System

Abstract

A system is provided.SOLUTION: A system comprising: means for setting a basic fee; means for obtaining a current time; means for obtaining a population density to be covered; means for obtaining a data usage; means for calculating a time-of-day weighting based on the current time; means for calculating a population-density weighting based on the population density to be covered; means for calculating a data-usage weighting based on the data usage; means for calculating a final fee by multiplying the basic fee by each of the weightings; and means for displaying the final fee.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] Conventional data communication fees charged by mobile network operators are often set in a fixed rate format, making it difficult to adapt to usage conditions, time periods, and regional population densities. As a result, network congestion and unfair pricing structures can occur, leading to user dissatisfaction and hindering the efficient use of network resources. The present invention aims to solve these problems and provide a system that enables flexible pricing according to usage conditions. [Means for solving the problem]

[0005] The present invention solves the above problems by providing a system including: means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a time-of-day weighting based on the current time, means for calculating a population density weighting based on the population density of the area covered, means for calculating a data usage weighting based on the data usage, means for calculating a final fee by multiplying the basic fee by the product of the weightings, and means for displaying the final fee. This system dynamically adjusts data communication fees, enabling efficient use of network resources and improved user satisfaction.

[0006] "Basic fee" is the initial fee per 1GB of data communication, and the final fee is calculated based on this fee.

[0007] The "current time" is the specific time at which the system is operating, and the weighting of the time zone is based on this time.

[0008] "Population density" is the number of people per unit area in the area covered by a particular base station and is used to determine high-density and low-density areas.

[0009] "Data usage" is the total amount of data handled by a specific base station within a certain period of time, and serves as a basis for weighting according to the intensity of communication.

[0010] "Weighting" is a coefficient applied to the base rate depending on factors that affect rate adjustments, and is set based on time of day, population density, and data usage.

[0011] "Final fee" is the final fee per GB calculated by reflecting various weightings on the basic fee, and is the fee that the user actually pays.

[0012] The "display means" refers to a means for visually conveying the calculated final fee to the user, and specifically includes a screen, a printer, and the like. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] The present invention relates to a system for dynamically adjusting data communication charges for mobile network operators, which calculates and provides a final charge to users based on a basic charge and factors such as time of day, population density, and data usage.

[0035] composition

[0036] The system is implemented by a server which includes the following main means:

[0037] Basic fee setting method

[0038] The server stores the base fee set by the administrator, which is the initial fee per GB and is the basis for calculating the final fee.

[0039] Time acquisition means

[0040] The server has a function for obtaining the current time. For example, it obtains the current time using a system clock. Based on this time information, the server weights the time zones.

[0041] Population density acquisition method

[0042] The server obtains the population density of the area covered by a particular base station, which can be obtained from a pre-configured database or real-time population data.

[0043] Data usage acquisition method

[0044] The server obtains the data usage handled by each base station, which is recorded as the total data volume within a certain period and becomes one of the factors for weighting.

[0045] Each weight calculation means

[0046] The server calculates each weighting based on the current time, population density, and data usage. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage, different coefficients are applied to heavy, medium, and light usage.

[0047] Final fare calculation method

[0048] The server calculates the final fee by multiplying the base fee by the product of each of the weightings above. This final fee becomes the final fee per 1GB applied to the user.

[0049] Final fare display method

[0050] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[0051] Specific examples

[0052] A specific example of use is shown below.

[0053] Example 1: Daytime, high density area, medium usage

[0054] Base price: $10 / GB

[0055] Current time: 2 PM

[0056] Population density: 1,200 people per square kilometer

[0057] Data usage: 60GB

[0058] The server applies a daytime weighting of 1.5 based on the time of day, 2:00 PM. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. And because the data usage is 60 GB, it applies a medium-use weighting of 1.2. The final cost is $10 1.5 1.2 1.2 = $21.6. The device displays this final cost to the user.

[0059] Example 2: Nighttime, low density area, light use

[0060] Base price: $10 / GB

[0061] Current time: 10 PM

[0062] Population density: 300 people per square kilometer

[0063] Data usage: 20GB

[0064] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Then, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. And because the data usage is 20 GB, it applies a light usage weighting of 1.0. The final cost is $10 0.75 0.8 1.0 = $6. The device displays this final cost to the user.

[0065] The above describes a specific embodiment of the present invention and an example of its processing. This system enables efficient and fair data communication fee setting, thereby improving user satisfaction.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[0069] Step 2:

[0070] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[0071] Step 3:

[0072] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[0073] Step 4:

[0074] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[0075] Step 5:

[0076] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[0077] Step 6:

[0078] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[0079] Step 7:

[0080] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[0081] Step 8:

[0082] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, and a data usage weighting of 1.2. The final charge is $10 1.5 1.2 1.2 = $21.6.

[0083] Step 9:

[0084] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $21.60." The user can check this and understand the usage charge.

[0085] This concludes the detailed flow of the system's processing steps, which enables dynamic pricing and provides users with fair and realistic prices.

[0086] Example 1

[0087] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0088] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variable factors such as time of day, population density, data usage, etc. In conventional technology, data communication charges are fixed, making it difficult to appropriately adjust charges in response to user behavior patterns and network load, resulting in a lack of fairness and efficiency.

[0089] 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.

[0090] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a time-of-day weighting based on the current time, means for calculating a population density weighting based on the population density of the area covered, means for calculating a data usage weighting based on the data usage, means for multiplying the basic fee by the product of the weightings, means for transmitting and displaying the final fee to the terminal, and means for taking into account the weighting coefficients set by the administrator when applying the weightings. This enables efficient and fair data communication fee setting and improves user satisfaction.

[0091] "Basic fee" refers to the initial fee per 1GB for communication services, which is the basis for calculating the final fee.

[0092] "Current time" is information indicating the current time obtained by the server using the system clock.

[0093] "Population density" is an index that indicates the number of people per unit area within the area covered by a particular base station.

[0094] "Data usage" is information indicating the total amount of data traffic handled by each base station within a certain period of time.

[0095] "Weighting" refers to a coefficient that varies depending on factors such as time of day, population density, and data usage, and is an adjustment factor used when calculating the final fee.

[0096] "Final Price" means the price per GB applied to a User calculated by multiplying the Basic Price by the product of each Weighting.

[0097] "Weighting coefficients set by the administrator" refers to the adjustment coefficients for each element (time period, population density, data usage) that are predetermined by the system administrator.

[0098] "Terminal" refers to a device that visually displays the final price sent from the server and presents it to the user.

[0099] MODE FOR CARRYING OUT THE INVENTION

[0100] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variables such as time of day, population density, data usage, etc. Specific embodiments of this system will be described below.

[0101] Setting the basic fee

[0102] The server remembers the base price set by the administrator. This base price is the initial price per GB and is the basis for calculating the final price. The base price is stored in a database and can be read, for example, using an SQL query.

[0103] Getting the current time

[0104] The server uses the system clock to obtain the current time. This time information is used in subsequent weighting calculations. APIs such as LocalDateTime.now() in Java or datetime.datetime.now() in Python can be used.

[0105] Obtaining population density

[0106] The server retrieves population density information for the area covered by the base station from a database or real-time population data service. For example, the population density data is retrieved using a query like "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[0107] Get data usage

[0108] The server obtains the data usage handled by each base station. This data is recorded as the total data volume for a certain period of time. It is read using an SQL query such as "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[0109] Calculating the weights

[0110] The server calculates the weighting of each element using the following procedure.

[0111] Time of Day Weighting: Based on the current time, apply different factors to daytime (e.g. 6am-6pm) and nighttime (e.g. 6pm-6am), for example 1.5 for daytime and 0.75 for nighttime.

[0112] Population density weighting: Apply a factor based on whether the area is high, medium, or low density, using 1.2 for high density areas, 1.0 for medium density areas, and 0.8 for low density areas.

[0113] Data usage weighting: Apply a factor based on heavy, medium, or light usage, using 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[0114] Final fare calculation

[0115] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The final fee is calculated using the following formula:

[0116] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[0117] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate will be $10 1.5 1.2 1.2 = $21.6.

[0118] Display of final price

[0119] The server sends the calculated final fare to the terminal, which then visually displays this information. For example, a GUI (Graphical User Interface) component is used to display the fare information on the screen of a smartphone or PC. The user can check the final fare through a web interface using HTML and CSS or through a UI component in a mobile application.

[0120] Specific examples

[0121] Example 1: Daytime, high density area, medium usage

[0122] Base price: $10 / GB

[0123] Current time: 2 PM

[0124] Population density: 1,200 people per square kilometer

[0125] Data usage: 60GB

[0126] The server applies a daytime weighting of 1.5 because it is 2 PM, a high density weighting of 1.2 because the population density is 1,200 people per square kilometer, and a medium usage weighting of 1.2 because the data usage is 60 GB. The final charge is $10 1.5 1.2 1.2 = $21.6. The device displays this final charge to the user.

[0127] Example 2: Nighttime, low density area, light use

[0128] Base price: $10 / GB

[0129] Current time: 10 PM

[0130] Population density: 300 people per square kilometer

[0131] Data usage: 20GB

[0132] The server applies a nighttime weighting of 0.75 because it is 10 PM, a low-density area weighting of 0.8 because the population density is 300 people per square kilometer, and a light usage weighting of 1.0 because the data usage is 20 GB. The final charge is $10 0.75 0.8 1.0 = $6. The device displays this final charge to the user.

[0133] Prompt Sentence Examples

[0134] "Using a system that dynamically adjusts data rates for mobile network operators, please calculate the final rate for a population density of 1,200 people per square kilometer at 2 p.m. with 60 GB of data usage."

[0135] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0136] Specific processing flow of the system program

[0137] Step 1: Get the base price

[0138] The server retrieves the base rate from the database by executing the query "SELECT base_rate FROM pricing_table WHERE id = 1" and receiving the result.

[0139] Input: Database query

[0140] Data processing: Get the base price value from the database

[0141] Output: Base rate (e.g. $10 / GB)

[0142] Step 2: Get the current time

[0143] The server obtains the current time using the system clock, for example, using the Java LocalDateTime.now() method.

[0144] Input: None

[0145] Data processing: Get the current time from the system clock

[0146] Output: Current time (e.g. 2 PM)

[0147] Step 3: Obtaining population density

[0148] The server retrieves the population density of the area covered by a particular base station, for example by running the query "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[0149] Input: Database query

[0150] Data processing: Getting population density values ​​from the database

[0151] Output: population density (e.g. 1200 people / km2)

[0152] Step 4: Get data usage

[0153] The server obtains the data usage handled by each base station. For example, it executes the query "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[0154] Input: Database query

[0155] Data processing: Retrieving data usage values ​​from the database

[0156] Output: Data usage (e.g. 60GB)

[0157] Step 5: Calculate weights by time period

[0158] The server determines the weighting of the time period based on the current time it has obtained, for example, if the current time is 2 PM, it will apply a daytime weighting of 1.5.

[0159] Input: Current time

[0160] Data processing: Determine weighting based on time period

[0161] Output: Time period weighting (e.g. 1.5)

[0162] Step 6: Calculate population density weights

[0163] The server determines the weighting factor based on the population density obtained. For example, if the population density is 1200 people per square kilometer, it applies a weighting factor of 1.2 to the high-density area.

[0164] Input: Population density

[0165] Data processing: Determine weights based on population density

[0166] Output: Population density weight (e.g. 1.2)

[0167] Step 7: Calculate weighting by data usage

[0168] The server determines the weighting factor based on the acquired data usage. For example, if the data usage is 60GB, a weighting factor of 1.2 for medium usage will be applied.

[0169] Input: Data usage

[0170] Data processing: Determine weighting based on data usage

[0171] Output: Data usage weight (e.g. 1.2)

[0172] Step 8: Calculate the final price

[0173] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The calculation is based on the following formula:

[0174] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[0175] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate is $10 1.5 1.2 1.2 = $21.6.

[0176] Input: Base rate, time of day weighting, population density weighting, data usage weighting

[0177] Data processing: Multiply each weighting factor to calculate the final price

[0178] Output: Final price (e.g. $21.6)

[0179] Step 9: View the final price

[0180] The server sends the calculated final fare to the terminal, which displays this information visually, for example, on a smartphone or PC screen using a GUI component.

[0181] Input: Final Price

[0182] Data processing: Send the final fare to the terminal and display it visually on the terminal

[0183] Output: Price displayed to user (e.g. $21.6)

[0184] Through the above specific processing steps, the user can visually check the dynamically calculated final fare.

[0185] (Application example 1)

[0186] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0187] Conventional data communication pricing systems adjust fees based on time of day, local population density, and data usage, but they do not address the dynamic adjustments required by the food delivery industry. As a result, they are unable to offer fair and optimal delivery fees to users, making it difficult to promote the use of delivery services. Furthermore, fluctuations in regional order volume make it difficult to pinpoint pricing, which can affect the efficiency of the overall service.

[0188] 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.

[0189] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area to be covered, means for acquiring data usage, means for calculating a weighting by time of day based on the current time, means for calculating a weighting by population density based on the population density of the area to be covered, means for calculating a weighting by data usage based on the data usage, means for multiplying the basic fee by the product of the weightings, means for acquiring order volume by region, means for calculating a weighting by order volume based on the order volume, and means for displaying the final fee. This makes it possible to dynamically calculate and display fair delivery fees according to time of day, regional population density, data usage, and order volume, even in the food delivery industry.

[0190] "Basic fee" refers to the initial setup fee for data communication and service provision.

[0191] The "means for obtaining the current time" is a function for obtaining the current time using the system clock or external time information.

[0192] The "means for obtaining the population density of the area covered" is a function for obtaining population density data of a specific area.

[0193] The "means for acquiring data usage" is a function for collecting data usage within a certain period of time.

[0194] The "means for calculating weighting by time period" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired current time.

[0195] The "means for calculating weighting based on population density" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired population density information.

[0196] The "means for calculating weighting based on data usage" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired data usage information.

[0197] The "means for calculating the final fee" is a function for calculating the final fee using the basic fee and each weighting coefficient.

[0198] The "means for obtaining order volume by region" is a function for collecting current order volume data for a specific area or store.

[0199] The "means for calculating weighting based on order volume" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired order volume information.

[0200] The "means for displaying the final fee" is a function for visually displaying the calculated final fee to the user or the service provider.

[0201] This invention relates to a system for dynamically adjusting delivery fees in the food delivery industry. The system aims to calculate and present to the user a final fee based on a base fee as well as the current time, the area's population density, data usage, and the order volume by region.

[0202] composition

[0203] The system is implemented by a server which includes the following main means:

[0204] 1. How to set a base fee

[0205] The server stores a base fee set by an administrator, which is the initial fee for the delivery service and is the basis for calculating the final fee.

[0206] 2. How to get the current time

[0207] The server uses the system clock to obtain the current time, and weights the time zones based on this time information.

[0208] 3. How to obtain the population density coverage

[0209] The server retrieves the population density of a particular area, which can be obtained from a pre-configured database or real-time population data.

[0210] 4. How to get data usage

[0211] The server collects data usage data for each store and area, which is recorded as the total data volume for a certain period of time and becomes a factor in weighting.

[0212] 5. How to obtain order volume by region

[0213] The server obtains the current order volume for a specific area or store via an API or other means.

[0214] 6. Weighting calculation methods

[0215] The server calculates each weighting based on the acquired current time, population density, data usage, and order volume. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage and order volume, different coefficients are applied to heavy, medium, and light usage.

[0216] 7. Final Fee Calculation Method

[0217] The server calculates the final fee by multiplying the base fee by the product of each of the weightings, and this final fee becomes the final fee applied to the user.

[0218] 8. Final price display method

[0219] The server sends the calculated final fare to the terminal and visually displays it to the user and / or delivery driver, and the terminal displays this information for the user to review.

[0220] Specific operation example

[0221] Example 1: Daytime, high density area, medium usage, high order volume

[0222] Basic fee: 500 yen

[0223] Current time: 1 PM

[0224] Population density: 1500 people / km²

[0225] Data usage: 60GB

[0226] Order quantity: 60

[0227] The server applies a daytime weighting of 1.5 based on the 1 PM time slot. Next, because the population density is 1500 people / km², it applies a high density area weighting of 1.2. Also, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the order volume is 60, it applies a high order volume weighting of 1.3. The final cost is 500 yen 1.5 1.2 1.2 1.3 = 1404 yen. The device displays this final cost to the user.

[0228] Hardware and software used

[0229] Hardware:

[0230] Server (CPU, memory, network interface)

[0231] Display devices (smartphones, etc.) used by users and delivery drivers

[0232] software:

[0233] Server program (Python, library for API requests, etc.)

[0234] Database (storing data such as population density, order volume, etc.)

[0235] Display application (for users and delivery drivers)

[0236] This system configuration will enable fair and dynamic pricing in the food delivery industry.

[0237] Prompt Sentence Examples

[0238] (prompt)

[0239] The current time is 1 PM, the population density of the delivery area is 1500 people / km², and the order volume is 60. If the basic delivery fee is 500 yen, calculate and display the corresponding final delivery fee.

[0240] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0241] Step 1:

[0242] The server sets the base fee. The input is the initial base fee set by the administrator, and this fee is stored in the server. The output is the stored base fee.

[0243] Step 2:

[0244] The server gets the current time. The input is the system clock, and the time information is retrieved. The output is the retrieved current time. This information is used to weight the time zones.

[0245] Step 3:

[0246] The server obtains the population density of the area it covers. The input is the area identification information, and the population density information is obtained from a database or real-time population data. The output is the obtained population density.

[0247] Step 4:

[0248] The server acquires the data usage of each area and store. The input is the identification information of the area or store, and the total data volume within a certain period is collected. The output is the acquired data usage.

[0249] Step 5:

[0250] The server obtains order volume by region. The input is area and store identification information, and current order volume data is collected via API, etc. The output is the obtained order volume.

[0251] Step 6:

[0252] The server calculates the weighting for each time zone based on the current time. The input is the current time, and different weighting factors are applied for daytime and nighttime. The output is the calculated time zone weighting factor.

[0253] Step 7:

[0254] The server calculates the population density weighting based on the acquired population density information. The input is the population density, and different weighting factors are applied to high-density, medium-density, and low-density areas. The output is the calculated population density weighting factor.

[0255] Step 8:

[0256] The server calculates the weighting factor based on the data usage information it has acquired. The input is the data usage, and different weighting factors are applied to heavy, medium, and light usage. The output is the calculated data usage weighting factor.

[0257] Step 9:

[0258] The server calculates the weighting by order volume based on the acquired order volume information. The input is the order volume, and different weighting coefficients are applied to high, medium, and low order volumes. The output is the calculated order volume weighting coefficient.

[0259] Step 10:

[0260] The server calculates the final fare by multiplying the base fare by each weighting factor. The input is the base fare and each weighting factor, which are multiplied together to calculate the final fare. The output is the calculated final fare.

[0261] Step 11:

[0262] The server sends the calculated final fare to the terminal. The input is the final fare, which is sent to the display terminals of the user and the delivery driver. The output is the visually displayed final fare.

[0263] 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.

[0264] The present invention combines an emotion engine with a system that dynamically adjusts data communication charges provided by mobile network operators, and aims to calculate and provide a final charge to users based on the basic charge as well as the time of day, population density, data usage, and the user's emotions.

[0265] composition

[0266] The system is implemented by a server which includes the following main means:

[0267] Basic fee setting method

[0268] The server stores the base rate set by the administrator, which is the initial rate per GB and the basis for calculating the final rate.

[0269] Time acquisition means

[0270] The server has a function for obtaining the current time. For example, the server obtains the current time using the system clock. Based on this time information, the time zone is weighted.

[0271] Population density acquisition method

[0272] The server obtains the population density of the area covered by a particular base station. This information is obtained from a pre-configured database or real-time population data.

[0273] Data usage acquisition method

[0274] The server collects the data usage handled by each base station, which is recorded as the total data volume for a certain period of time and becomes one of the factors for weighting.

[0275] Each weight calculation means

[0276] The server calculates the weighting based on the current time, population density, and data usage. The weighting by time of day applies different coefficients to daytime and nighttime, the weighting by population density applies different coefficients to high-density, medium-density, and low-density areas, and the weighting by data usage applies different coefficients to heavy, medium, and light usage.

[0277] Emotion engine and its weighting method

[0278] The server includes an emotion engine for recognizing the user's emotion, and further applies weighting based on the user's emotion acquired by the emotion engine. For example, if the user's emotion is positive, a weighting is applied to discount the fee, and if the user's emotion is negative, a weighting is applied to increase the fee.

[0279] Final fare calculation method

[0280] The server calculates the final fee by multiplying the base fee by the product of the weightings above. This final fee becomes the final fee per GB applied to the user.

[0281] Final fare display method

[0282] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[0283] Specific examples

[0284] A specific example of use is shown below.

[0285] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[0286] Base price: $10 / GB

[0287] Current time: 2 PM

[0288] Population density: 1,200 people per square kilometer

[0289] Data usage: 60GB

[0290] User Sentiment: Positive

[0291] The server applies a daytime weighting of 1.5 based on the 2:00 PM time slot. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. Furthermore, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the user's sentiment is positive, it applies a weighting of 0.9. The final cost is $10 1.5 1.2 1.2 0.9 = $19.44. The device displays this final cost to the user.

[0292] Example 2: Nighttime, low density area, light use, negative sentiment

[0293] Base price: $10 / GB

[0294] Current time: 10 PM

[0295] Population density: 300 people per square kilometer

[0296] Data usage: 20GB

[0297] User Sentiment: Negative

[0298] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Next, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. Also, because the data usage is 20 GB, it applies a light usage weighting of 1.0. Additionally, because the user's sentiment is negative, it applies a weighting of 1.1. The final cost is $10. 0.75 0.8 1.0 1.1 = $6.6. The device displays this final cost to the user.

[0299] The above describes a specific embodiment of the present invention and an example of its processing. This system dynamically adjusts data communication fees and provides a flexible fee structure that responds to usage conditions and emotions. This improves user satisfaction and enables efficient use of network resources.

[0300] The processing flow will be explained below.

[0301] Step 1:

[0302] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[0303] Step 2:

[0304] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[0305] Step 3:

[0306] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[0307] Step 4:

[0308] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[0309] Step 5:

[0310] The device activates an emotion engine to recognize the user's emotion. The emotion engine acquires the user's emotion as data using a voice recognition device or a facial expression recognition device. For example, the user's emotion is recognized as positive.

[0311] Step 6:

[0312] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[0313] Step 7:

[0314] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[0315] Step 8:

[0316] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[0317] Step 9:

[0318] The server refers to the emotion engine data based on the user's emotion and applies a separate weighting. For example, if the emotion is positive, a weighting of 0.9 is applied, and if negative, a weighting of 1.1 is applied. In this case, the user's emotion is positive, so a weighting of 0.9 is applied.

[0319] Step 10:

[0320] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, a data usage weighting of 1.2, and an emotion weighting of 0.9. The final charge is $10 1.5 1.2 1.2 0.9 = $19.44.

[0321] Step 11:

[0322] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $19.44." The user can confirm this and understand the usage charge.

[0323] This is the detailed flow of the system's processing steps, which enables dynamic pricing and provides a flexible pricing structure that responds to usage and emotions.

[0324] Example 2

[0325] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0326] Existing data communication fee setting methods are based on static standards, making it difficult to flexibly adjust fees according to actual usage and user conditions. In addition, because they do not take user emotions into consideration, they have the problem of not being able to maximize user satisfaction.

[0327] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for setting a basic fee, a means for acquiring the current time, a means for acquiring the population density of the area to be covered, a means for acquiring the amount of data usage, a means for calculating a weighting by time zone based on the current time, a means for calculating a weighting by population density based on the population density of the area to be covered, a means for calculating a weighting by data usage based on the amount of data usage, a means for recognizing the user's emotions and calculating a weighting, a means for calculating a final fee by multiplying the basic fee by the product of the weightings, and a means for displaying the final fee. This makes it possible to dynamically set data communication fees according to the usage status and the user's emotions.

[0328] The "means for setting the basic fee" is a device or system that has the function of storing the initial fee for data communication in a database.

[0329] The "means for obtaining the current time" refers to a device or system that has a function for the server to obtain the current time by referencing the system clock.

[0330] The "means for obtaining the population density of a coverage area" is a device or system that has the function of obtaining the population density within a specific area from a database or real-time data.

[0331] The "means for acquiring data usage" is a device or system that has the function of measuring the amount of data communication within a specific period and recording the value.

[0332] The "means for calculating weighting by time zone based on the current time" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired time information.

[0333] The "means for calculating weighting by population density based on the population density covered" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired population density information.

[0334] The "means for calculating weighting based on data usage amount" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired data usage amount information.

[0335] The "means for recognizing the user's emotions and calculating weighting" is a device or system that has the function of recognizing the user's emotional state using an emotion engine and calculating weighting coefficients based on that information.

[0336] The "means for calculating the final fee by multiplying the basic fee by the product of the weighting factors" refers to a device or system that has the function of multiplying the basic fee by various weighting factors to calculate the final fee.

[0337] The "means for displaying the final fare" is a device or system having the function of transmitting the calculated final fare to the terminal and visually displaying it to the user.

[0338] MODE FOR CARRYING OUT THE INVENTION

[0339] The present invention is a system for dynamically adjusting data communication charges offered by mobile network operators, which is combined with an emotion engine. The system aims to calculate and provide a final charge to users based on the base charge, time of day, population density, data usage, and user emotion.

[0340] Hardware and software used

[0341] The server is the central component of the system and contains the following main functions:

[0342] Database for which basic fees are set

[0343] The system clock from which the time is obtained

[0344] Real-time population database for obtaining population density

[0345] Log analysis tool for measuring data usage

[0346] Emotion engines (e.g., natural language processing models)

[0347] A terminal is a device with a graphical user interface (GUI) used to display the final fare to the user. Examples include smartphones and tablets.

[0348] Example of operation

[0349] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[0350] 1. The server has a base database fee of $10 / GB.

[0351] 2. The server gets the current time, "2:00 PM," from the system clock.

[0352] 3. The server obtains the population density of the base station coverage area as "1,200 people / square kilometer" from the real-time population data.

[0353] 4. The server retrieves the data usage for the past hour: "60GB."

[0354] 5. The server calculates a weighting of 1.5 for time of day (daytime), 1.2 for population density (high density area), and 1.2 for data usage (medium usage).

[0355] 6. The server runs the sentiment engine and applies a weighting of 0.9 because the user's sentiment is positive.

[0356] 7. The server calculates:

[0357] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[0358] = $10 1.5 1.2 1.2 0.9 = $19.44

[0359] 8. The server sends the final price of $19.44 to the terminal, which displays this price to the user.

[0360] Prompt Sentence Examples

[0361] Examples of prompts to be input to a generative AI model include:

[0362] "Calculate the final price based on the following conditions: base rate $10 / GB, time of day 2 PM, high density area, data usage 60 GB, and positive user sentiment."

[0363] As a result, the system of the present invention is able to dynamically set data communication charges according to the usage situation and the user's emotions, thereby improving user satisfaction and realizing efficient use of network resources.

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1: Set your base price

[0366] The server sets the base price in the database. It takes the base price specified by the administrator as input and stores it in the database. This input is given as "$10 / GB", the server saves this in the database and is ready to go.

[0367] Specific behavior:

[0368] The server receives the base fee value through the administrator interface.

[0369] The server establishes a connection to the database and stores the base price of $10 / GB.

[0370] Step 2: Get the current time

[0371] The server gets the current time using the system clock. This is the action where the server accesses the system clock and gets the current time. The input is the state of the internal system clock, and the output is the current time, "2:00 PM."

[0372] Specific behavior:

[0373] The server references the current time from the system clock and obtains "2:00 PM."

[0374] Step 3: Obtaining population density

[0375] The server gets the population density of the area covered by a particular base station. The input is a real-time population database, and the server accesses this database to get the required population density data. The output is "1200 people / square kilometer."

[0376] Specific behavior:

[0377] The server queries the population density database to obtain the population density of the base station coverage area.

[0378] The server obtains population density data of "1200 people per square kilometer."

[0379] Step 4: Get data usage

[0380] The server obtains the data usage at each base station. The input is the data usage information provided by the log analysis tool, and the server analyzes this information to measure the data usage. The output is "60GB."

[0381] Specific behavior:

[0382] The server measures data usage through a log analysis tool.

[0383] The server obtains the data usage for the past hour, which is "60GB."

[0384] Step 5: Calculate weights for time periods

[0385] The server calculates the time zone weighting based on the current time. The input is the current time obtained in step 2, and the output is a weighting of 1.5.

[0386] Specific behavior:

[0387] The server applies a daytime weighting of 1.5 based on the current time, 2:00 PM.

[0388] Step 6: Calculate population density weights

[0389] The server calculates the weighting based on the population density obtained. The input is the population density information obtained in step 3, and the output is a weighting of "1.2".

[0390] Specific behavior:

[0391] The server applies a weighting of 1.2 to high density areas based on a population density of 1,200 people per square kilometer.

[0392] Step 7: Calculate weighted data usage

[0393] The server calculates the weighting based on the acquired data usage. The input is the data usage information acquired in step 4, and the output is a weighting of 1.2.

[0394] Specific behavior:

[0395] The server applies a weighting of 1.2 for medium usage based on a data usage of 60GB.

[0396] Step 8: Use the Emotion Engine

[0397] The server uses the emotion engine to recognize the user's emotion and calculates the weighting based on that. The input is the user's emotion information from the emotion engine, and based on the "positive emotion," a weighting of "0.9" is applied.

[0398] Specific behavior:

[0399] The server runs an emotion engine and recognizes that the user's emotion is positive.

[0400] The server applies a weighting of 0.9 to positive sentiment.

[0401] Step 9: Calculate the final price

[0402] The server calculates the final price by multiplying the base price by each weighting. The input is the base price and each weighting, and the output is the final price of $19.44.

[0403] Specific behavior:

[0404] The server performs the following calculation:

[0405] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[0406] = $10 1.5 1.2 1.2 0.9 = $19.44

[0407] Step 10: View the final price

[0408] The server sends the calculated final fare to the terminal, which displays this information to the user. The input is the final fare information and the output is the display presented to the user.

[0409] Specific behavior:

[0410] The server sends the final price of $19.44 to the terminal.

[0411] The terminal visually displays the final price of $19.44 to the user.

[0412] (Application example 2)

[0413] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0414] Conventional fee structures have difficulty in setting flexible fees according to user emotions and usage conditions, which limits the improvement of user satisfaction and the efficient use of network resources. Furthermore, especially in electronic payment services, there is a demand for dynamic fee settings that reflect user emotions, and achieving this has been a challenge.

[0415] 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.

[0416] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a weighting by time zone based on the current time, means for calculating a weighting by population density based on the population density of the area covered, means for calculating a weighting by data usage based on the data usage, means for recognizing a user's emotion and applying a weighting based on the emotion, means for calculating a final fee by multiplying the basic fee by the product of the weightings, and means for displaying the final fee. This makes it possible to provide a flexible fee structure that reflects the user's emotion and usage status, thereby improving user satisfaction and enabling efficient use of network resources.

[0417] "Basic Charge" means the basic charge initially set for data communication or service usage.

[0418] "Current time" is information indicating the actual time obtained from the system clock or an external time server.

[0419] "Coverage density" is information that describes the number of people present in a particular area, usually expressed as people per square kilometer.

[0420] "Data Usage" means the total amount of data consumed within a particular period of time, generally measured in gigabytes (GB).

[0421] "Time of day weighting" is a correction factor applied to the charge based on the current time, and is set to a different value depending on the time of day, such as daytime or nighttime.

[0422] "Population density weighting" is a correction coefficient applied to fees based on the population density of the area covered, with different values ​​being set depending on the population density of the area.

[0423] "Weighting based on data usage" is a correction coefficient applied to the fee based on the amount of data usage, and different values ​​are set depending on the amount of usage.

[0424] The "means for recognizing a user's emotions" refers to a technology or device for determining the user's emotional state, such as emotion recognition software or a sensor.

[0425] The "means for applying weighting based on emotion" is a technique or device for calculating a correction factor to be applied to the fee based on the result of the user's emotion recognition.

[0426] The "final fee" is the final fee calculated by multiplying the basic fee by each weighting, and is the amount applied to the user.

[0427] The "means for displaying the final fare" refers to a technology or device for presenting the calculated final fare to the user, such as a smartphone screen or a notification system.

[0428] To implement this invention, the server, terminal, and user must work together, each using appropriate hardware and software to execute specific processes. The functions and processing methods of each are described in detail below.

[0429] Server Processing

[0430] The server sets the base fee and has a means to obtain the current time, the population density covered, data usage, and user sentiment. Based on this data, it calculates the weighting, calculates the final fee, and displays it. Specifically, the following hardware and software are used:

[0431] Hardware

[0432] System clock for obtaining time information

[0433] Database server for acquiring population density data

[0434] Emotion Recognition Sensor

[0435] software

[0436] Time server for obtaining time information

[0437] A database for managing population density information

[0438] Network monitoring tool to monitor data usage

[0439] Emotion recognition engine (e.g., emotion recognition API)

[0440] The server first obtains the current time using the system clock or time server and calculates weighting based on that time. Next, it obtains population density information for a specific area from a database and calculates weighting based on that population density. Next, it obtains data usage using a network monitoring tool and calculates weighting based on that usage. Finally, it recognizes the user's emotion using an emotion recognition engine and calculates weighting based on that emotion. The final charge is calculated based on these weightings and the result is sent to the terminal.

[0441] Terminal handling

[0442] The terminal is responsible for displaying the final price sent from the server to the user.

[0443] Hardware

[0444] Smartphone or tablet

[0445] display

[0446] software

[0447] Interface Applications

[0448] The terminal receives the final fare information sent from the server and visually displays it to the user using an interface application, allowing the user to check the final fare based on their own usage and emotions.

[0449] User Roles

[0450] The user is responsible for checking the information provided by the server through their device and providing emotional data as necessary. The user's emotions are collected through an emotion recognition application installed on a smartphone or tablet.

[0451] Specific examples

[0452] For example, if a user is in a high-density area at 10 AM, has moderate data usage of 1GB, and is expressing positive sentiment, the following will happen:

[0453] The basic fee is 5% (commission rate)

[0454] The current time is 10:00 AM, so a daytime weighting of 1.5 is applied

[0455] The population density is 1200 people per square kilometre, so a high density area weighting of 1.2 is applied.

[0456] Since the data usage is 1GB, a weighting of 1.0 is applied to the moderate usage

[0457] Since the user has a positive sentiment, a sentiment weighting of 0.9 is applied.

[0458] Based on these weights, the server calculates the final fare and sends it to the terminal, which displays the result to the user, who then confirms the final fare.

[0459] Prompt Sentence Examples

[0460] It works by inputting the following prompt sentence into the generative AI model:

[0461] Current time: 10:00 AM

[0462] Area population density: 1,200 people per square kilometer

[0463] Data usage: 1GB

[0464] User Sentiment: Positive

[0465] Basic fee: 5%

[0466] Prompt: Calculate the final settlement fee rate based on this situation.

[0467] This allows for flexible pricing based on user sentiment and usage.

[0468] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0469] Step 1:

[0470] The server sets an initial basic fee using the basic fee setting means. This determines the fee that will be the basis for specific data communication and service usage. This basic fee will be the basis for each weighting process that will be performed later.

[0471] Input: None

[0472] Output: Basic charge

[0473] Step 2:

[0474] The server obtains the current time from its system clock or a time server, which provides the current time information for subsequent time zone weighting calculations.

[0475] Input: None

[0476] Output: current time

[0477] Step 3:

[0478] The server retrieves population density information for the area it covers from a population density database, which allows it to understand the population density within a specific area.

[0479] Input: Region ID or location

[0480] Output: Covered population density

[0481] Step 4:

[0482] The server uses a network monitoring tool to monitor data usage over a certain period of time and acquires the data, thereby determining the current data usage.

[0483] Input: None

[0484] Output: Data usage

[0485] Step 5:

[0486] The server calculates the weighting for each time period based on the current time, for example, applying a weighting of 1.5 for daytime and 0.75 for nighttime.

[0487] Input: Current time

[0488] Output: Weighted by time period

[0489] Step 6:

[0490] The server calculates a density weight based on the population density of the area it covers: high density applies a weight of 1.2, medium density applies a weight of 1.0, and low density applies a weight of 0.8.

[0491] Input: Population density to cover

[0492] Output: Weighted by population density

[0493] Step 7:

[0494] The server calculates a weighting based on data usage, with a weighting of 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[0495] Input: Data usage

[0496] Output: Weighted by data usage

[0497] Step 8:

[0498] The server uses an emotion recognition engine to obtain the emotion data provided by the user and calculates a weighting based on the emotion: 0.9 for positive emotions, 1.0 for neutral, and 1.2 for negative emotions.

[0499] Input: User emotion data

[0500] Output: Emotion weighting

[0501] Step 9:

[0502] The server calculates the final charge by multiplying the basic charge by each weighting. Specifically, the server calculates the final charge by multiplying the basic charge by each weighting of time of day, population density, data usage, and emotion.

[0503] Inputs: Base price, Time of day weighting, Population density weighting, Data usage weighting, Sentiment weighting

[0504] Output: Final price

[0505] Step 10:

[0506] The server sends the calculated final fare to the terminal, which displays the final fare to the user using an interface application.

[0507] Input: Final Price

[0508] Output: The final price shown to the user

[0509] 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.

[0510] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0511] 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.

[0512] [Second embodiment]

[0513] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0514] 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.

[0515] 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).

[0516] 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.

[0517] 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.

[0518] 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).

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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.

[0524] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0525] The present invention relates to a system for dynamically adjusting data communication charges for mobile network operators, which calculates and provides a final charge to users based on a basic charge and factors such as time of day, population density, and data usage.

[0526] composition

[0527] The system is implemented by a server which includes the following main means:

[0528] Basic fee setting method

[0529] The server stores the base fee set by the administrator, which is the initial fee per GB and is the basis for calculating the final fee.

[0530] Time acquisition means

[0531] The server has a function for obtaining the current time. For example, it obtains the current time using a system clock. Based on this time information, the server weights the time zones.

[0532] Population density acquisition method

[0533] The server obtains the population density of the area covered by a particular base station, which can be obtained from a pre-configured database or real-time population data.

[0534] Data usage acquisition method

[0535] The server obtains the data usage handled by each base station, which is recorded as the total data volume within a certain period and becomes one of the factors for weighting.

[0536] Each weight calculation means

[0537] The server calculates each weighting based on the current time, population density, and data usage. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage, different coefficients are applied to heavy, medium, and light usage.

[0538] Final fare calculation method

[0539] The server calculates the final fee by multiplying the base fee by the product of each of the weightings above. This final fee becomes the final fee per 1GB applied to the user.

[0540] Final fare display method

[0541] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[0542] Specific examples

[0543] A specific example of use is shown below.

[0544] Example 1: Daytime, high density area, medium usage

[0545] Base price: $10 / GB

[0546] Current time: 2 PM

[0547] Population density: 1,200 people per square kilometer

[0548] Data usage: 60GB

[0549] The server applies a daytime weighting of 1.5 based on the time of day, 2:00 PM. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. And because the data usage is 60 GB, it applies a medium-use weighting of 1.2. The final cost is $10 1.5 1.2 1.2 = $21.6. The device displays this final cost to the user.

[0550] Example 2: Nighttime, low density area, light use

[0551] Base price: $10 / GB

[0552] Current time: 10 PM

[0553] Population density: 300 people per square kilometer

[0554] Data usage: 20GB

[0555] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Then, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. And because the data usage is 20 GB, it applies a light usage weighting of 1.0. The final cost is $10 0.75 0.8 1.0 = $6. The device displays this final cost to the user.

[0556] The above describes a specific embodiment of the present invention and an example of its processing. This system enables efficient and fair data communication fee setting, thereby improving user satisfaction.

[0557] The processing flow will be explained below.

[0558] Step 1:

[0559] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[0560] Step 2:

[0561] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[0562] Step 3:

[0563] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[0564] Step 4:

[0565] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[0566] Step 5:

[0567] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[0568] Step 6:

[0569] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[0570] Step 7:

[0571] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[0572] Step 8:

[0573] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, and a data usage weighting of 1.2. The final charge is $10 1.5 1.2 1.2 = $21.6.

[0574] Step 9:

[0575] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $21.60." The user can check this and understand the usage charge.

[0576] This concludes the detailed flow of the system's processing steps, which enables dynamic pricing and provides users with fair and realistic prices.

[0577] Example 1

[0578] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0579] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variable factors such as time of day, population density, data usage, etc. In conventional technology, data communication charges are fixed, making it difficult to appropriately adjust charges in response to user behavior patterns and network load, resulting in a lack of fairness and efficiency.

[0580] 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.

[0581] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a time-of-day weighting based on the current time, means for calculating a population density weighting based on the population density of the area covered, means for calculating a data usage weighting based on the data usage, means for multiplying the basic fee by the product of the weightings, means for transmitting and displaying the final fee to the terminal, and means for taking into account the weighting coefficients set by the administrator when applying the weightings. This enables efficient and fair data communication fee setting and improves user satisfaction.

[0582] "Basic fee" refers to the initial fee per 1GB for communication services, which is the basis for calculating the final fee.

[0583] "Current time" is information indicating the current time obtained by the server using the system clock.

[0584] "Population density" is an index that indicates the number of people per unit area within the area covered by a particular base station.

[0585] "Data usage" is information indicating the total amount of data traffic handled by each base station within a certain period of time.

[0586] "Weighting" refers to a coefficient that varies depending on factors such as time of day, population density, and data usage, and is an adjustment factor used when calculating the final fee.

[0587] "Final Price" means the price per GB applied to a User calculated by multiplying the Basic Price by the product of each Weighting.

[0588] "Weighting coefficients set by the administrator" refers to the adjustment coefficients for each element (time period, population density, data usage) that are predetermined by the system administrator.

[0589] "Terminal" refers to a device that visually displays the final price sent from the server and presents it to the user.

[0590] MODE FOR CARRYING OUT THE INVENTION

[0591] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variables such as time of day, population density, data usage, etc. Specific embodiments of this system will be described below.

[0592] Setting the basic fee

[0593] The server remembers the base price set by the administrator. This base price is the initial price per GB and is the basis for calculating the final price. The base price is stored in a database and can be read, for example, using an SQL query.

[0594] Getting the current time

[0595] The server uses the system clock to get the current time, which is used in the weighting calculations later. APIs such as LocalDateTime.now() in Java or datetime.datetime.now() in Python can be used.

[0596] Obtaining population density

[0597] The server retrieves population density information for the area covered by the base station from a database or real-time population data service. For example, the population density data is retrieved using a query like "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[0598] Get data usage

[0599] The server obtains the data usage handled by each base station. This data is recorded as the total data volume for a certain period of time. It is read using an SQL query such as "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[0600] Calculating the weights

[0601] The server calculates the weighting of each element using the following procedure.

[0602] Time of Day Weighting: Based on the current time, apply different factors to daytime (e.g. 6am-6pm) and nighttime (e.g. 6pm-6am), for example 1.5 for daytime and 0.75 for nighttime.

[0603] Population density weighting: Apply a factor based on whether the area is high, medium, or low density, using 1.2 for high density areas, 1.0 for medium density areas, and 0.8 for low density areas.

[0604] Data usage weighting: Apply a factor based on heavy, medium, or light usage, using 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[0605] Final fare calculation

[0606] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The final fee is calculated using the following formula:

[0607] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[0608] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate will be $10 1.5 1.2 1.2 = $21.6.

[0609] Display of final price

[0610] The server sends the calculated final fare to the terminal, which then visually displays this information. For example, a GUI (Graphical User Interface) component is used to display the fare information on the screen of a smartphone or PC. The user can check the final fare through a web interface using HTML and CSS or through a UI component in a mobile application.

[0611] Specific examples

[0612] Example 1: Daytime, high density area, medium usage

[0613] Base price: $10 / GB

[0614] Current time: 2 PM

[0615] Population density: 1,200 people per square kilometer

[0616] Data usage: 60GB

[0617] The server applies a daytime weighting of 1.5 because it is 2 PM, a high density weighting of 1.2 because the population density is 1,200 people per square kilometer, and a medium usage weighting of 1.2 because the data usage is 60 GB. The final charge is $10 1.5 1.2 1.2 = $21.6. The device displays this final charge to the user.

[0618] Example 2: Nighttime, low density area, light use

[0619] Base price: $10 / GB

[0620] Current time: 10 PM

[0621] Population density: 300 people per square kilometer

[0622] Data usage: 20GB

[0623] The server applies a nighttime weighting of 0.75 because it is 10 PM, a low-density area weighting of 0.8 because the population density is 300 people per square kilometer, and a light usage weighting of 1.0 because the data usage is 20 GB. The final charge is $10 0.75 0.8 1.0 = $6. The device displays this final charge to the user.

[0624] Prompt Sentence Examples

[0625] "Using a system that dynamically adjusts data rates for mobile network operators, please calculate the final rate for a population density of 1,200 people per square kilometer at 2 p.m. with 60 GB of data usage."

[0626] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0627] Specific processing flow of the system program

[0628] Step 1: Get the base price

[0629] The server retrieves the base rate from the database by executing the query "SELECT base_rate FROM pricing_table WHERE id = 1" and receiving the result.

[0630] Input: Database query

[0631] Data processing: Get the base price value from the database

[0632] Output: Base rate (e.g. $10 / GB)

[0633] Step 2: Get the current time

[0634] The server obtains the current time using the system clock, for example, using the Java LocalDateTime.now() method.

[0635] Input: None

[0636] Data processing: Get the current time from the system clock

[0637] Output: Current time (e.g. 2 PM)

[0638] Step 3: Obtaining population density

[0639] The server retrieves the population density of the area covered by a particular base station, for example by running the query "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[0640] Input: Database query

[0641] Data processing: Getting population density values ​​from the database

[0642] Output: population density (e.g. 1200 people / km2)

[0643] Step 4: Get data usage

[0644] The server obtains the data usage handled by each base station. For example, it executes the query "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[0645] Input: Database query

[0646] Data processing: Retrieving data usage values ​​from the database

[0647] Output: Data usage (e.g. 60GB)

[0648] Step 5: Calculate weights by time period

[0649] The server determines the weighting of the time period based on the current time it has obtained, for example, if the current time is 2 PM, it will apply a daytime weighting of 1.5.

[0650] Input: Current time

[0651] Data processing: Determine weighting based on time period

[0652] Output: Time period weighting (e.g. 1.5)

[0653] Step 6: Calculate population density weights

[0654] The server determines the weighting factor based on the population density obtained. For example, if the population density is 1200 people per square kilometer, it applies a weighting factor of 1.2 to the high-density area.

[0655] Input: Population density

[0656] Data processing: Determine weights based on population density

[0657] Output: Population density weight (e.g. 1.2)

[0658] Step 7: Calculate weighting by data usage

[0659] The server determines the weighting factor based on the acquired data usage. For example, if the data usage is 60GB, a weighting factor of 1.2 for medium usage will be applied.

[0660] Input: Data usage

[0661] Data processing: Determine weighting based on data usage

[0662] Output: Data usage weight (e.g. 1.2)

[0663] Step 8: Calculate the final price

[0664] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The calculation is based on the following formula:

[0665] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[0666] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate is $10 1.5 1.2 1.2 = $21.6.

[0667] Input: Base rate, time of day weighting, population density weighting, data usage weighting

[0668] Data processing: Multiply each weighting factor to calculate the final price

[0669] Output: Final price (e.g. $21.6)

[0670] Step 9: View the final price

[0671] The server sends the calculated final fare to the terminal, which displays this information visually, for example, on a smartphone or PC screen using a GUI component.

[0672] Input: Final Price

[0673] Data processing: Send the final fare to the terminal and display it visually on the terminal

[0674] Output: Price displayed to user (e.g. $21.6)

[0675] Through the above specific processing steps, the user can visually check the dynamically calculated final fare.

[0676] (Application example 1)

[0677] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0678] Conventional data communication pricing systems adjust fees based on time of day, local population density, and data usage, but they do not address the dynamic adjustments required by the food delivery industry. As a result, they are unable to offer fair and optimal delivery fees to users, making it difficult to promote the use of delivery services. Furthermore, fluctuations in regional order volume make it difficult to pinpoint pricing, which can affect the efficiency of the overall service.

[0679] 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.

[0680] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area to be covered, means for acquiring data usage, means for calculating a weighting by time of day based on the current time, means for calculating a weighting by population density based on the population density of the area to be covered, means for calculating a weighting by data usage based on the data usage, means for multiplying the basic fee by the product of the weightings, means for acquiring order volume by region, means for calculating a weighting by order volume based on the order volume, and means for displaying the final fee. This makes it possible to dynamically calculate and display fair delivery fees according to time of day, regional population density, data usage, and order volume, even in the food delivery industry.

[0681] "Basic fee" refers to the initial setup fee for data communication and service provision.

[0682] The "means for obtaining the current time" is a function for obtaining the current time using the system clock or external time information.

[0683] The "means for obtaining the population density of the area covered" is a function for obtaining population density data of a specific area.

[0684] The "means for acquiring data usage" is a function for collecting data usage within a certain period of time.

[0685] The "means for calculating weighting by time period" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired current time.

[0686] The "means for calculating weighting based on population density" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired population density information.

[0687] The "means for calculating weighting based on data usage" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired data usage information.

[0688] The "means for calculating the final fee" is a function for calculating the final fee using the basic fee and each weighting coefficient.

[0689] The "means for obtaining order volume by region" is a function for collecting current order volume data for a specific area or store.

[0690] The "means for calculating weighting based on order volume" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired order volume information.

[0691] The "means for displaying the final fee" is a function for visually displaying the calculated final fee to the user or the service provider.

[0692] This invention relates to a system for dynamically adjusting delivery fees in the food delivery industry. The system aims to calculate and present to the user a final fee based on a base fee as well as the current time, the area's population density, data usage, and the order volume by region.

[0693] composition

[0694] The system is implemented by a server which includes the following main means:

[0695] 1. How to set a base fee

[0696] The server stores a base fee set by an administrator, which is the initial fee for the delivery service and is the basis for calculating the final fee.

[0697] 2. How to get the current time

[0698] The server uses the system clock to obtain the current time, and weights the time zones based on this time information.

[0699] 3. How to obtain the population density coverage

[0700] The server retrieves the population density of a particular area, which can be obtained from a pre-configured database or real-time population data.

[0701] 4. How to get data usage

[0702] The server collects data usage data for each store and area, which is recorded as the total data volume for a certain period of time and becomes a factor in weighting.

[0703] 5. How to obtain order volume by region

[0704] The server obtains the current order volume for a specific area or store via an API or other means.

[0705] 6. Weighting calculation methods

[0706] The server calculates each weighting based on the acquired current time, population density, data usage, and order volume. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage and order volume, different coefficients are applied to heavy, medium, and light usage.

[0707] 7. Final Fee Calculation Method

[0708] The server calculates the final fee by multiplying the base fee by the product of each of the weightings, and this final fee becomes the final fee applied to the user.

[0709] 8. Final price display method

[0710] The server sends the calculated final fare to the terminal and visually displays it to the user and / or delivery driver, and the terminal displays this information for the user to review.

[0711] Specific operation example

[0712] Example 1: Daytime, high density area, medium usage, high order volume

[0713] Basic fee: 500 yen

[0714] Current time: 1 PM

[0715] Population density: 1500 people / km²

[0716] Data usage: 60GB

[0717] Order quantity: 60

[0718] The server applies a daytime weighting of 1.5 based on the 1 PM time slot. Next, because the population density is 1500 people / km², it applies a high density area weighting of 1.2. Also, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the order volume is 60, it applies a high order volume weighting of 1.3. The final cost is 500 yen 1.5 1.2 1.2 1.3 = 1404 yen. The device displays this final cost to the user.

[0719] Hardware and software used

[0720] Hardware:

[0721] Server (CPU, memory, network interface)

[0722] Display devices (smartphones, etc.) used by users and delivery drivers

[0723] software:

[0724] Server program (Python, library for API requests, etc.)

[0725] Database (storing data such as population density, order volume, etc.)

[0726] Display application (for users and delivery drivers)

[0727] This system configuration will enable fair and dynamic pricing in the food delivery industry.

[0728] Prompt Sentence Examples

[0729] (prompt)

[0730] The current time is 1 PM, the population density of the delivery area is 1500 people / km², and the order volume is 60. If the basic delivery fee is 500 yen, calculate and display the corresponding final delivery fee.

[0731] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0732] Step 1:

[0733] The server sets the base fee. The input is the initial base fee set by the administrator, and this fee is stored in the server. The output is the stored base fee.

[0734] Step 2:

[0735] The server gets the current time. The input is the system clock, and the time information is retrieved. The output is the retrieved current time. This information is used to weight the time zones.

[0736] Step 3:

[0737] The server obtains the population density of the area it covers. The input is the area identification information, and the population density information is obtained from a database or real-time population data. The output is the obtained population density.

[0738] Step 4:

[0739] The server acquires the data usage of each area and store. The input is the identification information of the area or store, and the total data volume within a certain period is collected. The output is the acquired data usage.

[0740] Step 5:

[0741] The server obtains order volume by region. The input is area and store identification information, and current order volume data is collected via API, etc. The output is the obtained order volume.

[0742] Step 6:

[0743] The server calculates the weighting for each time zone based on the current time. The input is the current time, and different weighting factors are applied for daytime and nighttime. The output is the calculated time zone weighting factor.

[0744] Step 7:

[0745] The server calculates the population density weighting based on the acquired population density information. The input is the population density, and different weighting factors are applied to high-density, medium-density, and low-density areas. The output is the calculated population density weighting factor.

[0746] Step 8:

[0747] The server calculates the weighting factor based on the data usage information it has acquired. The input is the data usage, and different weighting factors are applied to heavy, medium, and light usage. The output is the calculated data usage weighting factor.

[0748] Step 9:

[0749] The server calculates the weighting by order volume based on the acquired order volume information. The input is the order volume, and different weighting coefficients are applied to high, medium, and low order volumes. The output is the calculated order volume weighting coefficient.

[0750] Step 10:

[0751] The server calculates the final fare by multiplying the base fare by each weighting factor. The input is the base fare and each weighting factor, which are multiplied together to calculate the final fare. The output is the calculated final fare.

[0752] Step 11:

[0753] The server sends the calculated final fare to the terminal. The input is the final fare, which is sent to the display terminals of the user and the delivery driver. The output is the visually displayed final fare.

[0754] 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.

[0755] The present invention combines an emotion engine with a system that dynamically adjusts data communication charges provided by mobile network operators, and aims to calculate and provide a final charge to users based on the basic charge as well as the time of day, population density, data usage, and the user's emotions.

[0756] composition

[0757] The system is implemented by a server which includes the following main means:

[0758] Basic fee setting method

[0759] The server stores the base rate set by the administrator, which is the initial rate per GB and the basis for calculating the final rate.

[0760] Time acquisition means

[0761] The server has a function for obtaining the current time. For example, the server obtains the current time using the system clock. Based on this time information, the time zone is weighted.

[0762] Population density acquisition method

[0763] The server obtains the population density of the area covered by a particular base station. This information is obtained from a pre-configured database or real-time population data.

[0764] Data usage acquisition method

[0765] The server collects the data usage handled by each base station, which is recorded as the total data volume for a certain period of time and becomes one of the factors for weighting.

[0766] Each weight calculation means

[0767] The server calculates the weighting based on the current time, population density, and data usage. The weighting by time of day applies different coefficients to daytime and nighttime, the weighting by population density applies different coefficients to high-density, medium-density, and low-density areas, and the weighting by data usage applies different coefficients to heavy, medium, and light usage.

[0768] Emotion engine and its weighting method

[0769] The server includes an emotion engine for recognizing the user's emotion, and further applies weighting based on the user's emotion acquired by the emotion engine. For example, if the user's emotion is positive, a weighting is applied to discount the fee, and if the user's emotion is negative, a weighting is applied to increase the fee.

[0770] Final fare calculation method

[0771] The server calculates the final fee by multiplying the base fee by the product of the weightings above. This final fee becomes the final fee per GB applied to the user.

[0772] Final fare display method

[0773] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[0774] Specific examples

[0775] A specific example of use is shown below.

[0776] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[0777] Base price: $10 / GB

[0778] Current time: 2 PM

[0779] Population density: 1,200 people per square kilometer

[0780] Data usage: 60GB

[0781] User Sentiment: Positive

[0782] The server applies a daytime weighting of 1.5 based on the 2:00 PM time slot. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. Furthermore, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the user's sentiment is positive, it applies a weighting of 0.9. The final cost is $10 1.5 1.2 1.2 0.9 = $19.44. The device displays this final cost to the user.

[0783] Example 2: Nighttime, low density area, light use, negative sentiment

[0784] Base price: $10 / GB

[0785] Current time: 10 PM

[0786] Population density: 300 people per square kilometer

[0787] Data usage: 20GB

[0788] User Sentiment: Negative

[0789] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Next, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. Also, because the data usage is 20 GB, it applies a light usage weighting of 1.0. Additionally, because the user's sentiment is negative, it applies a weighting of 1.1. The final cost is $10. 0.75 0.8 1.0 1.1 = $6.6. The device displays this final cost to the user.

[0790] The above describes a specific embodiment of the present invention and an example of its processing. This system dynamically adjusts data communication fees and provides a flexible fee structure that responds to usage conditions and emotions. This improves user satisfaction and enables efficient use of network resources.

[0791] The processing flow will be explained below.

[0792] Step 1:

[0793] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[0794] Step 2:

[0795] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[0796] Step 3:

[0797] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[0798] Step 4:

[0799] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[0800] Step 5:

[0801] The device activates an emotion engine to recognize the user's emotion. The emotion engine acquires the user's emotion as data using a voice recognition device or a facial expression recognition device. For example, the user's emotion is recognized as positive.

[0802] Step 6:

[0803] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[0804] Step 7:

[0805] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[0806] Step 8:

[0807] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[0808] Step 9:

[0809] The server refers to the emotion engine data based on the user's emotion and applies a separate weighting. For example, if the emotion is positive, a weighting of 0.9 is applied, and if negative, a weighting of 1.1 is applied. In this case, the user's emotion is positive, so a weighting of 0.9 is applied.

[0810] Step 10:

[0811] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, a data usage weighting of 1.2, and an emotion weighting of 0.9. The final charge is $10 1.5 1.2 1.2 0.9 = $19.44.

[0812] Step 11:

[0813] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $19.44." The user can confirm this and understand the usage charge.

[0814] This is the detailed flow of the system's processing steps, which enables dynamic pricing and provides a flexible pricing structure that responds to usage and emotions.

[0815] Example 2

[0816] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0817] Existing data communication fee setting methods are based on static standards, making it difficult to flexibly adjust fees according to actual usage and user conditions. In addition, because they do not take user emotions into consideration, they have the problem of not being able to maximize user satisfaction.

[0818] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for setting a basic fee, a means for acquiring the current time, a means for acquiring the population density of the area to be covered, a means for acquiring the amount of data usage, a means for calculating a weighting by time zone based on the current time, a means for calculating a weighting by population density based on the population density of the area to be covered, a means for calculating a weighting by data usage based on the amount of data usage, a means for recognizing the user's emotions and calculating a weighting, a means for calculating a final fee by multiplying the basic fee by the product of the weightings, and a means for displaying the final fee. This makes it possible to dynamically set data communication fees according to the usage status and the user's emotions.

[0819] The "means for setting the basic fee" is a device or system that has the function of storing the initial fee for data communication in a database.

[0820] The "means for obtaining the current time" refers to a device or system that has a function for the server to obtain the current time by referencing the system clock.

[0821] The "means for obtaining the population density of a coverage area" is a device or system that has the function of obtaining the population density within a specific area from a database or real-time data.

[0822] The "means for acquiring data usage" is a device or system that has the function of measuring the amount of data communication within a specific period and recording the value.

[0823] The "means for calculating weighting by time zone based on the current time" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired time information.

[0824] The "means for calculating weighting by population density based on the population density covered" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired population density information.

[0825] The "means for calculating weighting based on data usage amount" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired data usage amount information.

[0826] The "means for recognizing the user's emotions and calculating weighting" is a device or system that has the function of recognizing the user's emotional state using an emotion engine and calculating weighting coefficients based on that information.

[0827] The "means for calculating the final fee by multiplying the basic fee by the product of the weighting factors" refers to a device or system that has the function of multiplying the basic fee by various weighting factors to calculate the final fee.

[0828] The "means for displaying the final fare" is a device or system having the function of transmitting the calculated final fare to the terminal and visually displaying it to the user.

[0829] MODE FOR CARRYING OUT THE INVENTION

[0830] The present invention is a system for dynamically adjusting data communication charges offered by mobile network operators, which is combined with an emotion engine. The system aims to calculate and provide a final charge to users based on the base charge, time of day, population density, data usage, and user emotion.

[0831] Hardware and software used

[0832] The server is the central component of the system and contains the following main functions:

[0833] Database for which basic fees are set

[0834] The system clock from which the time is obtained

[0835] Real-time population database for obtaining population density

[0836] Log analysis tool for measuring data usage

[0837] Emotion engines (e.g., natural language processing models)

[0838] A terminal is a device with a graphical user interface (GUI) used to display the final fare to the user. Examples include smartphones and tablets.

[0839] Example of operation

[0840] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[0841] 1. The server has a base database fee of $10 / GB.

[0842] 2. The server gets the current time, "2:00 PM," from the system clock.

[0843] 3. The server obtains the population density of the base station coverage area as "1,200 people / square kilometer" from the real-time population data.

[0844] 4. The server retrieves the data usage for the past hour: "60GB."

[0845] 5. The server calculates a weighting of 1.5 for time of day (daytime), 1.2 for population density (high density area), and 1.2 for data usage (medium usage).

[0846] 6. The server runs the sentiment engine and applies a weighting of 0.9 because the user's sentiment is positive.

[0847] 7. The server calculates:

[0848] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[0849] = $10 1.5 1.2 1.2 0.9 = $19.44

[0850] 8. The server sends the final price of $19.44 to the terminal, which displays this price to the user.

[0851] Prompt Sentence Examples

[0852] Examples of prompts to be input to a generative AI model include:

[0853] "Calculate the final price based on the following conditions: base rate $10 / GB, time of day 2 PM, high density area, data usage 60 GB, and positive user sentiment."

[0854] As a result, the system of the present invention is able to dynamically set data communication charges according to the usage situation and the user's emotions, thereby improving user satisfaction and realizing efficient use of network resources.

[0855] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0856] Step 1: Set your base price

[0857] The server sets the base price in the database. It takes the base price specified by the administrator as input and stores it in the database. This input is given as "$10 / GB", the server saves this in the database and is ready to go.

[0858] Specific behavior:

[0859] The server receives the base fee value through the administrator interface.

[0860] The server establishes a connection to the database and stores the base price of $10 / GB.

[0861] Step 2: Get the current time

[0862] The server gets the current time using the system clock. This is the action where the server accesses the system clock and gets the current time. The input is the state of the internal system clock, and the output is the current time, "2:00 PM."

[0863] Specific behavior:

[0864] The server references the current time from the system clock and obtains "2:00 PM."

[0865] Step 3: Obtaining population density

[0866] The server gets the population density of the area covered by a particular base station. The input is a real-time population database, and the server accesses this database to get the required population density data. The output is "1200 people / square kilometer."

[0867] Specific behavior:

[0868] The server queries the population density database to obtain the population density of the base station coverage area.

[0869] The server obtains population density data of "1200 people per square kilometer."

[0870] Step 4: Get data usage

[0871] The server obtains the data usage at each base station. The input is the data usage information provided by the log analysis tool, and the server analyzes this information to measure the data usage. The output is "60GB."

[0872] Specific behavior:

[0873] The server measures data usage through a log analysis tool.

[0874] The server obtains the data usage for the past hour, which is "60GB."

[0875] Step 5: Calculate weights for time periods

[0876] The server calculates the time zone weighting based on the current time. The input is the current time obtained in step 2, and the output is a weighting of 1.5.

[0877] Specific behavior:

[0878] The server applies a daytime weighting of 1.5 based on the current time, 2:00 PM.

[0879] Step 6: Calculate population density weights

[0880] The server calculates the weighting based on the population density obtained. The input is the population density information obtained in step 3, and the output is a weighting of "1.2".

[0881] Specific behavior:

[0882] The server applies a weighting of 1.2 to high density areas based on a population density of 1,200 people per square kilometer.

[0883] Step 7: Calculate weighted data usage

[0884] The server calculates the weighting based on the acquired data usage. The input is the data usage information acquired in step 4, and the output is a weighting of 1.2.

[0885] Specific behavior:

[0886] The server applies a weighting of 1.2 for medium usage based on a data usage of 60GB.

[0887] Step 8: Use the Emotion Engine

[0888] The server uses the emotion engine to recognize the user's emotion and calculates the weighting based on that. The input is the user's emotion information from the emotion engine, and based on the "positive emotion," a weighting of "0.9" is applied.

[0889] Specific behavior:

[0890] The server runs an emotion engine and recognizes that the user's emotion is positive.

[0891] The server applies a weighting of 0.9 to positive sentiment.

[0892] Step 9: Calculate the final price

[0893] The server calculates the final price by multiplying the base price by each weighting. The input is the base price and each weighting, and the output is the final price of $19.44.

[0894] Specific behavior:

[0895] The server performs the following calculation:

[0896] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[0897] = $10 1.5 1.2 1.2 0.9 = $19.44

[0898] Step 10: View the final price

[0899] The server sends the calculated final fare to the terminal, which displays this information to the user. The input is the final fare information and the output is the display presented to the user.

[0900] Specific behavior:

[0901] The server sends the final price of $19.44 to the terminal.

[0902] The terminal visually displays the final price of $19.44 to the user.

[0903] (Application example 2)

[0904] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0905] Conventional fee structures have difficulty in setting flexible fees according to user emotions and usage conditions, which limits the improvement of user satisfaction and the efficient use of network resources. Furthermore, especially in electronic payment services, there is a demand for dynamic fee settings that reflect user emotions, and achieving this has been a challenge.

[0906] 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.

[0907] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a weighting by time zone based on the current time, means for calculating a weighting by population density based on the population density of the area covered, means for calculating a weighting by data usage based on the data usage, means for recognizing a user's emotion and applying a weighting based on the emotion, means for calculating a final fee by multiplying the basic fee by the product of the weightings, and means for displaying the final fee. This makes it possible to provide a flexible fee structure that reflects the user's emotion and usage status, thereby improving user satisfaction and enabling efficient use of network resources.

[0908] "Basic Charge" means the basic charge initially set for data communication or service usage.

[0909] "Current time" is information indicating the actual time obtained from the system clock or an external time server.

[0910] "Coverage density" is information that describes the number of people present in a particular area, usually expressed as people per square kilometer.

[0911] "Data Usage" means the total amount of data consumed within a particular period of time, generally measured in gigabytes (GB).

[0912] "Time of day weighting" is a correction factor applied to the charge based on the current time, and is set to a different value depending on the time of day, such as daytime or nighttime.

[0913] "Population density weighting" is a correction coefficient applied to fees based on the population density of the area covered, with different values ​​being set depending on the population density of the area.

[0914] "Weighting based on data usage" is a correction coefficient applied to the fee based on the amount of data usage, and different values ​​are set depending on the amount of usage.

[0915] The "means for recognizing a user's emotions" refers to a technology or device for determining the user's emotional state, such as emotion recognition software or a sensor.

[0916] The "means for applying weighting based on emotion" is a technique or device for calculating a correction factor to be applied to the fee based on the result of the user's emotion recognition.

[0917] The "final fee" is the final fee calculated by multiplying the basic fee by each weighting, and is the amount applied to the user.

[0918] The "means for displaying the final fare" refers to a technology or device for presenting the calculated final fare to the user, such as a smartphone screen or a notification system.

[0919] To implement this invention, the server, terminal, and user must work together, each using appropriate hardware and software to execute specific processes. The functions and processing methods of each are described in detail below.

[0920] Server Processing

[0921] The server sets the base fee and has a means to obtain the current time, the population density covered, data usage, and user sentiment. Based on this data, it calculates the weighting, calculates the final fee, and displays it. Specifically, the following hardware and software are used:

[0922] Hardware

[0923] System clock for obtaining time information

[0924] Database server for acquiring population density data

[0925] Emotion Recognition Sensor

[0926] software

[0927] Time server for obtaining time information

[0928] A database for managing population density information

[0929] Network monitoring tool to monitor data usage

[0930] Emotion recognition engine (e.g., emotion recognition API)

[0931] The server first obtains the current time using the system clock or time server and calculates weighting based on that time. Next, it obtains population density information for a specific area from a database and calculates weighting based on that population density. Next, it obtains data usage using a network monitoring tool and calculates weighting based on that usage. Finally, it recognizes the user's emotion using an emotion recognition engine and calculates weighting based on that emotion. The final charge is calculated based on these weightings and the result is sent to the terminal.

[0932] Terminal handling

[0933] The terminal is responsible for displaying the final price sent from the server to the user.

[0934] Hardware

[0935] Smartphone or tablet

[0936] display

[0937] software

[0938] Interface Applications

[0939] The terminal receives the final fare information sent from the server and visually displays it to the user using an interface application, allowing the user to check the final fare based on their own usage and emotions.

[0940] User Roles

[0941] The user is responsible for checking the information provided by the server through their device and providing emotional data as necessary. The user's emotions are collected through an emotion recognition application installed on a smartphone or tablet.

[0942] Specific examples

[0943] For example, if a user is in a high-density area at 10 AM, has moderate data usage of 1GB, and is expressing positive sentiment, the following will happen:

[0944] The basic fee is 5% (commission rate)

[0945] The current time is 10:00 AM, so a daytime weighting of 1.5 is applied

[0946] The population density is 1200 people per square kilometre, so a high density area weighting of 1.2 is applied.

[0947] Since the data usage is 1GB, a weighting of 1.0 is applied to the moderate usage

[0948] Since the user has a positive sentiment, a sentiment weighting of 0.9 is applied.

[0949] Based on these weights, the server calculates the final fare and sends it to the terminal, which displays the result to the user, who then confirms the final fare.

[0950] Prompt Sentence Examples

[0951] It works by inputting the following prompt sentence into the generative AI model:

[0952] Current time: 10:00 AM

[0953] Area population density: 1,200 people per square kilometer

[0954] Data usage: 1GB

[0955] User Sentiment: Positive

[0956] Basic fee: 5%

[0957] Prompt: Calculate the final settlement fee rate based on this situation.

[0958] This allows for flexible pricing based on user sentiment and usage.

[0959] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0960] Step 1:

[0961] The server sets an initial basic fee using the basic fee setting means. This determines the fee that will be the basis for specific data communication and service usage. This basic fee will be the basis for each weighting process that will be performed later.

[0962] Input: None

[0963] Output: Basic charge

[0964] Step 2:

[0965] The server obtains the current time from its system clock or a time server, which provides the current time information for subsequent time zone weighting calculations.

[0966] Input: None

[0967] Output: current time

[0968] Step 3:

[0969] The server retrieves population density information for the area it covers from a population density database, which allows it to understand the population density within a specific area.

[0970] Input: Region ID or location

[0971] Output: Covered population density

[0972] Step 4:

[0973] The server uses a network monitoring tool to monitor data usage over a certain period of time and acquires the data, thereby determining the current data usage.

[0974] Input: None

[0975] Output: Data usage

[0976] Step 5:

[0977] The server calculates the weighting for each time period based on the current time, for example, applying a weighting of 1.5 for daytime and 0.75 for nighttime.

[0978] Input: Current time

[0979] Output: Weighted by time period

[0980] Step 6:

[0981] The server calculates a density weight based on the population density of the area it covers: high density applies a weight of 1.2, medium density applies a weight of 1.0, and low density applies a weight of 0.8.

[0982] Input: Population density to cover

[0983] Output: Weighted by population density

[0984] Step 7:

[0985] The server calculates a weighting based on data usage, with a weighting of 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[0986] Input: Data usage

[0987] Output: Weighted by data usage

[0988] Step 8:

[0989] The server uses an emotion recognition engine to obtain the emotion data provided by the user and calculates a weighting based on the emotion: 0.9 for positive emotions, 1.0 for neutral, and 1.2 for negative emotions.

[0990] Input: User emotion data

[0991] Output: Emotion weighting

[0992] Step 9:

[0993] The server calculates the final charge by multiplying the basic charge by each weighting. Specifically, the server calculates the final charge by multiplying the basic charge by each weighting of time of day, population density, data usage, and emotion.

[0994] Inputs: Base price, Time of day weighting, Population density weighting, Data usage weighting, Sentiment weighting

[0995] Output: Final price

[0996] Step 10:

[0997] The server sends the calculated final fare to the terminal, which displays the final fare to the user using an interface application.

[0998] Input: Final Price

[0999] Output: The final price shown to the user

[1000] 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.

[1001] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1002] 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.

[1003] [Third embodiment]

[1004] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1005] 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.

[1006] 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).

[1007] 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.

[1008] 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.

[1009] 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).

[1010] 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.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] 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.

[1015] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1016] The present invention relates to a system for dynamically adjusting data communication charges for mobile network operators, which calculates and provides a final charge to users based on a basic charge and factors such as time of day, population density, and data usage.

[1017] composition

[1018] The system is implemented by a server which includes the following main means:

[1019] Basic fee setting method

[1020] The server stores the base fee set by the administrator, which is the initial fee per GB and is the basis for calculating the final fee.

[1021] Time acquisition means

[1022] The server has a function for obtaining the current time. For example, it obtains the current time using a system clock. Based on this time information, the server weights the time zones.

[1023] Population density acquisition method

[1024] The server obtains the population density of the area covered by a particular base station, which can be obtained from a pre-configured database or real-time population data.

[1025] Data usage acquisition method

[1026] The server obtains the data usage handled by each base station, which is recorded as the total data volume within a certain period and becomes one of the factors for weighting.

[1027] Each weight calculation means

[1028] The server calculates each weighting based on the current time, population density, and data usage. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage, different coefficients are applied to heavy, medium, and light usage.

[1029] Final fare calculation method

[1030] The server calculates the final fee by multiplying the base fee by the product of each of the weightings above. This final fee becomes the final fee per 1GB applied to the user.

[1031] Final fare display method

[1032] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[1033] Specific examples

[1034] A specific example of use is shown below.

[1035] Example 1: Daytime, high density area, medium usage

[1036] Base price: $10 / GB

[1037] Current time: 2 PM

[1038] Population density: 1,200 people per square kilometer

[1039] Data usage: 60GB

[1040] The server applies a daytime weighting of 1.5 based on the time of day, 2:00 PM. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. And because the data usage is 60 GB, it applies a medium-use weighting of 1.2. The final cost is $10 1.5 1.2 1.2 = $21.6. The device displays this final cost to the user.

[1041] Example 2: Nighttime, low density area, light use

[1042] Base price: $10 / GB

[1043] Current time: 10 PM

[1044] Population density: 300 people per square kilometer

[1045] Data usage: 20GB

[1046] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Then, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. And because the data usage is 20 GB, it applies a light usage weighting of 1.0. The final cost is $10 0.75 0.8 1.0 = $6. The device displays this final cost to the user.

[1047] The above describes a specific embodiment of the present invention and an example of its processing. This system enables efficient and fair data communication fee setting, thereby improving user satisfaction.

[1048] The processing flow will be explained below.

[1049] Step 1:

[1050] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[1051] Step 2:

[1052] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[1053] Step 3:

[1054] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[1055] Step 4:

[1056] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[1057] Step 5:

[1058] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[1059] Step 6:

[1060] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[1061] Step 7:

[1062] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[1063] Step 8:

[1064] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, and a data usage weighting of 1.2. The final charge is $10 1.5 1.2 1.2 = $21.6.

[1065] Step 9:

[1066] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $21.60." The user can check this and understand the usage charge.

[1067] This concludes the detailed flow of the system's processing steps, which enables dynamic pricing and provides users with fair and realistic prices.

[1068] Example 1

[1069] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1070] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variable factors such as time of day, population density, data usage, etc. In conventional technology, data communication charges are fixed, making it difficult to appropriately adjust charges in response to user behavior patterns and network load, resulting in a lack of fairness and efficiency.

[1071] 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.

[1072] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a time-of-day weighting based on the current time, means for calculating a population density weighting based on the population density of the area covered, means for calculating a data usage weighting based on the data usage, means for multiplying the basic fee by the product of the weightings, means for transmitting and displaying the final fee to the terminal, and means for taking into account the weighting coefficients set by the administrator when applying the weightings. This enables efficient and fair data communication fee setting and improves user satisfaction.

[1073] "Basic fee" refers to the initial fee per 1GB for communication services, which is the basis for calculating the final fee.

[1074] "Current time" is information indicating the current time obtained by the server using the system clock.

[1075] "Population density" is an index that indicates the number of people per unit area within the area covered by a particular base station.

[1076] "Data usage" is information indicating the total amount of data traffic handled by each base station within a certain period of time.

[1077] "Weighting" refers to a coefficient that varies depending on factors such as time of day, population density, and data usage, and is an adjustment factor used when calculating the final fee.

[1078] "Final Price" means the price per GB applied to a User calculated by multiplying the Basic Price by the product of each Weighting.

[1079] "Weighting coefficients set by the administrator" refers to the adjustment coefficients for each element (time period, population density, data usage) that are predetermined by the system administrator.

[1080] "Terminal" refers to a device that visually displays the final price sent from the server and presents it to the user.

[1081] MODE FOR CARRYING OUT THE INVENTION

[1082] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variables such as time of day, population density, data usage, etc. Specific embodiments of this system will be described below.

[1083] Setting the basic fee

[1084] The server remembers the base price set by the administrator. This base price is the initial price per GB and is the basis for calculating the final price. The base price is stored in a database and can be read, for example, using an SQL query.

[1085] Getting the current time

[1086] The server uses the system clock to get the current time, which is used in the weighting calculations later. APIs such as LocalDateTime.now() in Java or datetime.datetime.now() in Python can be used.

[1087] Obtaining population density

[1088] The server retrieves population density information for the area covered by the base station from a database or real-time population data service. For example, the population density data is retrieved using a query like "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[1089] Get data usage

[1090] The server obtains the data usage handled by each base station. This data is recorded as the total data volume for a certain period of time. It is read using an SQL query such as "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[1091] Calculating the weights

[1092] The server calculates the weighting of each element using the following procedure.

[1093] Time of Day Weighting: Based on the current time, apply different factors to daytime (e.g. 6am-6pm) and nighttime (e.g. 6pm-6am), for example 1.5 for daytime and 0.75 for nighttime.

[1094] Population density weighting: Apply a factor based on whether the area is high, medium, or low density, using 1.2 for high density areas, 1.0 for medium density areas, and 0.8 for low density areas.

[1095] Data usage weighting: Apply a factor based on heavy, medium, or light usage, using 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[1096] Final fare calculation

[1097] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The final fee is calculated using the following formula:

[1098] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[1099] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate will be $10 1.5 1.2 1.2 = $21.6.

[1100] Display of final price

[1101] The server sends the calculated final fare to the terminal, which then visually displays this information. For example, a GUI (Graphical User Interface) component is used to display the fare information on the screen of a smartphone or PC. The user can check the final fare through a web interface using HTML and CSS or through a UI component in a mobile application.

[1102] Specific examples

[1103] Example 1: Daytime, high density area, medium usage

[1104] Base price: $10 / GB

[1105] Current time: 2 PM

[1106] Population density: 1,200 people per square kilometer

[1107] Data usage: 60GB

[1108] The server applies a daytime weighting of 1.5 because it is 2 PM, a high density weighting of 1.2 because the population density is 1,200 people per square kilometer, and a medium usage weighting of 1.2 because the data usage is 60 GB. The final charge is $10 1.5 1.2 1.2 = $21.6. The device displays this final charge to the user.

[1109] Example 2: Nighttime, low density area, light use

[1110] Base price: $10 / GB

[1111] Current time: 10 PM

[1112] Population density: 300 people per square kilometer

[1113] Data usage: 20GB

[1114] The server applies a nighttime weighting of 0.75 because it is 10 PM, a low-density area weighting of 0.8 because the population density is 300 people per square kilometer, and a light usage weighting of 1.0 because the data usage is 20 GB. The final charge is $10 0.75 0.8 1.0 = $6. The device displays this final charge to the user.

[1115] Prompt Sentence Examples

[1116] "Using a system that dynamically adjusts data rates for mobile network operators, please calculate the final rate for a population density of 1,200 people per square kilometer at 2 p.m. with 60 GB of data usage."

[1117] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1118] Specific processing flow of the system program

[1119] Step 1: Get the base price

[1120] The server retrieves the base rate from the database by executing the query "SELECT base_rate FROM pricing_table WHERE id = 1" and receiving the result.

[1121] Input: Database query

[1122] Data processing: Get the base price value from the database

[1123] Output: Base rate (e.g. $10 / GB)

[1124] Step 2: Get the current time

[1125] The server obtains the current time using the system clock, for example, using the Java LocalDateTime.now() method.

[1126] Input: None

[1127] Data processing: Get the current time from the system clock

[1128] Output: Current time (e.g. 2 PM)

[1129] Step 3: Obtaining population density

[1130] The server retrieves the population density of the area covered by a particular base station, for example by running the query "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[1131] Input: Database query

[1132] Data processing: Getting population density values ​​from the database

[1133] Output: population density (e.g. 1200 people / km2)

[1134] Step 4: Get data usage

[1135] The server obtains the data usage handled by each base station. For example, it executes the query "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[1136] Input: Database query

[1137] Data processing: Retrieving data usage values ​​from the database

[1138] Output: Data usage (e.g. 60GB)

[1139] Step 5: Calculate weights by time period

[1140] The server determines the weighting of the time period based on the current time it has obtained, for example, if the current time is 2 PM, it will apply a daytime weighting of 1.5.

[1141] Input: Current time

[1142] Data processing: Determine weighting based on time period

[1143] Output: Time period weighting (e.g. 1.5)

[1144] Step 6: Calculate population density weights

[1145] The server determines the weighting factor based on the population density obtained. For example, if the population density is 1200 people per square kilometer, it applies a weighting factor of 1.2 to the high-density area.

[1146] Input: Population density

[1147] Data processing: Determine weights based on population density

[1148] Output: Population density weight (e.g. 1.2)

[1149] Step 7: Calculate weighting by data usage

[1150] The server determines the weighting factor based on the acquired data usage. For example, if the data usage is 60GB, a weighting factor of 1.2 for medium usage will be applied.

[1151] Input: Data usage

[1152] Data processing: Determine weighting based on data usage

[1153] Output: Data usage weight (e.g. 1.2)

[1154] Step 8: Calculate the final price

[1155] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The calculation is based on the following formula:

[1156] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[1157] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate is $10 1.5 1.2 1.2 = $21.6.

[1158] Input: Base rate, time of day weighting, population density weighting, data usage weighting

[1159] Data processing: Multiply each weighting factor to calculate the final price

[1160] Output: Final price (e.g. $21.6)

[1161] Step 9: View the final price

[1162] The server sends the calculated final fare to the terminal, which displays this information visually, for example, on a smartphone or PC screen using a GUI component.

[1163] Input: Final Price

[1164] Data processing: Send the final fare to the terminal and display it visually on the terminal

[1165] Output: Price displayed to user (e.g. $21.6)

[1166] Through the above specific processing steps, the user can visually check the dynamically calculated final fare.

[1167] (Application example 1)

[1168] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1169] Conventional data communication pricing systems adjust fees based on time of day, local population density, and data usage, but they do not address the dynamic adjustments required by the food delivery industry. As a result, they are unable to offer fair and optimal delivery fees to users, making it difficult to promote the use of delivery services. Furthermore, fluctuations in regional order volume make it difficult to pinpoint pricing, which can affect the efficiency of the overall service.

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

[1171] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area to be covered, means for acquiring data usage, means for calculating a weighting by time of day based on the current time, means for calculating a weighting by population density based on the population density of the area to be covered, means for calculating a weighting by data usage based on the data usage, means for multiplying the basic fee by the product of the weightings, means for acquiring order volume by region, means for calculating a weighting by order volume based on the order volume, and means for displaying the final fee. This makes it possible to dynamically calculate and display fair delivery fees according to time of day, regional population density, data usage, and order volume, even in the food delivery industry.

[1172] "Basic fee" refers to the initial setup fee for data communication and service provision.

[1173] The "means for obtaining the current time" is a function for obtaining the current time using the system clock or external time information.

[1174] The "means for obtaining the population density of the area covered" is a function for obtaining population density data of a specific area.

[1175] The "means for acquiring data usage" is a function for collecting data usage within a certain period of time.

[1176] The "means for calculating weighting by time period" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired current time.

[1177] The "means for calculating weighting based on population density" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired population density information.

[1178] The "means for calculating weighting based on data usage" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired data usage information.

[1179] The "means for calculating the final fee" is a function for calculating the final fee using the basic fee and each weighting coefficient.

[1180] The "means for obtaining order volume by region" is a function for collecting current order volume data for a specific area or store.

[1181] The "means for calculating weighting based on order volume" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired order volume information.

[1182] The "means for displaying the final fee" is a function for visually displaying the calculated final fee to the user or the service provider.

[1183] This invention relates to a system for dynamically adjusting delivery fees in the food delivery industry. The system aims to calculate and present to the user a final fee based on a base fee as well as the current time, the area's population density, data usage, and the order volume by region.

[1184] composition

[1185] The system is implemented by a server which includes the following main means:

[1186] 1. How to set a base fee

[1187] The server stores a base fee set by an administrator, which is the initial fee for the delivery service and is the basis for calculating the final fee.

[1188] 2. How to get the current time

[1189] The server uses the system clock to obtain the current time, and weights the time zones based on this time information.

[1190] 3. How to obtain the population density coverage

[1191] The server retrieves the population density of a particular area, which can be obtained from a pre-configured database or real-time population data.

[1192] 4. How to get data usage

[1193] The server collects data usage data for each store and area, which is recorded as the total data volume for a certain period of time and becomes a factor in weighting.

[1194] 5. How to obtain order volume by region

[1195] The server obtains the current order volume for a specific area or store via an API or other means.

[1196] 6. Weighting calculation methods

[1197] The server calculates each weighting based on the acquired current time, population density, data usage, and order volume. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage and order volume, different coefficients are applied to heavy, medium, and light usage.

[1198] 7. Final Fee Calculation Method

[1199] The server calculates the final fee by multiplying the base fee by the product of each of the weightings, and this final fee becomes the final fee applied to the user.

[1200] 8. Final price display method

[1201] The server sends the calculated final fare to the terminal and visually displays it to the user and / or delivery driver, and the terminal displays this information for the user to review.

[1202] Specific operation example

[1203] Example 1: Daytime, high density area, medium usage, high order volume

[1204] Basic fee: 500 yen

[1205] Current time: 1 PM

[1206] Population density: 1500 people / km²

[1207] Data usage: 60GB

[1208] Order quantity: 60

[1209] The server applies a daytime weighting of 1.5 based on the 1 PM time slot. Next, because the population density is 1500 people / km², it applies a high density area weighting of 1.2. Also, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the order volume is 60, it applies a high order volume weighting of 1.3. The final cost is 500 yen 1.5 1.2 1.2 1.3 = 1404 yen. The device displays this final cost to the user.

[1210] Hardware and software used

[1211] Hardware:

[1212] Server (CPU, memory, network interface)

[1213] Display devices (smartphones, etc.) used by users and delivery drivers

[1214] software:

[1215] Server program (Python, library for API requests, etc.)

[1216] Database (storing data such as population density, order volume, etc.)

[1217] Display application (for users and delivery drivers)

[1218] This system configuration will enable fair and dynamic pricing in the food delivery industry.

[1219] Prompt Sentence Examples

[1220] (prompt)

[1221] The current time is 1 PM, the population density of the delivery area is 1500 people / km², and the order volume is 60. If the basic delivery fee is 500 yen, calculate and display the corresponding final delivery fee.

[1222] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1223] Step 1:

[1224] The server sets the base fee. The input is the initial base fee set by the administrator, and this fee is stored in the server. The output is the stored base fee.

[1225] Step 2:

[1226] The server gets the current time. The input is the system clock, and the time information is retrieved. The output is the retrieved current time. This information is used to weight the time zones.

[1227] Step 3:

[1228] The server obtains the population density of the area it covers. The input is the area identification information, and the population density information is obtained from a database or real-time population data. The output is the obtained population density.

[1229] Step 4:

[1230] The server acquires the data usage of each area and store. The input is the identification information of the area or store, and the total data volume within a certain period is collected. The output is the acquired data usage.

[1231] Step 5:

[1232] The server obtains order volume by region. The input is area and store identification information, and current order volume data is collected via API, etc. The output is the obtained order volume.

[1233] Step 6:

[1234] The server calculates the weighting for each time zone based on the current time. The input is the current time, and different weighting factors are applied for daytime and nighttime. The output is the calculated time zone weighting factor.

[1235] Step 7:

[1236] The server calculates the population density weighting based on the acquired population density information. The input is the population density, and different weighting factors are applied to high-density, medium-density, and low-density areas. The output is the calculated population density weighting factor.

[1237] Step 8:

[1238] The server calculates the weighting factor based on the data usage information it has acquired. The input is the data usage, and different weighting factors are applied to heavy, medium, and light usage. The output is the calculated data usage weighting factor.

[1239] Step 9:

[1240] The server calculates the weighting by order volume based on the acquired order volume information. The input is the order volume, and different weighting coefficients are applied to high, medium, and low order volumes. The output is the calculated order volume weighting coefficient.

[1241] Step 10:

[1242] The server calculates the final fare by multiplying the base fare by each weighting factor. The input is the base fare and each weighting factor, which are multiplied together to calculate the final fare. The output is the calculated final fare.

[1243] Step 11:

[1244] The server sends the calculated final fare to the terminal. The input is the final fare, which is sent to the display terminals of the user and the delivery driver. The output is the visually displayed final fare.

[1245] 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.

[1246] The present invention combines an emotion engine with a system that dynamically adjusts data communication charges provided by mobile network operators, and aims to calculate and provide a final charge to users based on the basic charge as well as the time of day, population density, data usage, and the user's emotions.

[1247] composition

[1248] The system is implemented by a server which includes the following main means:

[1249] Basic fee setting method

[1250] The server stores the base rate set by the administrator, which is the initial rate per GB and the basis for calculating the final rate.

[1251] Time acquisition means

[1252] The server has a function for obtaining the current time. For example, the server obtains the current time using the system clock. Based on this time information, the time zone is weighted.

[1253] Population density acquisition method

[1254] The server obtains the population density of the area covered by a particular base station. This information is obtained from a pre-configured database or real-time population data.

[1255] Data usage acquisition method

[1256] The server collects the data usage handled by each base station, which is recorded as the total data volume for a certain period of time and becomes one of the factors for weighting.

[1257] Each weight calculation means

[1258] The server calculates the weighting based on the current time, population density, and data usage. The weighting by time of day applies different coefficients to daytime and nighttime, the weighting by population density applies different coefficients to high-density, medium-density, and low-density areas, and the weighting by data usage applies different coefficients to heavy, medium, and light usage.

[1259] Emotion engine and its weighting method

[1260] The server includes an emotion engine for recognizing the user's emotion, and further applies weighting based on the user's emotion acquired by the emotion engine. For example, if the user's emotion is positive, a weighting is applied to discount the fee, and if the user's emotion is negative, a weighting is applied to increase the fee.

[1261] Final fare calculation method

[1262] The server calculates the final fee by multiplying the base fee by the product of the weightings above. This final fee becomes the final fee per GB applied to the user.

[1263] Final fare display method

[1264] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[1265] Specific examples

[1266] A specific example of use is shown below.

[1267] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[1268] Base price: $10 / GB

[1269] Current time: 2 PM

[1270] Population density: 1,200 people per square kilometer

[1271] Data usage: 60GB

[1272] User Sentiment: Positive

[1273] The server applies a daytime weighting of 1.5 based on the 2:00 PM time slot. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. Furthermore, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the user's sentiment is positive, it applies a weighting of 0.9. The final cost is $10 1.5 1.2 1.2 0.9 = $19.44. The device displays this final cost to the user.

[1274] Example 2: Nighttime, low density area, light use, negative sentiment

[1275] Base price: $10 / GB

[1276] Current time: 10 PM

[1277] Population density: 300 people per square kilometer

[1278] Data usage: 20GB

[1279] User Sentiment: Negative

[1280] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Next, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. Also, because the data usage is 20 GB, it applies a light usage weighting of 1.0. Additionally, because the user's sentiment is negative, it applies a weighting of 1.1. The final cost is $10. 0.75 0.8 1.0 1.1 = $6.6. The device displays this final cost to the user.

[1281] The above describes a specific embodiment of the present invention and an example of its processing. This system dynamically adjusts data communication fees and provides a flexible fee structure that responds to usage conditions and emotions. This improves user satisfaction and enables efficient use of network resources.

[1282] The processing flow will be explained below.

[1283] Step 1:

[1284] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[1285] Step 2:

[1286] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[1287] Step 3:

[1288] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[1289] Step 4:

[1290] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[1291] Step 5:

[1292] The device activates an emotion engine to recognize the user's emotion. The emotion engine acquires the user's emotion as data using a voice recognition device or a facial expression recognition device. For example, the user's emotion is recognized as positive.

[1293] Step 6:

[1294] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[1295] Step 7:

[1296] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[1297] Step 8:

[1298] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[1299] Step 9:

[1300] The server refers to the emotion engine data based on the user's emotion and applies a separate weighting. For example, if the emotion is positive, a weighting of 0.9 is applied, and if negative, a weighting of 1.1 is applied. In this case, the user's emotion is positive, so a weighting of 0.9 is applied.

[1301] Step 10:

[1302] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, a data usage weighting of 1.2, and an emotion weighting of 0.9. The final charge is $10 1.5 1.2 1.2 0.9 = $19.44.

[1303] Step 11:

[1304] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $19.44." The user can confirm this and understand the usage charge.

[1305] This is the detailed flow of the system's processing steps, which enables dynamic pricing and provides a flexible pricing structure that responds to usage and emotions.

[1306] Example 2

[1307] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1308] Existing data communication fee setting methods are based on static standards, making it difficult to flexibly adjust fees according to actual usage and user conditions. In addition, because they do not take user emotions into consideration, they have the problem of not being able to maximize user satisfaction.

[1309] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for setting a basic fee, a means for acquiring the current time, a means for acquiring the population density of the area to be covered, a means for acquiring the amount of data usage, a means for calculating a weighting by time zone based on the current time, a means for calculating a weighting by population density based on the population density of the area to be covered, a means for calculating a weighting by data usage based on the amount of data usage, a means for recognizing the user's emotions and calculating a weighting, a means for calculating a final fee by multiplying the basic fee by the product of the weightings, and a means for displaying the final fee. This makes it possible to dynamically set data communication fees according to the usage status and the user's emotions.

[1310] The "means for setting the basic fee" is a device or system that has the function of storing the initial fee for data communication in a database.

[1311] The "means for obtaining the current time" refers to a device or system that has a function for the server to obtain the current time by referencing the system clock.

[1312] The "means for obtaining the population density of a coverage area" is a device or system that has the function of obtaining the population density within a specific area from a database or real-time data.

[1313] The "means for acquiring data usage" is a device or system that has the function of measuring the amount of data communication within a specific period and recording the value.

[1314] The "means for calculating weighting by time zone based on the current time" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired time information.

[1315] The "means for calculating weighting by population density based on the population density covered" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired population density information.

[1316] The "means for calculating weighting based on data usage amount" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired data usage amount information.

[1317] The "means for recognizing the user's emotions and calculating weighting" is a device or system that has the function of recognizing the user's emotional state using an emotion engine and calculating weighting coefficients based on that information.

[1318] The "means for calculating the final fee by multiplying the basic fee by the product of the weighting factors" refers to a device or system that has the function of multiplying the basic fee by various weighting factors to calculate the final fee.

[1319] The "means for displaying the final fare" is a device or system having the function of transmitting the calculated final fare to the terminal and visually displaying it to the user.

[1320] MODE FOR CARRYING OUT THE INVENTION

[1321] The present invention is a system for dynamically adjusting data communication charges offered by mobile network operators, which is combined with an emotion engine. The system aims to calculate and provide a final charge to users based on the base charge, time of day, population density, data usage, and user emotion.

[1322] Hardware and software used

[1323] The server is the central component of the system and contains the following main functions:

[1324] Database for which basic fees are set

[1325] The system clock from which the time is obtained

[1326] Real-time population database for obtaining population density

[1327] Log analysis tool for measuring data usage

[1328] Emotion engines (e.g., natural language processing models)

[1329] A terminal is a device with a graphical user interface (GUI) used to display the final fare to the user. Examples include smartphones and tablets.

[1330] Example of operation

[1331] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[1332] 1. The server has a base database fee of $10 / GB.

[1333] 2. The server gets the current time, "2:00 PM," from the system clock.

[1334] 3. The server obtains the population density of the base station coverage area as "1,200 people / square kilometer" from the real-time population data.

[1335] 4. The server retrieves the data usage for the past hour: "60GB."

[1336] 5. The server calculates a weighting of 1.5 for time of day (daytime), 1.2 for population density (high density area), and 1.2 for data usage (medium usage).

[1337] 6. The server runs the sentiment engine and applies a weighting of 0.9 because the user's sentiment is positive.

[1338] 7. The server calculates:

[1339] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[1340] = $10 1.5 1.2 1.2 0.9 = $19.44

[1341] 8. The server sends the final price of $19.44 to the terminal, which displays this price to the user.

[1342] Prompt Sentence Examples

[1343] Examples of prompts to be input to a generative AI model include:

[1344] "Calculate the final price based on the following conditions: base rate $10 / GB, time of day 2 PM, high density area, data usage 60 GB, and positive user sentiment."

[1345] As a result, the system of the present invention is able to dynamically set data communication charges according to the usage situation and the user's emotions, thereby improving user satisfaction and realizing efficient use of network resources.

[1346] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1347] Step 1: Set your base price

[1348] The server sets the base price in the database. It takes the base price specified by the administrator as input and stores it in the database. This input is given as "$10 / GB", the server saves this in the database and is ready to go.

[1349] Specific behavior:

[1350] The server receives the base fee value through the administrator interface.

[1351] The server establishes a connection to the database and stores the base price of $10 / GB.

[1352] Step 2: Get the current time

[1353] The server gets the current time using the system clock. This is the action where the server accesses the system clock and gets the current time. The input is the state of the internal system clock, and the output is the current time, "2:00 PM."

[1354] Specific behavior:

[1355] The server references the current time from the system clock and obtains "2:00 PM."

[1356] Step 3: Obtaining population density

[1357] The server gets the population density of the area covered by a particular base station. The input is a real-time population database, and the server accesses this database to get the required population density data. The output is "1200 people / square kilometer."

[1358] Specific behavior:

[1359] The server queries the population density database to obtain the population density of the base station coverage area.

[1360] The server obtains population density data of "1200 people per square kilometer."

[1361] Step 4: Get data usage

[1362] The server obtains the data usage at each base station. The input is the data usage information provided by the log analysis tool, and the server analyzes this information to measure the data usage. The output is "60GB."

[1363] Specific behavior:

[1364] The server measures data usage through a log analysis tool.

[1365] The server obtains the data usage for the past hour, which is "60GB."

[1366] Step 5: Calculate weights for time periods

[1367] The server calculates the time zone weighting based on the current time. The input is the current time obtained in step 2, and the output is a weighting of 1.5.

[1368] Specific behavior:

[1369] The server applies a daytime weighting of 1.5 based on the current time, 2:00 PM.

[1370] Step 6: Calculate population density weights

[1371] The server calculates the weighting based on the population density obtained. The input is the population density information obtained in step 3, and the output is a weighting of "1.2".

[1372] Specific behavior:

[1373] The server applies a weighting of 1.2 to high density areas based on a population density of 1,200 people per square kilometer.

[1374] Step 7: Calculate weighted data usage

[1375] The server calculates the weighting based on the acquired data usage. The input is the data usage information acquired in step 4, and the output is a weighting of 1.2.

[1376] Specific behavior:

[1377] The server applies a weighting of 1.2 for medium usage based on a data usage of 60GB.

[1378] Step 8: Use the Emotion Engine

[1379] The server uses the emotion engine to recognize the user's emotion and calculates the weighting based on that. The input is the user's emotion information from the emotion engine, and based on the "positive emotion," a weighting of "0.9" is applied.

[1380] Specific behavior:

[1381] The server runs an emotion engine and recognizes that the user's emotion is positive.

[1382] The server applies a weighting of 0.9 to positive sentiment.

[1383] Step 9: Calculate the final price

[1384] The server calculates the final price by multiplying the base price by each weighting. The input is the base price and each weighting, and the output is the final price of $19.44.

[1385] Specific behavior:

[1386] The server performs the following calculation:

[1387] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[1388] = $10 1.5 1.2 1.2 0.9 = $19.44

[1389] Step 10: View the final price

[1390] The server sends the calculated final fare to the terminal, which displays this information to the user. The input is the final fare information and the output is the display presented to the user.

[1391] Specific behavior:

[1392] The server sends the final price of $19.44 to the terminal.

[1393] The terminal visually displays the final price of $19.44 to the user.

[1394] (Application example 2)

[1395] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1396] Conventional fee structures have difficulty in setting flexible fees according to user emotions and usage conditions, which limits the improvement of user satisfaction and the efficient use of network resources. Furthermore, especially in electronic payment services, there is a demand for dynamic fee settings that reflect user emotions, and achieving this has been a challenge.

[1397] 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.

[1398] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a weighting by time zone based on the current time, means for calculating a weighting by population density based on the population density of the area covered, means for calculating a weighting by data usage based on the data usage, means for recognizing a user's emotion and applying a weighting based on the emotion, means for calculating a final fee by multiplying the basic fee by the product of the weightings, and means for displaying the final fee. This makes it possible to provide a flexible fee structure that reflects the user's emotion and usage status, thereby improving user satisfaction and enabling efficient use of network resources.

[1399] "Basic Charge" means the basic charge initially set for data communication or service usage.

[1400] "Current time" is information indicating the actual time obtained from the system clock or an external time server.

[1401] "Coverage density" is information that describes the number of people present in a particular area, usually expressed as people per square kilometer.

[1402] "Data Usage" means the total amount of data consumed within a particular period of time, generally measured in gigabytes (GB).

[1403] "Time of day weighting" is a correction factor applied to the charge based on the current time, and is set to a different value depending on the time of day, such as daytime or nighttime.

[1404] "Population density weighting" is a correction coefficient applied to fees based on the population density of the area covered, with different values ​​being set depending on the population density of the area.

[1405] "Weighting based on data usage" is a correction coefficient applied to the fee based on the amount of data usage, and different values ​​are set depending on the amount of usage.

[1406] The "means for recognizing a user's emotions" refers to a technology or device for determining the user's emotional state, such as emotion recognition software or a sensor.

[1407] The "means for applying weighting based on emotion" is a technique or device for calculating a correction factor to be applied to the fee based on the result of the user's emotion recognition.

[1408] The "final fee" is the final fee calculated by multiplying the basic fee by each weighting, and is the amount applied to the user.

[1409] The "means for displaying the final fare" refers to a technology or device for presenting the calculated final fare to the user, such as a smartphone screen or a notification system.

[1410] To implement this invention, the server, terminal, and user must work together, each using appropriate hardware and software to execute specific processes. The functions and processing methods of each are described in detail below.

[1411] Server Processing

[1412] The server sets the base fee and has a means to obtain the current time, the population density covered, data usage, and user sentiment. Based on this data, it calculates the weighting, calculates the final fee, and displays it. Specifically, the following hardware and software are used:

[1413] Hardware

[1414] System clock for obtaining time information

[1415] Database server for acquiring population density data

[1416] Emotion Recognition Sensor

[1417] software

[1418] Time server for obtaining time information

[1419] A database for managing population density information

[1420] Network monitoring tool to monitor data usage

[1421] Emotion recognition engine (e.g., emotion recognition API)

[1422] The server first obtains the current time using the system clock or time server and calculates weighting based on that time. Next, it obtains population density information for a specific area from a database and calculates weighting based on that population density. Next, it obtains data usage using a network monitoring tool and calculates weighting based on that usage. Finally, it recognizes the user's emotion using an emotion recognition engine and calculates weighting based on that emotion. The final charge is calculated based on these weightings and the result is sent to the terminal.

[1423] Terminal handling

[1424] The terminal is responsible for displaying the final price sent from the server to the user.

[1425] Hardware

[1426] Smartphone or tablet

[1427] display

[1428] software

[1429] Interface Applications

[1430] The terminal receives the final fare information sent from the server and visually displays it to the user using an interface application, allowing the user to check the final fare based on their own usage and emotions.

[1431] User Roles

[1432] The user is responsible for checking the information provided by the server through their device and providing emotional data as necessary. The user's emotions are collected through an emotion recognition application installed on a smartphone or tablet.

[1433] Specific examples

[1434] For example, if a user is in a high-density area at 10 AM, has moderate data usage of 1GB, and is expressing positive sentiment, the following will happen:

[1435] The basic fee is 5% (commission rate)

[1436] The current time is 10:00 AM, so a daytime weighting of 1.5 is applied

[1437] The population density is 1200 people per square kilometre, so a high density area weighting of 1.2 is applied.

[1438] Since the data usage is 1GB, a weighting of 1.0 is applied to the moderate usage

[1439] Since the user has a positive sentiment, a sentiment weighting of 0.9 is applied.

[1440] Based on these weights, the server calculates the final fare and sends it to the terminal, which displays the result to the user, who then confirms the final fare.

[1441] Prompt Sentence Examples

[1442] It works by inputting the following prompt sentence into the generative AI model:

[1443] Current time: 10:00 AM

[1444] Area population density: 1,200 people per square kilometer

[1445] Data usage: 1GB

[1446] User Sentiment: Positive

[1447] Basic fee: 5%

[1448] Prompt: Calculate the final settlement fee rate based on this situation.

[1449] This allows for flexible pricing based on user sentiment and usage.

[1450] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1451] Step 1:

[1452] The server sets an initial basic fee using the basic fee setting means. This determines the fee that will be the basis for specific data communication and service usage. This basic fee will be the basis for each weighting process that will be performed later.

[1453] Input: None

[1454] Output: Basic charge

[1455] Step 2:

[1456] The server obtains the current time from its system clock or a time server, which provides the current time information for subsequent time zone weighting calculations.

[1457] Input: None

[1458] Output: current time

[1459] Step 3:

[1460] The server retrieves population density information for the area it covers from a population density database, which allows it to understand the population density within a specific area.

[1461] Input: Region ID or location

[1462] Output: Covered population density

[1463] Step 4:

[1464] The server uses a network monitoring tool to monitor data usage over a certain period of time and acquires the data, thereby determining the current data usage.

[1465] Input: None

[1466] Output: Data usage

[1467] Step 5:

[1468] The server calculates the weighting for each time period based on the current time, for example, applying a weighting of 1.5 for daytime and 0.75 for nighttime.

[1469] Input: Current time

[1470] Output: Weighted by time period

[1471] Step 6:

[1472] The server calculates a density weight based on the population density of the area it covers: high density applies a weight of 1.2, medium density applies a weight of 1.0, and low density applies a weight of 0.8.

[1473] Input: Population density to cover

[1474] Output: Weighted by population density

[1475] Step 7:

[1476] The server calculates a weighting based on data usage, with a weighting of 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[1477] Input: Data usage

[1478] Output: Weighted by data usage

[1479] Step 8:

[1480] The server uses an emotion recognition engine to obtain the emotion data provided by the user and calculates a weighting based on the emotion: 0.9 for positive emotions, 1.0 for neutral, and 1.2 for negative emotions.

[1481] Input: User emotion data

[1482] Output: Emotion weighting

[1483] Step 9:

[1484] The server calculates the final charge by multiplying the basic charge by each weighting. Specifically, the server calculates the final charge by multiplying the basic charge by each weighting of time of day, population density, data usage, and emotion.

[1485] Inputs: Base price, Time of day weighting, Population density weighting, Data usage weighting, Sentiment weighting

[1486] Output: Final price

[1487] Step 10:

[1488] The server sends the calculated final fare to the terminal, which displays the final fare to the user using an interface application.

[1489] Input: Final Price

[1490] Output: The final price shown to the user

[1491] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1492] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1493] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1494] [Fourth embodiment]

[1495] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1496] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1497] 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).

[1498] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1499] 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.

[1500] 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).

[1501] 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.

[1502] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1503] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1504] 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.

[1505] 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.

[1506] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1507] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1508] The present invention relates to a system for dynamically adjusting data communication charges for mobile network operators, which calculates and provides a final charge to users based on a basic charge and factors such as time of day, population density, and data usage.

[1509] composition

[1510] The system is implemented by a server which includes the following main means:

[1511] Basic fee setting method

[1512] The server stores the base fee set by the administrator, which is the initial fee per GB and is the basis for calculating the final fee.

[1513] Time acquisition means

[1514] The server has a function for obtaining the current time. For example, it obtains the current time using a system clock. Based on this time information, the server weights the time zones.

[1515] Population density acquisition method

[1516] The server obtains the population density of the area covered by a particular base station, which can be obtained from a pre-configured database or real-time population data.

[1517] Data usage acquisition method

[1518] The server obtains the data usage handled by each base station, which is recorded as the total data volume within a certain period and becomes one of the factors for weighting.

[1519] Each weight calculation means

[1520] The server calculates each weighting based on the current time, population density, and data usage. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage, different coefficients are applied to heavy, medium, and light usage.

[1521] Final fare calculation method

[1522] The server calculates the final fee by multiplying the base fee by the product of each of the weightings above. This final fee becomes the final fee per 1GB applied to the user.

[1523] Final fare display method

[1524] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[1525] Specific examples

[1526] A specific example of use is shown below.

[1527] Example 1: Daytime, high density area, medium usage

[1528] Base price: $10 / GB

[1529] Current time: 2 PM

[1530] Population density: 1,200 people per square kilometer

[1531] Data usage: 60GB

[1532] The server applies a daytime weighting of 1.5 based on the time of day, 2:00 PM. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. And because the data usage is 60 GB, it applies a medium-use weighting of 1.2. The final cost is $10 1.5 1.2 1.2 = $21.6. The device displays this final cost to the user.

[1533] Example 2: Nighttime, low density area, light use

[1534] Base price: $10 / GB

[1535] Current time: 10 PM

[1536] Population density: 300 people per square kilometer

[1537] Data usage: 20GB

[1538] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Then, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. And because the data usage is 20 GB, it applies a light usage weighting of 1.0. The final cost is $10 0.75 0.8 1.0 = $6. The device displays this final cost to the user.

[1539] The above describes a specific embodiment of the present invention and an example of its processing. This system enables efficient and fair data communication fee setting, thereby improving user satisfaction.

[1540] The processing flow will be explained below.

[1541] Step 1:

[1542] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[1543] Step 2:

[1544] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[1545] Step 3:

[1546] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[1547] Step 4:

[1548] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[1549] Step 5:

[1550] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[1551] Step 6:

[1552] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[1553] Step 7:

[1554] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[1555] Step 8:

[1556] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, and a data usage weighting of 1.2. The final charge is $10 1.5 1.2 1.2 = $21.6.

[1557] Step 9:

[1558] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $21.60." The user can check this and understand the usage charge.

[1559] This concludes the detailed flow of the system's processing steps, which enables dynamic pricing and provides users with fair and realistic prices.

[1560] Example 1

[1561] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1562] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variable factors such as time of day, population density, data usage, etc. In conventional technology, data communication charges are fixed, making it difficult to appropriately adjust charges in response to user behavior patterns and network load, resulting in a lack of fairness and efficiency.

[1563] 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.

[1564] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a time-of-day weighting based on the current time, means for calculating a population density weighting based on the population density of the area covered, means for calculating a data usage weighting based on the data usage, means for multiplying the basic fee by the product of the weightings, means for transmitting and displaying the final fee to the terminal, and means for taking into account the weighting coefficients set by the administrator when applying the weightings. This enables efficient and fair data communication fee setting and improves user satisfaction.

[1565] "Basic fee" refers to the initial fee per 1GB for communication services, which is the basis for calculating the final fee.

[1566] "Current time" is information indicating the current time obtained by the server using the system clock.

[1567] "Population density" is an index that indicates the number of people per unit area within the area covered by a particular base station.

[1568] "Data usage" is information indicating the total amount of data traffic handled by each base station within a certain period of time.

[1569] "Weighting" refers to a coefficient that varies depending on factors such as time of day, population density, and data usage, and is an adjustment factor used when calculating the final fee.

[1570] "Final Price" means the price per GB applied to a User calculated by multiplying the Basic Price by the product of each Weighting.

[1571] "Weighting coefficients set by the administrator" refers to the adjustment coefficients for each element (time period, population density, data usage) that are predetermined by the system administrator.

[1572] "Terminal" refers to a device that visually displays the final price sent from the server and presents it to the user.

[1573] MODE FOR CARRYING OUT THE INVENTION

[1574] The present invention relates to a system for dynamically adjusting communication charges for data communication services provided by mobile network operators in response to variables such as time of day, population density, data usage, etc. Specific embodiments of this system will be described below.

[1575] Setting the basic fee

[1576] The server remembers the base price set by the administrator. This base price is the initial price per GB and is the basis for calculating the final price. The base price is stored in a database and can be read, for example, using an SQL query.

[1577] Getting the current time

[1578] The server uses the system clock to get the current time, which is used in the weighting calculations later. APIs such as LocalDateTime.now() in Java or datetime.datetime.now() in Python can be used.

[1579] Obtaining population density

[1580] The server retrieves population density information for the area covered by the base station from a database or real-time population data service. For example, the population density data is retrieved using a query like "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[1581] Get data usage

[1582] The server obtains the data usage handled by each base station. This data is recorded as the total data volume for a certain period of time. It is read using an SQL query such as "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[1583] Calculating the weights

[1584] The server calculates the weighting of each element using the following procedure.

[1585] Time of Day Weighting: Based on the current time, apply different factors to daytime (e.g. 6am-6pm) and nighttime (e.g. 6pm-6am), for example 1.5 for daytime and 0.75 for nighttime.

[1586] Population density weighting: Apply a factor based on whether the area is high, medium, or low density, using 1.2 for high density areas, 1.0 for medium density areas, and 0.8 for low density areas.

[1587] Data usage weighting: Apply a factor based on heavy, medium, or light usage, using 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[1588] Final fare calculation

[1589] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The final fee is calculated using the following formula:

[1590] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[1591] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate will be $10 1.5 1.2 1.2 = $21.6.

[1592] Display of final price

[1593] The server sends the calculated final fare to the terminal, which then visually displays this information. For example, a GUI (Graphical User Interface) component is used to display the fare information on the screen of a smartphone or PC. The user can check the final fare through a web interface using HTML and CSS or through a UI component in a mobile application.

[1594] Specific examples

[1595] Example 1: Daytime, high density area, medium usage

[1596] Base price: $10 / GB

[1597] Current time: 2 PM

[1598] Population density: 1,200 people per square kilometer

[1599] Data usage: 60GB

[1600] The server applies a daytime weighting of 1.5 because it is 2 PM, a high density weighting of 1.2 because the population density is 1,200 people per square kilometer, and a medium usage weighting of 1.2 because the data usage is 60 GB. The final charge is $10 1.5 1.2 1.2 = $21.6. The device displays this final charge to the user.

[1601] Example 2: Nighttime, low density area, light use

[1602] Base price: $10 / GB

[1603] Current time: 10 PM

[1604] Population density: 300 people per square kilometer

[1605] Data usage: 20GB

[1606] The server applies a nighttime weighting of 0.75 because it is 10 PM, a low-density area weighting of 0.8 because the population density is 300 people per square kilometer, and a light usage weighting of 1.0 because the data usage is 20 GB. The final charge is $10 0.75 0.8 1.0 = $6. The device displays this final charge to the user.

[1607] Prompt Sentence Examples

[1608] "Using a system that dynamically adjusts data rates for mobile network operators, please calculate the final rate for a population density of 1,200 people per square kilometer at 2 p.m. with 60 GB of data usage."

[1609] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1610] Specific processing flow of the system program

[1611] Step 1: Get the base price

[1612] The server retrieves the base rate from the database by executing the query "SELECT base_rate FROM pricing_table WHERE id = 1" and receiving the result.

[1613] Input: Database query

[1614] Data processing: Get the base price value from the database

[1615] Output: Base rate (e.g. $10 / GB)

[1616] Step 2: Get the current time

[1617] The server obtains the current time using the system clock, for example, using the Java LocalDateTime.now() method.

[1618] Input: None

[1619] Data processing: Get the current time from the system clock

[1620] Output: Current time (e.g. 2 PM)

[1621] Step 3: Obtaining population density

[1622] The server retrieves the population density of the area covered by a particular base station, for example by running the query "SELECT population_density FROM area_data WHERE base_station_id = 'BS001'".

[1623] Input: Database query

[1624] Data processing: Getting population density values ​​from the database

[1625] Output: population density (e.g. 1200 people / km2)

[1626] Step 4: Get data usage

[1627] The server obtains the data usage handled by each base station. For example, it executes the query "SELECT SUM(data_usage) FROM usage_logs WHERE base_station_id = 'BS001' AND timestamp > '2023-01-01'".

[1628] Input: Database query

[1629] Data processing: Retrieving data usage values ​​from the database

[1630] Output: Data usage (e.g. 60GB)

[1631] Step 5: Calculate weights by time period

[1632] The server determines the weighting of the time period based on the current time it has obtained, for example, if the current time is 2 PM, it will apply a daytime weighting of 1.5.

[1633] Input: Current time

[1634] Data processing: Determine weighting based on time period

[1635] Output: Time period weighting (e.g. 1.5)

[1636] Step 6: Calculate population density weights

[1637] The server determines the weighting factor based on the population density obtained. For example, if the population density is 1200 people per square kilometer, it applies a weighting factor of 1.2 to the high-density area.

[1638] Input: Population density

[1639] Data processing: Determine weights based on population density

[1640] Output: Population density weight (e.g. 1.2)

[1641] Step 7: Calculate weighting by data usage

[1642] The server determines the weighting factor based on the acquired data usage. For example, if the data usage is 60GB, a weighting factor of 1.2 for medium usage will be applied.

[1643] Input: Data usage

[1644] Data processing: Determine weighting based on data usage

[1645] Output: Data usage weight (e.g. 1.2)

[1646] Step 8: Calculate the final price

[1647] The server multiplies the base fee by the product of each weighting factor to calculate the final fee. The calculation is based on the following formula:

[1648] Final charge = Basic charge Time of day weighting Population density weighting Data usage weighting

[1649] For example, if the base rate is $10, the time of day weighting is 1.5, the population density weighting is 1.2, and the data usage weighting is 1.2, the final rate is $10 1.5 1.2 1.2 = $21.6.

[1650] Input: Base rate, time of day weighting, population density weighting, data usage weighting

[1651] Data processing: Multiply each weighting factor to calculate the final price

[1652] Output: Final price (e.g. $21.6)

[1653] Step 9: View the final price

[1654] The server sends the calculated final fare to the terminal, which displays this information visually, for example, on a smartphone or PC screen using a GUI component.

[1655] Input: Final Price

[1656] Data processing: Send the final fare to the terminal and display it visually on the terminal

[1657] Output: Price displayed to user (e.g. $21.6)

[1658] Through the above specific processing steps, the user can visually check the dynamically calculated final fare.

[1659] (Application example 1)

[1660] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1661] Conventional data communication pricing systems adjust fees based on time of day, local population density, and data usage, but they do not address the dynamic adjustments required by the food delivery industry. As a result, they are unable to offer fair and optimal delivery fees to users, making it difficult to promote the use of delivery services. Furthermore, fluctuations in regional order volume make it difficult to pinpoint pricing, which can affect the efficiency of the overall service.

[1662] 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.

[1663] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area to be covered, means for acquiring data usage, means for calculating a weighting by time of day based on the current time, means for calculating a weighting by population density based on the population density of the area to be covered, means for calculating a weighting by data usage based on the data usage, means for multiplying the basic fee by the product of the weightings, means for acquiring order volume by region, means for calculating a weighting by order volume based on the order volume, and means for displaying the final fee. This makes it possible to dynamically calculate and display fair delivery fees according to time of day, regional population density, data usage, and order volume, even in the food delivery industry.

[1664] "Basic fee" refers to the initial setup fee for data communication and service provision.

[1665] The "means for obtaining the current time" is a function for obtaining the current time using the system clock or external time information.

[1666] The "means for obtaining the population density of the area covered" is a function for obtaining population density data of a specific area.

[1667] The "means for acquiring data usage" is a function for collecting data usage within a certain period of time.

[1668] The "means for calculating weighting by time period" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired current time.

[1669] The "means for calculating weighting based on population density" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired population density information.

[1670] The "means for calculating weighting based on data usage" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired data usage information.

[1671] The "means for calculating the final fee" is a function for calculating the final fee using the basic fee and each weighting coefficient.

[1672] The "means for obtaining order volume by region" is a function for collecting current order volume data for a specific area or store.

[1673] The "means for calculating weighting based on order volume" is a function for calculating a weighting coefficient to be applied to the fee based on the acquired order volume information.

[1674] The "means for displaying the final fee" is a function for visually displaying the calculated final fee to the user or the service provider.

[1675] This invention relates to a system for dynamically adjusting delivery fees in the food delivery industry. The system aims to calculate and present to the user a final fee based on a base fee as well as the current time, the area's population density, data usage, and the order volume by region.

[1676] composition

[1677] The system is implemented by a server which includes the following main means:

[1678] 1. How to set a base fee

[1679] The server stores a base fee set by an administrator, which is the initial fee for the delivery service and is the basis for calculating the final fee.

[1680] 2. How to get the current time

[1681] The server uses the system clock to obtain the current time, and weights the time zones based on this time information.

[1682] 3. How to obtain the population density coverage

[1683] The server retrieves the population density of a particular area, which can be obtained from a pre-configured database or real-time population data.

[1684] 4. How to get data usage

[1685] The server collects data usage data for each store and area, which is recorded as the total data volume for a certain period of time and becomes a factor in weighting.

[1686] 5. How to obtain order volume by region

[1687] The server obtains the current order volume for a specific area or store via an API or other means.

[1688] 6. Weighting calculation methods

[1689] The server calculates each weighting based on the acquired current time, population density, data usage, and order volume. For weighting by time of day, different coefficients are applied to daytime and nighttime. For weighting by population density, different coefficients are applied to high-density, medium-density, and low-density areas. For weighting by data usage and order volume, different coefficients are applied to heavy, medium, and light usage.

[1690] 7. Final Fee Calculation Method

[1691] The server calculates the final fee by multiplying the base fee by the product of each of the weightings, and this final fee becomes the final fee applied to the user.

[1692] 8. Final price display method

[1693] The server sends the calculated final fare to the terminal and visually displays it to the user and / or delivery driver, and the terminal displays this information for the user to review.

[1694] Specific operation example

[1695] Example 1: Daytime, high density area, medium usage, high order volume

[1696] Basic fee: 500 yen

[1697] Current time: 1 PM

[1698] Population density: 1500 people / km²

[1699] Data usage: 60GB

[1700] Order quantity: 60

[1701] The server applies a daytime weighting of 1.5 based on the 1 PM time slot. Next, because the population density is 1500 people / km², it applies a high density area weighting of 1.2. Also, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the order volume is 60, it applies a high order volume weighting of 1.3. The final cost is 500 yen 1.5 1.2 1.2 1.3 = 1404 yen. The device displays this final cost to the user.

[1702] Hardware and software used

[1703] Hardware:

[1704] Server (CPU, memory, network interface)

[1705] Display devices (smartphones, etc.) used by users and delivery drivers

[1706] software:

[1707] Server program (Python, library for API requests, etc.)

[1708] Database (storing data such as population density, order volume, etc.)

[1709] Display application (for users and delivery drivers)

[1710] This system configuration will enable fair and dynamic pricing in the food delivery industry.

[1711] Prompt Sentence Examples

[1712] (prompt)

[1713] The current time is 1 PM, the population density of the delivery area is 1500 people / km², and the order volume is 60. If the basic delivery fee is 500 yen, calculate and display the corresponding final delivery fee.

[1714] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1715] Step 1:

[1716] The server sets the base fee. The input is the initial base fee set by the administrator, and this fee is stored in the server. The output is the stored base fee.

[1717] Step 2:

[1718] The server gets the current time. The input is the system clock, and the time information is retrieved. The output is the retrieved current time. This information is used to weight the time zones.

[1719] Step 3:

[1720] The server obtains the population density of the area it covers. The input is the area identification information, and the population density information is obtained from a database or real-time population data. The output is the obtained population density.

[1721] Step 4:

[1722] The server acquires the data usage of each area and store. The input is the identification information of the area or store, and the total data volume within a certain period is collected. The output is the acquired data usage.

[1723] Step 5:

[1724] The server obtains order volume by region. The input is area and store identification information, and current order volume data is collected via API, etc. The output is the obtained order volume.

[1725] Step 6:

[1726] The server calculates the weighting for each time zone based on the current time. The input is the current time, and different weighting factors are applied for daytime and nighttime. The output is the calculated time zone weighting factor.

[1727] Step 7:

[1728] The server calculates the population density weighting based on the acquired population density information. The input is the population density, and different weighting factors are applied to high-density, medium-density, and low-density areas. The output is the calculated population density weighting factor.

[1729] Step 8:

[1730] The server calculates the weighting factor based on the data usage information it has acquired. The input is the data usage, and different weighting factors are applied to heavy, medium, and light usage. The output is the calculated data usage weighting factor.

[1731] Step 9:

[1732] The server calculates the weighting by order volume based on the acquired order volume information. The input is the order volume, and different weighting coefficients are applied to high, medium, and low order volumes. The output is the calculated order volume weighting coefficient.

[1733] Step 10:

[1734] The server calculates the final fare by multiplying the base fare by each weighting factor. The input is the base fare and each weighting factor, which are multiplied together to calculate the final fare. The output is the calculated final fare.

[1735] Step 11:

[1736] The server sends the calculated final fare to the terminal. The input is the final fare, which is sent to the display terminals of the user and the delivery driver. The output is the visually displayed final fare.

[1737] 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.

[1738] The present invention combines an emotion engine with a system that dynamically adjusts data communication charges provided by mobile network operators, and aims to calculate and provide a final charge to users based on the basic charge as well as the time of day, population density, data usage, and the user's emotions.

[1739] composition

[1740] The system is implemented by a server which includes the following main means:

[1741] Basic fee setting method

[1742] The server stores the base rate set by the administrator, which is the initial rate per GB and the basis for calculating the final rate.

[1743] Time acquisition means

[1744] The server has a function for obtaining the current time. For example, the server obtains the current time using the system clock. Based on this time information, the time zone is weighted.

[1745] Population density acquisition method

[1746] The server obtains the population density of the area covered by a particular base station. This information is obtained from a pre-configured database or real-time population data.

[1747] Data usage acquisition method

[1748] The server collects the data usage handled by each base station, which is recorded as the total data volume for a certain period of time and becomes one of the factors for weighting.

[1749] Each weight calculation means

[1750] The server calculates the weighting based on the current time, population density, and data usage. The weighting by time of day applies different coefficients to daytime and nighttime, the weighting by population density applies different coefficients to high-density, medium-density, and low-density areas, and the weighting by data usage applies different coefficients to heavy, medium, and light usage.

[1751] Emotion engine and its weighting method

[1752] The server includes an emotion engine for recognizing the user's emotion, and further applies weighting based on the user's emotion acquired by the emotion engine. For example, if the user's emotion is positive, a weighting is applied to discount the fee, and if the user's emotion is negative, a weighting is applied to increase the fee.

[1753] Final fare calculation method

[1754] The server calculates the final fee by multiplying the base fee by the product of the weightings above. This final fee becomes the final fee per GB applied to the user.

[1755] Final fare display method

[1756] The server sends the calculated final fare to the terminal and visually displays it to the user, who then displays this information for review.

[1757] Specific examples

[1758] A specific example of use is shown below.

[1759] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[1760] Base price: $10 / GB

[1761] Current time: 2 PM

[1762] Population density: 1,200 people per square kilometer

[1763] Data usage: 60GB

[1764] User Sentiment: Positive

[1765] The server applies a daytime weighting of 1.5 based on the 2:00 PM time slot. Then, because the population density is 1,200 people per square kilometer, it applies a high-density area weighting of 1.2. Furthermore, because the data usage is 60 GB, it applies a medium usage weighting of 1.2. Furthermore, because the user's sentiment is positive, it applies a weighting of 0.9. The final cost is $10 1.5 1.2 1.2 0.9 = $19.44. The device displays this final cost to the user.

[1766] Example 2: Nighttime, low density area, light use, negative sentiment

[1767] Base price: $10 / GB

[1768] Current time: 10 PM

[1769] Population density: 300 people per square kilometer

[1770] Data usage: 20GB

[1771] User Sentiment: Negative

[1772] The server applies a nighttime weighting of 0.75 based on the 10 PM time slot. Next, because the population density is 300 people per square kilometer, it applies a low-density area weighting of 0.8. Also, because the data usage is 20 GB, it applies a light usage weighting of 1.0. Additionally, because the user's sentiment is negative, it applies a weighting of 1.1. The final cost is $10. 0.75 0.8 1.0 1.1 = $6.6. The device displays this final cost to the user.

[1773] The above describes a specific embodiment of the present invention and an example of its processing. This system dynamically adjusts data communication fees and provides a flexible fee structure that responds to usage conditions and emotions. This improves user satisfaction and enables efficient use of network resources.

[1774] The processing flow will be explained below.

[1775] Step 1:

[1776] The server stores the base price set by the administrator. For example, the base price is set to $10 per GB. This base price is the basis for calculating the final price.

[1777] Step 2:

[1778] The device obtains the current time using the system clock. For example, the current time may be 2:00 PM. This time information is used in a later step to calculate the weights for the time periods.

[1779] Step 3:

[1780] The server obtains information about the population density of the area it covers. This information can be obtained from a pre-defined database or real-time population data. For example, the server obtains that the population density is 1200 people per square kilometer.

[1781] Step 4:

[1782] The server obtains the data usage handled by the base station. This is recorded as the total data volume for a certain period (e.g., an hour or a day). For example, the server recognizes that the data usage is 60 GB.

[1783] Step 5:

[1784] The device activates an emotion engine to recognize the user's emotion. The emotion engine acquires the user's emotion as data using a voice recognition device or a facial expression recognition device. For example, the user's emotion is recognized as positive.

[1785] Step 6:

[1786] The server calculates the weighting for each time period based on the current time. Specifically, if the current time is between 6:00 AM and 6:00 PM, a daytime weighting of 1.5 is applied, and if the current time is between 6:00 PM and 6:00 AM, a nighttime weighting of 0.75 is applied. In this case, since it is 2:00 PM, a weighting of 1.5 is applied.

[1787] Step 7:

[1788] The server calculates a weighting based on the population density of the area it covers. For example, if the population density is over 1000 people per square kilometer, it is considered a high density area and a weighting of 1.2 is applied. If the population density is 1200 people per square kilometer, a weighting of 1.2 is applied.

[1789] Step 8:

[1790] The server calculates a weighting based on the amount of data used at that base station. For example, if data usage exceeds 50GB, it is considered medium usage and a weighting of 1.2 is applied. If data usage is 60GB, a weighting of 1.2 is applied.

[1791] Step 9:

[1792] The server refers to the emotion engine data based on the user's emotion and applies a separate weighting. For example, if the emotion is positive, a weighting of 0.9 is applied, and if negative, a weighting of 1.1 is applied. In this case, the user's emotion is positive, so a weighting of 0.9 is applied.

[1793] Step 10:

[1794] The server calculates the final charge by multiplying the base charge by each weighting. Specifically, the base charge of $10 is multiplied by a time-of-day weighting of 1.5, a population density weighting of 1.2, a data usage weighting of 1.2, and an emotion weighting of 0.9. The final charge is $10 1.5 1.2 1.2 0.9 = $19.44.

[1795] Step 11:

[1796] After calculating the final charge, the server sends the information to the terminal. The terminal receives this information and visually displays the final charge to the user. Specifically, it displays "The final charge per GB is $19.44." The user can confirm this and understand the usage charge.

[1797] This is the detailed flow of the system's processing steps, which enables dynamic pricing and provides a flexible pricing structure that responds to usage and emotions.

[1798] Example 2

[1799] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1800] Existing data communication fee setting methods are based on static standards, making it difficult to flexibly adjust fees according to actual usage and user conditions. In addition, because they do not take user emotions into consideration, they have the problem of not being able to maximize user satisfaction.

[1801] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for setting a basic fee, a means for acquiring the current time, a means for acquiring the population density of the area to be covered, a means for acquiring the amount of data usage, a means for calculating a weighting by time zone based on the current time, a means for calculating a weighting by population density based on the population density of the area to be covered, a means for calculating a weighting by data usage based on the amount of data usage, a means for recognizing the user's emotions and calculating a weighting, a means for calculating a final fee by multiplying the basic fee by the product of the weightings, and a means for displaying the final fee. This makes it possible to dynamically set data communication fees according to the usage status and the user's emotions.

[1802] The "means for setting the basic fee" is a device or system that has the function of storing the initial fee for data communication in a database.

[1803] The "means for obtaining the current time" refers to a device or system that has a function for the server to obtain the current time by referencing the system clock.

[1804] The "means for obtaining the population density of a coverage area" is a device or system that has the function of obtaining the population density within a specific area from a database or real-time data.

[1805] The "means for acquiring data usage" is a device or system that has the function of measuring the amount of data communication within a specific period and recording the value.

[1806] The "means for calculating weighting by time zone based on the current time" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired time information.

[1807] The "means for calculating weighting by population density based on the population density covered" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired population density information.

[1808] The "means for calculating weighting based on data usage amount" is a device or system having a function for calculating an appropriate weighting coefficient based on the acquired data usage amount information.

[1809] The "means for recognizing the user's emotions and calculating weighting" is a device or system that has the function of recognizing the user's emotional state using an emotion engine and calculating weighting coefficients based on that information.

[1810] The "means for calculating the final fee by multiplying the basic fee by the product of the weighting factors" refers to a device or system that has the function of multiplying the basic fee by various weighting factors to calculate the final fee.

[1811] The "means for displaying the final fare" is a device or system having the function of transmitting the calculated final fare to the terminal and visually displaying it to the user.

[1812] MODE FOR CARRYING OUT THE INVENTION

[1813] The present invention is a system for dynamically adjusting data communication charges offered by mobile network operators, which is combined with an emotion engine. The system aims to calculate and provide a final charge to users based on the base charge, time of day, population density, data usage, and user emotion.

[1814] Hardware and software used

[1815] The server is the central component of the system and contains the following main functions:

[1816] Database for which basic fees are set

[1817] The system clock from which the time is obtained

[1818] Real-time population database for obtaining population density

[1819] Log analysis tool for measuring data usage

[1820] Emotion engines (e.g., natural language processing models)

[1821] A terminal is a device with a graphical user interface (GUI) used to display the final fare to the user. Examples include smartphones and tablets.

[1822] Example of operation

[1823] Example 1: Daytime, High-Density Area, Medium Usage, Positive Sentiment

[1824] 1. The server has a base database fee of $10 / GB.

[1825] 2. The server gets the current time, "2:00 PM," from the system clock.

[1826] 3. The server obtains the population density of the base station coverage area as "1,200 people / square kilometer" from the real-time population data.

[1827] 4. The server retrieves the data usage for the past hour: "60GB."

[1828] 5. The server calculates a weighting of 1.5 for time of day (daytime), 1.2 for population density (high density area), and 1.2 for data usage (medium usage).

[1829] 6. The server runs the sentiment engine and applies a weighting of 0.9 because the user's sentiment is positive.

[1830] 7. The server calculates:

[1831] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[1832] = $10 1.5 1.2 1.2 0.9 = $19.44

[1833] 8. The server sends the final price of $19.44 to the terminal, which displays this price to the user.

[1834] Prompt Sentence Examples

[1835] Examples of prompts to be input to a generative AI model include:

[1836] "Calculate the final price based on the following conditions: base rate $10 / GB, time of day 2 PM, high density area, data usage 60 GB, and positive user sentiment."

[1837] As a result, the system of the present invention is able to dynamically set data communication charges according to the usage situation and the user's emotions, thereby improving user satisfaction and realizing efficient use of network resources.

[1838] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1839] Step 1: Set your base price

[1840] The server sets the base price in the database. It takes the base price specified by the administrator as input and stores it in the database. This input is given as "$10 / GB", the server saves this in the database and is ready to go.

[1841] Specific behavior:

[1842] The server receives the base fee value through the administrator interface.

[1843] The server establishes a connection to the database and stores the base price of $10 / GB.

[1844] Step 2: Get the current time

[1845] The server gets the current time using the system clock. This is the action where the server accesses the system clock and gets the current time. The input is the state of the internal system clock, and the output is the current time, "2:00 PM."

[1846] Specific behavior:

[1847] The server references the current time from the system clock and obtains "2:00 PM."

[1848] Step 3: Obtaining population density

[1849] The server gets the population density of the area covered by a particular base station. The input is a real-time population database, and the server accesses this database to get the required population density data. The output is "1200 people / square kilometer."

[1850] Specific behavior:

[1851] The server queries the population density database to obtain the population density of the base station coverage area.

[1852] The server obtains population density data of "1200 people per square kilometer."

[1853] Step 4: Get data usage

[1854] The server obtains the data usage at each base station. The input is the data usage information provided by the log analysis tool, and the server analyzes this information to measure the data usage. The output is "60GB."

[1855] Specific behavior:

[1856] The server measures data usage through a log analysis tool.

[1857] The server obtains the data usage for the past hour, which is "60GB."

[1858] Step 5: Calculate weights for time periods

[1859] The server calculates the time zone weighting based on the current time. The input is the current time obtained in step 2, and the output is a weighting of 1.5.

[1860] Specific behavior:

[1861] The server applies a daytime weighting of 1.5 based on the current time, 2:00 PM.

[1862] Step 6: Calculate population density weights

[1863] The server calculates the weighting based on the population density obtained. The input is the population density information obtained in step 3, and the output is a weighting of "1.2".

[1864] Specific behavior:

[1865] The server applies a weighting of 1.2 to high density areas based on a population density of 1,200 people per square kilometer.

[1866] Step 7: Calculate weighted data usage

[1867] The server calculates the weighting based on the acquired data usage. The input is the data usage information acquired in step 4, and the output is a weighting of 1.2.

[1868] Specific behavior:

[1869] The server applies a weighting of 1.2 for medium usage based on a data usage of 60GB.

[1870] Step 8: Use the Emotion Engine

[1871] The server uses the emotion engine to recognize the user's emotion and calculates the weighting based on that. The input is the user's emotion information from the emotion engine, and based on the "positive emotion," a weighting of "0.9" is applied.

[1872] Specific behavior:

[1873] The server runs an emotion engine and recognizes that the user's emotion is positive.

[1874] The server applies a weighting of 0.9 to positive sentiment.

[1875] Step 9: Calculate the final price

[1876] The server calculates the final price by multiplying the base price by each weighting. The input is the base price and each weighting, and the output is the final price of $19.44.

[1877] Specific behavior:

[1878] The server performs the following calculation:

[1879] Final price = Base price Time of day weighting Population density weighting Data usage weighting Emotion weighting

[1880] = $10 1.5 1.2 1.2 0.9 = $19.44

[1881] Step 10: View the final price

[1882] The server sends the calculated final fare to the terminal, which displays this information to the user. The input is the final fare information and the output is the display presented to the user.

[1883] Specific behavior:

[1884] The server sends the final price of $19.44 to the terminal.

[1885] The terminal visually displays the final price of $19.44 to the user.

[1886] (Application example 2)

[1887] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1888] Conventional fee structures have difficulty in setting flexible fees according to user emotions and usage conditions, which limits the improvement of user satisfaction and the efficient use of network resources. Furthermore, especially in electronic payment services, there is a demand for dynamic fee settings that reflect user emotions, and achieving this has been a challenge.

[1889] 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.

[1890] In this invention, the server includes means for setting a basic fee, means for acquiring the current time, means for acquiring the population density of the area covered, means for acquiring data usage, means for calculating a weighting by time zone based on the current time, means for calculating a weighting by population density based on the population density of the area covered, means for calculating a weighting by data usage based on the data usage, means for recognizing a user's emotion and applying a weighting based on the emotion, means for calculating a final fee by multiplying the basic fee by the product of the weightings, and means for displaying the final fee. This makes it possible to provide a flexible fee structure that reflects the user's emotion and usage status, thereby improving user satisfaction and enabling efficient use of network resources.

[1891] "Basic Charge" means the basic charge initially set for data communication or service usage.

[1892] "Current time" is information indicating the actual time obtained from the system clock or an external time server.

[1893] "Coverage density" is information that describes the number of people present in a particular area, usually expressed as people per square kilometer.

[1894] "Data Usage" means the total amount of data consumed within a particular period of time, generally measured in gigabytes (GB).

[1895] "Time of day weighting" is a correction factor applied to the charge based on the current time, and is set to a different value depending on the time of day, such as daytime or nighttime.

[1896] "Population density weighting" is a correction coefficient applied to fees based on the population density of the area covered, with different values ​​being set depending on the population density of the area.

[1897] "Weighting based on data usage" is a correction coefficient applied to the fee based on the amount of data usage, and different values ​​are set depending on the amount of usage.

[1898] The "means for recognizing a user's emotions" refers to a technology or device for determining the user's emotional state, such as emotion recognition software or a sensor.

[1899] The "means for applying weighting based on emotion" is a technique or device for calculating a correction factor to be applied to the fee based on the result of the user's emotion recognition.

[1900] The "final fee" is the final fee calculated by multiplying the basic fee by each weighting, and is the amount applied to the user.

[1901] The "means for displaying the final fare" refers to a technology or device for presenting the calculated final fare to the user, such as a smartphone screen or a notification system.

[1902] To implement this invention, the server, terminal, and user must work together, each using appropriate hardware and software to execute specific processes. The functions and processing methods of each are described in detail below.

[1903] Server Processing

[1904] The server sets the base fee and has a means to obtain the current time, the population density covered, data usage, and user sentiment. Based on this data, it calculates the weighting, calculates the final fee, and displays it. Specifically, the following hardware and software are used:

[1905] Hardware

[1906] System clock for obtaining time information

[1907] Database server for acquiring population density data

[1908] Emotion Recognition Sensor

[1909] software

[1910] Time server for obtaining time information

[1911] A database for managing population density information

[1912] Network monitoring tool to monitor data usage

[1913] Emotion recognition engine (e.g., emotion recognition API)

[1914] The server first obtains the current time using the system clock or time server and calculates weighting based on that time. Next, it obtains population density information for a specific area from a database and calculates weighting based on that population density. Next, it obtains data usage using a network monitoring tool and calculates weighting based on that usage. Finally, it recognizes the user's emotion using an emotion recognition engine and calculates weighting based on that emotion. The final charge is calculated based on these weightings and the result is sent to the terminal.

[1915] Terminal handling

[1916] The terminal is responsible for displaying the final price sent from the server to the user.

[1917] Hardware

[1918] Smartphone or tablet

[1919] display

[1920] software

[1921] Interface Applications

[1922] The terminal receives the final fare information sent from the server and visually displays it to the user using an interface application, allowing the user to check the final fare based on their own usage and emotions.

[1923] User Roles

[1924] The user is responsible for checking the information provided by the server through their device and providing emotional data as necessary. The user's emotions are collected through an emotion recognition application installed on a smartphone or tablet.

[1925] Specific examples

[1926] For example, if a user is in a high-density area at 10 AM, has moderate data usage of 1GB, and is expressing positive sentiment, the following will happen:

[1927] The basic fee is 5% (commission rate)

[1928] The current time is 10:00 AM, so a daytime weighting of 1.5 is applied

[1929] The population density is 1200 people per square kilometre, so a high density area weighting of 1.2 is applied.

[1930] Since the data usage is 1GB, a weighting of 1.0 is applied to the moderate usage

[1931] Since the user has a positive sentiment, a sentiment weighting of 0.9 is applied.

[1932] Based on these weights, the server calculates the final fare and sends it to the terminal, which displays the result to the user, who then confirms the final fare.

[1933] Prompt Sentence Examples

[1934] It works by inputting the following prompt sentence into the generative AI model:

[1935] Current time: 10:00 AM

[1936] Area population density: 1,200 people per square kilometer

[1937] Data usage: 1GB

[1938] User Sentiment: Positive

[1939] Basic fee: 5%

[1940] Prompt: Calculate the final settlement fee rate based on this situation.

[1941] This allows for flexible pricing based on user sentiment and usage.

[1942] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1943] Step 1:

[1944] The server sets an initial basic fee using the basic fee setting means. This determines the fee that will be the basis for specific data communication and service usage. This basic fee will be the basis for each weighting process that will be performed later.

[1945] Input: None

[1946] Output: Basic charge

[1947] Step 2:

[1948] The server obtains the current time from its system clock or a time server, which provides the current time information for subsequent time zone weighting calculations.

[1949] Input: None

[1950] Output: current time

[1951] Step 3:

[1952] The server retrieves population density information for the area it covers from a population density database, which allows it to understand the population density within a specific area.

[1953] Input: Region ID or location

[1954] Output: Covered population density

[1955] Step 4:

[1956] The server uses a network monitoring tool to monitor data usage over a certain period of time and acquires the data, thereby determining the current data usage.

[1957] Input: None

[1958] Output: Data usage

[1959] Step 5:

[1960] The server calculates the weighting for each time period based on the current time, for example, applying a weighting of 1.5 for daytime and 0.75 for nighttime.

[1961] Input: Current time

[1962] Output: Weighted by time period

[1963] Step 6:

[1964] The server calculates a density weight based on the population density of the area it covers: high density applies a weight of 1.2, medium density applies a weight of 1.0, and low density applies a weight of 0.8.

[1965] Input: Population density to cover

[1966] Output: Weighted by population density

[1967] Step 7:

[1968] The server calculates a weighting based on data usage, with a weighting of 1.2 for heavy usage, 1.0 for medium usage, and 0.8 for light usage.

[1969] Input: Data usage

[1970] Output: Weighted by data usage

[1971] Step 8:

[1972] The server uses an emotion recognition engine to obtain the emotion data provided by the user and calculates a weighting based on the emotion: 0.9 for positive emotions, 1.0 for neutral, and 1.2 for negative emotions.

[1973] Input: User emotion data

[1974] Output: Emotion weighting

[1975] Step 9:

[1976] The server calculates the final charge by multiplying the basic charge by each weighting. Specifically, the server calculates the final charge by multiplying the basic charge by each weighting of time of day, population density, data usage, and emotion.

[1977] Inputs: Base price, Time of day weighting, Population density weighting, Data usage weighting, Sentiment weighting

[1978] Output: Final price

[1979] Step 10:

[1980] The server sends the calculated final fare to the terminal, which displays the final fare to the user using an interface application.

[1981] Input: Final Price

[1982] Output: The final price shown to the user

[1983] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1984] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1985] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1986] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1987] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1988] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1989] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1990] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1991] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1992] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1993] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1994] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1995] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1996] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1997] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1998] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1999] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2000] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[2001] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[2002] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[2003] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2004] The following is further disclosed regarding the above embodiment.

[2005] (Claim 1)

[2006] a means for setting a base fee;

[2007] A means of obtaining the current time;

[2008] A means of obtaining the population density to be covered;

[2009] A means for obtaining data usage;

[2010] means for calculating time zone weights based on the current time;

[2011] means for calculating a population density weight based on the population density covered;

[2012] means for calculating a data usage weight based on the data usage;

[2013] means for calculating a final fee by multiplying the basic fee by the product of each of the weightings;

[2014] a means for displaying the final price;

[2015] A system including:

[2016] (Claim 2)

[2017] 10. The system of claim 1, further comprising means for determining whether a current time is within a specified range, and applying a daytime weighting if the time period is daytime, and applying a nighttime weighting if the time period is nighttime.

[2018] (Claim 3)

[2019] 10. The system of claim 1, further comprising means for applying a high density area weighting when population density is above a predetermined threshold and applying a low density area weighting when population density is below the predetermined threshold.

[2020] "Example 1"

[2021] (Claim 1)

[2022] a means for setting a base fee;

[2023] A means of obtaining the current time;

[2024] A means of obtaining the population density to be covered;

[2025] A means for obtaining data usage;

[2026] means for calculating time zone weights based on the current time;

[2027] means for calculating a population density weight based on the population density covered;

[2028] means for calculating a data usage weight based on the data usage;

[2029] means for calculating a final fee by multiplying the basic fee by the product of each of the weightings;

[2030] means for transmitting and displaying the final fare to the terminal;

[2031] means for taking into account the weighting factors set by the administrator when applying each weighting;

[2032] A system including:

[2033] (Claim 2)

[2034] 10. The system of claim 1, further comprising means for determining whether a current time is within a specified range, and applying a daytime weighting if the time period is daytime, and applying a nighttime weighting if the time period is nighttime.

[2035] (Claim 3)

[2036] 10. The system of claim 1, further comprising means for applying a high density area weighting when population density is above a predetermined threshold and applying a low density area weighting when population density is below the predetermined threshold.

[2037] "Application Example 1"

[2038] (Claim 1)

[2039] a means for setting a base fee;

[2040] A means of obtaining the current time;

[2041] A means of obtaining the population density to be covered;

[2042] A means for obtaining data usage;

[2043] means for calculating time zone weights based on the current time;

[2044] means for calculating a population density weight based on the population density covered;

[2045] means for calculating a data usage weight based on the data usage;

[2046] means for calculating a final fee by multiplying the basic fee by the product of each of the weightings;

[2047] A means for obtaining order volume by region;

[2048] means for calculating an order volume weight based on the order volume;

[2049] a means for displaying the final price;

[2050] A system including:

[2051] (Claim 2)

[2052] 10. The system of claim 1, further comprising means for determining whether a current time is within a specified range, and applying a daytime weighting if the time period is daytime, and applying a nighttime weighting if the time period is nighttime.

[2053] (Claim 3)

[2054] 10. The system of claim 1, further comprising means for applying a high density area weighting when population density is above a predetermined threshold and applying a low density area weighting when population density is below the predetermined threshold.

[2055] "Example 2: Combining Emotion Engines"

[2056] (Claim 1)

[2057] a means for setting a base fee;

[2058] A means of obtaining the current time;

[2059] A means of obtaining the population density to be covered;

[2060] A means for obtaining data usage;

[2061] means for calculating time zone weights based on the current time;

[2062] means for calculating a population density weight based on the population density covered;

[2063] means for calculating a data usage weight based on the data usage;

[2064] means for recognizing a user's emotions and calculating weights;

[2065] means for calculating a final fee by multiplying the basic fee by the product of each of the weightings;

[2066] a means for displaying the final price;

[2067] An information processing system including:

[2068] (Claim 2)

[2069] 2. The information processing system of claim 1, further comprising means for determining whether a current time is within a specified range, and applying a daytime weighting if the time period is daytime, and applying a nighttime weighting if the time period is nighttime.

[2070] (Claim 3)

[2071] 10. The information processing system of claim 1, further comprising means for applying a weighting for high density areas when population density is above a predetermined threshold and applying a weighting for low density areas when population density is below the predetermined threshold.

[2072] "Application example 2 when combining emotion engines"

[2073] (Claim 1)

[2074] a means for setting a base fee;

[2075] A means of obtaining the current time;

[2076] A means of obtaining the population density to be covered;

[2077] A means for obtaining data usage;

[2078] means for calculating time zone weights based on the current time;

[2079] means for calculating a population density weight based on the population density covered;

[2080] means for calculating a data usage weight based on the data usage;

[2081] means for recognizing a user's emotion and applying a weighting based on the emotion;

[2082] means for calculating a final fee by multiplying the basic fee by the product of each of the weightings;

[2083] a means for displaying the final price;

[2084] A system including:

[2085] (Claim 2)

[2086] 10. The system of claim 1, further comprising means for determining whether a current time is within a specified range, and applying a daytime weighting if the time period is daytime, and applying a nighttime weighting if the time period is nighttime.

[2087] (Claim 3)

[2088] 10. The system of claim 1, further comprising means for applying a high density area weighting when population density is above a predetermined threshold and applying a low density area weighting when population density is below the predetermined threshold. [Explanation of symbols]

[2089] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for setting a base fee; A means of obtaining the current time; A means of obtaining the population density to be covered; A means for obtaining data usage; means for calculating time zone weights based on the current time; means for calculating a population density weight based on the population density covered; means for calculating a data usage weight based on the data usage; means for calculating a final fee by multiplying the basic fee by the product of each of the weightings; a means for displaying the final price; A system including:

2. 2. The system of claim 1, further comprising means for determining whether a current time is within a specified range and applying a daytime weighting if the time period is daytime and applying a nighttime weighting if the time period is nighttime.

3. The system of claim 1 , further comprising means for applying a high density area weighting when population density is above a predetermined threshold and applying a low density area weighting when population density is below the predetermined threshold.

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