumption and renewable energy usage timing for each communication device. The generative AI model makes these predictions based on past data and experience.

[0412] Step 5:

[0413] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions for each communication device, such as "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0414] Step 6:

[0415] The server transmits the generated operation instructions to each communication device, and upon receiving the instructions, the communication device adjusts its power consumption in real time based on the instructions.

[0416] Step 7:

[0417] The terminal (communication device) will switch its power source to renewable energy sources as needed, for example, by following instructions to prioritize the use of electricity from solar or wind power.

[0418] Step 8:

[0419] Users can monitor the system's operation and check performance data, such as availability, power consumption, and renewable energy usage.

[0420] Step 9:

[0421] If the user determines that the system's behavior or predictions are inappropriate or that there is room for improvement, the user provides feedback to the server, including specific problems and suggestions for improvement.

[0422] Step 10:

[0423] The server collects user feedback and performance data, and continuously learns and updates the generative AI model based on that data, thereby improving the accuracy of the next prediction.

[0424] Example 1

[0425] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0426] Currently, there is a demand for optimizing the power consumption of communication devices and efficiently using renewable energy. However, existing systems have difficulty managing power consumption while fully considering the operating status of communication devices and weather conditions. In addition, there is a lack of a mechanism to incorporate user feedback and update the AI ​​model, which makes it difficult to improve prediction accuracy.

[0427] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0428] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for the communication device to adjust power consumption in real time based on the generative AI model, and means for updating the generative AI model based on feedback from users. This allows for efficient management of power consumption of the communication devices and optimal utilization of renewable energy.

[0429] A "communication device" is an electronic device for transmitting and receiving data.

[0430] "Operational status" is data indicating the current operating state and performance indicators of a communication device.

[0431] "Weather conditions" refers to environmental data such as the weather and temperature of the area where the communication device is installed.

[0432] A "generative model implementation" is an algorithm and software for estimating optimal power consumption based on collected data.

[0433] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0434] "Renewable energy sources" are sustainable energy sources obtained from nature, such as solar power and wind power.

[0435] A "generative AI model" is an artificial intelligence model that predicts optimal power consumption for communication devices based on past data and experience.

[0436] "Real time" refers to a time period in which data processing and responses occur almost immediately.

[0437] "Feedback" refers to opinions and suggestions for corrections from users regarding the results of system operations.

[0438] "Update" means improving current models and systems based on new data and feedback.

[0439] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. An embodiment of the present invention will now be described in detail.

[0440] This system consists of a server, terminals (communications devices), and users. The server collects information on the operating status of each communications device and external weather conditions in real time, and calculates the optimal amount of electricity consumption and the timing of renewable energy usage based on this information. This is done using a generative AI model. Specifically, the generative AI model, built using TensorFlow, is trained and inference processed on AWS SageMaker.

[0441] Data collection

[0442] The server collects operational status data such as communication volume, operating time, and temperature from each communication device. The server also obtains weather data provided in JSON format from an external weather data provider and identifies relevant weather data based on the location information of the communication device. This data collection is performed using an automated process using AWS Lambda, and the collected data is stored in Amazon S3.

[0443] Using generative AI models

[0444] The server inputs the collected operational status data and weather data into a generative AI model. This generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on past data and experience. To implement the model, TensorFlow is used to build it, and AWS SageMaker is used to train and infer the model.

[0445] Generating Power Management Instructions

[0446] Based on the prediction results of the generative AI model, the server generates specific instructions for the communication device to control power consumption, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." These instructions are sent to the communication device using the MQTT protocol.

[0447] Power consumption regulation

[0448] The communication device adjusts power consumption in real time based on instructions from the server, and switches power to renewable energy sources at the set time. This is done using a general-purpose computer as an edge computing device, and transmits control instructions via Azure IoT Hub.

[0449] Feedback and Model Updates

[0450] Users monitor the system's operation status and provide feedback in real time. Using a dedicated monitoring application, they send the necessary feedback to the server based on the system's operation results. The server collects the user feedback using Google Cloud Pub / Sub and analyzes it using BigQuery. The generative AI model is then retrained based on the analysis results to improve the accuracy of the next prediction.

[0451] Specific examples

[0452] Here is an example prompt:

[0453] "Please obtain the current traffic volume and weather data of communication device A in real time."

[0454] "Based on the collected data, please predict the optimal power consumption and renewable energy usage timing between 2:00 PM and 4:00 PM."

[0455] "Based on the predictions of the generative AI model, please instruct communication device A to reduce power consumption by 30% and switch to solar power generation starting at 2:30 PM."

[0456] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0457] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0458] Step 1: Data collection

[0459] The server collects operational status data of each communication device and external weather data in real time. The inputs are operational status data (communication volume, operating time, temperature, etc.) sent from the communication device and weather information obtained from a weather data provider. The server automates this data collection using AWS Lambda and stores the collected data in Amazon S3. The output is well-formatted operational status data and weather data.

[0460] Step 2: Data Shaping

[0461] The server converts the collected operational status data and weather data into a format that can be input to the generative AI model. The input at this time is the raw data collected in step 1. Data reformatting involves imputing missing values, normalizing values, and encoding categorical data. The output is a dataset in a format that is compatible with the generative AI model.

[0462] Step 3: Input to the generative AI model

[0463] The server inputs the formatted dataset into the generative AI model to predict optimal power consumption and the timing of renewable energy usage. The input at this time is the dataset formatted in step 2. The output is the predicted optimal power consumption and timing of energy usage for each communication device. The server runs the generative AI model built with TensorFlow on AWS SageMaker.

[0464] Step 4: Generate Power Management Directives

[0465] The server generates specific power management instructions based on the prediction results of the generative AI model. The input at this time is the prediction results obtained in step 3. The server analyzes the prediction results and generates specific instructions for each communication device, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." The output is the power management instructions sent to the terminal.

[0466] Step 5: Sending instructions

[0467] The server sends the generated power management instructions to each communication device. The input at this time is the power management instruction generated in step 4. The server uses the MQTT protocol to send the power management instructions to the communication devices in real time. The output is the power management instruction received by the communication devices.

[0468] Step 6: Adjusting power consumption

[0469] The terminal (communication device) adjusts its power consumption in real time based on instructions received from the server. The input at this time is the power management instruction received in step 5. The terminal switches its power source to a renewable energy source (e.g., solar power generation) at the set time. The output is the adjusted power consumption state.

[0470] Step 7: Submit your feedback

[0471] The user monitors the system's operation status and provides real-time feedback to the server. The input is the user's observations and evaluations. The user checks the system's operation results using a dedicated monitoring application and enters the necessary feedback. The output is feedback data sent to the server.

[0472] Step 8: Update the generative AI model

[0473] The server receives feedback data from users and updates the generative AI model. The input is the feedback data collected in step 7. The server collects the feedback using Google Cloud Pub / Sub and analyzes it with BigQuery. The server retrains the generative AI model based on the analysis results to improve prediction accuracy. The output is an updated generative AI model.

[0474] (Application example 1)

[0475] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] In recent years, energy issues and increasing environmental impacts have become social challenges. In particular, physical stores operate a variety of electrical devices, and efficient management of their energy consumption is essential for achieving sustainable operations. However, conventional systems have difficulty optimizing power consumption in real time or appropriately controlling the timing of renewable energy usage. Furthermore, energy management methods within physical stores based on weather conditions and operating status are immature, making it difficult to effectively improve energy efficiency. New methods are needed to solve these problems and achieve energy conservation and sustainability in physical stores.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0478] In this invention, the server includes means for collecting the operating status and weather conditions of each electronic device, a generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for controlling the power consumption of the electronic devices based on the estimation results of the generative model means and means for switching power to a renewable energy source, and means for optimizing the energy consumption and usage timing of various devices using in-store operating status data and external weather data. This enables real-time optimization of energy consumption and effective use of renewable energy in physical stores.

[0479] "Electronic equipment" is a general term for all devices and equipment operating within a store, including, for example, lighting, heating and cooling, POS systems, and security cameras.

[0480] "Operating status" refers to data that indicates how frequently and at what output an electronic device is operating, and specifically includes operating time, power consumption, frequency of use, and the like.

[0481] "Weather conditions" refers to data related to the weather in the external environment, and specifically includes temperature, humidity, wind speed, rainfall, and the like.

[0482] "Means for collection" refers to systems and methods for collecting data on the operating status of electronic devices and weather conditions using various sensors and data acquisition devices.

[0483] "Generative modeling tools" refer to machine learning models and AI systems that use collected data to predict optimal electricity consumption and the timing of renewable energy use.

[0484] "Estimation results" refers to the predicted data and analysis results obtained by the generative model means, and specifically includes energy consumption and the scheduled time of use of renewable energy.

[0485] "Renewable energy sources" refers to sustainable energy sources such as solar, wind, and hydroelectric power.

[0486] "Means for performing power source switching" refers to a control device or system for automatically switching between renewable energy sources and conventional power sources as needed.

[0487] "Optimization measures" refers to the means and methods for efficiently adjusting the operation of each electronic device and minimizing energy consumption based on collected data and estimation results.

[0488] In order to carry out the present invention, an embodiment including the following system configuration and operation procedure is adopted.

[0489] System Configuration

[0490] 1. Server:

[0491] Data collection method: The server collects real-time operational status data of each electronic device in the physical store (lighting, heating and cooling, POS system, security camera), obtains weather conditions from an external weather data provider, and identifies relevant weather data based on the store's location information.

[0492] Generative model means: The server inputs the collected operational status data and weather data into a generative AI model, which is then used to predict optimal power consumption and the timing of renewable energy use.

[0493] Power management: The server analyzes the results of the generative AI model's estimations and sends instructions to each electronic device to control its energy consumption. It also switches the power source to renewable energy sources as needed.

[0494] 2. Terminals (electronic devices):

[0495] Based on instructions received from the server, each electronic device adjusts its power consumption in real time and switches to renewable energy at the specified times.

[0496] 3. User:

[0497] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model to improve prediction accuracy.

[0498] Processing Description

[0499] 1. Data collection methods:

[0500] The server uses sensors and data collection devices to collect data on the operating status of each electronic device in the physical store as well as external weather data. For example, the store's lighting has an illuminance sensor, the heating and cooling has a temperature sensor, the POS system has transaction data, and the security camera has operating time data.

[0501] 2. Generative modeling methods:

[0502] The server inputs the collected operational status data and weather data into a generative AI model, which predicts optimal energy usage and the timing of renewable energy use based on past data and experience. The generative AI model uses deep learning platforms such as Tensorflow and PyTorch.

[0503] 3. Power management measures:

[0504] The server sends instructions to each electronic device to adjust its energy consumption based on the predictions of the generative AI model, and also sends control signals to indicate when it needs to switch to renewable energy sources, allowing the electronic device to consume power efficiently and maximize the use of sustainable energy.

[0505] Specific examples

[0506] Consider a situation where the electronics in a brick-and-mortar store exceed their normal utilization rate one afternoon. In this case, the server operates as follows:

[0507] 1. The server collects real-time operating status data sent from electronic devices (e.g., lighting 75%, heating / cooling 60%, POS system 7%, security camera 3%) and related weather data (e.g., temperature 30°C, humidity 60%).

[0508] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and renewable energy usage timing for electronic devices between 2:00 PM and 4:00 PM.

[0509] 3. Based on the prediction results of the generative AI model, the server sends instructions to each electronic device to "adjust lighting consumption to 50%, heating and cooling consumption to 40%, and switch to solar power generation from 2:30 pm."

[0510] 4. Electronic devices adjust their power consumption in real time according to instructions from the server and switch to renewable energy at the specified times.

[0511] Example prompt for a generative AI model:

[0512] tokyo_store_lighting=75, hvac=60, pos_system=7, security_camera=3, temperature=30, humidity=60

[0513] In this way, the system of the present invention effectively manages energy consumption in physical stores and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reducing environmental impact.

[0514] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0515] Step 1:

[0516] The server collects operational status data from electronic devices in the physical store. Specifically, the server obtains real-time operational status data from various sensors and data acquisition devices on lighting, heating and cooling, POS systems, security cameras, etc. This operational status data includes each device's operating time, power consumption, frequency of use, etc. The input data is the operating status of each device, and the output is storage of this data in a database.

[0517] Step 2:

[0518] The server obtains weather condition data from an external weather data provider. Using the store's location information, the server obtains current weather data (e.g., temperature, humidity, wind speed, and rainfall) in real time from the weather provider's API. The input data is location information, and the output is weather data. This weather data is also stored in the database, just like the operating status data.

[0519] Step 3:

[0520] The server inputs the collected operational status data and weather data into the generative AI model. The generative AI model predicts optimal energy consumption and the timing for using renewable energy based on past data and experience. The input data are operational status data and weather data, and the output is predicted data for the estimated optimal energy consumption and the timing for using renewable energy.

[0521] Step 4:

[0522] The server analyzes the estimation results of the generative AI model and sends instructions to the electronic devices to adjust their power consumption. For example, it sends specific instructions such as "Adjust lighting consumption to 50% and heating / cooling consumption to 40% between 2:00 PM and 4:00 PM, and switch to solar power generation from 2:30 PM." The input data is the estimation results of the generative AI model, and the output is a control signal to the electronic devices.

[0523] Step 5:

[0524] The terminal (electronic device) adjusts its power consumption in real time based on instructions received from the server. Specifically, the electronic device controls power consumption according to instructions from the server and switches to renewable energy at the specified time. The input data is a control signal from the server, and the output is the adjusted power consumption and the switch to renewable energy.

[0525] Step 6:

[0526] Users monitor the system's operation and provide real-time feedback. The user's feedback is sent to the server, which then updates the generative AI model based on this feedback to further improve the accuracy of the next prediction. The input data is user feedback, and the output is the updated generative AI model.

[0527] Through these steps, the system of the present invention effectively manages energy consumption in physical stores and optimizes the use of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0528] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0529] The present invention is a system for improving the efficiency of power consumption in communication devices and achieving optimal utilization of renewable energy. Furthermore, by combining this system with an emotion engine that recognizes user emotions, it enables optimal energy management based on the user's emotions. Specific embodiments of the present invention are described below.

[0530] System Overview

[0531] The system of the present invention consists of the following main components:

[0532] 1. Data Collection Methods

[0533] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[0534] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[0535] 2. Generative AI Model Means

[0536] The server inputs the collected operational status data and weather data into a generative AI model, which then predicts optimal power consumption and the timing of renewable energy use.

[0537] 3. Power management measures

[0538] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[0539] 4. Emotion Engine

[0540] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0541] 5. Feedback channels

[0542] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[0543] Specific examples

[0544] For example, consider a situation where communication device A must process more data than its normal utilization rate in the afternoon of a certain day. The processing flow in this case is as follows:

[0545] 1. Collecting operational status data

[0546] The server acquires real-time operational status data transmitted from communication device A. The operational status data includes communication volume, operating time, and temperature.

[0547] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of communication device A.

[0548] 2. Using generative AI models

[0549] The server inputs the operational status data, weather data, and user emotion data obtained from the emotion engine into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy use.

[0550] 3. Sending operation instructions

[0551] The server analyzes the prediction results obtained from the generative AI model and sends instructions to communication device A to "reduce power consumption by 30% between 2:00 p.m. and 4:00 p.m. and switch to solar power generation."

[0552] The terminal (communication device A) adjusts power consumption in real time based on instructions from the server and switches to renewable energy at the specified time.

[0553] 4. Providing Feedback

[0554] The user monitors the operation results of communication device A and their own emotional data, for example, to check whether power consumption has been reduced as predicted and whether renewable energy has been used appropriately.

[0555] Users can provide feedback to the server as needed, which the server uses to update the generative AI model and improve the accuracy of the next prediction.

[0556] In this way, the system of the present invention, which combines an emotion engine, can improve the efficiency of power consumption in communication devices and optimize the use of renewable energy. Furthermore, by taking into account the user's emotion data, it becomes possible to manage energy in a way that takes into account the user's comfort and stress level.

[0557] The processing flow will be explained below.

[0558] Step 1:

[0559] The server collects operational status data from each communication device (terminal) in real time. Specifically, it periodically acquires data such as communication volume, operating time, and temperature, and centralizes the collected data.

[0560] Step 2:

[0561] The server retrieves the latest weather conditions from external weather data providers, including information on weather, temperature, solar radiation, wind speed, etc. It then matches the location information of the communication devices to identify the weather data relevant to each device.

[0562] Step 3:

[0563] The server collects emotional data through an emotion engine that recognizes the user's emotions. The emotional data is acquired in real time from the user's facial expressions and voice. The emotion engine quantifies the emotional data and indicates the user's stress level and comfort level.

[0564] Step 4:

[0565] The server inputs the collected operational status data, weather data, and emotion data into the generative AI model, performing preprocessing on the data, such as normalizing it and filling in missing values.

[0566] Step 5:

[0567] The generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on the given data. The generative AI model derives efficient energy management from past data and learning results.

[0568] Step 6:

[0569] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions based on them, such as "reduce electricity consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0570] Step 7:

[0571] The server then sends operational instructions based on the analysis results to each communication device, which then adjusts their power consumption in real time based on these instructions.

[0572] Step 8:

[0573] The terminal (communication device) can switch its power source to a renewable energy source as needed, for example, by setting it to prioritize the use of electricity from solar power or wind power.

[0574] Step 9:

[0575] The user monitors the results of the system's operation and also checks their own emotional data to assess how comfortable or stressed they feel.

[0576] Step 10:

[0577] Users provide feedback on the system's behavior as needed. The server uses this feedback to continuously learn and update the generative AI model, improving prediction accuracy.

[0578] Example 2

[0579] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0580] The power consumption of communication devices is increasing year by year, necessitating efficient power management. However, conventional methods do not adequately optimize the timing of renewable energy usage or power consumption. Furthermore, power management does not take into account the user's comfort and stress level. Therefore, a system that reduces power waste and realizes optimal use of renewable energy is needed.

[0581] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0582] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, a generative artificial intelligence model means for estimating efficient power consumption based on the operating status, weather conditions, and user emotion data, means for controlling the power consumption of the communication device based on the estimation result of the generative artificial intelligence model means and means for switching power to a renewable energy source, emotion recognition means for generating user emotion data, and means for updating the generative artificial intelligence model means based on performance data and emotion data. This makes it possible to improve the efficiency of power consumption of the communication device, optimally utilize renewable energy, and enable energy management that takes user comfort and stress levels into consideration.

[0583] A "communication device" is a device that can be connected to a network and send and receive data. Examples include smartphones, tablets, and personal computers.

[0584] "Operational status data" refers to data that indicates information related to the operation of a communication device. Specifically, this includes information such as communication volume, operating time, and temperature.

[0585] "Weather conditions" refers to data that indicates information about the weather at a particular location, such as temperature, humidity, wind speed, and air pressure.

[0586] A "generative AI model" is a machine learning model that makes predictions and optimizations based on collected data. It refers to a model built using artificial intelligence technology.

[0587] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0588] "Renewable energy sources" refers to energy sources that provide environmentally friendly energy, such as solar, wind, and hydropower.

[0589] "Emotion recognition means" refers to a technical means for analyzing a user's emotions and generating emotion data. For example, it includes technology that analyzes a user's facial expressions and voice using a camera or microphone.

[0590] "Performance data" refers to data that records the past operation results of the system. Specifically, it includes information such as power consumption and renewable energy usage.

[0591] "Feedback data" is data that records user evaluations and opinions on the behavior of the system. It is used to improve and optimize the system.

[0592] "Generative AI model means" refers to a technical means for estimating efficient power consumption using a generative AI model, utilizing machine learning algorithms and computational resources.

[0593] System Program Overview

[0594] This invention is a system that improves the efficiency of power consumption in communication devices and realizes optimal utilization of renewable energy. Furthermore, by combining it with an emotion engine that recognizes user emotions, optimal energy management based on the user's emotions is possible. Specific embodiments of the invention are described below.

[0595] Hardware and software used

[0596] Server: Collects data, runs generative AI models, sends instructions to control power consumption, generates emotion data, and processes feedback data.

[0597] Software used: TensorFlow, PyTorch, API communication module

[0598] Terminal (communication device): Sends operational status data of the communication device to the server and executes power consumption control instructions.

[0599] Example: Smartphone, tablet, PC

[0600] Users: Monitor the system's behavior and provide feedback.

[0601] Hardware used: Camera, microphone

[0602] Program processing

[0603] Data collection

[0604] The server obtains real-time operational status data from each communication device (terminal), including communication volume, operating time, temperature, etc. The server also obtains weather data such as temperature, humidity, wind speed, and air pressure from weather data providers via API, and identifies relevant weather data based on the location information of the communication device.

[0605] Predictions from generative AI models

[0606] The server inputs collected operational status data, weather data, and user sentiment data into a generative AI model built with TensorFlow and PyTorch to predict optimal power consumption and the timing of renewable energy use.

[0607] Controlling Power Consumption

[0608] The server analyzes the prediction results of the generative AI model and sends specific operational instructions to each communication device. The terminal (communication device) adjusts its power consumption in real time based on instructions from the server and switches to renewable energy at the specified timing.

[0609] Use of emotion engine

[0610] The server recognizes the user's emotions and generates emotional data based on them. This data, which indicates the user's stress level and comfort level, is collected using a camera and microphone. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0611] Providing feedback

[0612] The user monitors the system's operating status and their own emotional data. They check the system's operating results (for example, the reduction in power consumption of communication devices or the use of renewable energy) and provide feedback to the server as needed. The server then updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[0613] Specific examples

[0614] For example, suppose that one afternoon, communication device A needs to handle more data traffic than its normal utilization rate. In this case, the process flow is as follows:

[0615] 1. Collecting operational status data

[0616] The server acquires real-time operating status data (communication volume, operating time, temperature) sent from communication device A. The server also acquires weather conditions (temperature, humidity, wind speed) via API and identifies related weather data based on the location information of communication device A.

[0617] 2. Using generative AI models

[0618] The server inputs operational status data, weather data, and user emotion data into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy usage.

[0619] 3. Sending operation instructions

[0620] The server analyzes the prediction results obtained from the generative AI model and sends an instruction to communication device A to "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation." Communication device A adjusts its power consumption based on this instruction and switches to renewable energy at the specified time.

[0621] 4. Providing Feedback

[0622] The user monitors the operation results of communication device A and their own emotional data. For example, they check whether power consumption was reduced as predicted or whether renewable energy was used appropriately, and provide feedback to the server. The server then updates the generative AI model based on this information to improve the accuracy of the next prediction.

[0623] Prompt Sentence Examples

[0624] "Communication device A is expected to operate at a higher than usual rate between 2:00 and 4:00 PM. Please predict the optimal power consumption and timing for using renewable energy during this time. Current operation status data includes communication volume, operation time, and temperature, while weather data includes temperature, humidity, and wind speed. Also, please take into account user emotion data."

[0625] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0626] Step 1: Collect data

[0627] The server obtains real-time operational status data from each communication device (terminal). The operational status data includes communication volume, operating time, temperature, etc. The server also obtains current weather conditions (temperature, humidity, wind speed, air pressure, etc.) from a weather data provider via an API and identifies relevant weather data based on the location information of the communication device. In this step, the operational status data sent from each communication device and the weather data obtained from the weather data provider are input, and the server outputs a database that manages all of this data collectively.

[0628] Specific behavior:

[0629] The server uses an API to obtain real-time data on communication volume, operating time, and temperature from the communication device.

[0630] The server calls the weather data provider's API to obtain temperature, humidity, wind speed, and air pressure, and identifies data that matches the location information of the communication device.

[0631] Step 2: Generative AI model prediction

[0632] The server inputs the operational status data, weather data, and user emotion data collected in step 1 into a generative AI model. The generative AI model is executed using a machine learning framework (e.g., TensorFlow or PyTorch) on the server. This model predicts the optimal amount of electricity consumption and the timing to use renewable energy. In this step, the operational status data, weather data, and user emotion data are input, and the predicted results of the optimal amount of electricity consumption and the timing to use renewable energy are output.

[0633] Specific behavior:

[0634] The server runs the generative AI model using a machine learning framework and inputs operational status data, weather data, and emotion data.

[0635] The output of the generative AI model is a specific prediction result, such as "reduce electricity consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0636] Step 3: Sending operation instructions

[0637] The server analyzes the prediction results of the generative AI model obtained in step 2 and sends specific operation instructions to each communication device. In this step, the prediction results from the generative AI model are input, and specific operation instructions to the communication device are output.

[0638] Specific behavior:

[0639] The server analyzes the prediction results obtained from the generative AI model and creates optimal power consumption adjustment instructions for each communication device.

[0640] The server sends specific instructions to communication device A via an API, such as "Reduce power consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0641] Step 4: Adjusting power consumption

[0642] The terminal (communication device) adjusts power consumption in real time based on instructions from the server. In this step, the operation instructions from the server are the input, and the adjusted power consumption and renewable energy usage results are the output.

[0643] Specific behavior:

[0644] The communication device A receives and executes the instruction to reduce power consumption from the server.

[0645] Communication device A suspends certain processes and adjusts its operating mode to reduce power consumption.

[0646] Switch to renewable energy sources at designated times.

[0647] Step 5: Generate sentiment data and provide feedback

[0648] The user monitors the system's operation status and provides feedback to the server. The server then identifies the user's emotions through emotion recognition means and generates emotion data. In step 5, the system's operation results and the user's emotion data are input, and the feedback data and an updated generative AI model are output.

[0649] Specific behavior:

[0650] The server uses a camera and microphone to acquire the user's emotional data (stress level and comfort level), and generates data using an emotion engine.

[0651] Users can check the progress of power consumption reductions and renewable energy usage on the dashboard.

[0652] If desired, the user sends feedback to the server.

[0653] The server updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[0654] (Application example 2)

[0655] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0656] The present invention relates to a system that improves the efficiency of power consumption of communication devices and optimizes the use of renewable energy. In particular, the present invention aims to simultaneously achieve user comfort and efficient power consumption by enabling energy management that takes user emotions into account. Conventional systems simply manage power consumption based on the operating status of communication devices and weather conditions, and are unable to manage energy that takes user emotions and stress levels into account. This makes it difficult to improve power consumption efficiency without compromising user comfort.

[0657] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0658] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for acquiring user emotion data and inputting it to the generative model means, means for controlling the power consumption of the communication device based on the estimation result of the generative model means and means for switching power to a renewable energy source, means for optimizing the operating environment of the communication device based on the user emotion data, and means for feeding back the operation results of the system and the user emotion data. This enables energy management that takes user emotion into consideration, thereby making it possible to efficiently consume power in the communication device while maintaining user comfort.

[0659] The "operating status" indicates the operating state of the communication device, and includes information such as communication volume, operating time, and temperature.

[0660] "Weather conditions" refers to weather information, such as temperature, precipitation, and wind speed, associated with the location information of the communication device, obtained from a weather data provider.

[0661] "Generative model means" refers to an AI model used to predict optimal power consumption for communications devices and the timing of renewable energy use based on collected operational status data and weather data.

[0662] "Emotion data" is data generated based on the user's emotions, and indicates the stress level and comfort the user feels toward the system.

[0663] The "means for controlling power consumption" is a means for adjusting the amount of power consumed by the communication device based on the prediction results obtained from the generative model means.

[0664] "Renewable energy sources" refers to power sources that use energy obtained from the natural environment, such as solar power and wind power.

[0665] "Means for performing power supply switching" refers to means for switching the power supply from a conventional power source to a renewable energy source.

[0666] "Means for optimizing the operating environment" refers to means for adjusting the communication device and its surrounding environment (e.g., temperature, lighting, music, etc.) based on the user's emotional data, to create a comfortable environment for the user.

[0667] The "feedback means" is a means for providing the system's operational results and user emotion data to the server, and automatically updating and correcting the generative model means based on this.

[0668] The present invention provides a system for improving the efficiency of power consumption in communication devices and optimizing the use of renewable energy. The system also recognizes a user's emotions and manages energy based on those emotions, thereby managing power consumption while maintaining user comfort. Specific embodiments of the present invention are described below.

[0669] System Configuration

[0670] This system is broadly composed of the following components:

[0671] 1. Data Collection Methods

[0672] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[0673] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[0674] 2. Generative Modeling Methods

[0675] The server inputs the collected operational status data, weather data, and emotional data obtained from users into a generative AI model, which then predicts the optimal power consumption for the communications device and the timing of renewable energy usage.

[0676] 3. Power management measures

[0677] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[0678] 4. Emotion Engine

[0679] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0680] 5. Feedback channels

[0681] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[0682] Hardware and software used

[0683] The following hardware and software are used to implement the system:

[0684] Hardware: Smartphones, temperature sensors, humidity sensors, emotion recognition cameras, etc.

[0685] Software: Python, weather data API, energy management system, generative AI model

[0686] Data processing and calculation

[0687] The server centrally manages data collected from communication devices and various sensors and processes it in real time. The generative AI model uses this data to predict optimal power consumption and timing for using renewable energy. The server analyzes the results and generates specific instructions for each communication device. This series of processes includes the following data processing and calculations:

[0688] Data collection and pre-processing: Collect data from each communication device and weather data provider and convert it into the required format.

[0689] Use of generative AI models: Input data into generative AI models to get predicted power consumption and energy usage timing.

[0690] Instruction generation and transmission: Based on the prediction results, the server generates and transmits appropriate operation instructions to each communication device.

[0691] Specific examples

[0692] A cafe is introducing this system to optimize the in-store environment while reducing power consumption. When a customer enters the store, the system connects with the customer's smartphone and automatically optimizes the temperature, humidity, and lighting. It also adjusts the volume and selection of background music to help customers relax. The store's power consumption will be switched to renewable energy, making for an environmentally friendly operation.

[0693] Prompt Sentence Examples

[0694] "Please collect temperature, humidity, and customer sentiment data from within the store, and predict the optimal energy consumption and timing for using renewable energy based on the weather forecast for the day. Also, please output specific operating instructions to provide a comfortable environment for customers."

[0695] As described above, the system of the present invention can improve the efficiency of power consumption of communication devices while taking into consideration the user's emotions, and can realize the effective use of renewable energy.

[0696] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0697] Step 1:

[0698] The server collects operational status data from each communication device (terminal). This operational status data includes communication volume, operating time, temperature, etc. This makes it possible to understand the terminal's power consumption and operating status. The input is operational status data from the communication device, and the output is the server receiving and saving this data.

[0699] Step 2:

[0700] The server obtains weather conditions from a weather data provider, including temperature, precipitation, wind speed, etc., based on the location information of the communication device. The input is the location information of the communication device and weather data from the weather data provider, and the output is the identified weather data, which allows the influence of the external environment to be taken into account.

[0701] Step 3:

[0702] The server collects the user's emotional data, including the user's stress level and comfort level. The data is collected using emotion-recognition cameras and sensors and analyzed through an emotion engine. The input is information obtained from the user's facial expressions and behavior, and the output is analyzed emotional data.

[0703] Step 4:

[0704] The server inputs the collected operational status data, weather data, and emotion data into a generative AI model. Based on this data, the generative AI model predicts optimal power consumption and the timing of renewable energy usage. The input is the collected data, and the output is the predicted power consumption and timing of energy usage.

[0705] Step 5:

[0706] The server generates and transmits operational instructions to control the power consumption of the communications device based on the prediction results of the generative AI model. The terminal receives instructions from the server, adjusts power consumption in real time, and switches to renewable energy sources as needed. The input is the prediction result of the generative AI model, and the output is specific operational instructions for the communications device.

[0707] Step 6:

[0708] The user monitors the system's operation status and checks performance data and emotion data. For example, they check whether power consumption has been reduced as predicted or whether renewable energy has been used appropriately. The inputs are the system's operation results and emotion data, and the output is the monitoring results of these data.

[0709] Step 7:

[0710] Users can provide feedback to the server as needed. The server updates the generative AI model based on this feedback to improve the accuracy of the next prediction. The input is the user's feedback, and the output is the updated generative AI model.

[0711] Through the above steps, the server, terminal, and user work together to make the power consumption of the communication device more efficient, thereby optimizing the use of renewable energy while maintaining user comfort.

[0712] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0713] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0714] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0715] [Third embodiment]

[0716] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0717] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0718] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0719] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0720] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0721] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0722] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0723] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0724] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0725] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0726] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0727] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0728] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. The following describes specific embodiments of the present invention.

[0729] System Overview

[0730] The system of the present invention collects information on the operating status and weather conditions of each communication device, and then uses a generative AI model to calculate the optimal power consumption and power switching timing. The specific components and their operation are as follows:

[0731] 1. Data Collection Methods

[0732] The server collects operational status data from each communication device (terminal) in real time, including communication volume, operating time, temperature, etc.

[0733] The server obtains weather conditions from external weather data providers and identifies relevant weather data based on the location information of the communication device.

[0734] 2. Generative AI Model Means

[0735] The server inputs the collected operational status data and weather data into a generative AI model, which uses past data and experience to predict the optimal power consumption and timing for renewable energy use for each communication device.

[0736] 3. Power management measures

[0737] The server analyzes the prediction results of the generative AI model and, based on that, sends instructions to each communication device to control power consumption.

[0738] Based on instructions received from the server, the terminal (communication device) adjusts power consumption in real time and switches power to renewable energy sources as needed.

[0739] 4. Feedback methods

[0740] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model based on the collected feedback, improving the system's prediction accuracy.

[0741] Specific examples

[0742] For example, consider a situation where communication device A needs to handle more data traffic than its normal utilization rate in the afternoon. In this case, the server operates as follows:

[0743] 1. The server collects real-time operational status data and related weather data sent from communication device A.

[0744] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and timing for renewable energy use for communication device A between 2:00 PM and 4:00 PM.

[0745] 3. Based on the prediction results of the generative AI model, the server sends an instruction to communication device A to "reduce current power consumption by 30% and switch to solar power generation from 2:30 pm."

[0746] 4. Communication device A adjusts its power consumption in real time according to instructions from the server and switches to renewable energy at the specified time.

[0747] 5. Users monitor the system's performance and provide real-time feedback as needed, which the server uses to update the generative AI model and further improve the accuracy of future predictions.

[0748] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reduced environmental impact.

[0749] The processing flow will be explained below.

[0750] Step 1:

[0751] The server acquires operational status data from each communication device (terminal) in real time. Specifically, it periodically collects data such as the amount of communication traffic, operating time, and temperature sent from the communication device.

[0752] Step 2:

[0753] The server retrieves weather data from external weather data providers, including data on current weather, temperature, solar radiation, wind speed, etc., and further identifies weather conditions associated with each communication device in association with the communication device's location information.

[0754] Step 3:

[0755] The server preprocesses the operational status data collected in step 1 and the weather data acquired in step 2, preparing them for input into the generative AI model. This preprocessing includes imputing missing values ​​and standardizing the data.

[0756] Step 4:

[0757] The server inputs the preprocessed data into a generative AI model that predicts the optimal power consumption and renewable energy usage timing for each communication device. The generative AI model makes these predictions based on past data and experience.

[0758] Step 5:

[0759] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions for each communication device, such as "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0760] Step 6:

[0761] The server transmits the generated operation instructions to each communication device, and upon receiving the instructions, the communication device adjusts its power consumption in real time based on the instructions.

[0762] Step 7:

[0763] The terminal (communication device) will switch its power source to renewable energy sources as needed, for example, by following instructions to prioritize the use of electricity from solar or wind power.

[0764] Step 8:

[0765] Users can monitor the system's operation and check performance data, such as availability, power consumption, and renewable energy usage.

[0766] Step 9:

[0767] If the user determines that the system's behavior or predictions are inappropriate or that there is room for improvement, the user provides feedback to the server, including specific problems and suggestions for improvement.

[0768] Step 10:

[0769] The server collects user feedback and performance data, and continuously learns and updates the generative AI model based on that data, thereby improving the accuracy of the next prediction.

[0770] Example 1

[0771] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0772] Currently, there is a demand for optimizing the power consumption of communication devices and efficiently using renewable energy. However, existing systems have difficulty managing power consumption while fully considering the operating status of communication devices and weather conditions. In addition, there is a lack of a mechanism to incorporate user feedback and update the AI ​​model, which makes it difficult to improve prediction accuracy.

[0773] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0774] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for the communication device to adjust power consumption in real time based on the generative AI model, and means for updating the generative AI model based on feedback from users. This allows for efficient management of power consumption of the communication devices and optimal utilization of renewable energy.

[0775] A "communication device" is an electronic device for transmitting and receiving data.

[0776] "Operational status" is data indicating the current operating state and performance indicators of a communication device.

[0777] "Weather conditions" refers to environmental data such as the weather and temperature of the area where the communication device is installed.

[0778] A "generative model implementation" is an algorithm and software for estimating optimal power consumption based on collected data.

[0779] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0780] "Renewable energy sources" are sustainable energy sources obtained from nature, such as solar power and wind power.

[0781] A "generative AI model" is an artificial intelligence model that predicts optimal power consumption for communication devices based on past data and experience.

[0782] "Real time" refers to a time period in which data processing and responses occur almost immediately.

[0783] "Feedback" refers to opinions and suggestions for corrections from users regarding the results of system operations.

[0784] "Update" means improving current models and systems based on new data and feedback.

[0785] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. An embodiment of the present invention will now be described in detail.

[0786] This system consists of a server, terminals (communications devices), and users. The server collects information on the operating status of each communications device and external weather conditions in real time, and calculates the optimal amount of electricity consumption and the timing of renewable energy usage based on this information. This is done using a generative AI model. Specifically, the generative AI model, built using TensorFlow, is trained and inference processed on AWS SageMaker.

[0787] Data collection

[0788] The server collects operational status data such as communication volume, operating time, and temperature from each communication device. The server also obtains weather data provided in JSON format from an external weather data provider and identifies relevant weather data based on the location information of the communication device. This data collection is performed using an automated process using AWS Lambda, and the collected data is stored in Amazon S3.

[0789] Using generative AI models

[0790] The server inputs the collected operational status data and weather data into a generative AI model. This generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on past data and experience. To implement the model, TensorFlow is used to build it, and AWS SageMaker is used to train and infer the model.

[0791] Generating Power Management Instructions

[0792] Based on the prediction results of the generative AI model, the server generates specific instructions for the communication device to control power consumption, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." These instructions are sent to the communication device using the MQTT protocol.

[0793] Power consumption regulation

[0794] The communication device adjusts power consumption in real time based on instructions from the server, and switches power to renewable energy sources at the set time. This is done using a general-purpose computer as an edge computing device, and transmits control instructions via Azure IoT Hub.

[0795] Feedback and Model Updates

[0796] Users monitor the system's operation status and provide feedback in real time. Using a dedicated monitoring application, they send the necessary feedback to the server based on the system's operation results. The server collects the user feedback using Google Cloud Pub / Sub and analyzes it using BigQuery. The generative AI model is then retrained based on the analysis results to improve the accuracy of the next prediction.

[0797] Specific examples

[0798] Here is an example prompt:

[0799] "Please obtain the current traffic volume and weather data of communication device A in real time."

[0800] "Based on the collected data, please predict the optimal power consumption and renewable energy usage timing between 2:00 PM and 4:00 PM."

[0801] "Based on the predictions of the generative AI model, please instruct communication device A to reduce power consumption by 30% and switch to solar power generation starting at 2:30 PM."

[0802] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0803] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0804] Step 1: Data collection

[0805] The server collects operational status data of each communication device and external weather data in real time. The inputs are operational status data (communication volume, operating time, temperature, etc.) sent from the communication device and weather information obtained from a weather data provider. The server automates this data collection using AWS Lambda and stores the collected data in Amazon S3. The output is well-formatted operational status data and weather data.

[0806] Step 2: Data Shaping

[0807] The server converts the collected operational status data and weather data into a format that can be input to the generative AI model. The input at this time is the raw data collected in step 1. Data reformatting involves imputing missing values, normalizing values, and encoding categorical data. The output is a dataset in a format that is compatible with the generative AI model.

[0808] Step 3: Input to the generative AI model

[0809] The server inputs the formatted dataset into the generative AI model to predict optimal power consumption and the timing of renewable energy usage. The input at this time is the dataset formatted in step 2. The output is the predicted optimal power consumption and timing of energy usage for each communication device. The server runs the generative AI model built with TensorFlow on AWS SageMaker.

[0810] Step 4: Generate Power Management Directives

[0811] The server generates specific power management instructions based on the prediction results of the generative AI model. The input at this time is the prediction results obtained in step 3. The server analyzes the prediction results and generates specific instructions for each communication device, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." The output is the power management instructions sent to the terminal.

[0812] Step 5: Sending instructions

[0813] The server sends the generated power management instructions to each communication device. The input at this time is the power management instruction generated in step 4. The server uses the MQTT protocol to send the power management instructions to the communication devices in real time. The output is the power management instruction received by the communication devices.

[0814] Step 6: Adjusting power consumption

[0815] The terminal (communication device) adjusts its power consumption in real time based on instructions received from the server. The input at this time is the power management instruction received in step 5. The terminal switches its power source to a renewable energy source (e.g., solar power generation) at the set time. The output is the adjusted power consumption state.

[0816] Step 7: Submit your feedback

[0817] The user monitors the system's operation status and provides real-time feedback to the server. The input is the user's observations and evaluations. The user checks the system's operation results using a dedicated monitoring application and enters the necessary feedback. The output is feedback data sent to the server.

[0818] Step 8: Update the generative AI model

[0819] The server receives feedback data from users and updates the generative AI model. The input is the feedback data collected in step 7. The server collects the feedback using Google Cloud Pub / Sub and analyzes it with BigQuery. The server retrains the generative AI model based on the analysis results to improve prediction accuracy. The output is an updated generative AI model.

[0820] (Application example 1)

[0821] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0822] In recent years, energy issues and increasing environmental impacts have become social challenges. In particular, physical stores operate a variety of electrical devices, and efficient management of their energy consumption is essential for achieving sustainable operations. However, conventional systems have difficulty optimizing power consumption in real time or appropriately controlling the timing of renewable energy usage. Furthermore, energy management methods within physical stores based on weather conditions and operating status are immature, making it difficult to effectively improve energy efficiency. New methods are needed to solve these problems and achieve energy conservation and sustainability in physical stores.

[0823] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0824] In this invention, the server includes means for collecting the operating status and weather conditions of each electronic device, a generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for controlling the power consumption of the electronic devices based on the estimation results of the generative model means and means for switching power to a renewable energy source, and means for optimizing the energy consumption and usage timing of various devices using in-store operating status data and external weather data. This enables real-time optimization of energy consumption and effective use of renewable energy in physical stores.

[0825] "Electronic equipment" is a general term for all devices and equipment operating within a store, including, for example, lighting, heating and cooling, POS systems, and security cameras.

[0826] "Operating status" refers to data that indicates how frequently and at what output an electronic device is operating, and specifically includes operating time, power consumption, frequency of use, and the like.

[0827] "Weather conditions" refers to data related to the weather in the external environment, and specifically includes temperature, humidity, wind speed, rainfall, and the like.

[0828] "Means for collection" refers to systems and methods for collecting data on the operating status of electronic devices and weather conditions using various sensors and data acquisition devices.

[0829] "Generative modeling tools" refer to machine learning models and AI systems that use collected data to predict optimal electricity consumption and the timing of renewable energy use.

[0830] "Estimation results" refers to the predicted data and analysis results obtained by the generative model means, and specifically includes energy consumption and the scheduled time of use of renewable energy.

[0831] "Renewable energy sources" refers to sustainable energy sources such as solar, wind, and hydroelectric power.

[0832] "Means for performing power source switching" refers to a control device or system for automatically switching between renewable energy sources and conventional power sources as needed.

[0833] "Optimization measures" refers to the means and methods for efficiently adjusting the operation of each electronic device and minimizing energy consumption based on collected data and estimation results.

[0834] In order to carry out the present invention, an embodiment including the following system configuration and operation procedure is adopted.

[0835] System Configuration

[0836] 1. Server:

[0837] Data collection method: The server collects real-time operational status data of each electronic device in the physical store (lighting, heating and cooling, POS system, security camera), obtains weather conditions from an external weather data provider, and identifies relevant weather data based on the store's location information.

[0838] Generative model means: The server inputs the collected operational status data and weather data into a generative AI model, which is then used to predict optimal power consumption and the timing of renewable energy use.

[0839] Power management: The server analyzes the results of the generative AI model's estimations and sends instructions to each electronic device to control its energy consumption. It also switches the power source to renewable energy sources as needed.

[0840] 2. Terminals (electronic devices):

[0841] Based on instructions received from the server, each electronic device adjusts its power consumption in real time and switches to renewable energy at the specified times.

[0842] 3. User:

[0843] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model to improve prediction accuracy.

[0844] Processing Description

[0845] 1. Data collection methods:

[0846] The server uses sensors and data collection devices to collect data on the operating status of each electronic device in the physical store as well as external weather data. For example, the store's lighting has an illuminance sensor, the heating and cooling has a temperature sensor, the POS system has transaction data, and the security camera has operating time data.

[0847] 2. Generative modeling methods:

[0848] The server inputs the collected operational status data and weather data into a generative AI model, which predicts optimal energy usage and the timing of renewable energy use based on past data and experience. The generative AI model uses deep learning platforms such as Tensorflow and PyTorch.

[0849] 3. Power management measures:

[0850] The server sends instructions to each electronic device to adjust its energy consumption based on the predictions of the generative AI model, and also sends control signals to indicate when it needs to switch to renewable energy sources, allowing the electronic device to consume power efficiently and maximize the use of sustainable energy.

[0851] Specific examples

[0852] Consider a situation where the electronics in a brick-and-mortar store exceed their normal utilization rate one afternoon. In this case, the server operates as follows:

[0853] 1. The server collects real-time operating status data sent from electronic devices (e.g., lighting 75%, heating / cooling 60%, POS system 7%, security camera 3%) and related weather data (e.g., temperature 30°C, humidity 60%).

[0854] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and renewable energy usage timing for electronic devices between 2:00 PM and 4:00 PM.

[0855] 3. Based on the prediction results of the generative AI model, the server sends instructions to each electronic device to "adjust lighting consumption to 50%, heating and cooling consumption to 40%, and switch to solar power generation from 2:30 pm."

[0856] 4. Electronic devices adjust their power consumption in real time according to instructions from the server and switch to renewable energy at the specified times.

[0857] Example prompt for a generative AI model:

[0858] tokyo_store_lighting=75, hvac=60, pos_system=7, security_camera=3, temperature=30, humidity=60

[0859] In this way, the system of the present invention effectively manages energy consumption in physical stores and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reducing environmental impact.

[0860] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0861] Step 1:

[0862] The server collects operational status data from electronic devices in the physical store. Specifically, the server obtains real-time operational status data from various sensors and data acquisition devices on lighting, heating and cooling, POS systems, security cameras, etc. This operational status data includes each device's operating time, power consumption, frequency of use, etc. The input data is the operating status of each device, and the output is storage of this data in a database.

[0863] Step 2:

[0864] The server obtains weather condition data from an external weather data provider. Using the store's location information, the server obtains current weather data (e.g., temperature, humidity, wind speed, and rainfall) in real time from the weather provider's API. The input data is location information, and the output is weather data. This weather data is also stored in the database, just like the operating status data.

[0865] Step 3:

[0866] The server inputs the collected operational status data and weather data into the generative AI model. The generative AI model predicts optimal energy consumption and the timing for using renewable energy based on past data and experience. The input data are operational status data and weather data, and the output is predicted data for the estimated optimal energy consumption and the timing for using renewable energy.

[0867] Step 4:

[0868] The server analyzes the estimation results of the generative AI model and sends instructions to the electronic devices to adjust their power consumption. For example, it sends specific instructions such as "Adjust lighting consumption to 50% and heating / cooling consumption to 40% between 2:00 PM and 4:00 PM, and switch to solar power generation from 2:30 PM." The input data is the estimation results of the generative AI model, and the output is a control signal to the electronic devices.

[0869] Step 5:

[0870] The terminal (electronic device) adjusts its power consumption in real time based on instructions received from the server. Specifically, the electronic device controls power consumption according to instructions from the server and switches to renewable energy at the specified time. The input data is a control signal from the server, and the output is the adjusted power consumption and the switch to renewable energy.

[0871] Step 6:

[0872] Users monitor the system's operation and provide real-time feedback. The user's feedback is sent to the server, which then updates the generative AI model based on this feedback to further improve the accuracy of the next prediction. The input data is user feedback, and the output is the updated generative AI model.

[0873] Through these steps, the system of the present invention effectively manages energy consumption in physical stores and optimizes the use of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[0874] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0875] The present invention is a system for improving the efficiency of power consumption in communication devices and achieving optimal utilization of renewable energy. Furthermore, by combining this system with an emotion engine that recognizes user emotions, it enables optimal energy management based on the user's emotions. Specific embodiments of the present invention are described below.

[0876] System Overview

[0877] The system of the present invention consists of the following main components:

[0878] 1. Data Collection Methods

[0879] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[0880] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[0881] 2. Generative AI Model Means

[0882] The server inputs the collected operational status data and weather data into a generative AI model, which then predicts optimal power consumption and the timing of renewable energy use.

[0883] 3. Power management measures

[0884] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[0885] 4. Emotion Engine

[0886] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0887] 5. Feedback channels

[0888] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[0889] Specific examples

[0890] For example, consider a situation where communication device A must process more data than its normal utilization rate in the afternoon of a certain day. The processing flow in this case is as follows:

[0891] 1. Collecting operational status data

[0892] The server acquires real-time operational status data transmitted from communication device A. The operational status data includes communication volume, operating time, and temperature.

[0893] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of communication device A.

[0894] 2. Using generative AI models

[0895] The server inputs the operational status data, weather data, and user emotion data obtained from the emotion engine into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy use.

[0896] 3. Sending operation instructions

[0897] The server analyzes the prediction results obtained from the generative AI model and sends instructions to communication device A to "reduce power consumption by 30% between 2:00 p.m. and 4:00 p.m. and switch to solar power generation."

[0898] The terminal (communication device A) adjusts power consumption in real time based on instructions from the server and switches to renewable energy at the specified time.

[0899] 4. Providing Feedback

[0900] The user monitors the operation results of communication device A and their own emotional data, for example, to check whether power consumption has been reduced as predicted and whether renewable energy has been used appropriately.

[0901] Users can provide feedback to the server as needed, which the server uses to update the generative AI model and improve the accuracy of the next prediction.

[0902] In this way, the system of the present invention, which combines an emotion engine, can improve the efficiency of power consumption in communication devices and optimize the use of renewable energy. Furthermore, by taking into account the user's emotion data, it becomes possible to manage energy in a way that takes into account the user's comfort and stress level.

[0903] The processing flow will be explained below.

[0904] Step 1:

[0905] The server collects operational status data from each communication device (terminal) in real time. Specifically, it periodically acquires data such as communication volume, operating time, and temperature, and centralizes the collected data.

[0906] Step 2:

[0907] The server retrieves the latest weather conditions from external weather data providers, including information on weather, temperature, solar radiation, wind speed, etc. It then matches the location information of the communication devices to identify the weather data relevant to each device.

[0908] Step 3:

[0909] The server collects emotional data through an emotion engine that recognizes the user's emotions. The emotional data is acquired in real time from the user's facial expressions and voice. The emotion engine quantifies the emotional data and indicates the user's stress level and comfort level.

[0910] Step 4:

[0911] The server inputs the collected operational status data, weather data, and emotion data into the generative AI model, performing preprocessing on the data, such as normalizing it and filling in missing values.

[0912] Step 5:

[0913] The generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on the given data. The generative AI model derives efficient energy management from past data and learning results.

[0914] Step 6:

[0915] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions based on them, such as "reduce electricity consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[0916] Step 7:

[0917] The server then sends operational instructions based on the analysis results to each communication device, which then adjusts their power consumption in real time based on these instructions.

[0918] Step 8:

[0919] The terminal (communication device) can switch its power source to a renewable energy source as needed, for example, by setting it to prioritize the use of electricity from solar power or wind power.

[0920] Step 9:

[0921] The user monitors the results of the system's operation and also checks their own emotional data to assess how comfortable or stressed they feel.

[0922] Step 10:

[0923] Users provide feedback on the system's behavior as needed. The server uses this feedback to continuously learn and update the generative AI model, improving prediction accuracy.

[0924] Example 2

[0925] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0926] The power consumption of communication devices is increasing year by year, necessitating efficient power management. However, conventional methods do not adequately optimize the timing of renewable energy usage or power consumption. Furthermore, power management does not take into account the user's comfort and stress level. Therefore, a system that reduces power waste and realizes optimal use of renewable energy is needed.

[0927] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0928] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, a generative artificial intelligence model means for estimating efficient power consumption based on the operating status, weather conditions, and user emotion data, means for controlling the power consumption of the communication device based on the estimation result of the generative artificial intelligence model means and means for switching power to a renewable energy source, emotion recognition means for generating user emotion data, and means for updating the generative artificial intelligence model means based on performance data and emotion data. This makes it possible to improve the efficiency of power consumption of the communication device, optimally utilize renewable energy, and enable energy management that takes user comfort and stress levels into consideration.

[0929] A "communication device" is a device that can be connected to a network and send and receive data. Examples include smartphones, tablets, and personal computers.

[0930] "Operational status data" refers to data that indicates information related to the operation of a communication device. Specifically, this includes information such as communication volume, operating time, and temperature.

[0931] "Weather conditions" refers to data that indicates information about the weather at a particular location, such as temperature, humidity, wind speed, and air pressure.

[0932] A "generative AI model" is a machine learning model that makes predictions and optimizations based on collected data. It refers to a model built using artificial intelligence technology.

[0933] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[0934] "Renewable energy sources" refers to energy sources that provide environmentally friendly energy, such as solar, wind, and hydropower.

[0935] "Emotion recognition means" refers to a technical means for analyzing a user's emotions and generating emotion data. For example, it includes technology that analyzes a user's facial expressions and voice using a camera or microphone.

[0936] "Performance data" refers to data that records the past operation results of the system. Specifically, it includes information such as power consumption and renewable energy usage.

[0937] "Feedback data" is data that records user evaluations and opinions on the behavior of the system. It is used to improve and optimize the system.

[0938] "Generative AI model means" refers to a technical means for estimating efficient power consumption using a generative AI model, utilizing machine learning algorithms and computational resources.

[0939] System Program Overview

[0940] This invention is a system that improves the efficiency of power consumption in communication devices and realizes optimal utilization of renewable energy. Furthermore, by combining it with an emotion engine that recognizes user emotions, optimal energy management based on the user's emotions is possible. Specific embodiments of the invention are described below.

[0941] Hardware and software used

[0942] Server: Collects data, runs generative AI models, sends instructions to control power consumption, generates emotion data, and processes feedback data.

[0943] Software used: TensorFlow, PyTorch, API communication module

[0944] Terminal (communication device): Sends operational status data of the communication device to the server and executes power consumption control instructions.

[0945] Example: Smartphone, tablet, PC

[0946] Users: Monitor the system's behavior and provide feedback.

[0947] Hardware used: Camera, microphone

[0948] Program processing

[0949] Data collection

[0950] The server obtains real-time operational status data from each communication device (terminal), including communication volume, operating time, temperature, etc. The server also obtains weather data such as temperature, humidity, wind speed, and air pressure from weather data providers via API, and identifies relevant weather data based on the location information of the communication device.

[0951] Predictions from generative AI models

[0952] The server inputs collected operational status data, weather data, and user sentiment data into a generative AI model built with TensorFlow and PyTorch to predict optimal power consumption and the timing of renewable energy use.

[0953] Controlling Power Consumption

[0954] The server analyzes the prediction results of the generative AI model and sends specific operational instructions to each communication device. The terminal (communication device) adjusts its power consumption in real time based on instructions from the server and switches to renewable energy at the specified timing.

[0955] Use of emotion engine

[0956] The server recognizes the user's emotions and generates emotional data based on them. This data, which indicates the user's stress level and comfort level, is collected using a camera and microphone. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[0957] Providing feedback

[0958] The user monitors the system's operating status and their own emotional data. They check the system's operating results (for example, the reduction in power consumption of communication devices or the use of renewable energy) and provide feedback to the server as needed. The server then updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[0959] Specific examples

[0960] For example, suppose that one afternoon, communication device A needs to handle more data traffic than its normal utilization rate. In this case, the process flow is as follows:

[0961] 1. Collecting operational status data

[0962] The server acquires real-time operating status data (communication volume, operating time, temperature) sent from communication device A. The server also acquires weather conditions (temperature, humidity, wind speed) via API and identifies related weather data based on the location information of communication device A.

[0963] 2. Using generative AI models

[0964] The server inputs operational status data, weather data, and user emotion data into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy usage.

[0965] 3. Sending operation instructions

[0966] The server analyzes the prediction results obtained from the generative AI model and sends an instruction to communication device A to "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation." Communication device A adjusts its power consumption based on this instruction and switches to renewable energy at the specified time.

[0967] 4. Providing Feedback

[0968] The user monitors the operation results of communication device A and their own emotional data. For example, they check whether power consumption was reduced as predicted or whether renewable energy was used appropriately, and provide feedback to the server. The server then updates the generative AI model based on this information to improve the accuracy of the next prediction.

[0969] Prompt Sentence Examples

[0970] "Communication device A is expected to operate at a higher than usual rate between 2:00 and 4:00 PM. Please predict the optimal power consumption and timing for using renewable energy during this time. Current operation status data includes communication volume, operation time, and temperature, while weather data includes temperature, humidity, and wind speed. Also, please take into account user emotion data."

[0971] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0972] Step 1: Collect data

[0973] The server obtains real-time operational status data from each communication device (terminal). The operational status data includes communication volume, operating time, temperature, etc. The server also obtains current weather conditions (temperature, humidity, wind speed, air pressure, etc.) from a weather data provider via an API and identifies relevant weather data based on the location information of the communication device. In this step, the operational status data sent from each communication device and the weather data obtained from the weather data provider are input, and the server outputs a database that manages all of this data collectively.

[0974] Specific behavior:

[0975] The server uses an API to obtain real-time data on communication volume, operating time, and temperature from the communication device.

[0976] The server calls the weather data provider's API to obtain temperature, humidity, wind speed, and air pressure, and identifies data that matches the location information of the communication device.

[0977] Step 2: Generative AI model prediction

[0978] The server inputs the operational status data, weather data, and user emotion data collected in step 1 into a generative AI model. The generative AI model is executed using a machine learning framework (e.g., TensorFlow or PyTorch) on the server. This model predicts the optimal amount of electricity consumption and the timing to use renewable energy. In this step, the operational status data, weather data, and user emotion data are input, and the predicted results of the optimal amount of electricity consumption and the timing to use renewable energy are output.

[0979] Specific behavior:

[0980] The server runs the generative AI model using a machine learning framework and inputs operational status data, weather data, and emotion data.

[0981] The output of the generative AI model is a specific prediction result, such as "reduce electricity consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0982] Step 3: Sending operation instructions

[0983] The server analyzes the prediction results of the generative AI model obtained in step 2 and sends specific operation instructions to each communication device. In this step, the prediction results from the generative AI model are input, and specific operation instructions to the communication device are output.

[0984] Specific behavior:

[0985] The server analyzes the prediction results obtained from the generative AI model and creates optimal power consumption adjustment instructions for each communication device.

[0986] The server sends specific instructions to communication device A via an API, such as "Reduce power consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[0987] Step 4: Adjusting power consumption

[0988] The terminal (communication device) adjusts power consumption in real time based on instructions from the server. In this step, the operation instructions from the server are the input, and the adjusted power consumption and renewable energy usage results are the output.

[0989] Specific behavior:

[0990] The communication device A receives and executes the instruction to reduce power consumption from the server.

[0991] Communication device A suspends certain processes and adjusts its operating mode to reduce power consumption.

[0992] Switch to renewable energy sources at designated times.

[0993] Step 5: Generate sentiment data and provide feedback

[0994] The user monitors the system's operation status and provides feedback to the server. The server then identifies the user's emotions through emotion recognition means and generates emotion data. In step 5, the system's operation results and the user's emotion data are input, and the feedback data and an updated generative AI model are output.

[0995] Specific behavior:

[0996] The server uses a camera and microphone to acquire the user's emotional data (stress level and comfort level), and generates data using an emotion engine.

[0997] Users can check the progress of power consumption reductions and renewable energy usage on the dashboard.

[0998] If desired, the user sends feedback to the server.

[0999] The server updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[1000] (Application example 2)

[1001] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1002] The present invention relates to a system that improves the efficiency of power consumption of communication devices and optimizes the use of renewable energy. In particular, the present invention aims to simultaneously achieve user comfort and efficient power consumption by enabling energy management that takes user emotions into account. Conventional systems simply manage power consumption based on the operating status of communication devices and weather conditions, and are unable to manage energy that takes user emotions and stress levels into account. This makes it difficult to improve power consumption efficiency without compromising user comfort.

[1003] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1004] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for acquiring user emotion data and inputting it to the generative model means, means for controlling the power consumption of the communication device based on the estimation result of the generative model means and means for switching power to a renewable energy source, means for optimizing the operating environment of the communication device based on the user emotion data, and means for feeding back the operation results of the system and the user emotion data. This enables energy management that takes user emotion into consideration, thereby making it possible to efficiently consume power in the communication device while maintaining user comfort.

[1005] The "operating status" indicates the operating state of the communication device, and includes information such as communication volume, operating time, and temperature.

[1006] "Weather conditions" refers to weather information, such as temperature, precipitation, and wind speed, associated with the location information of the communication device, obtained from a weather data provider.

[1007] "Generative model means" refers to an AI model used to predict optimal power consumption for communications devices and the timing of renewable energy use based on collected operational status data and weather data.

[1008] "Emotion data" is data generated based on the user's emotions, and indicates the stress level and comfort the user feels toward the system.

[1009] The "means for controlling power consumption" is a means for adjusting the amount of power consumed by the communication device based on the prediction results obtained from the generative model means.

[1010] "Renewable energy sources" refers to power sources that use energy obtained from the natural environment, such as solar power and wind power.

[1011] "Means for performing power supply switching" refers to means for switching the power supply from a conventional power source to a renewable energy source.

[1012] "Means for optimizing the operating environment" refers to means for adjusting the communication device and its surrounding environment (e.g., temperature, lighting, music, etc.) based on the user's emotional data, to create a comfortable environment for the user.

[1013] The "feedback means" is a means for providing the system's operational results and user emotion data to the server, and automatically updating and correcting the generative model means based on this.

[1014] The present invention provides a system for improving the efficiency of power consumption in communication devices and optimizing the use of renewable energy. The system also recognizes a user's emotions and manages energy based on those emotions, thereby managing power consumption while maintaining user comfort. Specific embodiments of the present invention are described below.

[1015] System Configuration

[1016] This system is broadly composed of the following components:

[1017] 1. Data Collection Methods

[1018] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[1019] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[1020] 2. Generative Modeling Methods

[1021] The server inputs the collected operational status data, weather data, and emotional data obtained from users into a generative AI model, which then predicts the optimal power consumption for the communications device and the timing of renewable energy usage.

[1022] 3. Power management measures

[1023] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[1024] 4. Emotion Engine

[1025] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[1026] 5. Feedback channels

[1027] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[1028] Hardware and software used

[1029] The following hardware and software are used to implement the system:

[1030] Hardware: Smartphones, temperature sensors, humidity sensors, emotion recognition cameras, etc.

[1031] Software: Python, weather data API, energy management system, generative AI model

[1032] Data processing and calculation

[1033] The server centrally manages data collected from communication devices and various sensors and processes it in real time. The generative AI model uses this data to predict optimal power consumption and timing for using renewable energy. The server analyzes the results and generates specific instructions for each communication device. This series of processes includes the following data processing and calculations:

[1034] Data collection and pre-processing: Collect data from each communication device and weather data provider and convert it into the required format.

[1035] Use of generative AI models: Input data into generative AI models to get predicted power consumption and energy usage timing.

[1036] Instruction generation and transmission: Based on the prediction results, the server generates and transmits appropriate operation instructions to each communication device.

[1037] Specific examples

[1038] A cafe is introducing this system to optimize the in-store environment while reducing power consumption. When a customer enters the store, the system connects with the customer's smartphone and automatically optimizes the temperature, humidity, and lighting. It also adjusts the volume and selection of background music to help customers relax. The store's power consumption will be switched to renewable energy, making for an environmentally friendly operation.

[1039] Prompt Sentence Examples

[1040] "Please collect temperature, humidity, and customer sentiment data from within the store, and predict the optimal energy consumption and timing for using renewable energy based on the weather forecast for the day. Also, please output specific operating instructions to provide a comfortable environment for customers."

[1041] As described above, the system of the present invention can improve the efficiency of power consumption of communication devices while taking into consideration the user's emotions, and can realize the effective use of renewable energy.

[1042] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1043] Step 1:

[1044] The server collects operational status data from each communication device (terminal). This operational status data includes communication volume, operating time, temperature, etc. This makes it possible to understand the terminal's power consumption and operating status. The input is operational status data from the communication device, and the output is the server receiving and saving this data.

[1045] Step 2:

[1046] The server obtains weather conditions from a weather data provider, including temperature, precipitation, wind speed, etc., based on the location information of the communication device. The input is the location information of the communication device and weather data from the weather data provider, and the output is the identified weather data, which allows the influence of the external environment to be taken into account.

[1047] Step 3:

[1048] The server collects the user's emotional data, including the user's stress level and comfort level. The data is collected using emotion-recognition cameras and sensors and analyzed through an emotion engine. The input is information obtained from the user's facial expressions and behavior, and the output is analyzed emotional data.

[1049] Step 4:

[1050] The server inputs the collected operational status data, weather data, and emotion data into a generative AI model. Based on this data, the generative AI model predicts optimal power consumption and the timing of renewable energy usage. The input is the collected data, and the output is the predicted power consumption and timing of energy usage.

[1051] Step 5:

[1052] The server generates and transmits operational instructions to control the power consumption of the communications device based on the prediction results of the generative AI model. The terminal receives instructions from the server, adjusts power consumption in real time, and switches to renewable energy sources as needed. The input is the prediction result of the generative AI model, and the output is specific operational instructions for the communications device.

[1053] Step 6:

[1054] The user monitors the system's operation status and checks performance data and emotion data. For example, they check whether power consumption has been reduced as predicted or whether renewable energy has been used appropriately. The inputs are the system's operation results and emotion data, and the output is the monitoring results of these data.

[1055] Step 7:

[1056] Users can provide feedback to the server as needed. The server updates the generative AI model based on this feedback to improve the accuracy of the next prediction. The input is the user's feedback, and the output is the updated generative AI model.

[1057] Through the above steps, the server, terminal, and user work together to make the power consumption of the communication device more efficient, thereby optimizing the use of renewable energy while maintaining user comfort.

[1058] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1059] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1060] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1061] [Fourth embodiment]

[1062] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1063] 7, a 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.

[1064] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1065] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1066] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1067] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1068] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1069] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1070] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1071] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1072] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1073] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1074] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1075] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. The following describes specific embodiments of the present invention.

[1076] System Overview

[1077] The system of the present invention collects information on the operating status and weather conditions of each communication device, and then uses a generative AI model to calculate the optimal power consumption and power switching timing. The specific components and their operation are as follows:

[1078] 1. Data Collection Methods

[1079] The server collects operational status data from each communication device (terminal) in real time, including communication volume, operating time, temperature, etc.

[1080] The server obtains weather conditions from external weather data providers and identifies relevant weather data based on the location information of the communication device.

[1081] 2. Generative AI Model Means

[1082] The server inputs the collected operational status data and weather data into a generative AI model, which uses past data and experience to predict the optimal power consumption and timing for renewable energy use for each communication device.

[1083] 3. Power management measures

[1084] The server analyzes the prediction results of the generative AI model and, based on that, sends instructions to each communication device to control power consumption.

[1085] Based on instructions received from the server, the terminal (communication device) adjusts power consumption in real time and switches power to renewable energy sources as needed.

[1086] 4. Feedback methods

[1087] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model based on the collected feedback, improving the system's prediction accuracy.

[1088] Specific examples

[1089] For example, consider a situation where communication device A needs to handle more data traffic than its normal utilization rate in the afternoon. In this case, the server operates as follows:

[1090] 1. The server collects real-time operational status data and related weather data sent from communication device A.

[1091] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and timing for renewable energy use for communication device A between 2:00 PM and 4:00 PM.

[1092] 3. Based on the prediction results of the generative AI model, the server sends an instruction to communication device A to "reduce current power consumption by 30% and switch to solar power generation from 2:30 pm."

[1093] 4. Communication device A adjusts its power consumption in real time according to instructions from the server and switches to renewable energy at the specified time.

[1094] 5. Users monitor the system's performance and provide real-time feedback as needed, which the server uses to update the generative AI model and further improve the accuracy of future predictions.

[1095] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reduced environmental impact.

[1096] The processing flow will be explained below.

[1097] Step 1:

[1098] The server acquires operational status data from each communication device (terminal) in real time. Specifically, it periodically collects data such as the amount of communication traffic, operating time, and temperature sent from the communication device.

[1099] Step 2:

[1100] The server retrieves weather data from external weather data providers, including data on current weather, temperature, solar radiation, wind speed, etc., and further identifies weather conditions associated with each communication device in association with the communication device's location information.

[1101] Step 3:

[1102] The server preprocesses the operational status data collected in step 1 and the weather data acquired in step 2, preparing them for input into the generative AI model. This preprocessing includes imputing missing values ​​and standardizing the data.

[1103] Step 4:

[1104] The server inputs the preprocessed data into a generative AI model that predicts the optimal power consumption and renewable energy usage timing for each communication device. The generative AI model makes these predictions based on past data and experience.

[1105] Step 5:

[1106] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions for each communication device, such as "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[1107] Step 6:

[1108] The server transmits the generated operation instructions to each communication device, and upon receiving the instructions, the communication device adjusts its power consumption in real time based on the instructions.

[1109] Step 7:

[1110] The terminal (communication device) will switch its power source to renewable energy sources as needed, for example, by following instructions to prioritize the use of electricity from solar or wind power.

[1111] Step 8:

[1112] Users can monitor the system's operation and check performance data, such as availability, power consumption, and renewable energy usage.

[1113] Step 9:

[1114] If the user determines that the system's behavior or predictions are inappropriate or that there is room for improvement, the user provides feedback to the server, including specific problems and suggestions for improvement.

[1115] Step 10:

[1116] The server collects user feedback and performance data, and continuously learns and updates the generative AI model based on that data, thereby improving the accuracy of the next prediction.

[1117] Example 1

[1118] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1119] Currently, there is a demand for optimizing the power consumption of communication devices and efficiently using renewable energy. However, existing systems have difficulty managing power consumption while fully considering the operating status of communication devices and weather conditions. In addition, there is a lack of a mechanism to incorporate user feedback and update the AI ​​model, which makes it difficult to improve prediction accuracy.

[1120] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1121] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for the communication device to adjust power consumption in real time based on the generative AI model, and means for updating the generative AI model based on feedback from users. This allows for efficient management of power consumption of the communication devices and optimal utilization of renewable energy.

[1122] A "communication device" is an electronic device for transmitting and receiving data.

[1123] "Operational status" is data indicating the current operating state and performance indicators of a communication device.

[1124] "Weather conditions" refers to environmental data such as the weather and temperature of the area where the communication device is installed.

[1125] A "generative model implementation" is an algorithm and software for estimating optimal power consumption based on collected data.

[1126] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[1127] "Renewable energy sources" are sustainable energy sources obtained from nature, such as solar power and wind power.

[1128] A "generative AI model" is an artificial intelligence model that predicts optimal power consumption for communication devices based on past data and experience.

[1129] "Real time" refers to a time period in which data processing and responses occur almost immediately.

[1130] "Feedback" refers to opinions and suggestions for corrections from users regarding the results of system operations.

[1131] "Update" means improving current models and systems based on new data and feedback.

[1132] The present invention provides a system that improves the efficiency of power consumption in communication devices and optimally utilizes renewable energy. This enables sustainable operation of communication devices, reducing energy costs and environmental impact. An embodiment of the present invention will now be described in detail.

[1133] This system consists of a server, terminals (communications devices), and users. The server collects information on the operating status of each communications device and external weather conditions in real time, and calculates the optimal amount of electricity consumption and the timing of renewable energy usage based on this information. This is done using a generative AI model. Specifically, the generative AI model, built using TensorFlow, is trained and inference processed on AWS SageMaker.

[1134] Data collection

[1135] The server collects operational status data such as communication volume, operating time, and temperature from each communication device. The server also obtains weather data provided in JSON format from an external weather data provider and identifies relevant weather data based on the location information of the communication device. This data collection is performed using an automated process using AWS Lambda, and the collected data is stored in Amazon S3.

[1136] Using generative AI models

[1137] The server inputs the collected operational status data and weather data into a generative AI model. This generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on past data and experience. To implement the model, TensorFlow is used to build it, and AWS SageMaker is used to train and infer the model.

[1138] Generating Power Management Instructions

[1139] Based on the prediction results of the generative AI model, the server generates specific instructions for the communication device to control power consumption, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." These instructions are sent to the communication device using the MQTT protocol.

[1140] Power consumption regulation

[1141] The communication device adjusts power consumption in real time based on instructions from the server, and switches power to renewable energy sources at the set time. This is done using a general-purpose computer as an edge computing device, and transmits control instructions via Azure IoT Hub.

[1142] Feedback and Model Updates

[1143] Users monitor the system's operation status and provide feedback in real time. Using a dedicated monitoring application, they send the necessary feedback to the server based on the system's operation results. The server collects the user feedback using Google Cloud Pub / Sub and analyzes it using BigQuery. The generative AI model is then retrained based on the analysis results to improve the accuracy of the next prediction.

[1144] Specific examples

[1145] Here is an example prompt:

[1146] "Please obtain the current traffic volume and weather data of communication device A in real time."

[1147] "Based on the collected data, please predict the optimal power consumption and renewable energy usage timing between 2:00 PM and 4:00 PM."

[1148] "Based on the predictions of the generative AI model, please instruct communication device A to reduce power consumption by 30% and switch to solar power generation starting at 2:30 PM."

[1149] In this way, the system of the present invention effectively manages the power consumption of communication devices and realizes optimal utilization of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[1150] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1151] Step 1: Data collection

[1152] The server collects operational status data of each communication device and external weather data in real time. The inputs are operational status data (communication volume, operating time, temperature, etc.) sent from the communication device and weather information obtained from a weather data provider. The server automates this data collection using AWS Lambda and stores the collected data in Amazon S3. The output is well-formatted operational status data and weather data.

[1153] Step 2: Data Shaping

[1154] The server converts the collected operational status data and weather data into a format that can be input to the generative AI model. The input at this time is the raw data collected in step 1. Data reformatting involves imputing missing values, normalizing values, and encoding categorical data. The output is a dataset in a format that is compatible with the generative AI model.

[1155] Step 3: Input to the generative AI model

[1156] The server inputs the formatted dataset into the generative AI model to predict optimal power consumption and the timing of renewable energy usage. The input at this time is the dataset formatted in step 2. The output is the predicted optimal power consumption and timing of energy usage for each communication device. The server runs the generative AI model built with TensorFlow on AWS SageMaker.

[1157] Step 4: Generate Power Management Directives

[1158] The server generates specific power management instructions based on the prediction results of the generative AI model. The input at this time is the prediction results obtained in step 3. The server analyzes the prediction results and generates specific instructions for each communication device, such as "reduce power consumption by 30%" or "switch to solar power generation from 2:30 pm." The output is the power management instructions sent to the terminal.

[1159] Step 5: Sending instructions

[1160] The server sends the generated power management instructions to each communication device. The input at this time is the power management instruction generated in step 4. The server uses the MQTT protocol to send the power management instructions to the communication devices in real time. The output is the power management instruction received by the communication devices.

[1161] Step 6: Adjusting power consumption

[1162] The terminal (communication device) adjusts its power consumption in real time based on instructions received from the server. The input at this time is the power management instruction received in step 5. The terminal switches its power source to a renewable energy source (e.g., solar power generation) at the set time. The output is the adjusted power consumption state.

[1163] Step 7: Submit your feedback

[1164] The user monitors the system's operation status and provides real-time feedback to the server. The input is the user's observations and evaluations. The user checks the system's operation results using a dedicated monitoring application and enters the necessary feedback. The output is feedback data sent to the server.

[1165] Step 8: Update the generative AI model

[1166] The server receives feedback data from users and updates the generative AI model. The input is the feedback data collected in step 7. The server collects the feedback using Google Cloud Pub / Sub and analyzes it with BigQuery. The server retrains the generative AI model based on the analysis results to improve prediction accuracy. The output is an updated generative AI model.

[1167] (Application example 1)

[1168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1169] In recent years, energy issues and increasing environmental impacts have become social challenges. In particular, physical stores operate a variety of electrical devices, and efficient management of their energy consumption is essential for achieving sustainable operations. However, conventional systems have difficulty optimizing power consumption in real time or appropriately controlling the timing of renewable energy usage. Furthermore, energy management methods within physical stores based on weather conditions and operating status are immature, making it difficult to effectively improve energy efficiency. New methods are needed to solve these problems and achieve energy conservation and sustainability in physical stores.

[1170] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1171] In this invention, the server includes means for collecting the operating status and weather conditions of each electronic device, a generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for controlling the power consumption of the electronic devices based on the estimation results of the generative model means and means for switching power to a renewable energy source, and means for optimizing the energy consumption and usage timing of various devices using in-store operating status data and external weather data. This enables real-time optimization of energy consumption and effective use of renewable energy in physical stores.

[1172] "Electronic equipment" is a general term for all devices and equipment operating within a store, including, for example, lighting, heating and cooling, POS systems, and security cameras.

[1173] "Operating status" refers to data that indicates how frequently and at what output an electronic device is operating, and specifically includes operating time, power consumption, frequency of use, and the like.

[1174] "Weather conditions" refers to data related to the weather in the external environment, and specifically includes temperature, humidity, wind speed, rainfall, and the like.

[1175] "Means for collection" refers to systems and methods for collecting data on the operating status of electronic devices and weather conditions using various sensors and data acquisition devices.

[1176] "Generative modeling tools" refer to machine learning models and AI systems that use collected data to predict optimal electricity consumption and the timing of renewable energy use.

[1177] "Estimation results" refers to the predicted data and analysis results obtained by the generative model means, and specifically includes energy consumption and the scheduled time of use of renewable energy.

[1178] "Renewable energy sources" refers to sustainable energy sources such as solar, wind, and hydroelectric power.

[1179] "Means for performing power source switching" refers to a control device or system for automatically switching between renewable energy sources and conventional power sources as needed.

[1180] "Optimization measures" refers to the means and methods for efficiently adjusting the operation of each electronic device and minimizing energy consumption based on collected data and estimation results.

[1181] In order to carry out the present invention, an embodiment including the following system configuration and operation procedure is adopted.

[1182] System Configuration

[1183] 1. Server:

[1184] Data collection method: The server collects real-time operational status data of each electronic device in the physical store (lighting, heating and cooling, POS system, security camera), obtains weather conditions from an external weather data provider, and identifies relevant weather data based on the store's location information.

[1185] Generative model means: The server inputs the collected operational status data and weather data into a generative AI model, which is then used to predict optimal power consumption and the timing of renewable energy use.

[1186] Power management: The server analyzes the results of the generative AI model's estimations and sends instructions to each electronic device to control its energy consumption. It also switches the power source to renewable energy sources as needed.

[1187] 2. Terminals (electronic devices):

[1188] Based on instructions received from the server, each electronic device adjusts its power consumption in real time and switches to renewable energy at the specified times.

[1189] 3. User:

[1190] Users monitor the system's operation and send real-time feedback to the server, which then updates the generative AI model to improve prediction accuracy.

[1191] Processing Description

[1192] 1. Data collection methods:

[1193] The server uses sensors and data collection devices to collect data on the operating status of each electronic device in the physical store as well as external weather data. For example, the store's lighting has an illuminance sensor, the heating and cooling has a temperature sensor, the POS system has transaction data, and the security camera has operating time data.

[1194] 2. Generative modeling methods:

[1195] The server inputs the collected operational status data and weather data into a generative AI model, which predicts optimal energy usage and the timing of renewable energy use based on past data and experience. The generative AI model uses deep learning platforms such as Tensorflow and PyTorch.

[1196] 3. Power management measures:

[1197] The server sends instructions to each electronic device to adjust its energy consumption based on the predictions of the generative AI model, and also sends control signals to indicate when it needs to switch to renewable energy sources, allowing the electronic device to consume power efficiently and maximize the use of sustainable energy.

[1198] Specific examples

[1199] Consider a situation where the electronics in a brick-and-mortar store exceed their normal utilization rate one afternoon. In this case, the server operates as follows:

[1200] 1. The server collects real-time operating status data sent from electronic devices (e.g., lighting 75%, heating / cooling 60%, POS system 7%, security camera 3%) and related weather data (e.g., temperature 30°C, humidity 60%).

[1201] 2. The server inputs the collected data into a generative AI model to predict the optimal power consumption and renewable energy usage timing for electronic devices between 2:00 PM and 4:00 PM.

[1202] 3. Based on the prediction results of the generative AI model, the server sends instructions to each electronic device to "adjust lighting consumption to 50%, heating and cooling consumption to 40%, and switch to solar power generation from 2:30 pm."

[1203] 4. Electronic devices adjust their power consumption in real time according to instructions from the server and switch to renewable energy at the specified times.

[1204] Example prompt for a generative AI model:

[1205] tokyo_store_lighting=75, hvac=60, pos_system=7, security_camera=3, temperature=30, humidity=60

[1206] In this way, the system of the present invention effectively manages energy consumption in physical stores and realizes optimal utilization of renewable energy, thereby achieving improved energy efficiency and reducing environmental impact.

[1207] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1208] Step 1:

[1209] The server collects operational status data from electronic devices in the physical store. Specifically, the server obtains real-time operational status data from various sensors and data acquisition devices on lighting, heating and cooling, POS systems, security cameras, etc. This operational status data includes each device's operating time, power consumption, frequency of use, etc. The input data is the operating status of each device, and the output is storage of this data in a database.

[1210] Step 2:

[1211] The server obtains weather condition data from an external weather data provider. Using the store's location information, the server obtains current weather data (e.g., temperature, humidity, wind speed, and rainfall) in real time from the weather provider's API. The input data is location information, and the output is weather data. This weather data is also stored in the database, just like the operating status data.

[1212] Step 3:

[1213] The server inputs the collected operational status data and weather data into the generative AI model. The generative AI model predicts optimal energy consumption and the timing for using renewable energy based on past data and experience. The input data are operational status data and weather data, and the output is predicted data for the estimated optimal energy consumption and the timing for using renewable energy.

[1214] Step 4:

[1215] The server analyzes the estimation results of the generative AI model and sends instructions to the electronic devices to adjust their power consumption. For example, it sends specific instructions such as "Adjust lighting consumption to 50% and heating / cooling consumption to 40% between 2:00 PM and 4:00 PM, and switch to solar power generation from 2:30 PM." The input data is the estimation results of the generative AI model, and the output is a control signal to the electronic devices.

[1216] Step 5:

[1217] The terminal (electronic device) adjusts its power consumption in real time based on instructions received from the server. Specifically, the electronic device controls power consumption according to instructions from the server and switches to renewable energy at the specified time. The input data is a control signal from the server, and the output is the adjusted power consumption and the switch to renewable energy.

[1218] Step 6:

[1219] Users monitor the system's operation and provide real-time feedback. The user's feedback is sent to the server, which then updates the generative AI model based on this feedback to further improve the accuracy of the next prediction. The input data is user feedback, and the output is the updated generative AI model.

[1220] Through these steps, the system of the present invention effectively manages energy consumption in physical stores and optimizes the use of renewable energy, thereby improving energy efficiency and reducing environmental impact.

[1221] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1222] The present invention is a system for improving the efficiency of power consumption in communication devices and achieving optimal utilization of renewable energy. Furthermore, by combining this system with an emotion engine that recognizes user emotions, it enables optimal energy management based on the user's emotions. Specific embodiments of the present invention are described below.

[1223] System Overview

[1224] The system of the present invention consists of the following main components:

[1225] 1. Data Collection Methods

[1226] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[1227] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[1228] 2. Generative AI Model Means

[1229] The server inputs the collected operational status data and weather data into a generative AI model, which then predicts optimal power consumption and the timing of renewable energy use.

[1230] 3. Power management measures

[1231] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[1232] 4. Emotion Engine

[1233] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[1234] 5. Feedback channels

[1235] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[1236] Specific examples

[1237] For example, consider a situation where communication device A must process more data than its normal utilization rate in the afternoon of a certain day. The processing flow in this case is as follows:

[1238] 1. Collecting operational status data

[1239] The server acquires real-time operational status data transmitted from communication device A. The operational status data includes communication volume, operating time, and temperature.

[1240] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of communication device A.

[1241] 2. Using generative AI models

[1242] The server inputs the operational status data, weather data, and user emotion data obtained from the emotion engine into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy use.

[1243] 3. Sending operation instructions

[1244] The server analyzes the prediction results obtained from the generative AI model and sends instructions to communication device A to "reduce power consumption by 30% between 2:00 p.m. and 4:00 p.m. and switch to solar power generation."

[1245] The terminal (communication device A) adjusts power consumption in real time based on instructions from the server and switches to renewable energy at the specified time.

[1246] 4. Providing Feedback

[1247] The user monitors the operation results of communication device A and their own emotional data, for example, to check whether power consumption has been reduced as predicted and whether renewable energy has been used appropriately.

[1248] Users can provide feedback to the server as needed, which the server uses to update the generative AI model and improve the accuracy of the next prediction.

[1249] In this way, the system of the present invention, which combines an emotion engine, can improve the efficiency of power consumption in communication devices and optimize the use of renewable energy. Furthermore, by taking into account the user's emotion data, it becomes possible to manage energy in a way that takes into account the user's comfort and stress level.

[1250] The processing flow will be explained below.

[1251] Step 1:

[1252] The server collects operational status data from each communication device (terminal) in real time. Specifically, it periodically acquires data such as communication volume, operating time, and temperature, and centralizes the collected data.

[1253] Step 2:

[1254] The server retrieves the latest weather conditions from external weather data providers, including information on weather, temperature, solar radiation, wind speed, etc. It then matches the location information of the communication devices to identify the weather data relevant to each device.

[1255] Step 3:

[1256] The server collects emotional data through an emotion engine that recognizes the user's emotions. The emotional data is acquired in real time from the user's facial expressions and voice. The emotion engine quantifies the emotional data and indicates the user's stress level and comfort level.

[1257] Step 4:

[1258] The server inputs the collected operational status data, weather data, and emotion data into the generative AI model, performing preprocessing on the data, such as normalizing it and filling in missing values.

[1259] Step 5:

[1260] The generative AI model predicts the optimal power consumption and timing for renewable energy use for each communication device based on the given data. The generative AI model derives efficient energy management from past data and learning results.

[1261] Step 6:

[1262] The server analyzes the predictions obtained from the generative AI model and generates specific operational instructions based on them, such as "reduce electricity consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation."

[1263] Step 7:

[1264] The server then sends operational instructions based on the analysis results to each communication device, which then adjusts their power consumption in real time based on these instructions.

[1265] Step 8:

[1266] The terminal (communication device) can switch its power source to a renewable energy source as needed, for example, by setting it to prioritize the use of electricity from solar power or wind power.

[1267] Step 9:

[1268] The user monitors the results of the system's operation and also checks their own emotional data to assess how comfortable or stressed they feel.

[1269] Step 10:

[1270] Users provide feedback on the system's behavior as needed. The server uses this feedback to continuously learn and update the generative AI model, improving prediction accuracy.

[1271] Example 2

[1272] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1273] The power consumption of communication devices is increasing year by year, necessitating efficient power management. However, conventional methods do not adequately optimize the timing of renewable energy usage or power consumption. Furthermore, power management does not take into account the user's comfort and stress level. Therefore, a system that reduces power waste and realizes optimal use of renewable energy is needed.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1275] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, a generative artificial intelligence model means for estimating efficient power consumption based on the operating status, weather conditions, and user emotion data, means for controlling the power consumption of the communication device based on the estimation result of the generative artificial intelligence model means and means for switching power to a renewable energy source, emotion recognition means for generating user emotion data, and means for updating the generative artificial intelligence model means based on performance data and emotion data. This makes it possible to improve the efficiency of power consumption of the communication device, optimally utilize renewable energy, and enable energy management that takes user comfort and stress levels into consideration.

[1276] A "communication device" is a device that can be connected to a network and send and receive data. Examples include smartphones, tablets, and personal computers.

[1277] "Operational status data" refers to data that indicates information related to the operation of a communication device. Specifically, this includes information such as communication volume, operating time, and temperature.

[1278] "Weather conditions" refers to data that indicates information about the weather at a particular location, such as temperature, humidity, wind speed, and air pressure.

[1279] A "generative AI model" is a machine learning model that makes predictions and optimizations based on collected data. It refers to a model built using artificial intelligence technology.

[1280] "Power consumption" refers to the amount of power consumed by a communication device to operate.

[1281] "Renewable energy sources" refers to energy sources that provide environmentally friendly energy, such as solar, wind, and hydropower.

[1282] "Emotion recognition means" refers to a technical means for analyzing a user's emotions and generating emotion data. For example, it includes technology that analyzes a user's facial expressions and voice using a camera or microphone.

[1283] "Performance data" refers to data that records the past operation results of the system. Specifically, it includes information such as power consumption and renewable energy usage.

[1284] "Feedback data" is data that records user evaluations and opinions on the behavior of the system. It is used to improve and optimize the system.

[1285] "Generative AI model means" refers to a technical means for estimating efficient power consumption using a generative AI model, utilizing machine learning algorithms and computational resources.

[1286] System Program Overview

[1287] This invention is a system that improves the efficiency of power consumption in communication devices and realizes optimal utilization of renewable energy. Furthermore, by combining it with an emotion engine that recognizes user emotions, optimal energy management based on the user's emotions is possible. Specific embodiments of the invention are described below.

[1288] Hardware and software used

[1289] Server: Collects data, runs generative AI models, sends instructions to control power consumption, generates emotion data, and processes feedback data.

[1290] Software used: TensorFlow, PyTorch, API communication module

[1291] Terminal (communication device): Sends operational status data of the communication device to the server and executes power consumption control instructions.

[1292] Example: Smartphone, tablet, PC

[1293] Users: Monitor the system's behavior and provide feedback.

[1294] Hardware used: Camera, microphone

[1295] Program processing

[1296] Data collection

[1297] The server obtains real-time operational status data from each communication device (terminal), including communication volume, operating time, temperature, etc. The server also obtains weather data such as temperature, humidity, wind speed, and air pressure from weather data providers via API, and identifies relevant weather data based on the location information of the communication device.

[1298] Predictions from generative AI models

[1299] The server inputs collected operational status data, weather data, and user sentiment data into a generative AI model built with TensorFlow and PyTorch to predict optimal power consumption and the timing of renewable energy use.

[1300] Controlling Power Consumption

[1301] The server analyzes the prediction results of the generative AI model and sends specific operational instructions to each communication device. The terminal (communication device) adjusts its power consumption in real time based on instructions from the server and switches to renewable energy at the specified timing.

[1302] Use of emotion engine

[1303] The server recognizes the user's emotions and generates emotional data based on them. This data, which indicates the user's stress level and comfort level, is collected using a camera and microphone. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[1304] Providing feedback

[1305] The user monitors the system's operating status and their own emotional data. They check the system's operating results (for example, the reduction in power consumption of communication devices or the use of renewable energy) and provide feedback to the server as needed. The server then updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[1306] Specific examples

[1307] For example, suppose that one afternoon, communication device A needs to handle more data traffic than its normal utilization rate. In this case, the process flow is as follows:

[1308] 1. Collecting operational status data

[1309] The server acquires real-time operating status data (communication volume, operating time, temperature) sent from communication device A. The server also acquires weather conditions (temperature, humidity, wind speed) via API and identifies related weather data based on the location information of communication device A.

[1310] 2. Using generative AI models

[1311] The server inputs operational status data, weather data, and user emotion data into the generative AI model, which then provides optimal predictions of the power consumption of communication device A and the timing of renewable energy usage.

[1312] 3. Sending operation instructions

[1313] The server analyzes the prediction results obtained from the generative AI model and sends an instruction to communication device A to "reduce power consumption by 30% between 2:00 PM and 4:00 PM and switch to solar power generation." Communication device A adjusts its power consumption based on this instruction and switches to renewable energy at the specified time.

[1314] 4. Providing Feedback

[1315] The user monitors the operation results of communication device A and their own emotional data. For example, they check whether power consumption was reduced as predicted or whether renewable energy was used appropriately, and provide feedback to the server. The server then updates the generative AI model based on this information to improve the accuracy of the next prediction.

[1316] Prompt Sentence Examples

[1317] "Communication device A is expected to operate at a higher than usual rate between 2:00 and 4:00 PM. Please predict the optimal power consumption and timing for using renewable energy during this time. Current operation status data includes communication volume, operation time, and temperature, while weather data includes temperature, humidity, and wind speed. Also, please take into account user emotion data."

[1318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1319] Step 1: Collect data

[1320] The server obtains real-time operational status data from each communication device (terminal). The operational status data includes communication volume, operating time, temperature, etc. The server also obtains current weather conditions (temperature, humidity, wind speed, air pressure, etc.) from a weather data provider via an API and identifies relevant weather data based on the location information of the communication device. In this step, the operational status data sent from each communication device and the weather data obtained from the weather data provider are input, and the server outputs a database that manages all of this data collectively.

[1321] Specific behavior:

[1322] The server uses an API to obtain real-time data on communication volume, operating time, and temperature from the communication device.

[1323] The server calls the weather data provider's API to obtain temperature, humidity, wind speed, and air pressure, and identifies data that matches the location information of the communication device.

[1324] Step 2: Generative AI model prediction

[1325] The server inputs the operational status data, weather data, and user emotion data collected in step 1 into a generative AI model. The generative AI model is executed using a machine learning framework (e.g., TensorFlow or PyTorch) on the server. This model predicts the optimal amount of electricity consumption and the timing to use renewable energy. In this step, the operational status data, weather data, and user emotion data are input, and the predicted results of the optimal amount of electricity consumption and the timing to use renewable energy are output.

[1326] Specific behavior:

[1327] The server runs the generative AI model using a machine learning framework and inputs operational status data, weather data, and emotion data.

[1328] The output of the generative AI model is a specific prediction result, such as "reduce electricity consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[1329] Step 3: Sending operation instructions

[1330] The server analyzes the prediction results of the generative AI model obtained in step 2 and sends specific operation instructions to each communication device. In this step, the prediction results from the generative AI model are input, and specific operation instructions to the communication device are output.

[1331] Specific behavior:

[1332] The server analyzes the prediction results obtained from the generative AI model and creates optimal power consumption adjustment instructions for each communication device.

[1333] The server sends specific instructions to communication device A via an API, such as "Reduce power consumption by 30% between 2:00 and 4:00 p.m. and switch to solar power generation."

[1334] Step 4: Adjusting power consumption

[1335] The terminal (communication device) adjusts power consumption in real time based on instructions from the server. In this step, the operation instructions from the server are the input, and the adjusted power consumption and renewable energy usage results are the output.

[1336] Specific behavior:

[1337] The communication device A receives and executes the instruction to reduce power consumption from the server.

[1338] Communication device A suspends certain processes and adjusts its operating mode to reduce power consumption.

[1339] Switch to renewable energy sources at designated times.

[1340] Step 5: Generate sentiment data and provide feedback

[1341] The user monitors the system's operation status and provides feedback to the server. The server then identifies the user's emotions through emotion recognition means and generates emotion data. In step 5, the system's operation results and the user's emotion data are input, and the feedback data and an updated generative AI model are output.

[1342] Specific behavior:

[1343] The server uses a camera and microphone to acquire the user's emotional data (stress level and comfort level), and generates data using an emotion engine.

[1344] Users can check the progress of power consumption reductions and renewable energy usage on the dashboard.

[1345] If desired, the user sends feedback to the server.

[1346] The server updates the generative AI model based on the feedback, improving the accuracy of the next prediction.

[1347] (Application example 2)

[1348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1349] The present invention relates to a system that improves the efficiency of power consumption of communication devices and optimizes the use of renewable energy. In particular, the present invention aims to simultaneously achieve user comfort and efficient power consumption by enabling energy management that takes user emotions into account. Conventional systems simply manage power consumption based on the operating status of communication devices and weather conditions, and are unable to manage energy that takes user emotions and stress levels into account. This makes it difficult to improve power consumption efficiency without compromising user comfort.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1351] In this invention, the server includes means for collecting the operating status and weather conditions of each communication device, generative model means for estimating efficient power consumption based on the operating status and weather conditions, means for acquiring user emotion data and inputting it to the generative model means, means for controlling the power consumption of the communication device based on the estimation result of the generative model means and means for switching power to a renewable energy source, means for optimizing the operating environment of the communication device based on the user emotion data, and means for feeding back the operation results of the system and the user emotion data. This enables energy management that takes user emotion into consideration, thereby making it possible to efficiently consume power in the communication device while maintaining user comfort.

[1352] The "operating status" indicates the operating state of the communication device, and includes information such as communication volume, operating time, and temperature.

[1353] "Weather conditions" refers to weather information, such as temperature, precipitation, and wind speed, associated with the location information of the communication device, obtained from a weather data provider.

[1354] "Generative model means" refers to an AI model used to predict optimal power consumption for communications devices and the timing of renewable energy use based on collected operational status data and weather data.

[1355] "Emotion data" is data generated based on the user's emotions, and indicates the stress level and comfort the user feels toward the system.

[1356] The "means for controlling power consumption" is a means for adjusting the amount of power consumed by the communication device based on the prediction results obtained from the generative model means.

[1357] "Renewable energy sources" refers to power sources that use energy obtained from the natural environment, such as solar power and wind power.

[1358] "Means for performing power supply switching" refers to means for switching the power supply from a conventional power source to a renewable energy source.

[1359] "Means for optimizing the operating environment" refers to means for adjusting the communication device and its surrounding environment (e.g., temperature, lighting, music, etc.) based on the user's emotional data, to create a comfortable environment for the user.

[1360] The "feedback means" is a means for providing the system's operational results and user emotion data to the server, and automatically updating and correcting the generative model means based on this.

[1361] The present invention provides a system for improving the efficiency of power consumption in communication devices and optimizing the use of renewable energy. The system also recognizes a user's emotions and manages energy based on those emotions, thereby managing power consumption while maintaining user comfort. Specific embodiments of the present invention are described below.

[1362] System Configuration

[1363] This system is broadly composed of the following components:

[1364] 1. Data Collection Methods

[1365] The server collects operational status data from each communication device (terminal) in real time, including information such as communication volume, operating time, and temperature.

[1366] The server obtains weather conditions from a weather data provider and identifies relevant weather data based on the location information of the communication device.

[1367] 2. Generative Modeling Methods

[1368] The server inputs the collected operational status data, weather data, and emotional data obtained from users into a generative AI model, which then predicts the optimal power consumption for the communications device and the timing of renewable energy usage.

[1369] 3. Power management measures

[1370] The server analyzes the predictions of the generative AI model and sends specific operational instructions to each communication device to control power consumption. The terminal (communication device) adjusts its power consumption in real time based on the instructions and switches to renewable energy sources as necessary.

[1371] 4. Emotion Engine

[1372] The server recognizes the user's emotions and generates emotional data based on them. The emotional data indicates the user's stress level and comfort level with the system. The emotional data is used as input for the generative AI model and is reflected in the prediction results.

[1373] 5. Feedback channels

[1374] Users monitor the system's operation, check performance and emotion data, and provide real-time feedback to the server as needed. The server uses this feedback to update the generative AI model and improve the system's prediction accuracy.

[1375] Hardware and software used

[1376] The following hardware and software are used to implement the system:

[1377] Hardware: Smartphones, temperature sensors, humidity sensors, emotion recognition cameras, etc.

[1378] Software: Python, weather data API, energy management system, generative AI model

[1379] Data processing and calculation

[1380] The server centrally manages data collected from communication devices and various sensors and processes it in real time. The generative AI model uses this data to predict optimal power consumption and timing for using renewable energy. The server analyzes the results and generates specific instructions for each communication device. This series of processes includes the following data processing and calculations:

[1381] Data collection and pre-processing: Collect data from each communication device and weather data provider and convert it into the required format.

[1382] Use of generative AI models: Input data into generative AI models to get predicted power consumption and energy usage timing.

[1383] Instruction generation and transmission: Based on the prediction results, the server generates and transmits appropriate operation instructions to each communication device.

[1384] Specific examples

[1385] A cafe is introducing this system to optimize the in-store environment while reducing power consumption. When a customer enters the store, the system connects with the customer's smartphone and automatically optimizes the temperature, humidity, and lighting. It also adjusts the volume and selection of background music to help customers relax. The store's power consumption will be switched to renewable energy, making for an environmentally friendly operation.

[1386] Prompt Sentence Examples

[1387] "Please collect temperature, humidity, and customer sentiment data from within the store, and predict the optimal energy consumption and timing for using renewable energy based on the weather forecast for the day. Also, please output specific operating instructions to provide a comfortable environment for customers."

[1388] As described above, the system of the present invention can improve the efficiency of power consumption of communication devices while taking into consideration the user's emotions, and can realize the effective use of renewable energy.

[1389] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1390] Step 1:

[1391] The server collects operational status data from each communication device (terminal). This operational status data includes communication volume, operating time, temperature, etc. This makes it possible to understand the terminal's power consumption and operating status. The input is operational status data from the communication device, and the output is the server receiving and saving this data.

[1392] Step 2:

[1393] The server obtains weather conditions from a weather data provider, including temperature, precipitation, wind speed, etc., based on the location information of the communication device. The input is the location information of the communication device and weather data from the weather data provider, and the output is the identified weather data, which allows the influence of the external environment to be taken into account.

[1394] Step 3:

[1395] The server collects the user's emotional data, including the user's stress level and comfort level. The data is collected using emotion-recognition cameras and sensors and analyzed through an emotion engine. The input is information obtained from the user's facial expressions and behavior, and the output is analyzed emotional data.

[1396] Step 4:

[1397] The server inputs the collected operational status data, weather data, and emotion data into a generative AI model. Based on this data, the generative AI model predicts optimal power consumption and the timing of renewable energy usage. The input is the collected data, and the output is the predicted power consumption and timing of energy usage.

[1398] Step 5:

[1399] The server generates and transmits operational instructions to control the power consumption of the communications device based on the prediction results of the generative AI model. The terminal receives instructions from the server, adjusts power consumption in real time, and switches to renewable energy sources as needed. The input is the prediction result of the generative AI model, and the output is specific operational instructions for the communications device.

[1400] Step 6:

[1401] The user monitors the system's operation status and checks performance data and emotion data. For example, they check whether power consumption has been reduced as predicted or whether renewable energy has been used appropriately. The inputs are the system's operation results and emotion data, and the output is the monitoring results of these data.

[1402] Step 7:

[1403] Users can provide feedback to the server as needed. The server updates the generative AI model based on this feedback to improve the accuracy of the next prediction. The input is the user's feedback, and the output is the updated generative AI model.

[1404] Through the above steps, the server, terminal, and user work together to make the power consumption of the communication device more efficient, thereby optimizing the use of renewable energy while maintaining user comfort.

[1405] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1406] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1407] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1408] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1409] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1410] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1411] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1412] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1413] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1414] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1415] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1416] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1417] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1418] 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.

[1419] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1420] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1421] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1422] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1423] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1424] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1425] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1426] The following is further disclosed regarding the above embodiment.

[1427] (Claim 1)

[1428] means for collecting the operating status and weather conditions of each communication device;

[1429] a generative model means for estimating efficient power consumption based on the operating status and weather conditions;

[1430] The system includes means for controlling power consumption of a communication device based on the estimation results of the generative model means and means for performing power switching to a renewable energy source.

[1431] (Claim 2)

[1432] The system according to claim 1 , further comprising means for updating the generative model means based on an electric power supply and demand situation.

[1433] (Claim 3)

[1434] The system according to claim 1 , further comprising means for providing feedback on power consumption based on an estimation result of said generative model means and correcting said generative model means.

[1435] "Example 1"

[1436] (Claim 1)

[1437] means for collecting the operating status and weather conditions of each communication device;

[1438] a generative model means for estimating efficient power consumption based on the operating status and weather conditions;

[1439] means for controlling power consumption of a communication device based on an estimation result of said generative model means;

[1440] means for effecting a power switchover to renewable energy sources;

[1441] means for the communications device to adjust power consumption in real time based on the generative AI model;

[1442] A means to update generative AI models based on user feedback

[1443] A system including:

[1444] (Claim 2)

[1445] The system according to claim 1 , further comprising means for updating the generative model means based on an electric power supply and demand situation.

[1446] (Claim 3)

[1447] The system according to claim 1 , further comprising means for providing feedback on power consumption based on an estimation result of said generative model means and correcting said generative model means.

[1448] "Application Example 1"

[1449] (Claim 1)

[1450] A means for collecting the operating status and weather conditions of each electronic device;

[1451] a generative model means for estimating efficient power consumption based on the operating status and weather conditions;

[1452] means for controlling power consumption of electronic devices based on the estimation result of the generative model means and means for switching power sources to renewable energy sources;

[1453] A method for optimizing the energy consumption and usage timing of various devices using in-store operation status data and external weather data;

[1454] A system including:

[1455] (Claim 2)

[1456] The system according to claim 1 , further comprising means for updating the generative model means based on an electric power supply and demand situation.

[1457] (Claim 3)

[1458] The system according to claim 1 , further comprising means for providing feedback on power consumption based on an estimation result of said generative model means and correcting said generative model means.

[1459] "Example 2: Combining Emotion Engines"

[1460] (Claim 1)

[1461] means for collecting the operating status and weather conditions of each communication device;

[1462] a generating artificial intelligence modeling means for estimating efficient power consumption based on the operating status, weather conditions, and user emotion data;

[1463] means for controlling power consumption of the communication device based on the estimation result of the generating artificial intelligence model means and means for switching power source to a renewable energy source;

[1464] emotion recognition means for generating emotion data of a user;

[1465] The system includes a means for updating the generative artificial intelligence model means based on performance data and emotion data.

[1466] (Claim 2)

[1467] 10. The system of claim 1, further comprising means for updating said generative artificial intelligence model means based on power supply and demand conditions and feedback data from a user.

[1468] (Claim 3)

[1469] The system according to claim 1, further comprising means for providing feedback on power consumption based on an estimation result of said generative artificial intelligence model means and correcting said generative artificial intelligence model means.

[1470] "Application example 2 when combining emotion engines"

[1471] (Claim 1)

[1472] means for collecting the operating status and weather conditions of each communication device;

[1473] a generative model means for estimating efficient power consumption based on the operating status and weather conditions;

[1474] means for acquiring user emotion data and inputting the data to the generative model means;

[1475] means for controlling power consumption of a communication device based on an estimation result of the generative model means and means for switching power sources to renewable energy sources;

[1476] means for optimizing an operating environment of a communication device based on emotion data of a user;

[1477] a means for feeding back the operation results of the system and the user's emotional data;

[1478] A system including:

[1479] (Claim 2)

[1480] The system according to claim 1 , further comprising means for updating the generative model means based on an electric power supply and demand situation.

[1481] (Claim 3)

[1482] The system according to claim 1 , further comprising means for providing feedback on power consumption based on an estimation result of said generative model means and correcting said generative model means. [Explanation of symbols]

[1483] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for collecting the operating status and weather conditions of each communication device; a generative model means for estimating efficient power consumption based on the operating status and weather conditions; The system includes means for controlling power consumption of a communication device based on the estimation results of the generative model means and means for performing power switching to a renewable energy source.

2. The system according to claim 1 , further comprising means for updating the generative model means based on an electric power supply and demand situation.

3. The system according to claim 1 , further comprising means for providing feedback on power consumption based on an estimation result of said generative model means and correcting said generative model means.

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