system

Abstract

The system according to the embodiment aims to enable easy payment of parking fees through app payment. [Solution] A system according to an embodiment includes an acquisition unit, a calculation unit, and a payment unit. The acquisition unit acquires parking lot information. The calculation unit calculates a parking fee based on the information acquired by the acquisition unit. The payment unit pays the fee calculated by the calculation unit.

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 technology relies on cash payment for parking fees, leaving room for improvement in terms of convenience.

[0005] The system according to the embodiment aims to enable easy payment of parking fees through app payment. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a calculation unit, and a payment unit. The acquisition unit acquires information about parking lots. The calculation unit calculates parking fees based on the information acquired by the acquisition unit. The payment unit pays the fees calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows parking fees to be easily paid through app payment. [Brief explanation of the drawings]

[0008] [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. DETAILED DESCRIPTION OF THE INVENTION

[0009] 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.

[0010] First, the terms used in the following description will be explained.

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

[0012] 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.

[0013] 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.

[0014] 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), and Bluetooth (registered trademark).

[0015] 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."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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).

[0019] 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.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. 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 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. 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.

[0022] 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.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A parking lot payment system according to an embodiment of the present invention acquires parking lot information, calculates parking fees, and makes payments. In this parking lot payment system, a user inputs parking lot information using a dedicated app when parking their vehicle in a parking lot. The parking fee is calculated through the app when the parking time expires, and the user makes the payment within the app. For example, when a user parks their vehicle in a parking lot, the user inputs information such as the parking lot number and parking start time using the dedicated app. This information is managed within the app. Next, when the parking time expires, the parking fee is calculated through the app. The app automatically calculates the fee based on the parking start and end times. For example, if an hourly rate is set, the fee is calculated based on the parking time. The user makes the payment within the app. For example, payment can be made using a credit card or electronic money. This eliminates the need to carry cash and enables smooth payment. This system makes parking lots more convenient. Users no longer need to carry cash and can easily make payments through the app. Furthermore, this system reduces cash handling and simplifies management for parking lot managers. This parking lot payment system eliminates the need for users to carry cash and enables smooth payment.

[0029] A parking payment system according to an embodiment includes an acquisition unit, a calculation unit, and a settlement unit. The acquisition unit acquires information about the parking lot. The parking lot information includes, but is not limited to, the location, availability, and fee structure of the parking lot. For example, the acquisition unit can acquire the parking lot information by scanning a two-dimensional code (e.g., a QR code (registered trademark)) installed in the parking lot. The acquisition unit can also acquire the parking lot information using a sensor or camera in the parking lot. For example, the acquisition unit can acquire the availability of parking spaces in real time and suggest the optimal parking space to the user. The calculation unit calculates the parking fee based on the information acquired by the acquisition unit. The parking fee is automatically calculated based on, for example, the start and end times of parking, but is not limited to, an example. For example, the calculation unit can automatically apply a discount rate according to a specific time period or day of the week. The calculation unit can also apply benefits or discounts based on the user's membership status. The settlement unit settles the fee calculated by the calculation unit. Payment is made using, for example, a credit card or electronic money, but is not limited to, an example. For example, the settlement unit can encrypt and securely manage payment information. The payment unit can also estimate the user's emotions and suggest payment methods based on the estimated emotions. This allows the parking lot payment system according to the embodiment to perform an integrated process from obtaining parking lot information to calculating fees and making payments.

[0030] The acquisition unit can acquire parking lot information by scanning a two-dimensional code installed in the parking lot. The two-dimensional code is installed, for example, at the entrance to the parking lot or in each parking space, but is not limited to such an example. The acquisition unit can, for example, scan the two-dimensional code using a smartphone camera to acquire parking lot information. The acquisition unit can also read the two-dimensional code using a dedicated scanner. For example, the acquisition unit can scan a two-dimensional code installed at the entrance to the parking lot to acquire the location and availability of the parking lot. The acquisition unit can also scan a two-dimensional code installed in each parking space to acquire the availability of the parking space. In this way, parking lot information can be acquired quickly and accurately by scanning the two-dimensional code. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the results of scanning the two-dimensional code into the generation AI and have the generation AI analyze the parking lot information.

[0031] The payment unit can make payments using a credit card or electronic money. Examples of credit cards include, but are not limited to, Visa (registered trademark), MasterCard (registered trademark), and American Express (registered trademark). Examples of electronic money include, but are not limited to, Suica, PayPay (registered trademark), and Rakuten Edy (registered trademark). The payment unit can make payments using, for example, a credit card. For example, the payment unit inputs credit card information and makes a payment. The payment unit can also make payments using electronic money. For example, the payment unit makes a payment using an electronic money app. By using a credit card or electronic money, there is no need to carry cash. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without AI. For example, the payment unit can input credit card information or electronic money information into a generation AI and have the generation AI execute the payment processing.

[0032] The calculation unit can automatically calculate the fee based on the parking start and end times. The parking start and end times are obtained, for example, based on a method for recording parking entry and exit times, but are not limited to such examples. The calculation unit can, for example, input the parking start and end times and automatically calculate the fee. For example, the calculation unit can set an hourly rate and calculate the fee according to the parking time. The calculation unit can also automatically apply discounted rates according to specific times of day or days of the week. For example, the calculation unit can apply discounted rates during specific times on weekdays. The calculation unit can also apply special discounts on weekends and holidays. This allows for the rapid and accurate calculation of fees by automatically calculating the fee based on the parking start and end times. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input the parking start and end times into a generating AI and have the generating AI perform the fee calculation.

[0033] The payment unit can encrypt and securely manage payment information. Encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used, but are not limited to these examples. For example, the payment unit can encrypt payment information using AES. For example, the payment unit encrypts and securely manages payment information. The payment unit can also encrypt payment information using RSA. For example, the payment unit encrypts and securely manages payment information using RSA. This ensures the secure protection of user information by encrypting payment information. Some or all of the above-described processes in the payment unit may be performed using AI, or not. For example, the payment unit can input payment information into a generating AI and have the generating AI perform the encryption process.

[0034] The acquisition unit can acquire real-time information on the congestion status of a parking lot and suggest a suitable parking space to the user. For example, the acquisition unit can acquire real-time information on availability from parking lot sensors and suggest the optimal space to the user. For example, the acquisition unit can identify available spaces based on the sensor information of the parking lot and notify the user. The acquisition unit can also analyze camera footage of the parking lot to identify available spaces and notify the user. For example, the acquisition unit can analyze camera footage to identify available spaces. The acquisition unit can also acquire congestion status from the parking lot management system and prioritize the display of available areas. For example, the acquisition unit can identify available spaces based on information from the management system and notify the user. In this way, by acquiring real-time information on the congestion status of the parking lot, the system can suggest the optimal parking space to the user. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input sensor information and camera footage into a generating AI and have the generating AI perform the identification of available spaces.

[0035] The acquisition unit can automatically recognize the user's vehicle information when acquiring parking lot information, saving the user the effort of inputting it. For example, the acquisition unit can read the vehicle license plate with a camera in the parking lot and automatically acquire the user's vehicle information. For example, the acquisition unit can analyze the vehicle license plate captured by the camera and acquire the vehicle information. The acquisition unit can also automatically acquire vehicle information using the Bluetooth® connection between the user's smartphone and the vehicle. For example, the acquisition unit can acquire vehicle information via the Bluetooth connection. The acquisition unit can also detect vehicle characteristics with sensors in the parking lot and automatically acquire the user's vehicle information. For example, the acquisition unit can acquire vehicle information based on the vehicle characteristics detected by the sensors. This allows the user's vehicle information to be automatically recognized, saving the user the effort of inputting it. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data acquired by cameras and sensors into a generating AI and have the generating AI perform the recognition of vehicle information.

[0036] When acquiring parking lot information, the acquisition unit can suggest an appropriate parking space by referring to the user's past parking history. The acquisition unit, for example, suggests an optimal space based on parking spaces used by the user in the past. For example, the acquisition unit suggests an optimal parking space to the user based on the past parking history. The acquisition unit can also suggest a space that avoids congestion based on the user's past parking history. For example, the acquisition unit suggests a space that avoids congestion based on the past parking history. The acquisition unit can also analyze the user's past parking history and suggest the most efficient space. For example, the acquisition unit suggests an efficient space based on the past parking history. In this way, the optimal parking space can be suggested by referring to the user's past parking history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past parking history data to the generation AI and cause the generation AI to suggest an optimal parking space.

[0037] When acquiring parking information, the acquisition unit can prioritize displaying the nearest parking lot by taking into consideration the user's current location information. The acquisition unit, for example, prioritizes displaying the parking lot closest to the user's current location. For example, the acquisition unit identifies the user's current location based on GPS data and displays the nearest parking lot. The acquisition unit can also suggest the optimal parking lot based on the distance from the user's current location. For example, the acquisition unit calculates the distance from the current location and suggests the optimal parking lot. The acquisition unit can also update the user's current location information in real time and display the nearest parking lot. For example, the acquisition unit acquires current location information in real time and displays the nearest parking lot. In this way, the nearest parking lot can be prioritized by taking the user's current location information into consideration. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input current location information to the generation AI and cause the generation AI to identify the nearest parking lot.

[0038] The calculation unit can automatically apply a discount rate according to a specific time period or day of the week when calculating the parking fee. The calculation unit, for example, applies a discount rate to a specific time period on a weekday. For example, the calculation unit applies a discount rate between 9:00 AM and 5:00 PM on weekdays. The calculation unit can also apply a special discount on weekends and holidays. For example, the calculation unit applies a special discount on weekends and holidays. The calculation unit can also apply a discount rate for overnight parking. For example, the calculation unit applies a discount rate between 10:00 PM and 6:00 AM. This allows the user to enjoy a great deal by automatically applying a discount rate according to a specific time period or day of the week. Some or all of the above-described processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input data on a specific time period or day of the week into the generation AI and have the generation AI apply the discount rate.

[0039] The calculation unit can apply benefits and discounts based on the user's membership status when calculating the parking fee. For example, if the user is a gold member, the calculation unit applies a special discount. For example, the calculation unit applies a 10% discount to gold members. The calculation unit can also apply a certain discount if the user is a silver member. For example, the calculation unit applies a 5% discount to silver members. The calculation unit can also apply benefits to new members upon their first use if they are new members. For example, the calculation unit can provide free parking to new members upon their first use. In this way, member benefits can be provided by applying benefits and discounts based on the user's membership status. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input membership status data into a generation AI and have the generation AI apply benefits and discounts.

[0040] When calculating parking fees, the calculation unit can predict fees by referring to the user's past usage history. The calculation unit, for example, displays a predicted fee based on the user's past usage history. For example, the calculation unit calculates a predicted fee based on data on past parking dates and times and parking locations. The calculation unit can also predict fees based on the user's past usage patterns. For example, the calculation unit analyzes past usage patterns and displays a predicted fee. The calculation unit can also analyze the user's past usage history and propose an optimal fee. For example, the calculation unit proposes an optimal fee based on the past usage history. In this way, fees can be predicted by referring to the user's past usage history, and an optimal fee can be provided for the user. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input past usage history data into a generation AI and have the generation AI execute a fee prediction.

[0041] When calculating the parking fee, the calculation unit can compare the fee with that of other parking lots to propose an appropriate fee. The calculation unit, for example, compares the fee with that of nearby parking lots to propose the optimal fee. For example, the calculation unit obtains fee information of nearby parking lots, compares the fee, and proposes the optimal fee. The calculation unit can also propose the optimal fee by taking discount information of other parking lots into consideration. For example, the calculation unit proposes the optimal fee based on discount information of other parking lots. The calculation unit can also obtain fees of other parking lots in real time and propose the optimal fee. For example, the calculation unit obtains fee information of other parking lots in real time, compares the fee, and proposes the optimal fee. This makes it possible to propose the optimal fee for the user by comparing it with the fees of other parking lots. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input fee information of other parking lots into the generation AI and have the generation AI compare and propose fees.

[0042] At the time of payment, the payment unit can select an appropriate payment method by referring to the user's past payment history. For example, the payment unit prioritizes suggesting payment methods that the user has used in the past. For example, the payment unit suggests the optimal payment method to the user based on the past payment history. The payment unit can also select the optimal payment method from the user's past payment history. For example, the payment unit selects the optimal payment method based on the past payment history. The payment unit can also analyze the user's past payment patterns and suggest the most efficient payment method. For example, the payment unit suggests the optimal payment method based on the past payment patterns. In this way, the optimal payment method can be selected by referring to the user's past payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input past payment history data into a generation AI and have the generation AI select the optimal payment method.

[0043] The payment unit can provide installment payment options based on the user's current ability to pay at the time of payment. The payment unit, for example, evaluates the user's current ability to pay and proposes installment payment options. For example, the payment unit sets installment payment terms based on the user's income information or credit score. The payment unit can also provide installment payment options by referencing the user's past payment history. For example, the payment unit proposes installment payment options based on the user's past payment history. This makes it possible to provide flexible payment methods by providing installment payment options based on the user's current ability to pay. Some or all of the above-described processing in the payment unit may be performed using, or without, AI. For example, the payment unit can input payment ability data into a generation AI and have the generation AI propose installment payment options.

[0044] The payment unit can propose an appropriate payment method at the time of payment, taking into account the user's geographical location information. For example, if the user is in a specific region, the payment unit proposes payment methods available in that region. For example, the payment unit identifies the user's current location based on GPS data and proposes payment methods available in that region. The payment unit can also select the optimal payment method based on the user's current location information. For example, the payment unit selects the optimal payment method based on the current location information. The payment unit can also propose an internationally usable payment method if the user is overseas. For example, the payment unit proposes an internationally usable payment method based on the user's current location information. In this way, the optimal payment method can be proposed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input geographical location information to a generation AI and cause the generation AI to propose the optimal payment method.

[0045] The payment unit can analyze the user's social media activity at the time of payment and provide relevant benefits and discounts. The payment unit, for example, analyzes the user's social media activity and provides discounts based on a specific campaign. For example, the payment unit can provide discounts based on a specific campaign based on the content of the user's posts and the number of likes. The payment unit can also provide benefits based on information shared by the user on social media. For example, the payment unit can provide benefits based on the information shared by the user. The payment unit can also provide special discounts based on the number of the user's social media followers. For example, the payment unit can provide special discounts based on the number of followers. In this way, relevant benefits and discounts can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the payment unit may be performed using, or without, AI. For example, the payment unit can input social media activity data into a generation AI and cause the generation AI to provide benefits and discounts.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The parking payment system can also acquire fuel efficiency information from the user's vehicle and reflect it in the calculation of parking fees. For example, the acquisition unit acquires the vehicle's fuel efficiency data from the vehicle's OBD-II port and transmits it to the calculation unit. Based on the fuel efficiency data, the calculation unit can apply eco-discounts to the parking fees. Furthermore, if the user's vehicle is an electric vehicle, the acquisition unit can acquire the usage status of charging stations and add the charging fee to the parking fee. This allows users to receive discounts according to their fuel efficiency, promoting environmentally conscious parking use.

[0048] The parking payment system can further analyze users' parking frequency and offer benefits to frequent users. For example, the acquisition unit retrieves the user's parking usage history and transmits it to the calculation unit. The calculation unit calculates the benefit based on the usage frequency and transmits it to the payment unit. The payment unit can then present the user with the fee after applying the benefit. The acquisition unit can also provide a priority reservation function for a specific parking lot if the user frequently uses that lot. This allows users to receive benefits at the parking lots they frequently use, improving convenience.

[0049] The parking payment system can also monitor the user's health status and suggest parking spaces appropriate to their condition. For example, the acquisition unit acquires heart rate and step count data from the user's smartwatch and transmits it to the calculation unit. The calculation unit calculates the optimal parking space for the user based on the health data and transmits it to the provision unit. The provision unit can then suggest parking spaces to the user that are appropriate to their health status. Furthermore, the acquisition unit can prioritize suggesting barrier-free parking spaces if the user is elderly or disabled. This allows users to use parking spaces that are appropriate to their health status, enabling them to use the parking lot with peace of mind.

[0050] The parking payment system can also acquire maintenance information for the user's vehicle and notify the user when maintenance is required. For example, the acquisition unit acquires maintenance data from the vehicle's OBD-II port and sends it to the calculation unit. The calculation unit calculates when maintenance is required based on the maintenance data and sends it to the provision unit. The provision unit can notify the user of the need for maintenance. The acquisition unit can also provide the user with a link to schedule maintenance. This allows the user to perform vehicle maintenance at the appropriate time, improving vehicle safety.

[0051] The parking payment system can also acquire weather information when the user uses the parking lot and reflect it in suggesting parking spaces. For example, the acquisition unit acquires weather data and sends it to the calculation unit. The calculation unit calculates the optimal parking space for the user based on the weather data and sends it to the provision unit. The provision unit can suggest parking spaces to the user according to the weather conditions. The acquisition unit can also preferentially suggest indoor parking lots in bad weather. This allows the user to use a parking space according to the weather conditions and use the parking lot comfortably.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The acquisition unit acquires parking lot information. Parking lot information includes the location, availability, and fee structure of the parking lot. The acquisition unit can acquire parking lot information by scanning two-dimensional codes installed in the parking lot. Parking lot information can also be acquired using sensors and cameras in the parking lot. For example, the acquisition unit can acquire parking lot availability in real time and suggest the most suitable parking space to the user. Step 2: The calculation unit calculates the parking fee based on the information acquired by the acquisition unit. The parking fee is automatically calculated based on the parking start and end times. The calculation unit can automatically apply discounted rates depending on the time of day or day of the week. It can also apply benefits and discounts based on the user's membership status. Step 3: The payment unit settles the fee calculated by the calculation unit. Payment is made using a credit card or electronic money. The payment unit can encrypt and securely manage payment information. It can also estimate the user's emotions and suggest payment methods based on the estimated emotions.

[0054] (Example 2) A parking lot payment system according to an embodiment of the present invention acquires parking lot information, calculates parking fees, and makes payments. In this parking lot payment system, a user inputs parking lot information using a dedicated app when parking their vehicle in a parking lot. The parking fee is calculated through the app when the parking time expires, and the user makes the payment within the app. For example, when a user parks their vehicle in a parking lot, the user inputs information such as the parking lot number and parking start time using the dedicated app. This information is managed within the app. Next, when the parking time expires, the parking fee is calculated through the app. The app automatically calculates the fee based on the parking start and end times. For example, if an hourly rate is set, the fee is calculated based on the parking time. The user makes the payment within the app. For example, payment can be made using a credit card or electronic money. This eliminates the need to carry cash and enables smooth payment. This system makes parking lots more convenient. Users no longer need to carry cash and can easily make payments through the app. Furthermore, this system reduces cash handling and simplifies management for parking lot managers. This parking lot payment system eliminates the need for users to carry cash and enables smooth payment.

[0055] The parking payment system according to this embodiment comprises an acquisition unit, a calculation unit, and a payment unit. The acquisition unit acquires information about the parking lot. This information includes, but is not limited to, the location, availability, and fee structure of the parking lot. The acquisition unit can acquire information about the parking lot, for example, by scanning a two-dimensional code (e.g., a QR code) installed in the parking lot. The acquisition unit can also acquire information about the parking lot using sensors or cameras in the parking lot. For example, the acquisition unit can acquire the availability of parking spaces in real time and suggest the most suitable parking space to the user. The calculation unit calculates the parking fee based on the information acquired by the acquisition unit. The parking fee is calculated automatically, for example, based on the parking start and end times, but is not limited to this example. For example, the calculation unit can automatically apply discounted rates according to specific time slots or days of the week. The calculation unit can also apply benefits and discounts based on the user's membership status. The payment unit settles the fee calculated by the calculation unit. Payment is made, for example, using a credit card or electronic money, but is not limited to this example. For example, the payment unit can encrypt and securely manage the payment information. The payment unit can also estimate the user's emotions and suggest payment methods based on the estimated emotions. This allows the parking lot payment system according to the embodiment to perform an integrated process from obtaining parking lot information to calculating fees and making payments.

[0056] The acquisition unit can acquire parking lot information by scanning a two-dimensional code installed in the parking lot. The two-dimensional code is installed, for example, at the entrance to the parking lot or in each parking space, but is not limited to such an example. The acquisition unit can, for example, scan the two-dimensional code using a smartphone camera to acquire parking lot information. The acquisition unit can also read the two-dimensional code using a dedicated scanner. For example, the acquisition unit can scan a two-dimensional code installed at the entrance to the parking lot to acquire the location and availability of the parking lot. The acquisition unit can also scan a two-dimensional code installed in each parking space to acquire the availability of the parking space. In this way, parking lot information can be acquired quickly and accurately by scanning the two-dimensional code. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input the results of scanning the two-dimensional code into the generation AI and have the generation AI analyze the parking lot information.

[0057] The payment unit can make payments using a credit card or electronic money. Examples of credit cards include, but are not limited to, Visa, MasterCard, and American Express. Examples of electronic money include, but are not limited to, Suica, PayPay, and Rakuten Edy. The payment unit can make payments using, for example, a credit card. For example, the payment unit inputs credit card information and makes a payment. The payment unit can also make payments using electronic money. For example, the payment unit makes a payment using an electronic money app. By using a credit card or electronic money, there is no need to carry cash. Some or all of the above-described processing in the payment unit can be performed using, for example, AI, or can be performed without using AI. For example, the payment unit can input credit card information or electronic money information into a generation AI and have the generation AI execute the payment processing.

[0058] The calculation unit can automatically calculate the fee based on the parking start and end times. The parking start and end times are obtained, for example, based on a method for recording parking entry and exit times, but are not limited to such examples. The calculation unit can, for example, input the parking start and end times and automatically calculate the fee. For example, the calculation unit can set an hourly rate and calculate the fee according to the parking time. The calculation unit can also automatically apply discounted rates according to specific times of day or days of the week. For example, the calculation unit can apply discounted rates during specific times on weekdays. The calculation unit can also apply special discounts on weekends and holidays. This allows for the rapid and accurate calculation of fees by automatically calculating the fee based on the parking start and end times. Some or all of the above processing in the calculation unit may be performed using, for example, AI, or not using AI. For example, the calculation unit can input the parking start and end times into a generating AI and have the generating AI perform the fee calculation.

[0059] The payment unit can encrypt and securely manage payment information. Encryption algorithms such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) can be used, but are not limited to these examples. For example, the payment unit can encrypt payment information using AES. For example, the payment unit encrypts and securely manages payment information. The payment unit can also encrypt payment information using RSA. For example, the payment unit encrypts and securely manages payment information using RSA. This ensures the secure protection of user information by encrypting payment information. Some or all of the above-described processes in the payment unit may be performed using AI, or not. For example, the payment unit can input payment information into a generating AI and have the generating AI perform the encryption process.

[0060] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring parking information based on the estimated emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This improves user convenience by adjusting the timing of acquiring parking information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0061] The acquisition unit can acquire real-time information on the congestion status of a parking lot and suggest a suitable parking space to the user. For example, the acquisition unit can acquire real-time information on availability from parking lot sensors and suggest the optimal space to the user. For example, the acquisition unit can identify available spaces based on the sensor information of the parking lot and notify the user. The acquisition unit can also analyze camera footage of the parking lot to identify available spaces and notify the user. For example, the acquisition unit can analyze camera footage to identify available spaces. The acquisition unit can also acquire congestion status from the parking lot management system and prioritize the display of available areas. For example, the acquisition unit can identify available spaces based on information from the management system and notify the user. In this way, by acquiring real-time information on the congestion status of the parking lot, the system can suggest the optimal parking space to the user. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input sensor information and camera footage into a generating AI and have the generating AI perform the identification of available spaces.

[0062] The acquisition unit automatically recognizes the user's vehicle information when acquiring parking lot information, thereby eliminating the need for input. For example, the acquisition unit reads the vehicle license plate number using a parking lot camera and automatically acquires the user's vehicle information. For example, the acquisition unit analyzes the vehicle license plate photographed by the camera to acquire the vehicle information. The acquisition unit can also automatically acquire vehicle information using a Bluetooth connection between the user's smartphone and the vehicle. For example, the acquisition unit acquires vehicle information through the Bluetooth connection. The acquisition unit can also detect vehicle characteristics using a parking lot sensor and automatically acquire the user's vehicle information. For example, the acquisition unit acquires vehicle information based on the vehicle characteristics detected by the sensor. This automatically recognizes the user's vehicle information, eliminating the need for input. Some or all of the above-described processing in the acquisition unit may be performed using, for example, AI, or may be performed without AI. For example, the acquisition unit may input data acquired by a camera or sensor into a generation AI and cause the generation AI to recognize the vehicle information.

[0063] The acquisition unit can estimate the user's emotions and determine the priority of parking information to be acquired based on the estimated emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. The acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This improves user convenience by determining the priority of parking information according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the acquisition unit can be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0064] When acquiring parking lot information, the acquisition unit can suggest an appropriate parking space by referring to the user's past parking history. The acquisition unit, for example, suggests an optimal space based on parking spaces used by the user in the past. For example, the acquisition unit suggests an optimal parking space to the user based on the past parking history. The acquisition unit can also suggest a space that avoids congestion based on the user's past parking history. For example, the acquisition unit suggests a space that avoids congestion based on the past parking history. The acquisition unit can also analyze the user's past parking history and suggest the most efficient space. For example, the acquisition unit suggests an efficient space based on the past parking history. In this way, the optimal parking space can be suggested by referring to the user's past parking history. Some or all of the above-mentioned processing in the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input past parking history data to the generation AI and cause the generation AI to suggest an optimal parking space.

[0065] When acquiring parking information, the acquisition unit can prioritize displaying the nearest parking lot by taking into consideration the user's current location information. The acquisition unit, for example, prioritizes displaying the parking lot closest to the user's current location. For example, the acquisition unit identifies the user's current location based on GPS data and displays the nearest parking lot. The acquisition unit can also suggest the optimal parking lot based on the distance from the user's current location. For example, the acquisition unit calculates the distance from the current location and suggests the optimal parking lot. The acquisition unit can also update the user's current location information in real time and display the nearest parking lot. For example, the acquisition unit acquires current location information in real time and displays the nearest parking lot. In this way, the nearest parking lot can be prioritized by taking the user's current location information into consideration. Some or all of the above-described processing by the acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the acquisition unit can input current location information to the generation AI and cause the generation AI to identify the nearest parking lot.

[0066] The calculation unit can estimate the user's emotion and adjust the fee calculation method based on the estimated emotion. For example, the calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the calculation unit calculates an emotion score based on changes in facial expression. The calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the calculation unit analyzes the tone and speed of the voice and calculates the emotion score. The calculation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the calculation unit calculates the emotion score based on heart rate fluctuations. This improves user convenience by adjusting the fee calculation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's emotional data into the generation AI and have the generation AI adjust the fee calculation method.

[0067] The calculation unit can automatically apply a discount rate according to a specific time period or day of the week when calculating the parking fee. The calculation unit, for example, applies a discount rate to a specific time period on a weekday. For example, the calculation unit applies a discount rate between 9:00 AM and 5:00 PM on weekdays. The calculation unit can also apply a special discount on weekends and holidays. For example, the calculation unit applies a special discount on weekends and holidays. The calculation unit can also apply a discount rate for overnight parking. For example, the calculation unit applies a discount rate between 10:00 PM and 6:00 AM. This allows the user to enjoy a great deal by automatically applying a discount rate according to a specific time period or day of the week. Some or all of the above-described processing in the calculation unit may be performed using, or without, AI. For example, the calculation unit can input data on a specific time period or day of the week into the generation AI and have the generation AI apply the discount rate.

[0068] The calculation unit can apply benefits and discounts based on the user's membership status when calculating the parking fee. For example, if the user is a gold member, the calculation unit applies a special discount. For example, the calculation unit applies a 10% discount to gold members. The calculation unit can also apply a certain discount if the user is a silver member. For example, the calculation unit applies a 5% discount to silver members. The calculation unit can also apply benefits to new members upon their first use if they are new members. For example, the calculation unit can provide free parking to new members upon their first use. In this way, member benefits can be provided by applying benefits and discounts based on the user's membership status. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input membership status data into a generation AI and have the generation AI apply benefits and discounts.

[0069] The calculation unit can estimate the user's emotion and adjust the level of detail of the fee calculation based on the estimated emotion. For example, the calculation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the calculation unit calculates an emotion score based on changes in facial expression. The calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the calculation unit analyzes the tone and speed of the voice and calculates the emotion score. The calculation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the calculation unit calculates the emotion score based on heart rate fluctuations. This improves user convenience by adjusting the level of detail of the fee calculation according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the calculation unit can be performed using, for example, AI, or without AI. For example, the calculation unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the level of detail of the fee calculation.

[0070] When calculating parking fees, the calculation unit can predict fees by referring to the user's past usage history. The calculation unit, for example, displays a predicted fee based on the user's past usage history. For example, the calculation unit calculates a predicted fee based on data on past parking dates and times and parking locations. The calculation unit can also predict fees based on the user's past usage patterns. For example, the calculation unit analyzes past usage patterns and displays a predicted fee. The calculation unit can also analyze the user's past usage history and propose an optimal fee. For example, the calculation unit proposes an optimal fee based on the past usage history. In this way, fees can be predicted by referring to the user's past usage history, and an optimal fee can be provided for the user. Some or all of the above-described processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input past usage history data into a generation AI and have the generation AI execute a fee prediction.

[0071] When calculating the parking fee, the calculation unit can compare the fee with that of other parking lots to propose an appropriate fee. The calculation unit, for example, compares the fee with that of nearby parking lots to propose the optimal fee. For example, the calculation unit obtains fee information of nearby parking lots, compares the fee, and proposes the optimal fee. The calculation unit can also propose the optimal fee by taking discount information of other parking lots into consideration. For example, the calculation unit proposes the optimal fee based on discount information of other parking lots. The calculation unit can also obtain fees of other parking lots in real time and propose the optimal fee. For example, the calculation unit obtains fee information of other parking lots in real time, compares the fee, and proposes the optimal fee. This makes it possible to propose the optimal fee for the user by comparing it with the fees of other parking lots. Some or all of the above-mentioned processing in the calculation unit may be performed using, for example, AI, or may be performed without AI. For example, the calculation unit can input fee information of other parking lots into the generation AI and have the generation AI compare and propose fees.

[0072] The payment unit can estimate the user's emotions and propose a payment method based on the estimated emotions. For example, the payment unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the payment unit calculates an emotion score based on changes in facial expression. The payment unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the payment unit analyzes the tone and speed of the voice and calculates an emotion score. The payment unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the payment unit calculates an emotion score based on heart rate fluctuations. This improves user convenience by suggesting a payment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, AI, or without AI. For example, the payment unit can input the user's emotional data into the generation AI and have the generation AI propose a payment method.

[0073] At the time of payment, the payment unit can select an appropriate payment method by referring to the user's past payment history. For example, the payment unit prioritizes suggesting payment methods that the user has used in the past. For example, the payment unit suggests the optimal payment method to the user based on the past payment history. The payment unit can also select the optimal payment method from the user's past payment history. For example, the payment unit selects the optimal payment method based on the past payment history. The payment unit can also analyze the user's past payment patterns and suggest the most efficient payment method. For example, the payment unit suggests the optimal payment method based on the past payment patterns. In this way, the optimal payment method can be selected by referring to the user's past payment history. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input past payment history data into a generation AI and have the generation AI select the optimal payment method.

[0074] The payment unit can provide installment payment options based on the user's current ability to pay at the time of payment. The payment unit, for example, evaluates the user's current ability to pay and proposes installment payment options. For example, the payment unit sets installment payment terms based on the user's income information or credit score. The payment unit can also provide installment payment options by referencing the user's past payment history. For example, the payment unit proposes installment payment options based on the user's past payment history. This makes it possible to provide flexible payment methods by providing installment payment options based on the user's current ability to pay. Some or all of the above-described processing in the payment unit may be performed using, or without, AI. For example, the payment unit can input payment ability data into a generation AI and have the generation AI propose installment payment options.

[0075] The payment unit can estimate the user's emotions and determine payment priorities based on the estimated emotions. For example, the payment unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the payment unit calculates an emotion score based on changes in facial expressions. The payment unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the payment unit analyzes the tone and speed of the voice and calculates an emotion score. The payment unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the payment unit calculates an emotion score based on heart rate fluctuations. This improves user convenience by determining payment priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the payment unit can be performed using, for example, AI, or without AI. For example, the payment unit can input user emotion data into the generation AI and have the generation AI determine payment priorities.

[0076] The payment unit can propose an appropriate payment method at the time of payment, taking into account the user's geographical location information. For example, if the user is in a specific region, the payment unit proposes payment methods available in that region. For example, the payment unit identifies the user's current location based on GPS data and proposes payment methods available in that region. The payment unit can also select the optimal payment method based on the user's current location information. For example, the payment unit selects the optimal payment method based on the current location information. The payment unit can also propose an internationally usable payment method if the user is overseas. For example, the payment unit proposes an internationally usable payment method based on the user's current location information. In this way, the optimal payment method can be proposed by taking the user's geographical location information into consideration. Some or all of the above-described processing in the payment unit may be performed using, for example, AI, or may be performed without using AI. For example, the payment unit can input geographical location information to a generation AI and cause the generation AI to propose the optimal payment method.

[0077] The payment unit can analyze the user's social media activity at the time of payment and provide relevant benefits and discounts. The payment unit, for example, analyzes the user's social media activity and provides discounts based on a specific campaign. For example, the payment unit can provide discounts based on a specific campaign based on the content of the user's posts and the number of likes. The payment unit can also provide benefits based on information shared by the user on social media. For example, the payment unit can provide benefits based on the information shared by the user. The payment unit can also provide special discounts based on the number of the user's social media followers. For example, the payment unit can provide special discounts based on the number of followers. In this way, relevant benefits and discounts can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the payment unit may be performed using, or without, AI. For example, the payment unit can input social media activity data into a generation AI and cause the generation AI to provide benefits and discounts. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, calculation unit, and payment unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires parking lot information by scanning a two-dimensional code in the parking lot using the camera 42 of the smart device 14. The acquisition unit can also acquire information from sensors and cameras in the parking lot using the specific processing unit 290 of the data processing device 12. For example, the calculation unit calculates parking fees using the specific processing unit 290 of the data processing device 12. For example, the payment unit can perform payment using a credit card or electronic money using the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements including the acquisition unit, calculation unit, and payment unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit acquires parking lot information by scanning a two-dimensional code in the parking lot using the camera 42 of the smart glasses 214. The acquisition unit can also acquire information from sensors and cameras in the parking lot using the specific processing unit 290 of the data processing device 12. For example, the calculation unit calculates parking fees using the specific processing unit 290 of the data processing device 12. For example, the payment unit can perform payment using a credit card or electronic money using the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned acquisition unit, calculation unit, and payment unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit acquires parking lot information by scanning a two-dimensional code in the parking lot using the camera 42 of the headset type terminal 314. The acquisition unit can also acquire information from sensors and cameras in the parking lot using the specific processing unit 290 of the data processing device 12. For example, the calculation unit calculates the parking fee using the specific processing unit 290 of the data processing device 12. For example, the payment unit can perform payment using a credit card or electronic money using the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, calculation unit, and payment unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit acquires parking lot information by scanning a two-dimensional code in the parking lot using the camera 42 of the robot 414. The acquisition unit can also acquire information from sensors and cameras in the parking lot using the specific processing unit 290 of the data processing device 12. For example, the calculation unit calculates parking fees using the specific processing unit 290 of the data processing device 12. For example, the payment unit can perform payment using a credit card or electronic money using the control unit 46A of the robot 414.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The parking payment system can also acquire fuel efficiency information from the user's vehicle and reflect it in the calculation of parking fees. For example, the acquisition unit acquires the vehicle's fuel efficiency data from the vehicle's OBD-II port and transmits it to the calculation unit. Based on the fuel efficiency data, the calculation unit can apply eco-discounts to the parking fees. Furthermore, if the user's vehicle is an electric vehicle, the acquisition unit can acquire the usage status of charging stations and add the charging fee to the parking fee. This allows users to receive discounts according to their fuel efficiency, promoting environmentally conscious parking use.

[0080] The parking payment system can further analyze users' parking frequency and offer benefits to frequent users. For example, the acquisition unit retrieves the user's parking usage history and transmits it to the calculation unit. The calculation unit calculates the benefit based on the usage frequency and transmits it to the payment unit. The payment unit can then present the user with the fee after applying the benefit. The acquisition unit can also provide a priority reservation function for a specific parking lot if the user frequently uses that lot. This allows users to receive benefits at the parking lots they frequently use, improving convenience.

[0081] The parking payment system can also monitor the user's health status and suggest parking spaces appropriate to their condition. For example, the acquisition unit acquires heart rate and step count data from the user's smartwatch and transmits it to the calculation unit. The calculation unit calculates the optimal parking space for the user based on the health data and transmits it to the provision unit. The provision unit can then suggest parking spaces to the user that are appropriate to their health status. Furthermore, the acquisition unit can prioritize suggesting barrier-free parking spaces if the user is elderly or disabled. This allows users to use parking spaces that are appropriate to their health status, enabling them to use the parking lot with peace of mind.

[0082] The parking payment system can also acquire maintenance information for the user's vehicle and notify the user when maintenance is required. For example, the acquisition unit acquires maintenance data from the vehicle's OBD-II port and sends it to the calculation unit. The calculation unit calculates when maintenance is required based on the maintenance data and sends it to the provision unit. The provision unit can notify the user of the need for maintenance. The acquisition unit can also provide the user with a link to schedule maintenance. This allows the user to perform vehicle maintenance at the appropriate time, improving vehicle safety.

[0083] The parking payment system can also acquire weather information when the user uses the parking lot and reflect it in suggesting parking spaces. For example, the acquisition unit acquires weather data and sends it to the calculation unit. The calculation unit calculates the optimal parking space for the user based on the weather data and sends it to the provision unit. The provision unit can suggest parking spaces to the user according to the weather conditions. The acquisition unit can also preferentially suggest indoor parking lots in bad weather. This allows the user to use a parking space according to the weather conditions and use the parking lot comfortably.

[0084] The parking payment system can further estimate the user's emotions and customize the parking experience based on the estimated emotions. For example, the acquisition unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The calculation unit can provide the user with relaxing music based on the estimated emotion. Furthermore, the provision unit can simplify the parking procedure if the user is feeling stressed. This allows the user to enjoy a customized experience based on their emotions, making parking more comfortable.

[0085] The parking payment system can further estimate the user's emotions and provide a discount on the parking fee based on the estimated emotions. For example, the acquisition unit records the user's voice and estimates the emotion using voice analysis technology. The calculation unit can apply a special discount if the user is feeling stressed based on the estimated emotion. Furthermore, the provision unit can provide a discount coupon for the user's next use if the user is happy. This allows the user to receive benefits according to their emotions, making parking more enjoyable.

[0086] The parking payment system can further estimate the user's emotions and adjust the timing of notifying the user of parking availability based on the estimated emotions. For example, the acquisition unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. The calculation unit can notify the user of availability based on the estimated emotions when the user is relaxed. Furthermore, the provision unit can immediately notify the user of availability when the user is in a hurry. This allows the user to know the availability at a time that suits their emotions, making parking more convenient.

[0087] The parking payment system can further estimate the user's emotions, analyze the parking lot usage history based on the estimated emotions, and suggest the optimal parking space for the next use. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The calculation unit calculates the optimal parking space for the user based on the estimated emotion and past usage history, and transmits the calculated result to the provision unit. The provision unit can suggest the optimal parking space to the user based on the emotion. The acquisition unit can also suggest parking spaces that avoid parking spaces that the user has previously found stressful. This allows the user to use the optimal parking space based on their emotions, making parking more comfortable.

[0088] The parking payment system can further estimate the user's emotions and provide guidance on how to use the parking lot based on the estimated emotions. For example, the acquisition unit records the user's voice and estimates the emotion using voice analysis technology. The calculation unit can provide detailed guidance based on the estimated emotions if the user feels anxious. The provision unit can also provide simplified guidance if the user feels confident. This allows the user to receive guidance based on their emotions, making parking lot use smoother.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The acquisition unit acquires parking lot information. Parking lot information includes the location, availability, and fee structure of the parking lot. The acquisition unit can acquire parking lot information by scanning two-dimensional codes installed in the parking lot. Parking lot information can also be acquired using sensors and cameras in the parking lot. For example, the acquisition unit can acquire parking lot availability in real time and suggest the most suitable parking space to the user. Step 2: The calculation unit calculates the parking fee based on the information acquired by the acquisition unit. The parking fee is automatically calculated based on the parking start and end times. The calculation unit can automatically apply discounted rates depending on the time of day or day of the week. It can also apply benefits and discounts based on the user's membership status. Step 3: The payment unit settles the fee calculated by the calculation unit. Payment is made using a credit card or electronic money. The payment unit can encrypt and securely manage payment information. It can also estimate the user's emotions and suggest payment methods based on the estimated emotions.

[0091] 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.

[0092] 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> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0093] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0095] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0096] 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.

[0097] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0098] 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.

[0099] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0100] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0105] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0106] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0109] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0114] 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.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] 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 user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] 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.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0119] 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.

[0120] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and 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 and / or a LAN.

[0130] 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.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] 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 image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] 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.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the 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.

[0135] 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.

[0136] 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.

[0137] 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. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0144] 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.

[0145] FIG. 9 illustrates 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 behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions 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.

[0146] 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.

[0147] 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).

[0148] 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 expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, 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 expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, 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. A processor also includes 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.

[0156] The hardware resource that executes the specific process 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 process may be a single processor.

[0157] 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.

[0158] 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.

[0159] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0160] 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.

[0161] 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.

[0162] [Explanation of symbols]

[0163] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires parking lot information; a calculation unit that calculates a parking fee based on the information acquired by the acquisition unit; a settlement unit that settles the fee calculated by the calculation unit; Equipped with A system characterized by:

2. The acquisition unit Obtain parking lot information by scanning the 2D code installed in the parking lot.

2. The system of claim 1.

3. The settlement unit Pay using a credit card or electronic money 2. The system of claim 1.

4. The calculation unit Automatically calculate fees based on parking start and end times 2. The system of claim 1.

5. The settlement unit Encrypt and securely manage payment information 2. The system of claim 1.

6. The acquisition unit Estimates user emotions and adjusts the timing of obtaining parking information based on the estimated emotions.

2. The system of claim 1.

7. The acquisition unit Obtaining parking lot congestion status in real time and suggesting suitable parking spaces to users 2. The system of claim 1.

8. The acquisition unit When obtaining parking information, the system automatically recognizes the user's vehicle information, eliminating the need to enter it.

2. The system of claim 1.

Images