System

Abstract

To provide a system for promoting reduction of the amount of discharge of CO2 in a user's life.SOLUTION: A system includes: means for visualizing the amount of discharge of CO2 in a user's life; means for inputting information regarding at least one of electrical utilization, movements, meals, and consumer products in the user's house; means for generating a prompt sentence for designating production of a reduction target and a reduction plan of the amount of discharge of CO2 by the user on the basis of the information; means for generating a reduction target and a reduction plan of the amount of discharge of CO2 by the user; means for presenting the generated reduction target and reduction plan of the amount of discharge of CO2 by the user to the user; means for inputting progress information of a reduction plan executed by the user; means for evaluating a degree of achievement of a target by analyzing the inputted progress information; and means for imparting points to the user according to the evaluated degree of achievement of the target.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] Changing people's behavior is essential to achieving carbon neutrality by 2050. However, it is difficult for individuals to specifically grasp how much CO2 they emit in their daily lives and then take concrete action to reduce their CO2 emissions based on that information. [Means for solving the problem]

[0005] This invention provides a means for visualizing CO2 emissions in a user's daily life, a means for inputting information on the user's electricity use, transportation, meals, and consumer goods at their home, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, and a means for awarding points to users according to their achievement of the reduction targets. This allows users to specifically grasp their own CO2 emissions, and by being presented with reduction targets and reduction measures, they are able to take concrete action. Furthermore, since points are awarded according to the achievement of reduction targets, it is expected that users will maintain their motivation and promote behavioral change. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[0029] "Example 2"

[0030] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[0031] The processing flow of each embodiment will be described below.

[0032] "Example 1"

[0033] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[0034] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[0035] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[0036] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[0037] "Example 2"

[0038] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[0039] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[0040] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[0041] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[0042] Example 1

[0043] Next, a description will be given of Example 1 of Form 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."

[0044] In modern society, it is important to accurately grasp CO2 emissions in individuals' daily lives and present effective reduction measures. However, conventional systems have had difficulty presenting individualized reduction targets and measures based on users' lifestyles and behavioral patterns. Furthermore, there was a lack of mechanisms for awarding points as an incentive for achieving reduction targets, or for using those points as electronic money or coupons. This led to a decrease in users' motivation to reduce their CO2 emissions.

[0045] 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.

[0046] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement of the reduction targets; a means for transmitting the information input by the user to a cloud server; a means for the cloud server to analyze the received data and input it into a generative AI model; a means for the generative AI to analyze the user's behavioral patterns and calculate CO2 emissions; and a means for making the points available as electronic money or electronic coupons. This makes it possible to present individualized reduction targets and measures based on the user's lifestyle and behavioral patterns, and to award and use points as an incentive when the reduction targets are achieved.

[0047] "User" refers to an individual who uses the system to manage CO2 emissions in their daily lives and aims to achieve reduction targets.

[0048] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in a user's daily life, and is calculated based on information on electricity usage, transportation, meals, and consumer goods.

[0049] "Visualization means" refers to the function that calculates CO2 emissions based on information entered by the user and displays the results in the form of graphs, numbers, etc.

[0050] "Electricity usage" refers to the state of power consumption in a user's residence, and includes power consumption associated with the use of home appliances, lighting, and the like.

[0051] "Mobility" refers to the use of transportation means in the user's daily life, and includes means such as automobiles, public transportation, bicycles, and walking.

[0052] "Diet" refers to the types and amounts of food that a user consumes on a daily basis, and includes information on meat, vegetables, processed foods, etc.

[0053] "Consumer goods" refers to goods and services that users purchase and use, including home appliances, clothing, daily necessities, etc.

[0054] "Generative AI" refers to an artificial intelligence model that analyzes information entered by the user and presents CO2 emission reduction targets and reduction measures.

[0055] "Reduction target" refers to the amount of CO2 emissions reduction that the user aims to achieve, and is set individually by the generation AI.

[0056] "Reduction measures" refer to specific actions or changes that users should take to reduce CO2 emissions, and are suggested by the generative AI.

[0057] "Points" refer to rewards given to users when they achieve their reduction targets, and can be used as electronic money or electronic coupons.

[0058] "Cloud server" refers to a remote server that receives information entered by users and analyzes the data for input into a generative AI model.

[0059] "Behavioral patterns" refer to the behavioral tendencies and habits of users in their daily lives, and are the subject of analysis by generative AI.

[0060] "Electronic money" refers to a digital form of currency that can be used for purchases and payments using points earned by users.

[0061] A "coupon" refers to a ticket that allows a user to use points earned to receive discounts or free offers on specific products or services.

[0062] The present invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and reduction measures. A specific embodiment of this system will be described below.

[0063] User operations

[0064] Users download and install a smartphone application. They launch the application and enter information about their home electricity usage, transportation, meals, and consumer goods. For example, they enter their daily commute method (car, public transportation, bicycle, etc.), what they eat (meat, vegetables, processed foods, etc.), and information about the products they purchase (home appliances, clothing, daily necessities, etc.).

[0065] Device behavior

[0066] The device (smartphone) collects information entered by the user and sends it to a cloud server. The device then converts the entered data into an appropriate format and sends it in a format that is easy for the generative AI model to analyze. Specifically, the device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[0067] Server Operation

[0068] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information about the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model. The AI ​​model analyzes the data and extracts the user's behavioral patterns.

[0069] How generative AI works

[0070] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, the generative AI calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the vehicle model. The generative AI calculates the CO2 emissions (e.g., 2 kg CO2 / day) based on the user's commuting distance (e.g., 20 km) and the vehicle model's fuel efficiency (e.g., 10 km / L).

[0071] Based on the calculation results, the generative AI presents the user with a CO2 emission reduction target and reduction measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle. Based on the user's current CO2 emissions (e.g., 2 kg CO2 / day), the generative AI presents a reduction target (e.g., 1.5 kg CO2 / day) and specific measures (e.g., using public transportation).

[0072] Points allocation and management

[0073] When a user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards points when the target is achieved. Points can be viewed in the "Points Management" section of the application and can be exchanged for electronic money or electronic coupons.

[0074] Specific examples

[0075] Example 1

[0076] If the user commutes to work every day by car, the generative AI might suggest the following:

[0077] Using public transportation (e.g. buses and trains)

[0078] Commuting by bicycle (e.g., purchasing a bicycle and using it to commute to work)

[0079] Example 2

[0080] When a user achieves their reduction goal, the system will award them points as follows:

[0081] 100 points (can be used as electronic money)

[0082] 50 points (can be used as a coupon)

[0083] Prompt Sentence Examples

[0084] "If a user commutes to work every day by car, what CO2 emission reduction measures would the generative AI suggest?"

[0085] In this way, this system calculates CO2 emissions based on the information entered by the user, presents reduction targets and measures, and also awards points according to the degree of achievement, which can be used as electronic money or electronic coupons.

[0086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0087] Step 1:

[0088] The user enters information

[0089] Users launch a smartphone application and input information about their electricity usage, transportation, meals, and consumer goods at home. Specifically, they input their daily commute method (car, public transportation, bicycle, etc.), meal contents (meat, vegetables, processed foods, etc.), and information about purchased products (home appliances, clothing, daily necessities, etc.).

[0090] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0091] Output: Input lifestyle information data

[0092] Step 2:

[0093] The device sends data

[0094] The device (smartphone) collects information entered by the user and sends it to the cloud server. The device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[0095] Input: User's lifestyle information data

[0096] Output: JSON format data sent to the cloud server

[0097] Step 3:

[0098] The server receives and analyzes the data

[0099] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information on the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model.

[0100] Input: JSON format data sent to the cloud server

[0101] Output: Analyzed behavioral pattern data

[0102] Step 4:

[0103] Generative AI calculates CO2 emissions

[0104] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, it calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the car model.

[0105] Input: Analyzed behavioral pattern data

[0106] Output: Calculated CO2 emissions data

[0107] Step 5:

[0108] Generative AI presents reduction targets and measures

[0109] Based on the calculation results, the generative AI will suggest CO2 emission reduction targets and measures for the user. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[0110] Input: Calculated CO2 emissions data

[0111] Output: Presentation of reduction targets and reduction measures

[0112] Step 6:

[0113] The system awards points

[0114] When a user achieves their reduction target, the system awards them points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards them points when they achieve their target.

[0115] Input: Reduction target achievement status data

[0116] Output: Assigned point data

[0117] Step 7:

[0118] Users can use points

[0119] Users can check their points in the "Points Management" section of the application and exchange them for electronic money or electronic coupons.

[0120] Input: Assigned point data

[0121] Output: Point data used as electronic money or electronic coupons

[0122] (Application example 1)

[0123] Next, a description will be given of Application Example 1 of Embodiment 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."

[0124] In modern society, it is important to visualize the environmental impact of individual lifestyle habits and present specific reduction measures. However, conventional systems have difficulty collecting detailed data across a user's entire lifestyle and setting individualized reduction targets. Furthermore, the lack of incentives for users to achieve their reduction targets makes it difficult to encourage sustainable behavioral change. In particular, food delivery services face the challenge of preventing users from making environmentally conscious choices, as there are no systems that calculate the CO2 emissions of meals ordered and suggest environmentally friendly options.

[0125] 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.

[0126] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods; a means for using generative AI to present CO2 emission reduction targets and reduction measures; a means for awarding points based on the degree to which the user has achieved their reduction targets; a means for calculating CO2 emissions based on the ingredients and cooking methods of the meals the user orders; a means for the generative AI to suggest more environmentally friendly meal options to the user; a means for tracking the user's progress toward their reduction targets and awarding points based on the degree to which they have achieved their targets; and a means for exchanging the awarded points as electronic money or coupons. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier for users to make environmentally conscious meal choices, especially when using food delivery services.

[0127] "Means for visualizing CO2 emissions in a user's daily life" refers to techniques and methods for visually displaying the amount of carbon dioxide emissions associated with the activities and consumption in a user's daily life.

[0128] "Means for inputting information on electricity usage, transportation, meals, and consumer goods at a user's residence" refers to an interface or method for a user to input data on the electricity used, transportation, meals, and goods consumed at home into the system.

[0129] "Means for presenting CO2 emission reduction targets and reduction measures using generative AI" refers to technologies and methods that utilize generative artificial intelligence to set targets and suggest specific reduction methods for users to reduce their carbon dioxide emissions.

[0130] "Means for awarding points based on the degree of achievement when a user achieves a reduction target" refers to a system or method for awarding points based on the degree of achievement when a user achieves a carbon dioxide emission reduction target set by the user.

[0131] "Means for calculating CO2 emissions based on the ingredients and cooking method of the meal ordered by the user" refers to technology or methods for calculating the carbon dioxide emissions associated with the meal ordered by the user based on the ingredients and cooking method of the meal.

[0132] "Means for generative AI to suggest more environmentally friendly food options to users" refers to technologies and methods that use generative artificial intelligence to suggest food options that have a lower environmental impact to users.

[0133] "Means for tracking a user's progress toward a reduction target and awarding points based on the degree of achievement" refers to a system or method for tracking the progress of a user toward a carbon dioxide emission reduction target set by the user and awarding points based on the degree of achievement.

[0134] "Means for enabling the points awarded to a user to be used as electronic money or electronic coupons" refers to techniques or methods for enabling the points awarded to a user to be used as electronic money or electronic coupons.

[0135] As an embodiment of the present invention, the following system can be constructed.

[0136] System Overview

[0137] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has a function to calculate the CO2 emissions based on the ingredients and cooking method of the meal the user orders, and suggests environmentally friendly options. When a user achieves their reduction target, they are awarded points according to their achievement, which can be used as electronic money or electronic coupons.

[0138] Hardware and software used

[0139] Hardware: Smartphones, servers

[0140] Software: Python, generative AI model, API (for calculating CO2 emissions)

[0141] Data processing and calculation

[0142] Data Entry

[0143] Users use a smartphone application to input information about their home electricity usage, transportation, meals, and consumer goods, which allows detailed data on the user's life to be collected.

[0144] Calculating CO2 emissions

[0145] The server uses APIs to calculate the CO2 emissions for each item based on the data entered by the user, especially the ingredients and cooking method of the meal the user orders.

[0146] Presentation of reduction targets and reduction measures

[0147] The generative AI model analyzes the collected data and presents individualized CO2 emission reduction targets and strategies to the user. For example, if the user commutes by car every day, it suggests using public transportation or commuting by bicycle.

[0148] Proposing environmentally friendly options

[0149] The generative AI model calculates the CO2 footprint of the meal a user orders and suggests more environmentally friendly meal options, such as suggesting a vegetarian dish instead of a meat dish.

[0150] Progress tracking and points awarded

[0151] The server tracks the user's progress toward their reduction goal and awards points based on their achievement. These points can be used as electronic money or coupons.

[0152] Specific examples

[0153] Example user input

[0154] User ID: user123

[0155] Meals ordered: beef, chicken, rice

[0156] Reduction target: 15 kg CO2

[0157] Prompt Sentence Examples

[0158] User ID: user123

[0159] Meals ordered: beef, chicken, rice

[0160] Reduction target: 15 kg CO2

[0161] In this way, the generative AI model analyzes the data entered by the user and presents specific reduction measures and progress reports. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier to make environmentally conscious meal choices, especially when using food delivery services.

[0162] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0163] Step 1:

[0164] Users enter information about their home's electricity usage, transportation, meals, and consumer goods through a smartphone application.

[0165] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0166] Output: Input lifestyle information data

[0167] How it works: Users enter various data about their daily lives into the application's input form, such as recording details of their daily commute, meals, and electricity consumption.

[0168] Step 2:

[0169] The server uses an API to calculate the CO2 emissions for each item based on the data entered by the user.

[0170] Input: User's lifestyle information data

[0171] Output: CO2 emissions for each item

[0172] Specific operation: The server analyzes the input data and calls an external CO2 emission calculation API to calculate the CO2 emissions corresponding to each item. For example, it obtains CO2 emissions for electricity consumption or CO2 emissions based on transportation method.

[0173] Step 3:

[0174] A generative AI model analyzes the collected data and presents users with personalized CO2 emission reduction targets and measures.

[0175] Input: CO2 emissions for each item

[0176] Output: Individualized reduction targets and strategies

[0177] Specific operation: The server uses a generative AI model to analyze the user's lifestyle and behavioral patterns, and generates optimal reduction targets and specific reduction measures, such as suggesting the use of public transportation or energy-efficient home appliances.

[0178] Step 4:

[0179] The server calculates the CO2 emissions based on the ingredients and cooking method of the meal the user orders.

[0180] Input: Information about the meal the user wants to order (ingredients, cooking method)

[0181] Output: CO2 emissions associated with the meal

[0182] How it works: When a user uses a food delivery service, they input the information about the meal they want to order. Based on this information, the server calls an API to calculate the CO2 emissions corresponding to each ingredient and cooking method.

[0183] Step 5:

[0184] A generative AI model suggests more environmentally friendly food options to users.

[0185] Input: Diet-related CO2 emissions

[0186] Output: Eco-friendly food options

[0187] How it works: Based on the calculated CO2 emissions, the server uses a generative AI model to suggest environmentally friendly meal options to the user, for example, suggesting vegetable dishes instead of meat dishes.

[0188] Step 6:

[0189] The server tracks the user's progress towards their reduction goals and awards points based on their progress.

[0190] Input: User reduction goals and progress data

[0191] Output: Points awarded

[0192] What it does: The server periodically tracks the user's progress towards their reduction goal, calculates points based on the progress of the goal, and credits them to the user's account.

[0193] Step 7:

[0194] The server makes the awarded points available for use as electronic money or electronic coupons.

[0195] Input: Points awarded

[0196] Output: Electronic money or electronic coupons

[0197] Specific operation: The server converts the points awarded to the user into electronic money or electronic coupons, which the user can use. For example, it issues coupons that can be used at affiliated online stores or services.

[0198] Example 2

[0199] Next, a description will be given of Example 2 of Form 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."

[0200] In modern society, it is important to understand the environmental impact of each individual's daily activities and to reduce CO2 emissions through concrete actions. However, it is not easy for users to accurately grasp the CO2 emissions of their own lives and find effective reduction measures. Another issue is the lack of motivation to achieve reduction goals. To solve these problems, a system is needed that calculates CO2 emissions based on users' lifestyle information, presents individualized reduction goals and measures, and provides rewards based on the degree of achievement.

[0201] 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.

[0202] In this invention, the server includes: means for visualizing CO2 emissions in a user's daily life; means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at the user's residence; means for presenting CO2 emission reduction targets and reduction measures using generative AI; means for awarding points to the user based on the degree of achievement when the user achieves the reduction target; means for encrypting the information input by the user and transmitting it to the server; means for the generative AI model to calculate CO2 emissions based on the user's daily life information; means for the server to generate reduction targets and reduction measures based on the calculation results of the generative AI model; means for the user to implement the reduction measures and input their progress; means for the server to analyze the progress information and evaluate the degree of goal achievement; and means for the user to use the awarded points as electronic money or electronic coupons. This enables users to accurately grasp the CO2 emissions in their daily lives, implement effective reduction measures, and receive rewards based on the degree of achievement.

[0203] "User" refers to an individual who uses the system to input information about their lifestyle and aims to reduce CO2 emissions.

[0204] "Lifestyle information" refers to information about the user's electricity usage at home, transportation methods, meal frequency, and purchases of consumer goods.

[0205] "CO2 emissions" refers to the total amount of carbon dioxide emitted by the user's daily activities.

[0206] "Generative AI" refers to artificial intelligence that analyzes a user's lifestyle information and calculates CO2 emissions, presents reduction targets, and presents reduction measures.

[0207] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0208] "Reduction measures" refer to specific actions or methods that users should take to reduce CO2 emissions.

[0209] "Points" refer to rewards given to users when they achieve their reduction goals.

[0210] "Electronic money" refers to digital currency that users can use by exchanging the points they have been awarded.

[0211] "Coupon" refers to a discount ticket or special coupon that can be used by exchanging the points given to the user.

[0212] The "server" refers to a computer system that receives users' lifestyle information, analyzes it using generative AI, and generates reduction targets and reduction measures.

[0213] "Terminal" refers to a device through which a user inputs lifestyle information and communicates with a server.

[0214] "Encryption" refers to a technology that converts data to protect users' personal information and makes it impossible for third parties to decipher it.

[0215] "Progress information" refers to information about the results and progress of a user's implementation of a reduction measure.

[0216] "Goal achievement level" refers to an index that indicates the degree to which a user has achieved a set reduction target.

[0217] This invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, where users input their daily information and it is analyzed using generative AI.

[0218] A user starts a smartphone application and inputs information about their residential electricity usage, transportation, meal frequency, and consumer goods purchases. For example, they input information about their monthly electricity consumption of 300 kWh, daily commute by car, eating out three times a week, and monthly clothing purchases worth 5,000 yen.

[0219] The device (smartphone) encrypts the input information and sends it to the server. The server inputs the received information into a generative AI model and calculates the user's CO2 emissions. For example, OpenAI's GPT-4 (registered trademark) is used as the generative AI model.

[0220] The generative AI model calculates a user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item of electricity use, transportation, meals, and consumer goods to calculate overall emissions.

[0221] Based on the results of the generative AI model, the server generates a CO2 emission reduction target and specific measures for the user, such as suggesting using public transportation, cooking at home more, or using energy-efficient appliances.

[0222] The server then sends the generated reduction targets and measures to the user's device and displays them on the application. The user then implements the proposed reduction measures and enters their progress into the application. For example, they enter the number of days they used public transportation or the number of times they cooked at home.

[0223] The terminal transmits the progress information entered by the user to the server, which analyzes the received progress information and evaluates the user's achievement of the reduction target, for example, by calculating the percentage of the target achieved.

[0224] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The user can use the awarded points as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money.

[0225] As a concrete example, suppose the user enters the following information:

[0226] Residential electricity usage: 300 kWh per month

[0227] Transportation: Commute by car every day (20km one way)

[0228] Meals: Eating out three times a week

[0229] Consumer goods: 5,000 yen worth of clothing purchased per month

[0230] Based on this information, the generative AI model generates a prompt like this:

[0231] The user's electricity usage at home is 300kWh per month, he commutes 20km each way by car every day, eats out three times a week, and buys clothes worth 5,000 yen per month. Based on this information, please calculate the user's CO2 emissions and propose a reduction target and reduction measures.

[0232] The generative AI model analyzes this prompt and makes the following suggestions to the user:

[0233] Reduce electricity usage: Encourage the use of energy-efficient appliances

[0234] Changes in transportation: Suggestions for using public transportation or commuting by bicycle

[0235] Review your diet: Eat out less and cook more at home

[0236] Consumer goods purchases: Limit purchases to essentials

[0237] If the user implements these suggestions and achieves the reduction target, the server will give the user points, which the user can use as electronic money or electronic coupons.

[0238] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0239] Step 1:

[0240] The user starts the application and enters lifestyle information.

[0241] The user launches a smartphone application and inputs information about their residential electricity usage, transportation method, meal frequency, and consumer goods purchases. For example, they might input information about using 300 kWh of electricity per month, commuting to work by car every day, eating out three times a week, and purchasing 5,000 yen worth of clothing per month. The input information is temporarily saved within the application.

[0242] Step 2:

[0243] The terminal transmits the input information to the server.

[0244] The device encrypts the lifestyle information entered by the user and sends it to the server. The encrypted data is used to protect the user's privacy. The input data (information on electricity usage, transportation, meals, and consumer goods) is sent to the server.

[0245] Step 3:

[0246] The information received by the server is input into the generative AI model.

[0247] The server inputs the received lifestyle information into a generative AI model. For example, OpenAI's GPT-4 is used as the generative AI model. The generative AI model begins analysis based on the input data (information on electricity usage, transportation, meals, and consumer goods).

[0248] Step 4:

[0249] A generative AI model calculates the user's CO2 emissions.

[0250] The generative AI model calculates the user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item, such as electricity usage, transportation, meals, and consumer goods, to calculate overall emissions. The output data is the user's overall CO2 emissions.

[0251] Step 5:

[0252] The server generates reduction targets and reduction measures based on the calculation results of the generative AI model.

[0253] Based on the calculation results of the generative AI model, the server generates a CO2 emission reduction target and specific reduction measures for the user. For example, it suggests using public transportation, cooking at home more, and using energy-efficient appliances. The reduction target and reduction measures are obtained as output data.

[0254] Step 6:

[0255] The server presents the reduction target and reduction measures to the user.

[0256] The server sends the generated reduction targets and reduction measures to the user's device and presents them on the application. The user can then confirm the reduction targets and reduction measures through the application. The presented reduction targets and reduction measures are obtained as output data.

[0257] Step 7:

[0258] The user implements reduction measures and enters progress into the application.

[0259] The user implements the proposed reduction measures and inputs their progress into the application. For example, they input the number of days they used public transportation or the number of times they cooked at home. The input data provides progress information on the reduction measures they implemented.

[0260] Step 8:

[0261] The terminal sends progress information to the server.

[0262] The terminal transmits the progress information input by the user to the server. The input data (progress information) is transmitted to the server.

[0263] Step 9:

[0264] The server analyzes the progress information and evaluates the degree of goal achievement.

[0265] The server analyzes the received progress information and evaluates the user's achievement of the reduction target. For example, it calculates the percentage of the target that has been achieved. The target achievement level is obtained as output data.

[0266] Step 10:

[0267] The server awards points according to the degree of goal achievement.

[0268] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The awarded points are obtained as output data.

[0269] Step 11:

[0270] Users use the points as electronic money or electronic coupons.

[0271] Users can use the points they receive as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money. Information on the points used is obtained as output data.

[0272] (Application example 2)

[0273] Next, a description will be given of Application Example 2 of Form 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."

[0274] In modern society, reducing CO2 emissions in individual lives is an important issue. However, it is not easy for users to accurately grasp the CO2 emissions of their own lives and implement effective reduction measures. Furthermore, food delivery services, in particular, have difficulty considering the CO2 emissions of the food they order, and there is a lack of mechanisms to encourage environmentally friendly choices. This results in a lack of motivation for users to take environmentally conscious actions.

[0275] 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.

[0276] In this invention, the server includes a means for visualizing CO2 emissions in a user's daily life, a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, a means for awarding points to the user based on the degree of achievement when the reduction target is achieved, a means for calculating the CO2 emissions of food ordered by the user and suggesting environmentally friendly options, and a means for awarding points to the user when the user makes an environmentally friendly choice. This allows users to accurately grasp the CO2 emissions in their daily lives and implement effective reduction measures. It also promotes environmentally friendly choices in food delivery services, strengthening users' motivation to take environmentally conscious actions.

[0277] "User" refers to an individual who uses the system.

[0278] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[0279] "Visualization means" refers to methods and technologies that allow users to visually check their own CO2 emissions.

[0280] "Electricity usage" refers to the consumption of electricity in a user's residence.

[0281] "Movement" refers to the use of transportation means in the user's daily life.

[0282] "Meals" refers to the food and beverages consumed by a user.

[0283] "Consumer goods" refers to everyday items and services that users purchase and use.

[0284] "Means for inputting information" refers to the methods and techniques by which users provide data about their lives to the system.

[0285] "Generative AI" refers to artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and measures.

[0286] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0287] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[0288] "Means for awarding points" refers to a method or technology for awarding points as a reward when a user achieves a reduction target.

[0289] "CO2 emissions from food" refers to the total amount of carbon dioxide emitted from the production to consumption of the food ordered by the user.

[0290] "Environmentally friendly choices" refer to foods and actions that have a lower carbon footprint.

[0291] "Food delivery service" refers to a service that allows users to order food online and have it delivered.

[0292] The system for implementing this invention visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, and generative AI performs analysis based on lifestyle information entered by the user.

[0293] Hardware and Software

[0294] Hardware: Smartphone

[0295] Software: Python, JSON files, generative AI models

[0296] Data processing and calculation

[0297] 1. Enter user's lifestyle information:

[0298] Users enter information about their residential electricity usage, travel, meals, and consumer goods through a smartphone application, which is then stored in JSON format within the application.

[0299] 2. Calculating CO2 emissions:

[0300] The server calculates the CO2 emissions for each item based on the information entered by the user. For example, if a user orders a hamburger, it calculates the CO2 emissions generated from the production to consumption of that food.

[0301] 3. Generative AI analysis:

[0302] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[0303] 4. Propose environmentally friendly options:

[0304] The server calculates the CO2 emissions of the food the user orders and suggests environmentally friendly options, such as a salad instead of a hamburger.

[0305] 5. Points Awarding:

[0306] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement. These points can be used as electronic money or coupons.

[0307] Specific examples

[0308] If a user orders a hamburger, the application calculates the CO2 emissions of the food and suggests eco-friendly options such as a salad. If the user selects a salad, they will receive 10 points, which can be used as a discount on their next order.

[0309] Prompt Sentence Examples

[0310] An example of a prompt to input to a generative AI model is as follows:

[0311] Calculate the CO2 emissions of a user ordering a hamburger and suggest eco-friendly options, such as salads or vegetable-based dishes, and reward users for their eco-friendly choices.

[0312] In this way, users can accurately understand the CO2 emissions in their daily lives and take effective measures to reduce them. This also promotes environmentally friendly choices in food delivery services, strengthening users' motivation to take environmentally conscious actions.

[0313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0314] Step 1:

[0315] The user launches a smartphone application and enters information about their home's electricity usage, transportation, meals, and consumer goods.

[0316] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0317] Output: JSON formatted lifestyle information data

[0318] Specific operation: The user enters information about their daily life into the application's input form and presses the submit button. The application saves this information in JSON format.

[0319] Step 2:

[0320] The server receives the user's input information and calculates the CO2 emissions for each item.

[0321] Input: JSON formatted lifestyle information data

[0322] Output: CO2 emissions for each item

[0323] Specific operation: The server analyzes the received JSON data and calculates the CO2 emissions for each item (electricity usage, transportation, meals, consumer goods). For example, it calculates the CO2 emissions based on the power consumption from the electricity usage data.

[0324] Step 3:

[0325] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and reduction measures.

[0326] Input: CO2 emissions for each item

[0327] Output: Reduction targets and measures

[0328] Specific operation: The server inputs the CO2 emissions for each item into the generative AI model, which then analyzes them and generates individualized reduction targets and measures for the user, such as suggesting using public transportation or commuting by bicycle.

[0329] Step 4:

[0330] When a user uses a food delivery service, the service calculates the CO2 emissions of the food they order and suggests environmentally friendly options.

[0331] Input: Food information the user wants to order

[0332] Output: Food CO2 emissions and eco-friendly options

[0333] What it does: When a user selects a food item in a food delivery application, the server calculates the food's CO2 footprint and suggests environmentally friendly options (e.g., salads).

[0334] Step 5:

[0335] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement.

[0336] Input: User behavior data (reduction goal achievement status, making environmentally friendly choices)

[0337] Output: Points awarded

[0338] Specific operation: The server monitors the user's behavioral data, calculates points when reduction targets are achieved or environmentally friendly choices are made, and adds them to the user's account. For example, if a user chooses a salad, 10 points are added.

[0339] Step 6:

[0340] The user uses the awarded points as electronic money or electronic coupons.

[0341] Input: Points awarded

[0342] Output: Electronic money or electronic coupons

[0343] Specific operation: The user checks the points in the application and selects the option to use them as electronic money or electronic coupons. The server converts the points into electronic money or electronic coupons and provides them to the user.

[0344] 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.

[0345] "Example 1"

[0346] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[0347] "Example 2"

[0348] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[0349] The processing flow of each embodiment will be described below.

[0350] "Example 1"

[0351] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[0352] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[0353] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[0354] Step 4: The emotion engine recognizes the user's emotional state.

[0355] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[0356] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[0357] "Example 2"

[0358] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[0359] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[0360] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[0361] Step 4: The emotion engine recognizes the user's emotional state.

[0362] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[0363] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[0364] Example 1

[0365] Next, a description will be given of Example 1 of Form 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."

[0366] Conventional CO2 emission reduction systems present uniform reduction targets and measures without considering the user's lifestyle or emotional state, making it difficult to maintain user motivation. In addition, the rewards for achieving reduction targets are also uniform, making it difficult to respond flexibly to the efforts and circumstances of individual users.

[0367] 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.

[0368] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement when the reduction target is achieved; a means for recognizing the user's emotional state and adjusting the reduction targets and reduction measures based on the emotional state; and a means for adjusting the amount of points awarded based on the user's emotional state. This makes it possible to present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective CO2 emission reduction while maintaining the user's motivation.

[0369] A "user" is an individual who uses the system to manage the CO2 emissions in their daily lives and achieve their reduction targets.

[0370] "CO2 emissions" is the total amount of carbon dioxide emitted as a result of the user's daily activities.

[0371] "Visualization" is the process that allows users to visually check their own CO2 emissions.

[0372] "Electricity usage" refers to the state of electricity consumption in the user's residence.

[0373] "Movement" refers to the state of use of transportation means by the user in daily life.

[0374] "Diet" is information about the types and frequency of food consumed by the user.

[0375] "Consumer goods" are items such as daily necessities and clothing that users purchase.

[0376] "Generative AI" is artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and reduction measures.

[0377] The "reduction target" is the amount of CO2 emission reduction that the user must achieve.

[0378] "Reduction measures" are specific actions or methods that users should take to reduce CO2 emissions.

[0379] "Points" are rewards given to users when they achieve their reduction targets.

[0380] "Emotional state" refers to the user's psychological state or mood.

[0381] "Adjustment" is the process of changing reduction targets, measures, and point award amounts based on the user's emotional state.

[0382] This invention is a system for managing CO2 emissions in a user's daily life and achieving reduction targets. This system is implemented as a smartphone application, and users input their own lifestyle information, which is analyzed using generative AI, and CO2 emission reduction targets and reduction measures are presented.

[0383] Hardware and software used

[0384] Hardware: Smartphone (e.g. iPhone (registered trademark), ANDROID (registered trademark) device)

[0385] Software: Smartphone applications, generative AI (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva)

[0386] Specific operation of the system

[0387] User Input

[0388] Users enter information about their electricity usage at home, transportation, meals, and consumer goods through a smartphone application. For example, a user might enter information such as "I use 200 kWh of electricity per month," "I commute to work by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month."

[0389] Sending information

[0390] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the data to the server via the Internet.

[0391] Analysis of information

[0392] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data.

[0393] Calculating CO2 emissions

[0394] The generative AI calculates the user's CO2 emissions based on the prompt text. For example, it generates the following prompt text and performs analysis:

[0395] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[0396] Presentation of reduction targets and reduction measures

[0397] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures, such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months."

[0398] Tuning the Emotion Engine

[0399] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine will relax the reduction target and suggest a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine will increase the reduction target and suggest a more aggressive reduction strategy.

[0400] Confirmation of achievement of reduction targets

[0401] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application.

[0402] Points awarded

[0403] The server receives the user's report and checks the degree of achievement of the reduction target. Points are awarded according to the degree of achievement. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons.

[0404] In this way, the system of the present invention can present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective reduction of CO2 emissions while maintaining the user's motivation.

[0405] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0406] Step 1:

[0407] Entering user information

[0408] Users open a smartphone application and enter information about their home electricity usage, transportation, meals, and consumer goods. Specifically, users enter information such as "I use 200 kWh of electricity per month," "I commute by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month." The input data is temporarily stored within the application.

[0409] Step 2:

[0410] Sending information

[0411] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the input data to the server via the Internet in JSON format. For example, data is sent in the format {"electricity": 200, "commute": "car", "dining": 3, "shopping": 1}. The input is the user's lifestyle information, and the output is the JSON data sent to the server.

[0412] Step 3:

[0413] Analysis of information

[0414] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data. For example, it generates the following prompt:

[0415] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[0416] The input is the JSON data sent to the server, and the output is the generated prompt text.

[0417] Step 4:

[0418] Calculating CO2 emissions

[0419] The generative AI calculates the user's CO2 emissions based on the prompt text. The analysis results in the user's annual CO2 emissions. For example, the generative AI calculates the user's CO2 emissions to be approximately 3.5 tons per year. The input is the generated prompt text, and the output is the calculated CO2 emissions.

[0420] Step 5:

[0421] Presentation of reduction targets and reduction measures

[0422] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures. For example, it presents specific measures such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months." The input is the calculated CO2 emissions, and the output is the presented reduction targets and measures.

[0423] Step 6:

[0424] Tuning the Emotion Engine

[0425] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and proposes a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine raises the reduction target and proposes a more aggressive reduction strategy. The input is the user's emotional state, and the output is the adjusted reduction target and reduction strategy.

[0426] Step 7:

[0427] Confirmation of achievement of reduction targets

[0428] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application. The input is the reduction measures implemented by the user, and the output is the reported progress.

[0429] Step 8:

[0430] Points awarded

[0431] The server receives the user's report and checks the progress of the reduction target. Points are awarded according to the progress. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons. The input is the reported progress, and the output is the awarded points.

[0432] (Application example 1)

[0433] Next, a description will be given of Application Example 1 of Embodiment 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."

[0434] Reducing personal CO2 emissions is an important issue in modern society. However, it is not easy for users to accurately understand their own CO2 emissions and implement effective reduction measures. Furthermore, the lack of personalized suggestions that take into account the user's emotional state makes it difficult to encourage sustainable behavioral change. Furthermore, food delivery services lack mechanisms for suggesting environmentally friendly options, making it difficult for users to make environmentally conscious choices.

[0435] 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.

[0436] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user based on the degree to which the reduction target is achieved; a means for calculating the CO2 emissions of meals ordered by the user; a means for the generative AI to suggest environmentally friendly meal options to the user; and a means for recognizing the user's emotional state and adjusting the suggestions and point awarding based on the user's emotional state. This allows users to accurately grasp the CO2 emissions of their daily lives and implement personalized reduction measures according to their emotional state. Furthermore, since environmentally friendly options are suggested for food delivery services, it becomes easier for users to make environmentally conscious choices.

[0437] "User" refers to an individual who uses the system.

[0438] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[0439] "Visualization means" refers to a method or device that allows users to visually check their own CO2 emissions.

[0440] "Electricity usage" refers to the consumption of electricity in a user's residence.

[0441] "Movement" refers to the use of transportation means in the user's daily life.

[0442] "Meal" refers to the food consumed by the user and how it is prepared.

[0443] "Consumer goods" refers to everyday items and services that users purchase and use.

[0444] "Means for inputting information" refers to a method or device by which a user provides the required data to the system.

[0445] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate results.

[0446] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0447] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[0448] The "means for awarding points" refers to a method or device for awarding points as a reward when a user achieves a reduction target.

[0449] "Meal CO2 emissions" refers to the total amount of carbon dioxide associated with the production, transportation, and cooking of the food a user orders.

[0450] "Eco-friendly food options" refer to foods and cooking methods that have a low carbon footprint.

[0451] "Emotional state" refers to a user's psychological state or mood.

[0452] "Means for adjusting suggestions" refers to a method or device for changing the reduction measures or options presented based on the user's emotional state.

[0453] "Means for adjusting point awarding" refers to a method or device for changing the amount of points awarded based on the user's emotional state.

[0454] The embodiments of the present invention will be described in detail below.

[0455] System Overview

[0456] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has the function of calculating the CO2 emissions of meals ordered by the user and suggesting environmentally friendly options. It also recognizes the user's emotional state and adjusts the suggestions and point awards accordingly.

[0457] Hardware and software used

[0458] Hardware: Smartphone

[0459] Software: Python, emotion recognition API (e.g., Microsoft® Azure® Emotion API), generative AI model

[0460] Data processing and calculation

[0461] 1. Calculating CO2 emissions

[0462] The server calculates CO2 emissions based on the information entered by the user about electricity usage, travel, meals, and consumer goods, using pre-set CO2 emission coefficients for each item.

[0463] 2. Presentation of reduction targets and reduction measures

[0464] Using a generative AI model, the system presents personalized reduction targets and strategies based on the user's lifestyle and behavioral patterns. For example, if a user commutes by car every day, the system suggests using public transportation or commuting by bicycle.

[0465] 3. Calculation and proposal of CO2 emissions from meals

[0466] The system calculates the CO2 emissions of the meal a user orders and uses generative AI to suggest eco-friendly meal options, such as suggesting chicken or vegetarian options if the user orders beef.

[0467] 4. Recognizing and regulating emotional states

[0468] The system uses an emotion recognition API to recognize the user's emotional state. Based on this, it adjusts the content of suggestions and points awarded. For example, if the user is feeling stressed, it will relax the reduction target and suggest stress-reducing measures.

[0469] Specific examples

[0470] If the user orders "beef," the system will suggest "chicken" and award 10 points if the user feels "delighted."

[0471] Prompt Sentence Examples

[0472] The user ordered beef. The emotion engine recognized that the user was happy. The generative AI suggested chicken and awarded it 10 points.

[0473] This allows users to accurately understand the CO2 emissions in their daily lives and implement personalized reduction measures according to their emotional state. It also makes it easier for users to make environmentally conscious choices when using food delivery services, as it suggests environmentally friendly options.

[0474] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0475] Step 1:

[0476] Users use their smartphones to input information about electricity usage, transportation, meals, and consumer goods at home.

[0477] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0478] Output: Input lifestyle information data

[0479] Specific actions: The user opens the smartphone app and enters specific values ​​and options for each item. For example, for electricity usage, the user enters the monthly electricity consumption, and for transportation, the user enters the commute method and distance.

[0480] Step 2:

[0481] The server receives the lifestyle information entered and calculates CO2 emissions.

[0482] Input: Lifestyle information data

[0483] Output: CO2 emissions data

[0484] Specific operation: The server calculates the overall CO2 emissions using the CO2 emission coefficients set in advance for each item. For example, electricity usage is calculated by multiplying it by the CO2 emission coefficient for the power consumption.

[0485] Step 3:

[0486] The server uses a generative AI model to present reduction targets and measures based on the user's lifestyle and behavioral patterns.

[0487] Input: CO2 emissions data, lifestyle information data

[0488] Output: Reduction targets and measures

[0489] Specific operation: The server uses a generative AI model to analyze the user's lifestyle information and generate optimal reduction targets and specific reduction measures. For example, it suggests using public transportation for users who commute by car.

[0490] Step 4:

[0491] When a user orders a meal through a food delivery app, the server calculates the CO2 emissions of the meal being ordered.

[0492] Input: Order information (type of meal)

[0493] Output: CO2 emissions data for meals

[0494] What it does: When a user selects a meal in a food delivery app, the CO2 emissions of that meal are calculated. For example, if you order beef, the CO2 emissions associated with producing and transporting it are calculated.

[0495] Step 5:

[0496] The server uses a generative AI model to suggest environmentally friendly meal options.

[0497] Input: CO2 emissions data for meals, order information

[0498] Output: Eco-friendly food options

[0499] How it works: The server uses a generative AI model to suggest greener options based on the carbon footprint of the meal ordered, for example, suggesting chicken or vegetarian options if you order beef.

[0500] Step 6:

[0501] The server uses an emotion recognition API to recognize the user's emotional state.

[0502] Input: User emotion data (face image and voice)

[0503] Output: Emotional state data

[0504] Specific operation: When a user provides emotional data using the smartphone's camera or microphone, the server analyzes the data using an emotion recognition API and recognizes the user's emotional state.

[0505] Step 7:

[0506] The server adjusts the suggestions and points awarded based on the emotional state.

[0507] Input: Emotional state data, reduction goals and strategies, environmentally friendly food options

[0508] Output: Adjusted proposal and points awarded

[0509] Specific operation: Based on the emotional state data, the server relaxes the reduction target if the user is feeling stressed, and raises the target if the user is feeling happy. It also adjusts the amount of points awarded according to the emotional state. For example, if the user is feeling happy, 10 points will be awarded.

[0510] Step 8:

[0511] When a user achieves a reduction target, the server awards points according to the degree of achievement.

[0512] Input: Achievement data

[0513] Output: Points awarded

[0514] Specific operation: When a user achieves the set reduction goal, the server calculates points according to the achievement level and adds them to the user's account. These points can be used as electronic money or electronic coupons.

[0515] Example 2

[0516] Next, a description will be given of Example 2 of Form 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."

[0517] Reducing personal CO2 emissions is an important issue in modern society. However, it is not easy for users to accurately grasp the CO2 emissions in their own lives and find effective reduction measures. It is also difficult to maintain motivation to achieve reduction goals. Furthermore, a lack of personalized support that takes into account the user's emotional state can lead to stress and burden. To solve these issues, a system is needed that visualizes the CO2 emissions in a user's life, presents individualized reduction goals and measures, and provides support that takes into account the user's emotional state.

[0518] 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.

[0519] In this invention, the server includes a means for visualizing the CO2 emissions in a user's daily life, a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at the user's residence, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, a means for awarding points to the user based on the degree of achievement of the reduction target, a means for recognizing the user's emotional state and adjusting the reduction target and reduction measures based on the user's emotional state, and a means for adjusting the amount of points awarded based on the user's emotional state. This allows users to accurately grasp the CO2 emissions in their daily lives and find effective reduction measures. It also makes it easier for users to maintain motivation to achieve their reduction targets and allows them to receive personalized support that takes their emotional state into account.

[0520] "User" refers to any individual or entity that uses the System.

[0521] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[0522] "Visualization means" refers to methods and technologies that allow users to visually check their own CO2 emissions.

[0523] "Electricity usage" refers to the consumption of electricity in a user's residence.

[0524] "Movement" refers to the means of transportation used by the user, such as commuting or traveling.

[0525] "Meal" refers to the food a user consumes and how it is prepared.

[0526] "Consumer Goods" refers to goods and services that are purchased and used by users.

[0527] "Input means" refers to the methods or techniques by which a user provides information to a system.

[0528] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate results.

[0529] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0530] "Reduction measures" refers to specific methods and proposals for reducing CO2 emissions.

[0531] "Means of presentation" refers to the methods and techniques that generative AI uses to communicate analysis results to users.

[0532] "Means for awarding points" refers to a method or technology for awarding points as a reward when a user achieves a reduction target.

[0533] "Emotional state" refers to the user's mental and emotional state.

[0534] "Means for recognizing" refers to methods or techniques for detecting and determining a user's emotional state.

[0535] "Adjustment means" refers to methods and techniques for changing reduction targets, measures, and points awarded based on the user's emotional state.

[0536] The present invention provides a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and reduction measures. This system is implemented as a smartphone application. Specific embodiments are described below.

[0537] Hardware and software used

[0538] Hardware: Smartphone (e.g. iPhone, Android device)

[0539] Software: Smartphone applications, generative AI (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[0540] System Overview

[0541] Users input information about their home electricity usage, transportation, meals, and consumer goods through a smartphone application. This information is analyzed by generative AI, which calculates the user's CO2 emissions. Based on this information, generative AI presents the user with CO2 emission reduction targets and reduction measures. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[0542] Specific actions

[0543] 1. Data Entry

[0544] Users open a smartphone application and enter information about their residential electricity usage, transportation, meals, and consumer goods. For example, they enter information such as "I commute to work by car every day," "My monthly electricity usage is 300 kWh," and "I eat out three times a week."

[0545] 2. Data Transmission

[0546] The device sends the entered data to the server. Specifically, the application encrypts the data in the background and sends it to the server using the HTTPS protocol.

[0547] 3. Data Analysis

[0548] The server passes the received data to the generative AI and requests it to analyze it. The server converts the data into an appropriate format and sends a request to the generative AI's API.

[0549] 4. Calculating CO2 emissions

[0550] Generative AI calculates CO2 emissions based on user input data, generating results such as "CO2 emissions from commuting by car amount to 5 kg per day" or "CO2 emissions from electricity use in one month amount to 150 kg."

[0551] 5. Presentation of reduction targets and measures

[0552] Based on the calculation results, the generative AI generates a CO2 emission reduction target and measures for the user and returns them to the server. For example, it may suggest measures such as "suggesting the use of public transportation or commuting by bicycle" or "switching to LED light bulbs to reduce electricity usage by 20%."

[0553] 6. Displaying the results

[0554] The server sends the reduction targets and measures received from the generative AI to the device, converts the data into an appropriate format, and sends it to the device.

[0555] The terminal displays the received reduction targets and measures to the user. Specifically, the reduction targets and measures are displayed on the application UI so that the user can check them.

[0556] 7. Recognizing emotional states

[0557] The device uses an emotion engine to recognize the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes emotional states such as "feeling stressed" or "feeling happy."

[0558] 8. Adjustment of reduction targets and measures

[0559] The emotion engine adjusts reduction goals and strategies based on the user's perceived emotional state. For example, if the user is feeling stressed, the emotion engine may lower the reduction goal and suggest strategies such as "suggest ways to relax."

[0560] 9. Points Award

[0561] When a user achieves a reduction target, the server awards points according to the degree of achievement. For example, the server may perform a process such as "if the reduction target is achieved, 100 points will be awarded."

[0562] The emotion engine adjusts the amount of points awarded based on the user's emotional state, for example, awarding more points if the user is feeling happy.

[0563] Examples and prompts

[0564] Example: If a user commutes by car every day, the generative AI will suggest taking public transport or commuting by bicycle. If the user is feeling stressed, the emotion engine will relax the reduction goal and suggest ways to reduce stress.

[0565] Example prompt: "I drive to work every day. What are some suggestions for reducing my carbon footprint?"

[0566] In this way, the system of the present invention visualizes the CO2 emissions in the user's daily life, presents reduction targets and measures, and uses an emotion engine to take the user's emotional state into account to provide more personalized support.

[0567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0568] Step 1:

[0569] Data Entry

[0570] Users open a smartphone application and enter information about their residential electricity usage, transportation, meals, and consumer goods.

[0571] Input: Information such as "I commute to work by car every day," "My monthly electricity usage is 300kWh," and "I eat out three times a week."

[0572] Output: The input data is saved in the application.

[0573] Step 2:

[0574] Data transmission

[0575] The terminal transmits the input data to the server.

[0576] Input: Lifestyle information entered by the user.

[0577] Data processing: The application encrypts the data and sends it using the HTTPS protocol.

[0578] Output: Encrypted data reaches the server.

[0579] Step 3:

[0580] Data analysis

[0581] The server passes the received data to the generative AI and requests it to analyze it.

[0582] Input: Encrypted lifestyle information data.

[0583] Data processing: The server converts the data into an appropriate format and sends a request to the generative AI API.

[0584] Output: An analysis request is sent to the generative AI.

[0585] Step 4:

[0586] Calculating CO2 emissions

[0587] The generative AI calculates CO2 emissions based on user input data.

[0588] Input: Lifestyle information data sent from the server.

[0589] Data calculation: Generative AI analyzes data and generates calculations such as "CO2 emissions from commuting by car amount to 5 kg per day" and "CO2 emissions from electricity use in one month amount to 150 kg."

[0590] Output: Calculated CO2 emissions.

[0591] Step 5:

[0592] Presentation of reduction targets and measures

[0593] The generative AI generates the user's CO2 emission reduction targets and reduction measures based on the calculation results and returns them to the server.

[0594] Input: Calculated CO2 emissions.

[0595] Data calculation: Generative AI generates reduction targets and measures, such as "suggesting the use of public transportation or commuting by bicycle" or "switching to LED light bulbs to reduce electricity consumption by 20%."

[0596] Output: Reduction targets and measures.

[0597] Step 6:

[0598] Displaying the results

[0599] The server transmits the reduction targets and measures received from the generative AI to the terminal.

[0600] Input: Reduction targets and measures.

[0601] Data processing: The server converts the data into an appropriate format and sends it to the device.

[0602] Output: Reduction targets and measures arrive at the terminal.

[0603] The terminal displays the received reduction target and measures to the user.

[0604] Input: Reduction targets and measures sent from the server.

[0605] Specific operation: The reduction targets and measures are displayed in the application UI so that the user can check them.

[0606] Output: The user confirms the reduction targets and measures.

[0607] Step 7:

[0608] Recognition of emotional states

[0609] The terminal uses an emotion engine to recognize the user's emotional state.

[0610] Input: The user's facial expression and tone of voice.

[0611] Data calculation: Using a camera and microphone, the system analyzes the user's facial expressions and tone of voice to recognize their emotional state, such as "feeling stressed" or "feeling happy."

[0612] Output: Perceived emotional state.

[0613] Step 8:

[0614] Adjustment of reduction targets and measures

[0615] The emotion engine adjusts reduction goals and strategies based on the perceived emotional state.

[0616] Input: Perceived emotional state.

[0617] Data calculation: Adjust reduction goals and strategies based on emotional state. For example, if the user is feeling stressed, the reduction goal may be relaxed and "suggest ways to relax."

[0618] Output: Adjusted reduction targets and measures.

[0619] Step 9:

[0620] Points awarded

[0621] When a user achieves a reduction target, the server awards points according to the degree of achievement.

[0622] Input: User achievement.

[0623] Data calculation: Calculate and award points based on the level of achievement. For example, "If the reduction target is achieved, 100 points will be awarded."

[0624] Output: Points awarded.

[0625] The emotion engine adjusts the amount of points awarded based on the user's emotional state.

[0626] Input: Perceived emotional state.

[0627] Data calculation: Adjust the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, more points will be awarded.

[0628] Output: Adjusted points awarded.

[0629] (Application example 2)

[0630] Next, a description will be given of Application Example 2 of Form 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."

[0631] In modern society, it is important to visualize the environmental impact of individual lifestyle habits and present specific reduction targets and measures. However, existing systems lack personalized suggestions that take into account the user's emotional state, making it difficult to maintain user motivation. Furthermore, food delivery services lack mechanisms to provide options that take into account the environmental impact of the meals they order. This makes it difficult for users to make environmentally friendly choices.

[0632] 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.

[0633] In this invention, the server includes: a means for visualizing CO2 emissions in the user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement when the reduction target is achieved; a means for calculating the CO2 emissions of meals ordered by the user and suggesting environmentally friendly options; and a means for recognizing the user's emotional state and adjusting the reduction targets and reduction measures based on the emotional state. This enables personalized support for CO2 emission reduction that takes the user's emotional state into consideration, and makes it possible to provide environmentally friendly options even in food delivery services.

[0634] "Means for visualizing CO2 emissions in a user's daily life" refers to a means for visually displaying the carbon dioxide emissions associated with electricity use, transportation, meals, and use of consumer goods in a user's daily life.

[0635] "Means for inputting information on electricity usage, transportation, meals, and consumer goods at the user's residence" refers to a means for a user to input information on electricity consumption, transportation, meals, and consumer goods usage at home into the system.

[0636] "Means for presenting CO2 emission reduction targets and reduction measures using generative AI" refers to a means for utilizing generative artificial intelligence to propose targets and specific measures for reducing the user's CO2 emissions.

[0637] "Means for awarding points to a user based on the degree of achievement when the user achieves a reduction target" refers to means for awarding points to a user based on the degree of achievement when the user achieves a set CO2 emission reduction target.

[0638] "Means for calculating the carbon footprint of meals ordered by a user and suggesting environmentally friendly options" refers to means for calculating the carbon footprint of meals ordered by a user through a food delivery service and suggesting environmentally friendly alternative meal options.

[0639] "Means for recognizing the emotional state of a user and adjusting reduction targets and measures based on that emotional state" refers to means for recognizing the emotions of a user and adjusting CO2 emission reduction targets and measures in accordance with those emotions.

[0640] The following system configuration will be described as an embodiment of the present invention.

[0641] System Configuration

[0642] The system includes a user's smartphone, a server, a generative AI model, and an emotion recognition engine. Users input information about their residential electricity usage, transportation, meals, and consumer goods through a smartphone application. The server receives this information and uses the generative AI model to calculate the user's CO2 emissions and presents reduction targets and measures. It also uses the emotion recognition engine to recognize the user's emotional state and adjusts reduction targets and measures based on that emotional state.

[0643] Program processing explanation

[0644] Hardware

[0645] Smartphone

[0646] server

[0647] software

[0648] Smartphone application

[0649] Generative AI Model

[0650] Emotion Recognition Engine

[0651] Data processing and calculation

[0652] 1. Collecting user input information

[0653] Users enter information about their residential electricity usage, travel, meals, and consumer goods through a smartphone application, which is then sent to a server.

[0654] 2. Calculating CO2 emissions

[0655] The server uses a generative AI model to calculate CO2 emissions based on user input. For example, if a user orders a beef burger, the server calculates the CO2 emissions of that meal.

[0656] 3. Presentation of reduction targets and measures

[0657] Based on the calculation results, the server presents the user with a reduction target and specific reduction measures, for example, suggesting a "vegetarian burger" instead of a "beef burger."

[0658] 4. Recognizing and regulating emotional states

[0659] The emotion recognition engine recognizes the user's emotional state and adjusts the reduction goal and measures based on the emotional state. For example, if the user is feeling stressed, the reduction goal is relaxed and measures to reduce stress are suggested.

[0660] 5. Points Award

[0661] When a user achieves their reduction target, the server awards them points according to their achievement. These points can be used as electronic money or electronic coupons.

[0662] Specific examples

[0663] For example, if a user orders a beef burger and the emotion recognition engine detects stress, the app will suggest chicken salad or a vegetarian burger, lowering the target weight loss. If the user follows the suggestion and selects the vegetarian burger, points will be awarded according to the weight loss achieved.

[0664] Prompt Sentence Examples

[0665] An example of a prompt for a generative AI model is:

[0666] Calculate the CO2 emissions of the meals the user chooses and suggest eco-friendly meal options based on their emotional state. If the user is feeling stressed, ease their reduction goal and offer options to reduce stress. Also, reward them with points when they achieve their reduction goal.

[0667] In this way, personalized CO2 emission reduction support that takes into account the user's emotional state becomes possible, providing environmentally friendly options for food delivery services.

[0668] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0669] Step 1:

[0670] Collecting user input

[0671] Users use a smartphone application to input information about their residential electricity usage, travel, meals, and consumer goods. This information is sent to a server. Input data includes, for example, "I ordered a beef burger." The server receives this information and stores it in a database.

[0672] Step 2:

[0673] Calculating CO2 emissions

[0674] The server uses a generative AI model to calculate CO2 emissions based on the information entered by the user. Specifically, the server retrieves the CO2 emissions of a "beef burger" from a database and calculates that value. For example, suppose the CO2 emissions of a "beef burger" are 27 kg. The calculation results are stored on the server.

[0675] Step 3:

[0676] Presentation of reduction targets and measures

[0677] Based on the calculation results, the server presents the user with a reduction target and specific reduction measures. For example, the server might suggest a "vegetarian burger" instead of a "beef burger." This suggestion is notified to the user via a smartphone application. The suggestion is based on a prompt generated by the generative AI model.

[0678] Step 4:

[0679] Recognizing and regulating emotional states

[0680] The emotion recognition engine recognizes the user's emotional state. When the user is using a smartphone application, emotional data is collected via the camera and microphone. For example, if the user is feeling stressed, the emotion recognition engine sends that information to the server. The server then adjusts the reduction targets and measures based on this information. Specifically, it relaxes the reduction targets and presents measures to reduce stress.

[0681] Step 5:

[0682] Points awarded

[0683] When a user achieves their reduction goal, the server awards them points according to their achievement. For example, if a user selects a "vegetarian burger," points are calculated based on the reduction amount. The server adds these points to the user's account and notifies the user via a smartphone application. These points can be used as electronic money or electronic coupons.

[0684] In this way, personalized CO2 emission reduction support that takes into account the user's emotional state becomes possible, providing environmentally friendly options for food delivery services.

[0685] 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.

[0686] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0687] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[0688] 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.

[0689] [Second embodiment]

[0690] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0691] 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.

[0692] 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).

[0693] 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.

[0694] 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.

[0695] 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).

[0696] 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.

[0697] 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.

[0698] 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.

[0699] 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.

[0700] 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.

[0701] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0702] "Example 1"

[0703] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[0704] "Example 2"

[0705] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[0706] The processing flow of each embodiment will be described below.

[0707] "Example 1"

[0708] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[0709] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[0710] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[0711] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[0712] "Example 2"

[0713] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[0714] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[0715] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[0716] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[0717] Example 1

[0718] Next, a description will be given of Example 1 of Form 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."

[0719] In modern society, it is important to accurately grasp CO2 emissions in individuals' daily lives and present effective reduction measures. However, conventional systems have had difficulty presenting individualized reduction targets and measures based on users' lifestyles and behavioral patterns. Furthermore, there was a lack of mechanisms for awarding points as an incentive for achieving reduction targets, or for using those points as electronic money or coupons. This led to a decrease in users' motivation to reduce their CO2 emissions.

[0720] 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.

[0721] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement of the reduction targets; a means for transmitting the information input by the user to a cloud server; a means for the cloud server to analyze the received data and input it into a generative AI model; a means for the generative AI to analyze the user's behavioral patterns and calculate CO2 emissions; and a means for making the points available as electronic money or electronic coupons. This makes it possible to present individualized reduction targets and measures based on the user's lifestyle and behavioral patterns, and to award and use points as an incentive when the reduction targets are achieved.

[0722] "User" refers to an individual who uses the system to manage CO2 emissions in their daily lives and aims to achieve reduction targets.

[0723] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in a user's daily life, and is calculated based on information on electricity usage, transportation, meals, and consumer goods.

[0724] "Visualization means" refers to the function that calculates CO2 emissions based on information entered by the user and displays the results in the form of graphs, numbers, etc.

[0725] "Electricity usage" refers to the state of power consumption in a user's residence, and includes power consumption associated with the use of home appliances, lighting, and the like.

[0726] "Mobility" refers to the use of transportation means in the user's daily life, and includes means such as automobiles, public transportation, bicycles, and walking.

[0727] "Diet" refers to the types and amounts of food that a user consumes on a daily basis, and includes information on meat, vegetables, processed foods, etc.

[0728] "Consumer goods" refers to goods and services that users purchase and use, including home appliances, clothing, daily necessities, etc.

[0729] "Generative AI" refers to an artificial intelligence model that analyzes information entered by the user and presents CO2 emission reduction targets and reduction measures.

[0730] "Reduction target" refers to the amount of CO2 emissions reduction that the user aims to achieve, and is set individually by the generation AI.

[0731] "Reduction measures" refer to specific actions or changes that users should take to reduce CO2 emissions, and are suggested by the generative AI.

[0732] "Points" refer to rewards given to users when they achieve their reduction targets, and can be used as electronic money or electronic coupons.

[0733] "Cloud server" refers to a remote server that receives information entered by users and analyzes the data for input into a generative AI model.

[0734] "Behavioral patterns" refer to the behavioral tendencies and habits of users in their daily lives, and are the subject of analysis by generative AI.

[0735] "Electronic money" refers to a digital form of currency that can be used for purchases and payments using points earned by users.

[0736] A "coupon" refers to a ticket that allows a user to use points earned to receive discounts or free offers on specific products or services.

[0737] The present invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and reduction measures. A specific embodiment of this system will be described below.

[0738] User operations

[0739] Users download and install a smartphone application. They launch the application and enter information about their home electricity usage, transportation, meals, and consumer goods. For example, they enter their daily commute method (car, public transportation, bicycle, etc.), what they eat (meat, vegetables, processed foods, etc.), and information about the products they purchase (home appliances, clothing, daily necessities, etc.).

[0740] Device behavior

[0741] The device (smartphone) collects information entered by the user and sends it to a cloud server. The device then converts the entered data into an appropriate format and sends it in a format that is easy for the generative AI model to analyze. Specifically, the device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[0742] Server Operation

[0743] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information about the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model. The AI ​​model analyzes the data and extracts the user's behavioral patterns.

[0744] How generative AI works

[0745] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, the generative AI calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the vehicle model. The generative AI calculates the CO2 emissions (e.g., 2 kg CO2 / day) based on the user's commuting distance (e.g., 20 km) and the vehicle model's fuel efficiency (e.g., 10 km / L).

[0746] Based on the calculation results, the generative AI presents the user with a CO2 emission reduction target and reduction measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle. Based on the user's current CO2 emissions (e.g., 2 kg CO2 / day), the generative AI presents a reduction target (e.g., 1.5 kg CO2 / day) and specific measures (e.g., using public transportation).

[0747] Points allocation and management

[0748] When a user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards points when the target is achieved. Points can be viewed in the "Points Management" section of the application and can be exchanged for electronic money or electronic coupons.

[0749] Specific examples

[0750] Example 1

[0751] If the user commutes to work every day by car, the generative AI might suggest the following:

[0752] Using public transportation (e.g. buses and trains)

[0753] Commuting by bicycle (e.g., purchasing a bicycle and using it to commute to work)

[0754] Example 2

[0755] When a user achieves their reduction goal, the system will award them points as follows:

[0756] 100 points (can be used as electronic money)

[0757] 50 points (can be used as a coupon)

[0758] Prompt Sentence Examples

[0759] "If a user commutes to work every day by car, what CO2 emission reduction measures would the generative AI suggest?"

[0760] In this way, this system calculates CO2 emissions based on the information entered by the user, presents reduction targets and measures, and also awards points according to the degree of achievement, which can be used as electronic money or electronic coupons.

[0761] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0762] Step 1:

[0763] The user enters information

[0764] Users launch a smartphone application and input information about their electricity usage, transportation, meals, and consumer goods at home. Specifically, they input their daily commute method (car, public transportation, bicycle, etc.), meal contents (meat, vegetables, processed foods, etc.), and information about purchased products (home appliances, clothing, daily necessities, etc.).

[0765] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0766] Output: Input lifestyle information data

[0767] Step 2:

[0768] The device sends data

[0769] The device (smartphone) collects information entered by the user and sends it to the cloud server. The device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[0770] Input: User's lifestyle information data

[0771] Output: JSON format data sent to the cloud server

[0772] Step 3:

[0773] The server receives and analyzes the data

[0774] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information on the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model.

[0775] Input: JSON format data sent to the cloud server

[0776] Output: Analyzed behavioral pattern data

[0777] Step 4:

[0778] Generative AI calculates CO2 emissions

[0779] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, it calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the car model.

[0780] Input: Analyzed behavioral pattern data

[0781] Output: Calculated CO2 emissions data

[0782] Step 5:

[0783] Generative AI presents reduction targets and measures

[0784] Based on the calculation results, the generative AI will suggest CO2 emission reduction targets and measures for the user. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[0785] Input: Calculated CO2 emissions data

[0786] Output: Presentation of reduction targets and reduction measures

[0787] Step 6:

[0788] The system awards points

[0789] When a user achieves their reduction target, the system awards them points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards them points when they achieve their target.

[0790] Input: Reduction target achievement status data

[0791] Output: Assigned point data

[0792] Step 7:

[0793] Users can use points

[0794] Users can check their points in the "Points Management" section of the application and exchange them for electronic money or electronic coupons.

[0795] Input: Assigned point data

[0796] Output: Point data used as electronic money or electronic coupons

[0797] (Application example 1)

[0798] Next, a description will be given of Application Example 1 of Form 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."

[0799] In modern society, it is important to visualize the environmental impact of individual lifestyle habits and present specific reduction measures. However, conventional systems have difficulty collecting detailed data across a user's entire lifestyle and setting individualized reduction targets. Furthermore, the lack of incentives for users to achieve their reduction targets makes it difficult to encourage sustainable behavioral change. In particular, food delivery services face the challenge of preventing users from making environmentally conscious choices, as there are no systems that calculate the CO2 emissions of meals ordered and suggest environmentally friendly options.

[0800] 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.

[0801] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods; a means for using generative AI to present CO2 emission reduction targets and reduction measures; a means for awarding points based on the degree to which the user has achieved their reduction targets; a means for calculating CO2 emissions based on the ingredients and cooking methods of the meals the user orders; a means for the generative AI to suggest more environmentally friendly meal options to the user; a means for tracking the user's progress toward their reduction targets and awarding points based on the degree to which they have achieved their targets; and a means for exchanging the awarded points as electronic money or coupons. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier for users to make environmentally conscious meal choices, especially when using food delivery services.

[0802] "Means for visualizing CO2 emissions in a user's daily life" refers to techniques and methods for visually displaying the amount of carbon dioxide emissions associated with the activities and consumption in a user's daily life.

[0803] "Means for inputting information on electricity usage, transportation, meals, and consumer goods at a user's residence" refers to an interface or method for a user to input data on the electricity used, transportation, meals, and goods consumed at home into the system.

[0804] "Means for presenting CO2 emission reduction targets and reduction measures using generative AI" refers to technologies and methods that utilize generative artificial intelligence to set targets and suggest specific reduction methods for users to reduce their carbon dioxide emissions.

[0805] "Means for awarding points based on the degree of achievement when a user achieves a reduction target" refers to a system or method for awarding points based on the degree of achievement when a user achieves a carbon dioxide emission reduction target set by the user.

[0806] "Means for calculating CO2 emissions based on the ingredients and cooking method of the meal ordered by the user" refers to technology or methods for calculating the carbon dioxide emissions associated with the meal ordered by the user based on the ingredients and cooking method of the meal.

[0807] "Means for generative AI to suggest more environmentally friendly food options to users" refers to technologies and methods that use generative artificial intelligence to suggest food options that have a lower environmental impact to users.

[0808] "Means for tracking a user's progress toward a reduction target and awarding points based on the degree of achievement" refers to a system or method for tracking the progress of a user toward a carbon dioxide emission reduction target set by the user and awarding points based on the degree of achievement.

[0809] "Means for enabling the points awarded to a user to be used as electronic money or electronic coupons" refers to techniques or methods for enabling the points awarded to a user to be used as electronic money or electronic coupons.

[0810] As an embodiment of the present invention, the following system can be constructed.

[0811] System Overview

[0812] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has a function to calculate the CO2 emissions based on the ingredients and cooking method of the meal the user orders, and suggests environmentally friendly options. When a user achieves their reduction target, they are awarded points according to their achievement, which can be used as electronic money or electronic coupons.

[0813] Hardware and software used

[0814] Hardware: Smartphones, servers

[0815] Software: Python, generative AI model, API (for calculating CO2 emissions)

[0816] Data processing and calculation

[0817] Data Entry

[0818] Users use a smartphone application to input information about their home electricity usage, transportation, meals, and consumer goods, which allows detailed data on the user's life to be collected.

[0819] CO2 emissions calculation

[0820] The server uses APIs to calculate the CO2 emissions for each item based on the data entered by the user, especially the ingredients and cooking method of the meal the user orders.

[0821] Presentation of reduction targets and reduction measures

[0822] The generative AI model analyzes the collected data and presents individualized CO2 emission reduction targets and strategies to the user. For example, if a user commutes by car every day, it suggests using public transportation or commuting by bicycle.

[0823] Proposing environmentally friendly options

[0824] The generative AI model calculates the CO2 footprint of the meal a user orders and suggests more environmentally friendly meal options, such as suggesting a vegetarian dish instead of a meat dish.

[0825] Progress tracking and points awarded

[0826] The server tracks the user's progress toward their reduction goal and awards points based on their achievement. These points can be used as electronic money or coupons.

[0827] Specific examples

[0828] Example user input

[0829] User ID: user123

[0830] Meals ordered: beef, chicken, rice

[0831] Reduction target: 15 kg CO2

[0832] Prompt Sentence Examples

[0833] User ID: user123

[0834] Meals ordered: beef, chicken, rice

[0835] Reduction target: 15 kg CO2

[0836] In this way, the generative AI model analyzes the data entered by the user and presents specific reduction measures and progress reports. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier to make environmentally conscious meal choices, especially when using food delivery services.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] Users enter information about their home's electricity usage, transportation, meals, and consumer goods through a smartphone application.

[0840] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0841] Output: Input lifestyle information data

[0842] How it works: Users enter various data about their daily lives into the application's input form, such as recording details of their daily commute, meals, and electricity consumption.

[0843] Step 2:

[0844] The server uses an API to calculate the CO2 emissions for each item based on the data entered by the user.

[0845] Input: User's lifestyle information data

[0846] Output: CO2 emissions for each item

[0847] Specific operation: The server analyzes the input data and calls an external CO2 emission calculation API to calculate the CO2 emissions corresponding to each item. For example, it obtains CO2 emissions for electricity consumption or CO2 emissions based on transportation method.

[0848] Step 3:

[0849] A generative AI model analyzes the collected data and presents users with personalized CO2 emission reduction targets and measures.

[0850] Input: CO2 emissions for each item

[0851] Output: Individualized reduction targets and strategies

[0852] Specific operation: The server uses a generative AI model to analyze the user's lifestyle and behavioral patterns, and generates optimal reduction targets and specific reduction measures, such as suggesting the use of public transportation or energy-efficient home appliances.

[0853] Step 4:

[0854] The server calculates the CO2 emissions based on the ingredients and cooking method of the meal the user orders.

[0855] Input: Information about the meal the user wants to order (ingredients, cooking method)

[0856] Output: CO2 emissions associated with the meal

[0857] How it works: When a user uses a food delivery service, they input the information about the meal they want to order. Based on this information, the server calls an API to calculate the CO2 emissions corresponding to each ingredient and cooking method.

[0858] Step 5:

[0859] A generative AI model suggests more environmentally friendly food options to users.

[0860] Input: Diet-related CO2 emissions

[0861] Output: Eco-friendly food options

[0862] How it works: Based on the calculated CO2 emissions, the server uses a generative AI model to suggest environmentally friendly meal options to the user, for example, suggesting vegetable dishes instead of meat dishes.

[0863] Step 6:

[0864] The server tracks the user's progress towards their reduction goals and awards points based on their progress.

[0865] Input: User reduction goals and progress data

[0866] Output: Points awarded

[0867] What it does: The server periodically tracks the user's progress towards their reduction goal, calculates points based on the progress of the goal, and credits them to the user's account.

[0868] Step 7:

[0869] The server makes the awarded points available for use as electronic money or electronic coupons.

[0870] Input: Points awarded

[0871] Output: Electronic money or electronic coupons

[0872] Specific operation: The server converts the points awarded to the user into electronic money or electronic coupons, which the user can use. For example, it issues coupons that can be used at affiliated online stores or services.

[0873] Example 2

[0874] Next, a description will be given of Example 2 of Form 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."

[0875] In modern society, it is important to understand the environmental impact of each individual's daily activities and to reduce CO2 emissions through concrete actions. However, it is not easy for users to accurately grasp the CO2 emissions of their own lives and find effective reduction measures. Another issue is the lack of motivation to achieve reduction goals. To solve these problems, a system is needed that calculates CO2 emissions based on users' lifestyle information, presents individualized reduction goals and measures, and provides rewards based on the degree of achievement.

[0876] 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.

[0877] In this invention, the server includes: means for visualizing CO2 emissions in a user's daily life; means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at the user's residence; means for presenting CO2 emission reduction targets and reduction measures using generative AI; means for awarding points to the user based on the degree of achievement when the user achieves the reduction target; means for encrypting the information input by the user and transmitting it to the server; means for the generative AI model to calculate CO2 emissions based on the user's daily life information; means for the server to generate reduction targets and reduction measures based on the calculation results of the generative AI model; means for the user to implement the reduction measures and input their progress; means for the server to analyze the progress information and evaluate the degree of goal achievement; and means for the user to use the awarded points as electronic money or electronic coupons. This enables users to accurately grasp the CO2 emissions in their daily lives, implement effective reduction measures, and receive rewards based on the degree of achievement.

[0878] "User" refers to an individual who uses the system to input information about their lifestyle and aims to reduce CO2 emissions.

[0879] "Lifestyle information" refers to information about the user's electricity usage at home, transportation methods, meal frequency, and purchases of consumer goods.

[0880] "CO2 emissions" refers to the total amount of carbon dioxide emitted by the user's daily activities.

[0881] "Generative AI" refers to artificial intelligence that analyzes a user's lifestyle information and calculates CO2 emissions, presents reduction targets, and presents reduction measures.

[0882] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0883] "Reduction measures" refer to specific actions or methods that users should take to reduce CO2 emissions.

[0884] "Points" refer to rewards given to users when they achieve their reduction goals.

[0885] "Electronic money" refers to digital currency that users can use by exchanging the points they have been awarded.

[0886] "Coupon" refers to a discount ticket or special coupon that can be used by exchanging the points given to the user.

[0887] The "server" refers to a computer system that receives users' lifestyle information, analyzes it using generative AI, and generates reduction targets and reduction measures.

[0888] "Terminal" refers to a device through which a user inputs lifestyle information and communicates with a server.

[0889] "Encryption" refers to a technology that converts data to protect users' personal information and makes it impossible for third parties to decipher it.

[0890] "Progress information" refers to information about the results and progress of a user's implementation of a reduction measure.

[0891] "Goal achievement level" refers to an index that indicates the degree to which a user has achieved the reduction target that was set for them.

[0892] This invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, where users input their daily information and it is analyzed using generative AI.

[0893] A user starts a smartphone application and inputs information about their residential electricity usage, transportation, meal frequency, and consumer goods purchases. For example, they input information about their monthly electricity consumption of 300 kWh, daily commute by car, eating out three times a week, and monthly clothing purchases worth 5,000 yen.

[0894] The device (smartphone) encrypts the input information and sends it to the server. The server inputs the received information into a generative AI model and calculates the user's CO2 emissions. For example, OpenAI's GPT-4 is used as the generative AI model.

[0895] The generative AI model calculates a user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item of electricity use, transportation, meals, and consumer goods to calculate overall emissions.

[0896] Based on the results of the generative AI model, the server generates a CO2 emission reduction target and specific measures for the user, such as suggesting using public transportation, cooking at home more, or using energy-efficient appliances.

[0897] The server then sends the generated reduction targets and measures to the user's device and displays them on the application. The user then implements the proposed reduction measures and enters their progress into the application. For example, they enter the number of days they used public transportation or the number of times they cooked at home.

[0898] The terminal transmits the progress information entered by the user to the server, which analyzes the received progress information and evaluates the user's achievement of the reduction target, for example, by calculating the percentage of the target achieved.

[0899] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The user can use the awarded points as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money.

[0900] As a concrete example, suppose the user enters the following information:

[0901] Residential electricity usage: 300 kWh per month

[0902] Transportation: Commute by car every day (20km one way)

[0903] Meals: Eating out three times a week

[0904] Consumer goods: 5,000 yen worth of clothing purchased per month

[0905] Based on this information, the generative AI model generates a prompt like this:

[0906] The user's electricity usage at home is 300kWh per month, he commutes 20km each way by car every day, eats out three times a week, and buys clothes worth 5,000 yen per month. Based on this information, please calculate the user's CO2 emissions and propose a reduction target and reduction measures.

[0907] The generative AI model analyzes this prompt and makes the following suggestions to the user:

[0908] Reduce electricity usage: Encourage the use of energy-efficient appliances

[0909] Changes in transportation: Suggestions for using public transportation or commuting by bicycle

[0910] Review your diet: Eat out less and cook more at home

[0911] Consumer goods purchases: Limit purchases to essentials

[0912] If the user implements these suggestions and achieves the reduction target, the server will give the user points, which the user can use as electronic money or electronic coupons.

[0913] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0914] Step 1:

[0915] The user starts the application and enters lifestyle information.

[0916] The user launches a smartphone application and inputs information about their residential electricity usage, transportation method, meal frequency, and consumer goods purchases. For example, they might input information about using 300 kWh of electricity per month, commuting to work by car every day, eating out three times a week, and purchasing 5,000 yen worth of clothing per month. The input information is temporarily saved within the application.

[0917] Step 2:

[0918] The terminal transmits the input information to the server.

[0919] The device encrypts the lifestyle information entered by the user and sends it to the server. The encrypted data is used to protect the user's privacy. The input data (information on electricity usage, transportation, meals, and consumer goods) is sent to the server.

[0920] Step 3:

[0921] The information received by the server is input into the generative AI model.

[0922] The server inputs the received lifestyle information into a generative AI model. For example, OpenAI's GPT-4 is used as the generative AI model. The generative AI model begins analysis based on the input data (information on electricity usage, transportation, meals, and consumer goods).

[0923] Step 4:

[0924] A generative AI model calculates the user's CO2 emissions.

[0925] The generative AI model calculates the user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item, such as electricity usage, transportation, meals, and consumer goods, to calculate overall emissions. The output data is the user's overall CO2 emissions.

[0926] Step 5:

[0927] The server generates reduction targets and reduction measures based on the calculation results of the generative AI model.

[0928] Based on the calculation results of the generative AI model, the server generates a CO2 emission reduction target and specific reduction measures for the user. For example, it suggests using public transportation, cooking at home more, and using energy-efficient appliances. The reduction target and reduction measures are obtained as output data.

[0929] Step 6:

[0930] The server presents the reduction target and reduction measures to the user.

[0931] The server sends the generated reduction targets and reduction measures to the user's device and displays them on the application. The user can then confirm the reduction targets and reduction measures through the application. The presented reduction targets and reduction measures are obtained as output data.

[0932] Step 7:

[0933] The user implements reduction measures and enters progress into the application.

[0934] The user implements the proposed reduction measures and inputs their progress into the application. For example, they input the number of days they used public transportation or the number of times they cooked at home. The input data provides progress information on the reduction measures they implemented.

[0935] Step 8:

[0936] The terminal sends progress information to the server.

[0937] The terminal transmits the progress information input by the user to the server. The input data (progress information) is transmitted to the server.

[0938] Step 9:

[0939] The server analyzes the progress information and evaluates the degree of goal achievement.

[0940] The server analyzes the received progress information and evaluates the user's achievement of the reduction target. For example, it calculates the percentage of the target that has been achieved. The target achievement level is obtained as output data.

[0941] Step 10:

[0942] The server awards points according to the degree of goal achievement.

[0943] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The awarded points are obtained as output data.

[0944] Step 11:

[0945] Users use the points as electronic money or electronic coupons.

[0946] Users can use the points they receive as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money. Information on the points used is obtained as output data.

[0947] (Application example 2)

[0948] Next, a description will be given of Application Example 2 of Form 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."

[0949] In modern society, reducing CO2 emissions in individual lives is an important issue. However, it is not easy for users to accurately grasp the CO2 emissions in their own lives and implement effective reduction measures. In particular, food delivery services have difficulty considering the CO2 emissions of the food they order, and there is a lack of mechanisms to encourage environmentally friendly choices. This creates a problem of a lack of motivation for users to take environmentally conscious actions.

[0950] 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.

[0951] In this invention, the server includes a means for visualizing CO2 emissions in a user's daily life, a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, a means for awarding points to the user based on the degree of achievement when the reduction target is achieved, a means for calculating the CO2 emissions of food ordered by the user and suggesting environmentally friendly options, and a means for awarding points to the user when the user makes an environmentally friendly choice. This allows users to accurately grasp the CO2 emissions in their daily lives and implement effective reduction measures. It also promotes environmentally friendly choices in food delivery services, strengthening users' motivation to take environmentally conscious actions.

[0952] "User" refers to an individual who uses the system.

[0953] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[0954] "Visualization means" refers to methods and technologies that allow users to visually check their own CO2 emissions.

[0955] "Electricity usage" refers to the consumption of electricity in a user's residence.

[0956] "Movement" refers to the use of transportation means in the user's daily life.

[0957] "Meals" refers to the food and beverages consumed by a user.

[0958] "Consumer goods" refers to everyday items and services that users purchase and use.

[0959] "Means for inputting information" refers to the methods and techniques by which users provide data about their lives to the system.

[0960] "Generative AI" refers to artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and measures.

[0961] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[0962] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[0963] "Means for awarding points" refers to a method or technology for awarding points as a reward when a user achieves a reduction target.

[0964] "CO2 emissions from food" refers to the total amount of carbon dioxide emitted from the production to consumption of the food ordered by the user.

[0965] "Environmentally friendly choices" refer to foods and actions that have a lower carbon footprint.

[0966] "Food delivery service" refers to a service that allows users to order food online and have it delivered.

[0967] The system for implementing this invention visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, and generative AI performs analysis based on lifestyle information entered by the user.

[0968] Hardware and Software

[0969] Hardware: Smartphone

[0970] Software: Python, JSON files, generative AI models

[0971] Data processing and calculation

[0972] 1. Enter user's lifestyle information:

[0973] Users enter information about their residential electricity usage, travel, meals, and consumer goods through a smartphone application, which is then stored in JSON format within the application.

[0974] 2. Calculating CO2 emissions:

[0975] The server calculates the CO2 emissions for each item based on the information entered by the user. For example, if a user orders a hamburger, it calculates the CO2 emissions generated from the production to consumption of that food.

[0976] 3. Generative AI analysis:

[0977] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[0978] 4. Propose environmentally friendly options:

[0979] The server calculates the CO2 emissions of the food the user orders and suggests environmentally friendly options, such as a salad instead of a hamburger.

[0980] 5. Points Awarding:

[0981] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement. These points can be used as electronic money or coupons.

[0982] Specific examples

[0983] If a user orders a hamburger, the application calculates the CO2 emissions of the food and suggests eco-friendly options such as a salad. If the user selects a salad, they will receive 10 points, which can be used as a discount on their next order.

[0984] Prompt Sentence Examples

[0985] An example of a prompt to input to a generative AI model is as follows:

[0986] Calculate the CO2 emissions of a user ordering a hamburger and suggest eco-friendly options, such as salads or vegetable-based dishes, and reward users for their eco-friendly choices.

[0987] In this way, users can accurately understand the CO2 emissions in their daily lives and take effective measures to reduce them.Furthermore, food delivery services can also promote environmentally friendly choices, strengthening users' motivation to take environmentally conscious actions.

[0988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0989] Step 1:

[0990] The user launches a smartphone application and enters information about their home's electricity usage, transportation, meals, and consumer goods.

[0991] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[0992] Output: JSON formatted lifestyle information data

[0993] Specific operation: The user enters information about their daily life into the application's input form and presses the submit button. The application saves this information in JSON format.

[0994] Step 2:

[0995] The server receives the user's input information and calculates the CO2 emissions for each item.

[0996] Input: JSON formatted lifestyle information data

[0997] Output: CO2 emissions for each item

[0998] Specific operation: The server analyzes the received JSON data and calculates the CO2 emissions for each item (electricity usage, transportation, meals, consumer goods). For example, it calculates the CO2 emissions based on the power consumption from the electricity usage data.

[0999] Step 3:

[1000] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and reduction measures.

[1001] Input: CO2 emissions for each item

[1002] Output: Reduction targets and measures

[1003] Specific operation: The server inputs the CO2 emissions for each item into the generative AI model, which then analyzes them and generates individualized reduction targets and measures for the user, such as suggesting using public transportation or commuting by bicycle.

[1004] Step 4:

[1005] When a user uses a food delivery service, the service calculates the CO2 emissions of the food they order and suggests environmentally friendly options.

[1006] Input: Food information the user wants to order

[1007] Output: Food CO2 emissions and eco-friendly options

[1008] What it does: When a user selects a food item in a food delivery application, the server calculates the food's CO2 footprint and suggests environmentally friendly options (e.g., salads).

[1009] Step 5:

[1010] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement.

[1011] Input: User behavior data (reduction goal achievement status, making environmentally friendly choices)

[1012] Output: Points awarded

[1013] Specific operation: The server monitors the user's behavioral data, calculates points when reduction targets are achieved or environmentally friendly choices are made, and adds them to the user's account. For example, if a user chooses a salad, 10 points are added.

[1014] Step 6:

[1015] The user uses the awarded points as electronic money or electronic coupons.

[1016] Input: Points awarded

[1017] Output: Electronic money or electronic coupons

[1018] Specific operation: The user checks the points in the application and selects the option to use them as electronic money or electronic coupons. The server converts the points into electronic money or electronic coupons and provides them to the user.

[1019] 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.

[1020] "Example 1"

[1021] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[1022] "Example 2"

[1023] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[1024] The processing flow of each embodiment will be described below.

[1025] "Example 1"

[1026] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[1027] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[1028] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[1029] Step 4: The emotion engine recognizes the user's emotional state.

[1030] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[1031] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[1032] "Example 2"

[1033] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[1034] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[1035] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[1036] Step 4: The emotion engine recognizes the user's emotional state.

[1037] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[1038] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[1039] Example 1

[1040] Next, a description will be given of Example 1 of Form 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."

[1041] Conventional CO2 emission reduction systems present uniform reduction targets and measures without considering the user's lifestyle or emotional state, making it difficult to maintain user motivation. In addition, the rewards for achieving reduction targets are also uniform, making it difficult to respond flexibly to the efforts and circumstances of individual users.

[1042] 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.

[1043] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement when the reduction target is achieved; a means for recognizing the user's emotional state and adjusting the reduction targets and reduction measures based on the emotional state; and a means for adjusting the amount of points awarded based on the user's emotional state. This makes it possible to present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective CO2 emission reduction while maintaining the user's motivation.

[1044] A "user" is an individual who uses the system to manage the CO2 emissions in their daily lives and achieve their reduction targets.

[1045] "CO2 emissions" is the total amount of carbon dioxide emitted as a result of the user's daily activities.

[1046] "Visualization" is the process that allows users to visually check their own CO2 emissions.

[1047] "Electricity usage" refers to the state of electricity consumption in the user's residence.

[1048] "Movement" refers to the state of use of transportation means by the user in daily life.

[1049] "Diet" is information about the types and frequency of food consumed by the user.

[1050] "Consumer goods" are items such as daily necessities and clothing that users purchase.

[1051] "Generative AI" is artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and reduction measures.

[1052] The "reduction target" is the amount of CO2 emission reduction that the user must achieve.

[1053] "Reduction measures" are specific actions or methods that users should take to reduce CO2 emissions.

[1054] "Points" are rewards given to users when they achieve their reduction targets.

[1055] "Emotional state" refers to the user's psychological state or mood.

[1056] "Adjustment" is the process of changing reduction targets, measures, and point award amounts based on the user's emotional state.

[1057] This invention is a system for managing CO2 emissions in a user's daily life and achieving reduction targets. This system is implemented as a smartphone application, and users input their own lifestyle information, which is analyzed using generative AI, and CO2 emission reduction targets and reduction measures are presented.

[1058] Hardware and software used

[1059] Hardware: Smartphone (e.g. iPhone, Android device)

[1060] Software: Smartphone applications, generative AI (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva)

[1061] Specific operation of the system

[1062] User Input

[1063] Users enter information about their electricity usage at home, transportation, meals, and consumer goods through a smartphone application. For example, a user might enter information such as "I use 200 kWh of electricity per month," "I commute to work by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month."

[1064] Sending information

[1065] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the data to the server via the Internet.

[1066] Analysis of information

[1067] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data.

[1068] CO2 emissions calculation

[1069] The generative AI calculates the user's CO2 emissions based on the prompt text. For example, it generates the following prompt text and performs analysis:

[1070] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[1071] Presentation of reduction targets and reduction measures

[1072] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures, such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months."

[1073] Tuning the Emotion Engine

[1074] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine will relax the reduction target and suggest a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine will raise the reduction target and suggest a more aggressive reduction strategy.

[1075] Confirmation of achievement of reduction targets

[1076] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application.

[1077] Points awarded

[1078] The server receives the user's report and checks the degree of achievement of the reduction target. Points are awarded according to the degree of achievement. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons.

[1079] In this way, the system of the present invention can present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective reduction of CO2 emissions while maintaining the user's motivation.

[1080] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1081] Step 1:

[1082] Entering user information

[1083] Users open a smartphone application and enter information about their home electricity usage, transportation, meals, and consumer goods. Specifically, users enter information such as "I use 200 kWh of electricity per month," "I commute by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month." The input data is temporarily stored within the application.

[1084] Step 2:

[1085] Sending information

[1086] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the input data to the server via the Internet in JSON format. For example, data is sent in the format {"electricity": 200, "commute": "car", "dining": 3, "shopping": 1}. The input is the user's lifestyle information, and the output is the JSON data sent to the server.

[1087] Step 3:

[1088] Analysis of information

[1089] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data. For example, it generates the following prompt:

[1090] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[1091] The input is the JSON data sent to the server, and the output is the generated prompt text.

[1092] Step 4:

[1093] CO2 emissions calculation

[1094] The generative AI calculates the user's CO2 emissions based on the prompt sentence. The analysis result is the user's annual CO2 emissions. For example, the generative AI calculates the user's CO2 emissions to be approximately 3.5 tons per year. The input is the generated prompt sentence, and the output is the calculated CO2 emissions.

[1095] Step 5:

[1096] Presentation of reduction targets and reduction measures

[1097] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures. For example, it presents specific measures such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months." The input is the calculated CO2 emissions, and the output is the presented reduction targets and measures.

[1098] Step 6:

[1099] Tuning the Emotion Engine

[1100] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and proposes a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine raises the reduction target and proposes a more aggressive reduction strategy. The input is the user's emotional state, and the output is the adjusted reduction target and reduction strategy.

[1101] Step 7:

[1102] Confirmation of achievement of reduction targets

[1103] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application. The input is the reduction measures implemented by the user, and the output is the reported progress.

[1104] Step 8:

[1105] Points awarded

[1106] The server receives the user's report and checks the progress of the reduction target. Points are awarded according to the progress. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons. The input is the reported progress, and the output is the awarded points.

[1107] (Application example 1)

[1108] Next, a description will be given of Application Example 1 of Form 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."

[1109] Reducing personal CO2 emissions is an important issue in modern society. However, it is not easy for users to accurately understand their own CO2 emissions and implement effective reduction measures. Furthermore, the lack of personalized suggestions that take into account the user's emotional state makes it difficult to encourage sustainable behavioral change. Furthermore, food delivery services lack mechanisms for suggesting environmentally friendly options, making it difficult for users to make environmentally conscious choices.

[1110] 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.

[1111] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user based on the degree to which the reduction target is achieved; a means for calculating the CO2 emissions of meals ordered by the user; a means for the generative AI to suggest environmentally friendly meal options to the user; and a means for recognizing the user's emotional state and adjusting the suggestions and point awarding based on the user's emotional state. This allows users to accurately grasp the CO2 emissions of their daily lives and implement personalized reduction measures according to their emotional state. Furthermore, since environmentally friendly options are suggested for food delivery services, it becomes easier for users to make environmentally conscious choices.

[1112] "User" refers to an individual who uses the system.

[1113] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[1114] "Visualization means" refers to a method or device that allows users to visually check their own CO2 emissions.

[1115] "Electricity usage" refers to the consumption of electricity in a user's residence.

[1116] "Movement" refers to the use of transportation means in the user's daily life.

[1117] "Meal" refers to the food consumed by the user and how it is prepared.

[1118] "Consumer goods" refers to everyday items and services that users purchase and use.

[1119] "Means for inputting information" refers to a method or device by which a user provides the required data to the system.

[1120] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate results.

[1121] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[1122] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[1123] The "means for awarding points" refers to a method or device for awarding points as a reward when a user achieves a reduction target.

[1124] "Meal CO2 emissions" refers to the total amount of carbon dioxide associated with the production, transportation, and cooking of the food a user orders.

[1125] "Eco-friendly food options" refer to foods and cooking methods that have a low carbon footprint.

[1126] "Emotional state" refers to a user's psychological state or mood.

[1127] "Means for adjusting suggestions" refers to a method or device for changing the reduction measures or options presented based on the user's emotional state.

[1128] "Means for adjusting point awarding" refers to a method or device for changing the amount of points awarded based on the user's emotional state.

[1129] The embodiments of the present invention will be described in detail below.

[1130] System Overview

[1131] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has the function of calculating the CO2 emissions of meals ordered by the user and suggesting environmentally friendly options. It also recognizes the user's emotional state and adjusts the suggestions and point awards accordingly.

[1132] Hardware and software used

[1133] Hardware: Smartphone

[1134] Software: Python, emotion recognition API (e.g., Microsoft Azure Emotion API), generative AI model

[1135] Data processing and calculation

[1136] 1. Calculating CO2 emissions

[1137] The server calculates CO2 emissions based on the information entered by the user about electricity usage, travel, meals, and consumer goods, using pre-set CO2 emission coefficients for each item.

[1138] 2. Presentation of reduction targets and reduction measures

[1139] Using a generative AI model, the system presents personalized reduction targets and strategies based on the user's lifestyle and behavioral patterns. For example, if a user commutes by car every day, the system suggests using public transportation or commuting by bicycle.

[1140] 3. Calculation and proposal of CO2 emissions from meals

[1141] The system calculates the CO2 emissions of the meal a user orders and uses generative AI to suggest eco-friendly meal options, such as suggesting chicken or vegetarian options if the user orders beef.

[1142] 4. Recognizing and regulating emotional states

[1143] The system uses an emotion recognition API to recognize the user's emotional state. Based on this, it adjusts the content of suggestions and points awarded. For example, if the user is feeling stressed, it will relax the reduction target and suggest stress-reducing measures.

[1144] Specific examples

[1145] If the user orders "beef," the system will suggest "chicken" and award 10 points if the user feels "delighted."

[1146] Prompt Sentence Examples

[1147] The user ordered beef. The emotion engine recognized that the user was happy. The generative AI suggested chicken and awarded it 10 points.

[1148] This allows users to accurately understand the CO2 emissions in their daily lives and implement personalized reduction measures according to their emotional state. In addition, environmentally friendly options will be suggested for food delivery services, making it easier for users to make environmentally conscious choices.

[1149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1150] Step 1:

[1151] Users use their smartphones to input information about electricity usage, transportation, meals, and consumer goods at home.

[1152] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[1153] Output: Input lifestyle information data

[1154] Specific actions: The user opens the smartphone app and enters specific values ​​and options for each item. For example, for electricity usage, the user enters the monthly electricity consumption, and for transportation, the user enters the commute method and distance.

[1155] Step 2:

[1156] The server receives the lifestyle information entered and calculates CO2 emissions.

[1157] Input: Lifestyle information data

[1158] Output: CO2 emissions data

[1159] Specific operation: The server calculates the overall CO2 emissions using the CO2 emission coefficients set in advance for each item. For example, electricity usage is calculated by multiplying it by the CO2 emission coefficient for the power consumption.

[1160] Step 3:

[1161] The server uses a generative AI model to present reduction targets and measures based on the user's lifestyle and behavioral patterns.

[1162] Input: CO2 emissions data, lifestyle information data

[1163] Output: Reduction targets and measures

[1164] Specific operation: The server uses a generative AI model to analyze the user's lifestyle information and generate optimal reduction targets and specific reduction measures. For example, it suggests using public transportation for users who commute by car.

[1165] Step 4:

[1166] When a user orders a meal through a food delivery app, the server calculates the CO2 emissions of the meal being ordered.

[1167] Input: Order information (type of meal)

[1168] Output: CO2 emissions data for meals

[1169] What it does: When a user selects a meal in a food delivery app, the CO2 emissions of that meal are calculated. For example, if you order beef, the CO2 emissions associated with producing and transporting it are calculated.

[1170] Step 5:

[1171] The server uses a generative AI model to suggest environmentally friendly meal options.

[1172] Input: CO2 emissions data for meals, order information

[1173] Output: Eco-friendly food options

[1174] How it works: The server uses a generative AI model to suggest greener options based on the carbon footprint of the meal ordered, for example, suggesting chicken or vegetarian options if you order beef.

[1175] Step 6:

[1176] The server uses an emotion recognition API to recognize the user's emotional state.

[1177] Input: User emotion data (face image and voice)

[1178] Output: Emotional state data

[1179] Specific operation: When a user provides emotional data using the smartphone's camera or microphone, the server analyzes the data using an emotion recognition API and recognizes the user's emotional state.

[1180] Step 7:

[1181] The server adjusts the suggestions and points awarded based on the emotional state.

[1182] Input: Emotional state data, reduction goals and strategies, environmentally friendly food options

[1183] Output: Adjusted proposal and points awarded

[1184] Specific operation: Based on the emotional state data, the server relaxes the reduction target if the user is feeling stressed, and raises the target if the user is feeling happy. It also adjusts the amount of points awarded according to the emotional state. For example, if the user is feeling happy, 10 points will be awarded.

[1185] Step 8:

[1186] When a user achieves a reduction target, the server awards points according to the degree of achievement.

[1187] Input: Achievement data

[1188] Output: Points awarded

[1189] Specific operation: When a user achieves the set reduction goal, the server calculates points according to the achievement level and adds them to the user's account. These points can be used as electronic money or electronic coupons.

[1190] Example 2

[1191] Next, a description will be given of Example 2 of Form 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."

[1192] Reducing personal CO2 emissions is an important issue in modern society. However, it is not easy for users to accurately grasp the CO2 emissions in their own lives and find effective reduction measures. It is also difficult to maintain motivation to achieve reduction goals. Furthermore, a lack of personalized support that takes into account the user's emotional state can lead to stress and burden. To solve these issues, a system is needed that visualizes the CO2 emissions in a user's life, presents individualized reduction goals and measures, and provides support that takes into account the user's emotional state.

[1193] 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.

[1194] In this invention, the server includes a means for visualizing the CO2 emissions in a user's daily life, a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at the user's residence, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, a means for awarding points to the user based on the degree of achievement of the reduction target, a means for recognizing the user's emotional state and adjusting the reduction target and reduction measures based on the user's emotional state, and a means for adjusting the amount of points awarded based on the user's emotional state. This allows users to accurately grasp the CO2 emissions in their daily lives and find effective reduction measures. It also makes it easier for users to maintain motivation to achieve their reduction targets and allows them to receive personalized support that takes their emotional state into account.

[1195] "User" refers to any individual or entity that uses the System.

[1196] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[1197] "Visualization means" refers to methods and technologies that allow users to visually check their own CO2 emissions.

[1198] "Electricity usage" refers to the consumption of electricity in a user's residence.

[1199] "Movement" refers to the means of transportation used by the user, such as commuting or traveling.

[1200] "Meal" refers to the food a user consumes and how it is prepared.

[1201] "Consumer Goods" refers to goods and services that are purchased and used by users.

[1202] "Input means" refers to the methods or techniques by which a user provides information to a system.

[1203] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate results.

[1204] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[1205] "Reduction measures" refers to specific methods and proposals for reducing CO2 emissions.

[1206] "Means of presentation" refers to the methods and techniques that generative AI uses to communicate analysis results to users.

[1207] "Means for awarding points" refers to a method or technology for awarding points as a reward when a user achieves a reduction target.

[1208] "Emotional state" refers to the user's mental and emotional state.

[1209] "Means for recognizing" refers to methods or techniques for detecting and determining a user's emotional state.

[1210] "Adjustment means" refers to methods and techniques for changing reduction targets, measures, and points awarded based on the user's emotional state.

[1211] The present invention provides a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and reduction measures. This system is implemented as a smartphone application. Specific embodiments are described below.

[1212] Hardware and software used

[1213] Hardware: Smartphone (e.g. iPhone, Android device)

[1214] Software: Smartphone applications, generative AI (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva SDK)

[1215] System Overview

[1216] Users input information about their home electricity usage, transportation, meals, and consumer goods through a smartphone application. This information is analyzed by generative AI, which calculates the user's CO2 emissions. Based on this information, generative AI presents the user with CO2 emission reduction targets and reduction measures. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[1217] Specific actions

[1218] 1. Data Entry

[1219] Users open a smartphone application and enter information about their residential electricity usage, transportation, meals, and consumer goods. For example, they enter information such as "I commute to work by car every day," "My monthly electricity usage is 300 kWh," and "I eat out three times a week."

[1220] 2. Data Transmission

[1221] The device sends the entered data to the server. Specifically, the application encrypts the data in the background and sends it to the server using the HTTPS protocol.

[1222] 3. Data Analysis

[1223] The server passes the received data to the generative AI and requests it to analyze it. The server converts the data into an appropriate format and sends a request to the generative AI's API.

[1224] 4. Calculating CO2 emissions

[1225] Generative AI calculates CO2 emissions based on user input data, generating results such as "CO2 emissions from commuting by car amount to 5 kg per day" or "CO2 emissions from electricity use in one month amount to 150 kg."

[1226] 5. Presentation of reduction targets and measures

[1227] Based on the calculation results, the generative AI generates a CO2 emission reduction target and measures for the user and returns them to the server. For example, it may suggest measures such as "suggesting the use of public transportation or commuting by bicycle" or "switching to LED light bulbs to reduce electricity usage by 20%."

[1228] 6. Displaying the results

[1229] The server sends the reduction targets and measures received from the generative AI to the device, converts the data into an appropriate format, and sends it to the device.

[1230] The terminal displays the received reduction targets and measures to the user. Specifically, the reduction targets and measures are displayed on the application UI so that the user can check them.

[1231] 7. Recognizing emotional states

[1232] The device uses an emotion engine to recognize the user's emotional state. For example, it uses a camera and microphone to analyze the user's facial expressions and tone of voice, and recognizes emotional states such as "feeling stressed" or "feeling happy."

[1233] 8. Adjustment of reduction targets and measures

[1234] The emotion engine adjusts reduction goals and strategies based on the user's perceived emotional state. For example, if the user is feeling stressed, the emotion engine may relax the reduction goal and suggest strategies such as "suggest ways to relax."

[1235] 9. Points Award

[1236] When a user achieves a reduction target, the server awards points according to the degree of achievement. For example, the server may perform a process such as "if the reduction target is achieved, 100 points will be awarded."

[1237] The emotion engine adjusts the amount of points awarded based on the user's emotional state, for example, awarding more points if the user is feeling happy.

[1238] Examples and prompts

[1239] Example: If a user commutes by car every day, the generative AI will suggest taking public transport or commuting by bicycle. If the user is feeling stressed, the emotion engine will relax the reduction goal and suggest ways to reduce stress.

[1240] Example prompt: "I drive to work every day. What are some suggestions for reducing my carbon footprint?"

[1241] In this way, the system of the present invention visualizes the CO2 emissions in the user's daily life, presents reduction targets and measures, and takes into account the user's emotional state using an emotion engine to provide more personalized support.

[1242] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1243] Step 1:

[1244] Data Entry

[1245] Users open a smartphone application and enter information about their residential electricity usage, transportation, meals, and consumer goods.

[1246] Input: Information such as "I commute to work by car every day," "My monthly electricity usage is 300kWh," and "I eat out three times a week."

[1247] Output: The input data is saved in the application.

[1248] Step 2:

[1249] Data transmission

[1250] The terminal transmits the input data to the server.

[1251] Input: Lifestyle information entered by the user.

[1252] Data processing: The application encrypts the data and sends it using the HTTPS protocol.

[1253] Output: Encrypted data reaches the server.

[1254] Step 3:

[1255] Data analysis

[1256] The server passes the received data to the generative AI and requests it to analyze it.

[1257] Input: Encrypted lifestyle information data.

[1258] Data processing: The server converts the data into an appropriate format and sends a request to the generative AI API.

[1259] Output: An analysis request is sent to the generative AI.

[1260] Step 4:

[1261] CO2 emissions calculation

[1262] The generative AI calculates CO2 emissions based on user input data.

[1263] Input: Lifestyle information data sent from the server.

[1264] Data calculation: Generative AI analyzes data and generates calculations such as "CO2 emissions from commuting by car amount to 5 kg per day" and "CO2 emissions from electricity use in one month amount to 150 kg."

[1265] Output: Calculated CO2 emissions.

[1266] Step 5:

[1267] Presentation of reduction targets and measures

[1268] The generative AI generates the user's CO2 emission reduction targets and reduction measures based on the calculation results and returns them to the server.

[1269] Input: Calculated CO2 emissions.

[1270] Data calculation: Generative AI generates reduction targets and measures, such as "suggesting the use of public transportation or commuting by bicycle" or "switching to LED light bulbs to reduce electricity consumption by 20%."

[1271] Output: Reduction targets and measures.

[1272] Step 6:

[1273] Displaying the results

[1274] The server transmits the reduction targets and measures received from the generative AI to the terminal.

[1275] Input: Reduction targets and measures.

[1276] Data processing: The server converts the data into an appropriate format and sends it to the device.

[1277] Output: Reduction targets and measures arrive at the terminal.

[1278] The terminal displays the received reduction target and measures to the user.

[1279] Input: Reduction targets and measures sent from the server.

[1280] Specific operation: The reduction targets and measures are displayed in the application UI so that the user can check them.

[1281] Output: The user confirms the reduction targets and measures.

[1282] Step 7:

[1283] Recognition of emotional states

[1284] The terminal uses an emotion engine to recognize the user's emotional state.

[1285] Input: The user's facial expression and tone of voice.

[1286] Data calculation: Using a camera and microphone, the system analyzes the user's facial expressions and tone of voice to recognize their emotional state, such as "feeling stressed" or "feeling happy."

[1287] Output: Perceived emotional state.

[1288] Step 8:

[1289] Adjustment of reduction targets and measures

[1290] The emotion engine adjusts reduction goals and strategies based on the perceived emotional state.

[1291] Input: Perceived emotional state.

[1292] Data calculation: Adjust reduction goals and strategies based on emotional state. For example, if the user is feeling stressed, the reduction goal may be relaxed and "suggest ways to relax."

[1293] Output: Adjusted reduction targets and measures.

[1294] Step 9:

[1295] Points awarded

[1296] When a user achieves a reduction target, the server awards points according to the degree of achievement.

[1297] Input: User achievement.

[1298] Data calculation: Calculate and award points based on the level of achievement. For example, "If the reduction target is achieved, 100 points will be awarded."

[1299] Output: Points awarded.

[1300] The emotion engine adjusts the amount of points awarded based on the user's emotional state.

[1301] Input: Perceived emotional state.

[1302] Data calculation: Adjust the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, more points will be awarded.

[1303] Output: Adjusted points awarded.

[1304] (Application example 2)

[1305] Next, a description will be given of Application Example 2 of Form 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."

[1306] In modern society, it is important to visualize the environmental impact of individual lifestyle habits and present specific reduction targets and measures. However, existing systems lack personalized suggestions that take into account the user's emotional state, making it difficult to maintain user motivation. Furthermore, food delivery services lack mechanisms to provide options that take into account the environmental impact of the meals they order. This makes it difficult for users to make environmentally friendly choices.

[1307] 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.

[1308] In this invention, the server includes: a means for visualizing CO2 emissions in the user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement when the reduction target is achieved; a means for calculating the CO2 emissions of meals ordered by the user and suggesting environmentally friendly options; and a means for recognizing the user's emotional state and adjusting the reduction targets and reduction measures based on the emotional state. This enables personalized support for CO2 emission reduction that takes the user's emotional state into consideration, and makes it possible to provide environmentally friendly options even in food delivery services.

[1309] "Means for visualizing CO2 emissions in a user's daily life" refers to a means for visually displaying the carbon dioxide emissions associated with electricity use, transportation, meals, and use of consumer goods in a user's daily life.

[1310] "Means for inputting information on electricity usage, transportation, meals, and consumer goods at the user's residence" refers to a means for a user to input information on electricity consumption, transportation, meals, and consumer goods usage at home into the system.

[1311] "Means for presenting CO2 emission reduction targets and reduction measures using generative AI" refers to a means for utilizing generative artificial intelligence to propose targets and specific measures for reducing the user's CO2 emissions.

[1312] "Means for awarding points to a user based on the degree of achievement when the user achieves a reduction target" refers to means for awarding points to a user based on the degree of achievement when the user achieves a set CO2 emission reduction target.

[1313] "Means for calculating the carbon footprint of meals ordered by a user and suggesting environmentally friendly options" refers to means for calculating the carbon footprint of meals ordered by a user through a food delivery service and suggesting environmentally friendly alternative meal options.

[1314] "Means for recognizing the emotional state of a user and adjusting reduction targets and measures based on that emotional state" refers to means for recognizing the emotions of a user and adjusting CO2 emission reduction targets and measures in accordance with those emotions.

[1315] The following system configuration will be described as an embodiment of the present invention.

[1316] System Configuration

[1317] The system includes a user's smartphone, a server, a generative AI model, and an emotion recognition engine. Users input information about their residential electricity usage, transportation, meals, and consumer goods through a smartphone application. The server receives this information and uses the generative AI model to calculate the user's CO2 emissions and presents reduction targets and measures. The emotion recognition engine also recognizes the user's emotional state and adjusts the reduction targets and measures based on that emotional state.

[1318] Program processing explanation

[1319] Hardware

[1320] Smartphone

[1321] server

[1322] software

[1323] Smartphone application

[1324] Generative AI Model

[1325] Emotion Recognition Engine

[1326] Data processing and calculation

[1327] 1. Collecting user input information

[1328] Users enter information about their residential electricity usage, travel, meals, and consumer goods through a smartphone application, which is then sent to a server.

[1329] 2. Calculating CO2 emissions

[1330] The server uses a generative AI model to calculate CO2 emissions based on user input. For example, if a user orders a beef burger, the server calculates the CO2 emissions of that meal.

[1331] 3. Presentation of reduction targets and measures

[1332] Based on the calculation results, the server presents the user with a reduction target and specific reduction measures, for example, suggesting a "vegetarian burger" instead of a "beef burger."

[1333] 4. Recognizing and regulating emotional states

[1334] The emotion recognition engine recognizes the user's emotional state and adjusts the reduction goal and measures based on the emotional state. For example, if the user is feeling stressed, the reduction goal is relaxed and measures to reduce stress are suggested.

[1335] 5. Points Award

[1336] When a user achieves their reduction target, the server awards them points according to their achievement. These points can be used as electronic money or electronic coupons.

[1337] Specific examples

[1338] For example, if a user orders a beef burger and the emotion recognition engine detects stress, the app will suggest chicken salad or a vegetarian burger, lowering the target weight loss. If the user follows the suggestion and selects the vegetarian burger, points will be awarded according to the weight loss achieved.

[1339] Prompt Sentence Examples

[1340] An example of a prompt for a generative AI model is:

[1341] Calculate the CO2 emissions of the meals the user chooses and suggest eco-friendly meal options based on their emotional state. If the user is feeling stressed, ease their reduction goal and offer options to reduce stress. Also, reward them with points when they achieve their reduction goal.

[1342] In this way, personalized CO2 emission reduction support that takes into account the user's emotional state becomes possible, providing environmentally friendly options for food delivery services.

[1343] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1344] Step 1:

[1345] Collecting user input

[1346] Users use a smartphone application to input information about their residential electricity usage, travel, meals, and consumer goods. This information is sent to a server. Input data includes, for example, "I ordered a beef burger." The server receives this information and stores it in a database.

[1347] Step 2:

[1348] CO2 emissions calculation

[1349] The server uses a generative AI model to calculate CO2 emissions based on the information entered by the user. Specifically, the server retrieves the CO2 emissions of a "beef burger" from a database and calculates that value. For example, suppose the CO2 emissions of a "beef burger" are 27 kg. The calculation results are stored on the server.

[1350] Step 3:

[1351] Presentation of reduction targets and measures

[1352] Based on the calculation results, the server presents the user with a reduction target and specific reduction measures. For example, the server might suggest a "vegetarian burger" instead of a "beef burger." This suggestion is notified to the user via a smartphone application. The suggestion is based on a prompt generated by the generative AI model.

[1353] Step 4:

[1354] Recognizing and regulating emotional states

[1355] The emotion recognition engine recognizes the user's emotional state. When the user is using a smartphone application, emotional data is collected via the camera and microphone. For example, if the user is feeling stressed, the emotion recognition engine sends that information to the server. The server then adjusts the reduction targets and measures based on this information. Specifically, it relaxes the reduction targets and presents measures to reduce stress.

[1356] Step 5:

[1357] Points awarded

[1358] When a user achieves their reduction goal, the server awards them points according to their achievement. For example, if a user selects a "vegetarian burger," points are calculated based on the reduction amount. The server adds these points to the user's account and notifies the user via a smartphone application. These points can be used as electronic money or electronic coupons.

[1359] In this way, personalized CO2 emission reduction support that takes into account the user's emotional state becomes possible, providing environmentally friendly options for food delivery services.

[1360] 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.

[1361] 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> ) 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.

[1362] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are listed.

[1363] 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.

[1364] [Third embodiment]

[1365] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1366] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1367] 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).

[1368] 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.

[1369] 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.

[1370] 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).

[1371] 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.

[1372] 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.

[1373] 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.

[1374] 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.

[1375] 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.

[1376] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1377] "Example 1"

[1378] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[1379] "Example 2"

[1380] The system of the present invention is implemented as a smartphone application. Through the application, users input information about their residential electricity usage, transportation, meals, and consumer goods. This information is analyzed by a generative AI, which calculates the user's CO2 emissions. Based on this information, the generative AI presents the user with CO2 emission reduction targets and reduction measures. For example, if a user commutes by car every day, the generative AI can suggest using public transportation or commuting by bicycle. Furthermore, if the user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons.

[1381] The processing flow of each embodiment will be described below.

[1382] "Example 1"

[1383] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[1384] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[1385] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[1386] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[1387] "Example 2"

[1388] Step 1: The user opens the smartphone application and enters information about their residential electricity usage, transportation, meals, and consumer goods. For example, for electricity usage, they enter the reading from their electric meter. For transportation, they enter the distance traveled by car and information about the use of public transportation. For meals, they enter the type of meals they eat and how often they eat out. For consumer goods, they enter the type and quantity of products they have purchased.

[1389] Step 2: The generative AI calculates the user's CO2 emissions based on the input information. This calculation is performed using the CO2 emission coefficient corresponding to each piece of information. For example, for electricity usage, the calculation is made using the CO2 emission coefficient for each power company. For travel, the calculation is made using the CO2 emission coefficient according to the fuel efficiency of the car and the type of public transportation. For meals, the calculation is made using the CO2 emission coefficient for each food item. For consumer goods, the calculation is made using the CO2 emission coefficient for each product category.

[1390] Step 3: The generative AI presents the user with a CO2 emission reduction target and measures based on the calculated CO2 emissions. The reduction target is expressed as a reduction rate relative to current emissions. Reduction measures are presented as specific action suggestions. For example, if the distance traveled by car is long, it suggests using public transportation or commuting by bicycle. Also, if the CO2 emissions from meals are high, it suggests reducing meat intake and using locally produced ingredients.

[1391] Step 4: If the user achieves the reduction target, the system will award points according to the degree of achievement. Points can be used as electronic money or electronic coupons. For example, 100 points can be exchanged for 1 dollar of electronic money. They can also be exchanged for coupons that can be used at specific stores.

[1392] Example 1

[1393] Next, a description will be given of Example 1 of Form 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."

[1394] In modern society, it is important to accurately grasp CO2 emissions in individuals' daily lives and present effective reduction measures. However, conventional systems have had difficulty presenting individualized reduction targets and measures based on users' lifestyles and behavioral patterns. Furthermore, there was a lack of mechanisms for awarding points as an incentive for achieving reduction targets, or for using those points as electronic money or coupons. This led to a decrease in users' motivation to reduce their CO2 emissions.

[1395] 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.

[1396] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement of the reduction targets when the user achieves the reduction targets; a means for transmitting the information input by the user to a cloud server; a means for the cloud server to analyze the received data and input it into a generative AI model; a means for the generative AI to analyze the user's behavioral patterns and calculate CO2 emissions; and a means for making the points available as electronic money or electronic coupons. This makes it possible to present individualized reduction targets and measures based on the user's lifestyle and behavioral patterns, and to award and use points as an incentive when the reduction targets are achieved.

[1397] "User" refers to an individual who uses the system to manage CO2 emissions in their daily lives and aims to achieve reduction targets.

[1398] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in a user's daily life, and is calculated based on information on electricity usage, transportation, meals, and consumer goods.

[1399] "Visualization means" refers to the function that calculates CO2 emissions based on information entered by the user and displays the results in the form of graphs, numbers, etc.

[1400] "Electricity usage" refers to the state of power consumption in a user's residence, and includes power consumption associated with the use of home appliances, lighting, and the like.

[1401] "Mobility" refers to the use of transportation means in the user's daily life, and includes means such as automobiles, public transportation, bicycles, and walking.

[1402] "Diet" refers to the types and amounts of food that a user consumes on a daily basis, and includes information on meat, vegetables, processed foods, etc.

[1403] "Consumer goods" refers to goods and services that users purchase and use, including home appliances, clothing, daily necessities, etc.

[1404] "Generative AI" refers to an artificial intelligence model that analyzes information entered by the user and presents CO2 emission reduction targets and reduction measures.

[1405] "Reduction target" refers to the amount of CO2 emissions reduction that the user aims to achieve, and is set individually by the generation AI.

[1406] "Reduction measures" refer to specific actions or changes that users should take to reduce CO2 emissions, and are suggested by the generative AI.

[1407] "Points" refer to rewards given to users when they achieve their reduction targets, and can be used as electronic money or electronic coupons.

[1408] "Cloud server" refers to a remote server that receives information entered by users and analyzes the data for input into a generative AI model.

[1409] "Behavioral patterns" refer to the behavioral tendencies and habits of users in their daily lives, and are the subject of analysis by generative AI.

[1410] "Electronic money" refers to a digital form of currency that can be used for purchases and payments using points earned by users.

[1411] A "coupon" refers to a ticket that allows a user to use points earned to receive discounts or free offers on specific products or services.

[1412] The present invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and reduction measures. A specific embodiment of this system will be described below.

[1413] User operations

[1414] Users download and install a smartphone application. They launch the application and enter information about their home electricity usage, transportation, meals, and consumer goods. For example, they enter their daily commute method (car, public transportation, bicycle, etc.), what they eat (meat, vegetables, processed foods, etc.), and information about the products they purchase (home appliances, clothing, daily necessities, etc.).

[1415] Device behavior

[1416] The device (smartphone) collects information entered by the user and sends it to a cloud server. The device then converts the entered data into an appropriate format and sends it in a format that is easy for the generative AI model to analyze. Specifically, the device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[1417] Server Operation

[1418] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information about the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model. The AI ​​model analyzes the data and extracts the user's behavioral patterns.

[1419] How generative AI works

[1420] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, the generative AI calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the vehicle model. The generative AI calculates the CO2 emissions (e.g., 2 kg CO2 / day) based on the user's commuting distance (e.g., 20 km) and the vehicle model's fuel efficiency (e.g., 10 km / L).

[1421] Based on the calculation results, the generative AI presents the user with a CO2 emission reduction target and reduction measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle. Based on the user's current CO2 emissions (e.g., 2 kg CO2 / day), the generative AI presents a reduction target (e.g., 1.5 kg CO2 / day) and specific measures (e.g., using public transportation).

[1422] Points allocation and management

[1423] When a user achieves their reduction target, the system awards points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards points when the target is achieved. Points can be viewed in the "Points Management" section of the application and can be exchanged for electronic money or electronic coupons.

[1424] Specific examples

[1425] Example 1

[1426] If the user commutes to work every day by car, the generative AI might suggest the following:

[1427] Using public transportation (e.g. buses and trains)

[1428] Commuting by bicycle (e.g., purchasing a bicycle and using it to commute to work)

[1429] Example 2

[1430] When a user achieves their reduction goal, the system will award them points as follows:

[1431] 100 points (can be used as electronic money)

[1432] 50 points (can be used as a coupon)

[1433] Prompt Sentence Examples

[1434] "If a user commutes to work every day by car, what CO2 emission reduction measures would the generative AI suggest?"

[1435] In this way, this system calculates CO2 emissions based on the information entered by the user, presents reduction targets and measures, and also awards points according to the degree of achievement, which can be used as electronic money or electronic coupons.

[1436] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1437] Step 1:

[1438] The user enters information

[1439] Users launch a smartphone application and input information about their electricity usage, transportation, meals, and consumer goods at home. Specifically, they input their daily commute method (car, public transportation, bicycle, etc.), meal contents (meat, vegetables, processed foods, etc.), and information about purchased products (home appliances, clothing, daily necessities, etc.).

[1440] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[1441] Output: Input lifestyle information data

[1442] Step 2:

[1443] The device sends data

[1444] The device (smartphone) collects information entered by the user and sends it to the cloud server. The device converts the entered data into JSON format and sends it to the cloud server using the HTTPS protocol.

[1445] Input: User's lifestyle information data

[1446] Output: JSON format data sent to the cloud server

[1447] Step 3:

[1448] The server receives and analyzes the data

[1449] The server receives the data sent from the device and analyzes it using a generative AI model. Specifically, it analyzes the user's behavioral patterns based on information on the user's electricity usage, movement, meals, and consumer goods. The server stores the received data in a database and inputs it into the generative AI model.

[1450] Input: JSON format data sent to the cloud server

[1451] Output: Analyzed behavioral pattern data

[1452] Step 4:

[1453] Generative AI calculates CO2 emissions

[1454] The generative AI calculates the user's CO2 emissions based on the analysis results. For example, if a user commutes to work by car every day, it calculates the CO2 emissions based on the commuting distance and the fuel efficiency of the car model.

[1455] Input: Analyzed behavioral pattern data

[1456] Output: Calculated CO2 emissions data

[1457] Step 5:

[1458] Generative AI presents reduction targets and measures

[1459] Based on the calculation results, the generative AI will suggest CO2 emission reduction targets and measures for the user. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[1460] Input: Calculated CO2 emissions data

[1461] Output: Presentation of reduction targets and reduction measures

[1462] Step 6:

[1463] The system awards points

[1464] When a user achieves their reduction target, the system awards them points according to their achievement. These points can be used as electronic money or electronic coupons. The system monitors the user's progress toward achieving their reduction target and awards them points when they achieve their target.

[1465] Input: Reduction target achievement status data

[1466] Output: Assigned point data

[1467] Step 7:

[1468] Users can use points

[1469] Users can check their points in the "Points Management" section of the application and exchange them for electronic money or electronic coupons.

[1470] Input: Assigned point data

[1471] Output: Point data used as electronic money or electronic coupons

[1472] (Application example 1)

[1473] Next, a description will be given of Application Example 1 of Form 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."

[1474] In modern society, it is important to visualize the environmental impact of individual lifestyle habits and present specific reduction measures. However, conventional systems have difficulty collecting detailed data across a user's entire lifestyle and setting individualized reduction targets. Furthermore, the lack of incentives for users to achieve their reduction targets makes it difficult to encourage sustainable behavioral change. In particular, food delivery services face the challenge of preventing users from making environmentally conscious choices, as there are no systems that calculate the CO2 emissions of meals ordered and suggest environmentally friendly options.

[1475] 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.

[1476] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods; a means for using generative AI to present CO2 emission reduction targets and reduction measures; a means for awarding points based on the degree to which the user has achieved their reduction targets; a means for calculating CO2 emissions based on the ingredients and cooking methods of the meals the user orders; a means for the generative AI to suggest more environmentally friendly meal options to the user; a means for tracking the user's progress toward their reduction targets and awarding points based on the degree to which they have achieved their targets; and a means for exchanging the awarded points as electronic money or coupons. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier for users to make environmentally conscious meal choices, especially when using food delivery services.

[1477] "Means for visualizing CO2 emissions in a user's daily life" refers to techniques and methods for visually displaying the amount of carbon dioxide emissions associated with the activities and consumption in a user's daily life.

[1478] "Means for inputting information on electricity usage, transportation, meals, and consumer goods at a user's residence" refers to an interface or method for a user to input data on the electricity used, transportation, meals, and goods consumed at home into the system.

[1479] "Means for presenting CO2 emission reduction targets and reduction measures using generative AI" refers to technologies and methods that utilize generative artificial intelligence to set targets and suggest specific reduction methods for users to reduce their carbon dioxide emissions.

[1480] "Means for awarding points based on the degree of achievement when a user achieves a reduction target" refers to a system or method for awarding points based on the degree of achievement when a user achieves a carbon dioxide emission reduction target set by the user.

[1481] "Means for calculating CO2 emissions based on the ingredients and cooking method of the meal ordered by the user" refers to technology or methods for calculating the carbon dioxide emissions associated with the meal ordered by the user based on the ingredients and cooking method of the meal.

[1482] "Means for generative AI to suggest more environmentally friendly food options to users" refers to technologies and methods that use generative artificial intelligence to suggest food options that have a lower environmental impact to users.

[1483] "Means for tracking a user's progress toward a reduction target and awarding points based on the degree of achievement" refers to a system or method for tracking the progress of a user toward a carbon dioxide emission reduction target set by the user and awarding points based on the degree of achievement.

[1484] "Means for enabling the points awarded to a user to be used as electronic money or electronic coupons" refers to techniques or methods for enabling the points awarded to a user to be used as electronic money or electronic coupons.

[1485] As an embodiment of the present invention, the following system can be constructed.

[1486] System Overview

[1487] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has a function to calculate the CO2 emissions based on the ingredients and cooking method of the meal the user orders, and suggests environmentally friendly options. When a user achieves their reduction target, they are awarded points according to their achievement, which can be used as electronic money or electronic coupons.

[1488] Hardware and software used

[1489] Hardware: Smartphones, servers

[1490] Software: Python, generative AI model, API (for calculating CO2 emissions)

[1491] Data processing and calculation

[1492] Data Entry

[1493] Users use a smartphone application to input information about their home electricity usage, transportation, meals, and consumer goods, which allows detailed data on the user's life to be collected.

[1494] CO2 emissions calculation

[1495] The server uses APIs to calculate the CO2 emissions for each item based on the data entered by the user, particularly the ingredients and cooking method of the meal the user orders.

[1496] Presentation of reduction targets and reduction measures

[1497] The generative AI model analyzes the collected data and presents individualized CO2 emission reduction targets and strategies to the user. For example, if a user commutes by car every day, it suggests using public transportation or commuting by bicycle.

[1498] Proposing environmentally friendly options

[1499] The generative AI model calculates the CO2 footprint of the meal a user orders and suggests more environmentally friendly meal options, such as suggesting a vegetarian dish instead of a meat dish.

[1500] Progress tracking and points awarded

[1501] The server tracks the user's progress toward their reduction goal and awards points based on their achievement. These points can be used as electronic money or coupons.

[1502] Specific examples

[1503] Example user input

[1504] User ID: user123

[1505] Meals ordered: beef, chicken, rice

[1506] Reduction target: 15 kg CO2

[1507] Prompt Sentence Examples

[1508] User ID: user123

[1509] Meals ordered: beef, chicken, rice

[1510] Reduction target: 15 kg CO2

[1511] In this way, the generative AI model analyzes the data entered by the user and presents specific reduction measures and progress reports. This allows users to specifically understand the environmental impact of their lifestyle habits and promote sustainable behavioral change. This makes it easier to make environmentally conscious meal choices, especially when using food delivery services.

[1512] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1513] Step 1:

[1514] Users enter information about their home's electricity usage, transportation, meals, and consumer goods through a smartphone application.

[1515] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[1516] Output: Input lifestyle information data

[1517] How it works: Users enter various data about their daily lives into the application's input form, such as recording details of their daily commute, meals, and electricity consumption.

[1518] Step 2:

[1519] The server uses an API to calculate the CO2 emissions for each item based on the data entered by the user.

[1520] Input: User's lifestyle information data

[1521] Output: CO2 emissions for each item

[1522] Specific operation: The server analyzes the input data and calls an external CO2 emission calculation API to calculate the CO2 emissions corresponding to each item. For example, it obtains CO2 emissions for electricity consumption or CO2 emissions based on transportation method.

[1523] Step 3:

[1524] A generative AI model analyzes the collected data and presents users with personalized CO2 emission reduction targets and measures.

[1525] Input: CO2 emissions for each item

[1526] Output: Individualized reduction targets and strategies

[1527] Specific operation: The server uses a generative AI model to analyze the user's lifestyle and behavioral patterns, and generates optimal reduction targets and specific reduction measures, such as suggesting the use of public transportation or energy-efficient home appliances.

[1528] Step 4:

[1529] The server calculates the CO2 emissions based on the ingredients and cooking method of the meal the user orders.

[1530] Input: Information about the meal the user wants to order (ingredients, cooking method)

[1531] Output: CO2 emissions associated with the meal

[1532] How it works: When a user uses a food delivery service, they input the information about the meal they want to order. Based on this information, the server calls an API to calculate the CO2 emissions corresponding to each ingredient and cooking method.

[1533] Step 5:

[1534] A generative AI model suggests more environmentally friendly food options to users.

[1535] Input: CO2 emissions associated with meals

[1536] Output: Eco-friendly food options

[1537] How it works: Based on the calculated CO2 emissions, the server uses a generative AI model to suggest environmentally friendly meal options to the user, for example, suggesting vegetable dishes instead of meat dishes.

[1538] Step 6:

[1539] The server tracks the user's progress towards their reduction goals and awards points based on their achievement.

[1540] Input: User reduction goals and progress data

[1541] Output: Points awarded

[1542] What it does: The server periodically tracks the user's progress towards their reduction goal, calculates points based on the progress of the goal, and credits them to the user's account.

[1543] Step 7:

[1544] The server makes the awarded points available for use as electronic money or electronic coupons.

[1545] Input: Points awarded

[1546] Output: Electronic money or electronic coupons

[1547] Specific operation: The server converts the points awarded to the user into electronic money or electronic coupons, which the user can use. For example, it issues coupons that can be used at affiliated online stores or services.

[1548] Example 2

[1549] Next, a description will be given of Example 2 of Form 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."

[1550] In modern society, it is important to understand the environmental impact of each individual's daily activities and to reduce CO2 emissions through concrete actions. However, it is not easy for users to accurately grasp the CO2 emissions of their own lives and find effective reduction measures. Another issue is the lack of motivation to achieve reduction goals. To solve these problems, a system is needed that calculates CO2 emissions based on users' lifestyle information, presents individualized reduction goals and measures, and provides rewards based on the degree of achievement.

[1551] 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.

[1552] In this invention, the server includes: means for visualizing CO2 emissions in a user's daily life; means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at the user's residence; means for presenting CO2 emission reduction targets and reduction measures using generative AI; means for awarding points to the user based on the degree of achievement when the user achieves the reduction target; means for encrypting the information input by the user and transmitting it to the server; means for the generative AI model to calculate CO2 emissions based on the user's daily life information; means for the server to generate reduction targets and reduction measures based on the calculation results of the generative AI model; means for the user to implement the reduction measures and input their progress; means for the server to analyze the progress information and evaluate the degree of goal achievement; and means for the user to use the awarded points as electronic money or electronic coupons. This enables users to accurately grasp the CO2 emissions in their daily lives, implement effective reduction measures, and receive rewards based on the degree of achievement.

[1553] "User" refers to an individual who uses the system to input information about their lifestyle and aims to reduce CO2 emissions.

[1554] "Lifestyle information" refers to information about the user's electricity usage at home, transportation methods, meal frequency, and purchases of consumer goods.

[1555] "CO2 emissions" refers to the total amount of carbon dioxide emitted by the user's daily activities.

[1556] "Generative AI" refers to artificial intelligence that analyzes a user's lifestyle information and calculates CO2 emissions, presents reduction targets, and presents reduction measures.

[1557] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[1558] "Reduction measures" refer to specific actions or methods that users should take to reduce CO2 emissions.

[1559] "Points" refer to rewards given to users when they achieve their reduction goals.

[1560] "Electronic money" refers to digital currency that users can use by exchanging the points they have been awarded.

[1561] "Coupon" refers to a discount ticket or special coupon that can be used by exchanging the points given to the user.

[1562] The "server" refers to a computer system that receives users' lifestyle information, analyzes it using generative AI, and generates reduction targets and reduction measures.

[1563] "Terminal" refers to a device through which a user inputs lifestyle information and communicates with a server.

[1564] "Encryption" refers to a technology that converts data to protect users' personal information and makes it impossible for third parties to decipher it.

[1565] "Progress information" refers to information about the results and progress of a user's implementation of a reduction measure.

[1566] "Goal achievement level" refers to an index that indicates the degree to which a user has achieved a set reduction target.

[1567] This invention is a system that visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, where users input their daily information and it is analyzed using generative AI.

[1568] A user starts a smartphone application and inputs information about their residential electricity usage, transportation, meal frequency, and consumer goods purchases. For example, they input information about their monthly electricity consumption of 300 kWh, daily commute by car, eating out three times a week, and monthly clothing purchases worth 5,000 yen.

[1569] The device (smartphone) encrypts the input information and sends it to the server. The server inputs the received information into a generative AI model and calculates the user's CO2 emissions. For example, OpenAI's GPT-4 is used as the generative AI model.

[1570] The generative AI model calculates a user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item of electricity use, transportation, meals, and consumer goods to calculate overall emissions.

[1571] Based on the results of the generative AI model, the server generates a CO2 emission reduction target and specific measures for the user, such as suggesting using public transportation, cooking at home more, or using energy-efficient appliances.

[1572] The server then sends the generated reduction targets and measures to the user's device and displays them on the application. The user then implements the proposed reduction measures and enters their progress into the application. For example, they enter the number of days they used public transportation or the number of times they cooked at home.

[1573] The terminal transmits the progress information entered by the user to the server, which analyzes the received progress information and evaluates the user's achievement of the reduction target, for example, by calculating the percentage of the target achieved.

[1574] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The user can use the awarded points as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money.

[1575] As a concrete example, suppose the user enters the following information:

[1576] Residential electricity usage: 300 kWh per month

[1577] Transportation: Commute by car every day (20km one way)

[1578] Meals: Eating out three times a week

[1579] Consumer goods: 5,000 yen worth of clothing purchased per month

[1580] Based on this information, the generative AI model generates a prompt like this:

[1581] The user's electricity usage at home is 300kWh per month, he commutes 20km each way by car every day, eats out three times a week, and buys clothes worth 5,000 yen per month. Based on this information, please calculate the user's CO2 emissions and propose a reduction target and reduction measures.

[1582] The generative AI model analyzes this prompt and makes the following suggestions to the user:

[1583] Reduce electricity usage: Encourage the use of energy-efficient appliances

[1584] Changes in transportation: Suggestions for using public transportation or commuting by bicycle

[1585] Review your diet: Eat out less and cook more at home

[1586] Consumer goods purchases: Limit purchases to essentials

[1587] If the user implements these suggestions and achieves the reduction target, the server will give the user points, which the user can use as electronic money or electronic coupons.

[1588] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1589] Step 1:

[1590] The user starts the application and enters lifestyle information.

[1591] The user launches a smartphone application and inputs information about their residential electricity usage, transportation method, meal frequency, and consumer goods purchases. For example, they might input information about using 300 kWh of electricity per month, commuting to work by car every day, eating out three times a week, and purchasing 5,000 yen worth of clothing per month. The input information is temporarily saved within the application.

[1592] Step 2:

[1593] The terminal transmits the input information to the server.

[1594] The device encrypts the lifestyle information entered by the user and sends it to the server. The encrypted data is used to protect the user's privacy. The input data (information on electricity usage, transportation, meals, and consumer goods) is sent to the server.

[1595] Step 3:

[1596] The information received by the server is input into the generative AI model.

[1597] The server inputs the received lifestyle information into a generative AI model. For example, OpenAI's GPT-4 is used as the generative AI model. The generative AI model begins analysis based on the input data (information on electricity usage, transportation, meals, and consumer goods).

[1598] Step 4:

[1599] A generative AI model calculates the user's CO2 emissions.

[1600] The generative AI model calculates the user's CO2 emissions based on input lifestyle information. For example, it calculates CO2 emissions for each item, such as electricity usage, transportation, meals, and consumer goods, to calculate overall emissions. The output data is the user's overall CO2 emissions.

[1601] Step 5:

[1602] The server generates reduction targets and reduction measures based on the calculation results of the generative AI model.

[1603] Based on the calculation results of the generative AI model, the server generates a CO2 emission reduction target and specific reduction measures for the user. For example, it suggests using public transportation, cooking at home more, and using energy-efficient appliances. The reduction target and reduction measures are obtained as output data.

[1604] Step 6:

[1605] The server presents the reduction target and reduction measures to the user.

[1606] The server sends the generated reduction targets and reduction measures to the user's device and presents them on the application. The user can then confirm the reduction targets and reduction measures through the application. The presented reduction targets and reduction measures are obtained as output data.

[1607] Step 7:

[1608] The user implements reduction measures and enters progress into the application.

[1609] The user implements the proposed reduction measures and inputs their progress into the application. For example, they input the number of days they used public transportation or the number of times they cooked at home. The input data provides progress information on the reduction measures they implemented.

[1610] Step 8:

[1611] The terminal sends progress information to the server.

[1612] The terminal transmits the progress information input by the user to the server. The input data (progress information) is transmitted to the server.

[1613] Step 9:

[1614] The server analyzes the progress information and evaluates the degree of goal achievement.

[1615] The server analyzes the received progress information and evaluates the user's achievement of the reduction target. For example, it calculates the percentage of the target that has been achieved. The target achievement level is obtained as output data.

[1616] Step 10:

[1617] The server awards points according to the degree of goal achievement.

[1618] The server awards points to the user according to the degree of goal achievement. For example, if 80% of the goal is achieved, 80 points are awarded. The awarded points are obtained as output data.

[1619] Step 11:

[1620] Users use the points as electronic money or electronic coupons.

[1621] Users can use the points they receive as electronic money or electronic coupons. For example, 100 points can be exchanged for 500 yen worth of electronic money. Information on the points used is obtained as output data.

[1622] (Application example 2)

[1623] Next, a description will be given of Application Example 2 of Form 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."

[1624] In modern society, reducing CO2 emissions in individual lives is an important issue. However, it is not easy for users to accurately grasp the CO2 emissions in their own lives and implement effective reduction measures. In particular, food delivery services have difficulty considering the CO2 emissions of the food they order, and there is a lack of mechanisms to encourage environmentally friendly choices. This creates a problem of a lack of motivation for users to take environmentally conscious actions.

[1625] 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.

[1626] In this invention, the server includes a means for visualizing CO2 emissions in a user's daily life, a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home, a means for presenting CO2 emission reduction targets and reduction measures using generative AI, a means for awarding points to the user based on the degree of achievement when the reduction target is achieved, a means for calculating the CO2 emissions of food ordered by the user and suggesting environmentally friendly options, and a means for awarding points to the user when the user makes an environmentally friendly choice. This allows users to accurately grasp the CO2 emissions in their daily lives and implement effective reduction measures. It also promotes environmentally friendly choices in food delivery services, strengthening users' motivation to take environmentally conscious actions.

[1627] "User" refers to an individual who uses the system.

[1628] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[1629] "Visualization means" refers to methods and technologies that allow users to visually check their own CO2 emissions.

[1630] "Electricity usage" refers to the consumption of electricity in a user's residence.

[1631] "Movement" refers to the use of transportation means in the user's daily life.

[1632] "Meals" refers to the food and beverages consumed by a user.

[1633] "Consumer goods" refers to everyday items and services that users purchase and use.

[1634] "Means for inputting information" refers to the methods and techniques by which users provide data about their lives to the system.

[1635] "Generative AI" refers to artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and measures.

[1636] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[1637] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[1638] "Means for awarding points" refers to a method or technology for awarding points as a reward when a user achieves a reduction target.

[1639] "CO2 emissions from food" refers to the total amount of carbon dioxide emitted from the production to consumption of the food ordered by the user.

[1640] "Environmentally friendly choices" refer to foods and actions that have a lower carbon footprint.

[1641] "Food delivery service" refers to a service that allows users to order food online and have it delivered.

[1642] The system for implementing this invention visualizes CO2 emissions in a user's daily life and presents reduction targets and measures. The system is implemented as a smartphone application, and generative AI performs analysis based on lifestyle information entered by the user.

[1643] Hardware and Software

[1644] Hardware: Smartphone

[1645] Software: Python, JSON files, generative AI models

[1646] Data processing and calculation

[1647] 1. Enter user's lifestyle information:

[1648] Users enter information about their residential electricity usage, travel, meals, and consumer goods through a smartphone application, which is then stored in JSON format within the application.

[1649] 2. Calculating CO2 emissions:

[1650] The server calculates the CO2 emissions for each item based on the information entered by the user. For example, if a user orders a hamburger, it calculates the CO2 emissions generated from the production to consumption of that food.

[1651] 3. Generative AI analysis:

[1652] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and measures. For example, if the user commutes by car every day, it will suggest using public transportation or commuting by bicycle.

[1653] 4. Propose environmentally friendly options:

[1654] The server calculates the CO2 emissions of the food the user orders and suggests environmentally friendly options, such as a salad instead of a hamburger.

[1655] 5. Points Awarding:

[1656] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement. These points can be used as electronic money or coupons.

[1657] Specific examples

[1658] If a user orders a hamburger, the application calculates the CO2 emissions of the food and suggests eco-friendly options such as a salad. If the user selects a salad, they will receive 10 points, which can be used as a discount on their next order.

[1659] Prompt Sentence Examples

[1660] An example of a prompt to input to a generative AI model is as follows:

[1661] Calculate the CO2 emissions of a user ordering a hamburger and suggest eco-friendly options, such as salads or vegetable-based dishes, and reward users for their eco-friendly choices.

[1662] In this way, users can accurately understand the CO2 emissions in their daily lives and take effective measures to reduce them. This also promotes environmentally friendly choices in food delivery services, strengthening users' motivation to take environmentally conscious actions.

[1663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1664] Step 1:

[1665] The user launches a smartphone application and enters information about their home's electricity usage, transportation, meals, and consumer goods.

[1666] Input: User's lifestyle information (electricity usage, transportation, meals, consumer goods)

[1667] Output: JSON formatted lifestyle information data

[1668] Specific operation: The user enters information about their daily life into the application's input form and presses the submit button. The application saves this information in JSON format.

[1669] Step 2:

[1670] The server receives the user's input information and calculates the CO2 emissions for each item.

[1671] Input: JSON formatted lifestyle information data

[1672] Output: CO2 emissions for each item

[1673] Specific operation: The server analyzes the received JSON data and calculates the CO2 emissions for each item (electricity usage, transportation, meals, consumer goods). For example, it calculates the CO2 emissions based on the power consumption from the electricity usage data.

[1674] Step 3:

[1675] The generative AI model analyzes the user's lifestyle information and presents CO2 emission reduction targets and measures.

[1676] Input: CO2 emissions for each item

[1677] Output: Reduction targets and measures

[1678] Specific operation: The server inputs the CO2 emissions for each item into the generative AI model, which then analyzes them and generates individualized reduction targets and measures for the user, such as suggesting using public transportation or commuting by bicycle.

[1679] Step 4:

[1680] When a user uses a food delivery service, the service calculates the CO2 emissions of the food they order and suggests environmentally friendly options.

[1681] Input: Food information the user wants to order

[1682] Output: Food CO2 emissions and eco-friendly options

[1683] What it does: When a user selects a food item in a food delivery application, the server calculates the food's CO2 footprint and suggests environmentally friendly options (e.g., salads).

[1684] Step 5:

[1685] When users achieve their reduction targets or make environmentally friendly choices, the system will award them points according to their achievement.

[1686] Input: User behavior data (reduction goal achievement status, making environmentally friendly choices)

[1687] Output: Points awarded

[1688] Specific operation: The server monitors the user's behavioral data, calculates points when reduction targets are achieved or environmentally friendly choices are made, and adds them to the user's account. For example, if a user chooses a salad, 10 points are added.

[1689] Step 6:

[1690] The user uses the awarded points as electronic money or electronic coupons.

[1691] Input: Points awarded

[1692] Output: Electronic money or electronic coupons

[1693] Specific operation: The user checks the points in the application and selects the option to use them as electronic money or electronic coupons. The server converts the points into electronic money or electronic coupons and provides them to the user.

[1694] 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.

[1695] "Example 1"

[1696] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[1697] "Example 2"

[1698] One embodiment of the present invention provides a system incorporating an emotion engine that recognizes a user's emotions. This system visualizes CO2 emissions in a user's daily life and inputs information about the user's residential electricity usage, transportation, meals, and consumer goods. It then uses generative AI to present CO2 emission reduction targets and reduction measures. Furthermore, when the user achieves the reduction target, points are awarded based on the degree of achievement. In this system, the emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction measures based on the user's emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and presents reduction measures to alleviate stress. Similarly, if the user is feeling happy, the emotion engine raises the reduction target and presents more aggressive reduction measures. Furthermore, the emotion engine adjusts the amount of points awarded based on the user's emotional state. For example, if the user is feeling happy, the emotion engine awards more points. This enables more personalized CO2 emission reduction support that takes the user's emotional state into account.

[1699] The processing flow of each embodiment will be described below.

[1700] "Example 1"

[1701] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[1702] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[1703] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[1704] Step 4: The emotion engine recognizes the user's emotional state.

[1705] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[1706] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[1707] "Example 2"

[1708] Step 1: The user enters information about the CO2 emissions in their daily lives (electricity use at home, transportation, meals, and consumer goods) into the system.

[1709] Step 2: The system calculates the CO2 emissions based on the input information and presents it to the user in a visualized form.

[1710] Step 3: The generative AI generates reduction targets and measures based on the calculated CO2 emissions and presents them to the user.

[1711] Step 4: The emotion engine recognizes the user's emotional state.

[1712] Step 5: The emotion engine adjusts the reduction goal and reduction measures based on the recognized emotional state and presents them to the user.

[1713] Step 6: If the user achieves the reduction goal, the system will award points according to the achievement level. The emotion engine will adjust the amount of points awarded based on the user's emotional state.

[1714] Example 1

[1715] Next, a description will be given of Example 1 of Form 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."

[1716] Conventional CO2 emission reduction systems present uniform reduction targets and measures without considering the user's lifestyle or emotional state, making it difficult to maintain user motivation. In addition, the rewards for achieving reduction targets are also uniform, making it difficult to respond flexibly to the efforts and circumstances of individual users.

[1717] 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.

[1718] In this invention, the server includes: a means for visualizing CO2 emissions in a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user according to the degree of achievement when the reduction target is achieved; a means for recognizing the user's emotional state and adjusting the reduction targets and reduction measures based on the emotional state; and a means for adjusting the amount of points awarded based on the user's emotional state. This makes it possible to present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective CO2 emission reduction while maintaining the user's motivation.

[1719] A "user" is an individual who uses the system to manage the CO2 emissions in their daily lives and achieve their reduction targets.

[1720] "CO2 emissions" is the total amount of carbon dioxide emitted as a result of the user's daily activities.

[1721] "Visualization" is the process that allows users to visually check their own CO2 emissions.

[1722] "Electricity usage" refers to the state of electricity consumption in the user's residence.

[1723] "Movement" refers to the state of use of transportation means by the user in daily life.

[1724] "Diet" is information about the types and frequency of food consumed by the user.

[1725] "Consumer goods" are items such as daily necessities and clothing that users purchase.

[1726] "Generative AI" is artificial intelligence that analyzes user input information and generates CO2 emission reduction targets and reduction measures.

[1727] The "reduction target" is the amount of CO2 emission reduction that the user must achieve.

[1728] "Reduction measures" are specific actions or methods that users should take to reduce CO2 emissions.

[1729] "Points" are rewards given to users when they achieve their reduction targets.

[1730] "Emotional state" refers to the user's psychological state or mood.

[1731] "Adjustment" is the process of changing reduction targets, measures, and point award amounts based on the user's emotional state.

[1732] This invention is a system for managing CO2 emissions in a user's daily life and achieving reduction targets. This system is implemented as a smartphone application, and users input their own lifestyle information, which is analyzed using generative AI, and CO2 emission reduction targets and reduction measures are presented.

[1733] Hardware and software used

[1734] Hardware: Smartphone (e.g. iPhone, Android device)

[1735] Software: Smartphone applications, generative AI (e.g., OpenAI GPT-4), emotion engines (e.g., Affectiva)

[1736] Specific operation of the system

[1737] User Input

[1738] Users enter information about their electricity usage at home, transportation, meals, and consumer goods through a smartphone application. For example, a user might enter information such as "I use 200 kWh of electricity per month," "I commute to work by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month."

[1739] Sending information

[1740] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the data to the server via the Internet.

[1741] Analysis of information

[1742] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data.

[1743] CO2 emissions calculation

[1744] The generative AI calculates the user's CO2 emissions based on the prompt text. For example, it generates the following prompt text and performs analysis:

[1745] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[1746] Presentation of reduction targets and reduction measures

[1747] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures, such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months."

[1748] Tuning the Emotion Engine

[1749] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine will relax the reduction target and suggest a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine will increase the reduction target and suggest a more aggressive reduction strategy.

[1750] Confirmation of achievement of reduction targets

[1751] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application.

[1752] Points awarded

[1753] The server receives the user's report and checks the degree of achievement of the reduction target. Points are awarded according to the degree of achievement. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons.

[1754] In this way, the system of the present invention can present individualized reduction targets and measures that take into account the user's lifestyle habits and emotional state, enabling effective reduction of CO2 emissions while maintaining the user's motivation.

[1755] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1756] Step 1:

[1757] Entering user information

[1758] Users open a smartphone application and enter information about their home electricity usage, transportation, meals, and consumer goods. Specifically, users enter information such as "I use 200 kWh of electricity per month," "I commute by car every day (10 km one way)," "I eat out three times a week," and "I buy new clothes once a month." The input data is temporarily stored within the application.

[1759] Step 2:

[1760] Sending information

[1761] The device sends the information entered by the user to the server. Specifically, the smartphone application sends the input data to the server via the Internet in JSON format. For example, data is sent in the format {"electricity": 200, "commute": "car", "dining": 3, "shopping": 1}. The input is the user's lifestyle information, and the output is the JSON data sent to the server.

[1762] Step 3:

[1763] Analysis of information

[1764] The server passes the received user information to the generative AI and begins analysis. The generative AI (e.g., OpenAI GPT-4) generates a prompt to calculate CO2 emissions based on the user's input data. For example, it generates the following prompt:

[1765] The user's electricity usage at home is 200kWh per month, they commute to work by car every day (10km one way), they eat out three times a week, and they buy new clothes once a month. Based on this information, please calculate the user's CO2 emissions.

[1766] The input is the JSON data sent to the server, and the output is the generated prompt text.

[1767] Step 4:

[1768] CO2 emissions calculation

[1769] The generative AI calculates the user's CO2 emissions based on the prompt text. The analysis results in the user's annual CO2 emissions. For example, the generative AI calculates the user's CO2 emissions to be approximately 3.5 tons per year. The input is the generated prompt text, and the output is the calculated CO2 emissions.

[1770] Step 5:

[1771] Presentation of reduction targets and reduction measures

[1772] The server receives the analysis results from the generative AI and presents the user with CO2 emission reduction targets and measures. For example, it presents specific measures such as "consider using public transportation or commuting by bicycle," "reduce the frequency of eating out to once a week," and "reduce clothing purchases to once every two months." The input is the calculated CO2 emissions, and the output is the presented reduction targets and measures.

[1773] Step 6:

[1774] Tuning the Emotion Engine

[1775] The emotion engine recognizes the user's emotional state and adjusts the reduction target and reduction strategy based on the emotional state. For example, if the user is feeling stressed, the emotion engine relaxes the reduction target and proposes a reduction strategy to alleviate stress. On the other hand, if the user is feeling happy, the emotion engine raises the reduction target and proposes a more aggressive reduction strategy. The input is the user's emotional state, and the output is the adjusted reduction target and reduction strategy.

[1776] Step 7:

[1777] Confirmation of achievement of reduction targets

[1778] The user implements the proposed reduction measures and reports their progress through the application. For example, if the user commutes by public transportation, they enter that information into the application. The input is the reduction measures implemented by the user, and the output is the reported progress.

[1779] Step 8:

[1780] Points awarded

[1781] The server receives the user's report and checks the progress of the reduction target. Points are awarded according to the progress. For example, if a user commutes by public transportation, 50 points are awarded. These points can be used as electronic money or electronic coupons. The input is the reported progress, and the output is the awarded points.

[1782] (Application example 1)

[1783] Next, a description will be given of Application Example 1 of Form 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."

[1784] Reducing personal CO2 emissions is an important issue in modern society. However, it is not easy for users to accurately understand their own CO2 emissions and implement effective reduction measures. Furthermore, the lack of personalized suggestions that take into account the user's emotional state makes it difficult to encourage sustainable behavioral change. Furthermore, food delivery services lack mechanisms for suggesting environmentally friendly options, making it difficult for users to make environmentally conscious choices.

[1785] 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.

[1786] In this invention, the server includes: a means for visualizing the CO2 emissions of a user's daily life; a means for inputting information on the user's electricity usage, transportation, meals, and consumer goods at their home; a means for presenting CO2 emission reduction targets and reduction measures using generative AI; a means for awarding points to the user based on the degree to which the reduction target is achieved; a means for calculating the CO2 emissions of meals ordered by the user; a means for the generative AI to suggest environmentally friendly meal options to the user; and a means for recognizing the user's emotional state and adjusting the suggestions and point awarding based on the user's emotional state. This allows users to accurately grasp the CO2 emissions of their daily lives and implement personalized reduction measures according to their emotional state. Furthermore, since environmentally friendly options are suggested for food delivery services, it becomes easier for users to make environmentally conscious choices.

[1787] "User" refers to an individual who uses the system.

[1788] "CO2 emissions in daily life" refers to the total amount of carbon dioxide generated in the user's daily life.

[1789] "Visualization means" refers to a method or device that allows users to visually check their own CO2 emissions.

[1790] "Electricity usage" refers to the consumption of electricity in a user's residence.

[1791] "Movement" refers to the use of transportation means in the user's daily life.

[1792] "Meal" refers to the food consumed by the user and how it is prepared.

[1793] "Consumer goods" refers to everyday items and services that users purchase and use.

[1794] "Means for inputting information" refers to a method or device by which a user provides the required data to the system.

[1795] "Generative AI" refers to a system that uses artificial intelligence technology to analyze data and generate results.

[1796] "Reduction target" refers to the amount of CO2 emission reduction that the user must achieve.

[1797] "Reduction measures" refer to specific methods and actions that users should take to reduce CO2 emissions.

[1798] The "means for awarding points" refers to a method or device for awarding points as a reward when a user achieves a reduction target.

[1799] "Meal CO2 emissions" refers to the total amount of carbon dioxide associated with the production, transportation, and cooking of the food a user orders.

[1800] "Eco-friendly food options" refer to foods and cooking methods that have a low carbon footprint.

[1801] "Emotional state" refers to a user's psychological state or mood.

[1802] "Means for adjusting suggestions" refers to a method or device for changing the reduction measures or options presented based on the user's emotional state.

[1803] "Means for adjusting point awarding" refers to a method or device for changing the amount of points awarded based on the user's emotional state.

[1804] The embodiments of the present invention will be described in detail below.

[1805] System Overview

[1806] This system visualizes the CO2 emissions in a user's daily life and presents reduction targets and measures. It also has the function of calculating the CO2 emissions of meals ordered by the user and suggesting environmentally friendly options. It also recognizes the user's emotional state and adjusts the suggestions and point awards accordingly.

[1807] Hardware and software used

[1808] Hardware: Smartphone

[1809] Software: Python, emotion recognition API (e.g., Microsoft Azure Emotion API), generative AI model

[1810] Data processing and calculation

[1811] 1. Calculating CO2 emissions

[1812] The server calculates CO2 emissions based on the information entered by the user about electricity usage, travel, meals, and consumer goods, using pre-set CO2 emission coefficients for each item.

[1813] 2. Presentation of reduction targets and reduction measures

[1814] Using a generative AI model, the system presents personalized reduction targets and strategies based on the user's lifestyle and behavioral patterns. For example, if a user commutes by car every day, the system suggests using public transportation or commuting by bicycle.

[1815] 3. Calculation and proposal of CO2 emissions from meals

[1816] The system calculates the CO2 emissions of the meal a user orders and uses generative AI to suggest eco-friendly meal options, such as suggesting chicken or vegetarian options if the user orders beef.

[1817] 4...

Claims

1. A means for visualizing the amount of CO2 emissions in a user's life; means for inputting information regarding at least one of electricity usage, transportation, food, and consumer goods in the user's residence; A means for generating a prompt sentence for instructing the user to generate a reduction target and a reduction measure for the user's CO2 emission based on the information; A means for generating a reduction target and a reduction measure for the user's CO2 emissions using the generated prompt sentence and a generation AI model; A means for presenting the generated CO2 emission reduction target and reduction measures of the user to the user; A means for inputting progress information of the reduction measures implemented by the user; A means for analyzing the input progress information and evaluating the degree of goal achievement; and means for awarding points to the user according to the assessed degree of goal achievement.

2. The system according to claim 1 , wherein the means for generating a reduction target and a reduction measure for the user's CO 2 emissions generates an individualized reduction target and a reduction measure based on at least one of the user's lifestyle habits and behavior patterns.

3. The system according to claim 1 , further comprising a means for exchanging said points for at least one of electronic money and electronic coupons available to said user.

Images