system

Abstract

The system according to this embodiment aims to realize internet voting with improved security and reliability. [Solution] The system according to the embodiment comprises a reception unit, a selection unit, a generation unit, an encryption unit, and a transmission unit. The reception unit receives voting information from users. The selection unit selects candidates based on the information received by the reception unit. The generation unit generates voting data based on the candidates selected by the selection unit. The encryption unit encrypts the voting data generated by the generation unit. The transmission unit transmits the voting data encrypted by the encryption unit to a central aggregation server.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the safety and reliability of Internet voting are not sufficiently ensured, and there is room for improvement.

[0005] The system according to the embodiment aims to realize Internet voting with improved safety and reliability.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a selection unit, a generation unit, an encryption unit, and a transmission unit. The reception unit receives voting information from users. The selection unit selects candidates based on the information received by the reception unit. The generation unit generates voting data based on the candidates selected by the selection unit. The encryption unit encrypts the voting data generated by the generation unit. The transmission unit transmits the voting data encrypted by the encryption unit to a central aggregation server. [Effects of the Invention]

[0007] The system according to this embodiment can realize internet voting with improved security and reliability. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The internet voting system according to an embodiment of the present invention is an internet voting system using a smartphone application and My Number Card to address the low voter turnout in Japan. This internet voting system allows users to download an application from an app store and register their personal information using their My Number Card information. Next, the user's identity is verified by comparing their face with the My Number Card image using the smartphone camera and entering their My Number PIN. This allows the user to utilize the candidate selection and matching function. The user confirms their selected candidate on a confirmation screen and then casts their vote. Voting data is encrypted and transmitted to a central aggregation server. An AI agent works in conjunction at each step to improve security, reliability, and user experience. This system allows for unlimited voting during the early voting period, providing a new voting method for people living abroad for work or study. It is also expected to improve voter turnout among socially vulnerable groups (such as the elderly and people with disabilities) and young people. The AI ​​agent optimizes the facial recognition, interactive voting assistant, security, and encryption processes to ensure the safety and reliability of voting. As a result, the internet voting system enables users to vote efficiently and securely.

[0029] The internet voting system according to this embodiment comprises a reception unit, a selection unit, a generation unit, an encryption unit, and a transmission unit. The reception unit receives voting information from users. For example, the reception unit receives information from users who download an app and register personal information using their My Number Card. The reception unit can verify the user's identity by comparing their face with the face on the My Number Card using the smartphone camera and by having them enter their My Number PIN. The selection unit selects candidates based on the information received by the reception unit. For example, the selection unit presents candidates that match the user's views. The selection unit can also create a table showing the positions of each candidate on major policy issues for each electoral district and proportional representation candidate and present candidates that match the user's views. The generation unit generates voting data based on the candidates selected by the selection unit. For example, the generation unit generates voting data based on the candidates selected by the user. The generation unit can also generate voting data that allows for multiple re-votes during the voting period. The encryption unit encrypts the voting data generated by the generation unit. For example, the encryption unit encrypts the generated voting data. The transmission unit sends the encrypted voting data, which has been encrypted by the encryption unit, to a central aggregation server. For example, the transmission unit sends encrypted voting data to a central aggregation server. This enables the internet voting system according to the embodiment to efficiently receive user voting information, select candidates, generate voting data, encrypt it, and transmit it.

[0030] The reception desk receives voting information from users. Specifically, this involves a process where users download a dedicated application and register their personal information using their My Number Card information. Users verify their identity by using their smartphone camera to compare their face with the photo on their My Number Card and by entering their My Number PIN. This identity verification process ensures high security by using dual authentication with facial recognition technology and a PIN. Facial recognition technology analyzes the feature points of the user's face and determines whether it matches the photo on their My Number Card. This prevents unauthorized access and impersonation. The reception desk securely manages users' personal information and plays a crucial role as the first step in the voting process. Furthermore, the reception desk encrypts and stores the information registered by users and implements security measures to prevent unauthorized access by third parties. This protects user privacy and allows them to vote with peace of mind.

[0031] The selection unit selects candidates based on information received by the reception unit. Specifically, it has the function of presenting candidates that match the user's thoughts and opinions. The selection unit displays the position of each candidate on major policy issues in a table format for each constituency and proportional representation candidate, allowing the user to select the candidate that best aligns with their own views. For example, by having the user answer a questionnaire about policy issues they are interested in, the unit recommends the most suitable candidate based on their answers. The selection unit uses AI to analyze the user's answers and compare them with the policy positions of the candidates. The AI ​​uses natural language processing technology to understand the user's answers and compare them with the policy positions of the candidates to identify the most suitable candidate. This process makes it easy for the user to find the candidate that best aligns with their opinions. The selection unit plays a role in improving the convenience of the user when selecting candidates and promoting voting behavior.

[0032] The generation unit generates voting data based on the candidates selected by the selection unit. Specifically, it creates official voting data based on the information of the candidate selected by the user. In the voting data generation process, the generation unit provides a mechanism that allows users to re-vote any number of times during the voting period. This makes it easy for users to change their vote if they wish to do so. The generation unit performs data integrity checks to ensure the consistency and accuracy of the voting data. For example, even if the same user votes multiple times, the final voting data is uniquely determined. Furthermore, the generation unit implements security measures to prevent fraudulent operations and data tampering during the voting data generation process. As a result, the generation unit can generate highly reliable voting data and improve the reliability of the entire system.

[0033] The encryption unit encrypts the voting data generated by the generation unit. Specifically, it encrypts the generated voting data using advanced encryption algorithms to ensure data confidentiality. The encryption unit uses encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) to encrypt the voting data. This prevents unauthorized access to the voting data by third parties. The encryption unit strictly manages encryption keys during the encryption process and takes measures to prevent the leakage of encryption keys. The encrypted voting data remains encrypted and protected until it is sent to the central tabulation server by the transmission unit. This allows the encryption unit to ensure the confidentiality and security of the voting data and improve the overall system security.

[0034] The transmission unit sends the encrypted voting data, encrypted by the encryption unit, to the central tabulation server. Specifically, it transmits the encrypted voting data using a secure communication protocol to ensure data integrity and confidentiality. The transmission unit uses secure communication protocols such as TLS (Transport Layer Security) or SSL (Secure Sockets Layer) to transmit the voting data. This prevents the data from being intercepted or tampered with by third parties during transmission. The transmission unit has a function to monitor the data transmission status in real time during the transmission process and to retransmit the data if a transmission error or communication failure occurs. This ensures that the voting data reliably reaches the central tabulation server, improving the reliability of the entire system. Furthermore, the transmission unit improves the transparency and auditability of the system by recording logs of transmitted data, allowing for later review of the transmission history.

[0035] The reception desk can verify the identity of a person by comparing their face with the image on their My Number Card using their smartphone camera and then requiring them to enter their My Number PIN. For example, the reception desk can verify the identity by comparing their face with the image on their My Number Card using their smartphone camera and then requiring them to enter their My Number PIN. This makes it possible to verify identity using both the smartphone camera and the My Number Card.

[0036] The selection unit can present candidates that match the user's preferences. For example, the selection unit can present candidates that match the user's preferences. This makes it possible to present candidates based on the user's preferences.

[0037] The generation unit can generate voting data based on the candidate selected by the user. For example, the generation unit generates voting data based on the candidate selected by the user. This makes it possible to generate voting data based on the candidate selected by the user.

[0038] The encryption unit can encrypt the generated voting data. For example, the encryption unit encrypts the generated voting data. This makes it possible to encrypt the generated voting data.

[0039] The transmission unit can send encrypted voting data to a central counting server. For example, the transmission unit sends encrypted voting data to a central counting server. This enables the transmission of encrypted voting data.

[0040] The selection section can create a table showing the position of each candidate on major policy issues for both constituency and proportional representation, and then present candidates that match the user's views. For example, the selection section can create a table showing the position of each candidate on major policy issues for both constituency and proportional representation, and then present candidates that match the user's views. This makes it possible to present candidates that match the user's views by creating a table showing the position of each candidate on major policy issues for both constituency and proportional representation.

[0041] The generation unit can generate voting data that allows for multiple re-votes during the voting period. For example, the generation unit generates voting data that allows for multiple re-votes during the voting period. This makes it possible to generate voting data that allows for multiple re-votes during the voting period.

[0042] The reception desk can analyze a user's past voting history and select the most suitable identity verification method. For example, the reception desk may prioritize suggesting identity verification methods the user has used in the past. The reception desk can also select the fastest and most reliable identity verification method based on the user's past voting history. The reception desk can also analyze a user's past voting history and optimize the identity verification procedure. This makes it possible to select the most suitable identity verification method by analyzing the user's past voting history.

[0043] The reception desk can filter users based on their current lifestyle and areas of interest during identity verification. For example, the reception desk can suggest the most suitable identity verification method based on the user's current lifestyle. The reception desk can also customize the identity verification procedure based on the user's areas of interest. The reception desk can also optimize the identity verification procedure considering the user's lifestyle and areas of interest. This allows for more appropriate identity verification by filtering based on the user's current lifestyle and areas of interest.

[0044] The reception desk can prioritize the most relevant verification method during identity verification, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest an identity verification method appropriate for that region. The reception desk can also select the optimal identity verification method based on the user's geographical location. The reception desk can also optimize the identity verification procedure, taking into account the user's geographical location. This allows for more appropriate identity verification by prioritizing the most relevant verification method based on the user's geographical location.

[0045] The reception desk can analyze a user's social media activity during identity verification and implement relevant verification methods. For example, the reception desk can analyze a user's social media activity and propose the most suitable identity verification method. The reception desk can also customize the identity verification procedure based on the user's social media activity. Furthermore, the reception desk can optimize the identity verification procedure by considering the user's social media activity. This allows for the implementation of relevant verification methods by analyzing the user's social media activity.

[0046] The selection unit can select the most suitable candidate by referring to the user's past voting history when presenting candidates. For example, the selection unit can suggest the most suitable candidate based on the candidates the user has voted for in the past. The selection unit can also select the candidate of the user's greatest interest from their past voting history. The selection unit can also analyze the user's past voting history and suggest the most suitable candidate. This makes it possible to select the most suitable candidate by referring to the user's past voting history.

[0047] The selection unit can filter candidates based on the user's current living situation and areas of interest when presenting them. For example, the selection unit can suggest the most suitable candidate based on the user's current living situation. The selection unit can also filter candidate information based on the user's areas of interest. The selection unit can also optimize candidate information considering the user's living situation and areas of interest. This allows for the presentation of more appropriate candidates by filtering based on the user's current living situation and areas of interest.

[0048] The selection function can prioritize presenting highly relevant candidates by considering the user's geographical location when displaying candidates. For example, if the user is in a specific region, the selection function will suggest candidates suitable for that region. The selection function can also select the most suitable candidate based on the user's geographical location. The selection function can also optimize candidate information by considering the user's geographical location. This allows for the presentation of more appropriate candidates by prioritizing highly relevant candidates while considering the user's geographical location.

[0049] The selection unit can analyze the user's social media activity when presenting candidates and suggest relevant candidates. For example, the selection unit can analyze the user's social media activity and suggest the most suitable candidates. The selection unit can also filter candidate information based on the user's social media activity. The selection unit can also optimize candidate information considering the user's social media activity. This makes it possible to present relevant candidates by analyzing the user's social media activity.

[0050] The generation unit can generate optimal data by referring to the user's past voting history when generating voting data. For example, the generation unit can generate optimal voting data based on the candidates the user has voted for in the past. The generation unit can also generate the most relevant voting data from the user's past voting history. The generation unit can also analyze the user's past voting history and generate optimal voting data. This makes it possible to generate optimal voting data by referring to the user's past voting history.

[0051] The generation unit can filter voting data based on the user's current lifestyle and areas of interest during generation. For example, the generation unit generates optimal voting data according to the user's current lifestyle. The generation unit can also filter voting data based on the user's areas of interest. The generation unit can also optimize voting data by considering the user's lifestyle and areas of interest. This makes it possible to generate more appropriate voting data by filtering based on the user's current lifestyle and areas of interest.

[0052] The generation unit can prioritize generating highly relevant data by considering the user's geographical location when generating voting data. For example, if a user is in a specific region, the generation unit will generate voting data appropriate for that region. The generation unit can also generate optimal voting data based on the user's geographical location. The generation unit can also optimize voting data by considering the user's geographical location. This makes it possible to generate more appropriate voting data by prioritizing the generation of highly relevant data by considering the user's geographical location.

[0053] The generation unit can analyze users' social media activity and generate relevant data when generating voting data. For example, the generation unit can analyze users' social media activity and generate optimal voting data. The generation unit can also filter voting data based on users' social media activity. The generation unit can also optimize voting data by taking users' social media activity into consideration. This makes it possible to generate relevant data by analyzing users' social media activity.

[0054] The encryption unit can select the optimal encryption method by referring to the user's past voting history during encryption. For example, the encryption unit may prioritize suggesting encryption methods that the user has used in the past. The encryption unit can also select the most secure encryption method from the user's past voting history. The encryption unit can also analyze the user's past voting history and suggest the optimal encryption method. This makes it possible to select the optimal encryption method by referring to the user's past voting history.

[0055] The encryption unit can filter data based on the user's current lifestyle and areas of interest during encryption. For example, it can suggest the optimal encryption method based on the user's current lifestyle. The encryption unit can also customize the encryption procedure based on the user's areas of interest. Furthermore, it can optimize the encryption procedure by considering the user's lifestyle and areas of interest. This allows for more appropriate encryption by filtering based on the user's current lifestyle and areas of interest.

[0056] The encryption unit can prioritize the most relevant encryption method during encryption, taking into account the user's geographical location. For example, if the user is in a specific region, the encryption unit will suggest an encryption method suitable for that region. The encryption unit can also select the optimal encryption method based on the user's geographical location. The encryption unit can also optimize the encryption procedure, taking the user's geographical location into consideration. This allows for more appropriate encryption by prioritizing the most relevant encryption method based on the user's geographical location.

[0057] The encryption unit can analyze the user's social media activity during encryption and implement the relevant encryption method. For example, the encryption unit can analyze the user's social media activity and propose the optimal encryption method. The encryption unit can also customize the encryption procedure based on the user's social media activity. Furthermore, the encryption unit can optimize the encryption procedure by considering the user's social media activity. This makes it possible to implement the relevant encryption method by analyzing the user's social media activity.

[0058] The sending unit can select the optimal sending method by referring to the user's past voting history at the time of sending. For example, the sending unit may prioritize suggesting sending methods that the user has used in the past. The sending unit can also select the fastest and most reliable sending method from the user's past voting history. The sending unit can also analyze the user's past voting history and suggest the optimal sending method. This makes it possible to select the optimal sending method by referring to the user's past voting history.

[0059] The transmission unit can filter messages based on the user's current lifestyle and areas of interest during transmission. For example, the transmission unit can suggest the optimal transmission method based on the user's current lifestyle. The transmission unit can also customize the transmission procedure based on the user's areas of interest. Furthermore, the transmission unit can optimize the transmission procedure by considering the user's lifestyle and areas of interest. This allows for more appropriate transmissions by filtering messages based on the user's current lifestyle and areas of interest.

[0060] The transmission unit can prioritize the most relevant transmission method by considering the user's geographical location during transmission. For example, if the user is in a specific region, the transmission unit will suggest a transmission method suitable for that region. The transmission unit can also select the optimal transmission method based on the user's geographical location. The transmission unit can also optimize the transmission procedure by considering the user's geographical location. This allows for more appropriate transmission by prioritizing the most relevant transmission method by considering the user's geographical location.

[0061] The sending unit can analyze the user's social media activity and implement the appropriate sending method during transmission. For example, the sending unit can analyze the user's social media activity and suggest the optimal sending method. The sending unit can also customize the sending procedure based on the user's social media activity. The sending unit can also optimize the sending procedure considering the user's social media activity. This makes it possible to implement the appropriate sending method by analyzing the user's social media activity.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The reception desk can verify the user's identity using their biometric authentication information. For example, using fingerprint or iris authentication can ensure higher security. If the user chooses fingerprint authentication, the reception desk can verify their identity using the fingerprint sensor on their smartphone. If the user chooses iris authentication, the reception desk can verify their identity by scanning their iris using the smartphone's camera. This allows users to choose from multiple biometric authentication methods, improving both security and convenience.

[0064] The selection function can analyze the user's voting history and prioritize displaying information on candidates they have voted for in the past. For example, by displaying the policies and activities of candidates the user has previously supported, it makes it easier for the user to decide whether to vote for that candidate again. Based on the information of candidates the user has previously voted for, the selection function can also suggest new candidates with similar policies. This allows the user to choose a more appropriate candidate while referring to their past voting history.

[0065] The generation unit can include additional information to ensure voting transparency when generating user voting data. For example, the voting data can include information such as the date, time, and location of the vote, and the device used. The generation unit can also include a summary of the user's voting history in the voting data. This can improve the transparency and reliability of voting.

[0066] The encryption unit can use different encryption algorithms depending on the user's choice when encrypting the generated voting data. For example, if the user requires high security, a stronger encryption algorithm can be used. The encryption unit can also use a lightweight encryption algorithm if the user requires fast processing. This allows for flexible encryption tailored to the user's needs.

[0067] The transmission unit can adjust the transmission method according to the user's network conditions when sending encrypted voting data. For example, if the user has a high-speed internet connection, a large amount of data can be sent at once. Conversely, if the user has a slow internet connection, the data can be sent in smaller chunks. This improves the reliability and efficiency of the transmission.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives voting information from users. For example, a user downloads the app and registers their personal information using their My Number Card information. The reception desk can verify the user's identity by comparing their face with the My Number Card using the smartphone camera and by having them enter their My Number PIN. Step 2: The selection unit selects candidates based on the information received by the reception unit. For example, it may present candidates that match the user's views. The selection unit can also create a table showing the positions of each candidate on major policy issues for each constituency and proportional representation candidate, and then present candidates that match the user's views. Step 3: The generation unit generates voting data based on the candidates selected by the selection unit. For example, it generates voting data based on the candidates selected by the user. The generation unit can also generate voting data that allows for multiple re-votes during the voting period. Step 4: The encryption unit encrypts the voting data generated by the generation unit. For example, it encrypts the generated voting data. Step 5: The transmission unit sends the encrypted voting data, which has been encrypted by the encryption unit, to the central counting server. For example, it sends encrypted voting data to the central counting server.

[0070] (Example of form 2) The internet voting system according to an embodiment of the present invention is an internet voting system using a smartphone application and My Number Card to address the low voter turnout in Japan. This internet voting system allows users to download an application from an app store and register their personal information using their My Number Card information. Next, the user's identity is verified by comparing their face with the My Number Card image using the smartphone camera and entering their My Number PIN. This allows the user to utilize the candidate selection and matching function. The user confirms their selected candidate on a confirmation screen and then casts their vote. Voting data is encrypted and transmitted to a central aggregation server. An AI agent works in conjunction at each step to improve security, reliability, and user experience. This system allows for unlimited voting during the early voting period, providing a new voting method for people living abroad for work or study. It is also expected to improve voter turnout among socially vulnerable groups (such as the elderly and people with disabilities) and young people. The AI ​​agent optimizes the facial recognition, interactive voting assistant, security, and encryption processes to ensure the safety and reliability of voting. As a result, the internet voting system enables users to vote efficiently and securely.

[0071] The internet voting system according to this embodiment comprises a reception unit, a selection unit, a generation unit, an encryption unit, and a transmission unit. The reception unit receives voting information from users. For example, the reception unit receives information from users who download an app and register personal information using their My Number Card. The reception unit can verify the user's identity by comparing their face with the face on the My Number Card using the smartphone camera and by having them enter their My Number PIN. The selection unit selects candidates based on the information received by the reception unit. For example, the selection unit presents candidates that match the user's views. The selection unit can also create a table showing the positions of each candidate on major policy issues for each electoral district and proportional representation candidate and present candidates that match the user's views. The generation unit generates voting data based on the candidates selected by the selection unit. For example, the generation unit generates voting data based on the candidates selected by the user. The generation unit can also generate voting data that allows for multiple re-votes during the voting period. The encryption unit encrypts the voting data generated by the generation unit. For example, the encryption unit encrypts the generated voting data. The transmission unit sends the encrypted voting data, which has been encrypted by the encryption unit, to a central aggregation server. For example, the transmission unit sends encrypted voting data to a central aggregation server. This enables the internet voting system according to the embodiment to efficiently receive user voting information, select candidates, generate voting data, encrypt it, and transmit it.

[0072] The reception desk receives voting information from users. Specifically, this involves a process where users download a dedicated application and register their personal information using their My Number Card information. Users verify their identity by using their smartphone camera to compare their face with the photo on their My Number Card and by entering their My Number PIN. This identity verification process ensures high security by using dual authentication with facial recognition technology and a PIN. Facial recognition technology analyzes the feature points of the user's face and determines whether it matches the photo on their My Number Card. This prevents unauthorized access and impersonation. The reception desk securely manages users' personal information and plays a crucial role as the first step in the voting process. Furthermore, the reception desk encrypts and stores the information registered by users and implements security measures to prevent unauthorized access by third parties. This protects user privacy and allows them to vote with peace of mind.

[0073] The selection unit selects candidates based on information received by the reception unit. Specifically, it has the function of presenting candidates that match the user's thoughts and opinions. The selection unit displays the position of each candidate on major policy issues in a table format for each constituency and proportional representation candidate, allowing the user to select the candidate that best aligns with their own views. For example, by having the user answer a questionnaire about policy issues they are interested in, the unit recommends the most suitable candidate based on their answers. The selection unit uses AI to analyze the user's answers and compare them with the policy positions of the candidates. The AI ​​uses natural language processing technology to understand the user's answers and compare them with the policy positions of the candidates to identify the most suitable candidate. This process makes it easy for the user to find the candidate that best aligns with their opinions. The selection unit plays a role in improving the convenience of the user when selecting candidates and promoting voting behavior.

[0074] The generation unit generates voting data based on the candidates selected by the selection unit. Specifically, it creates official voting data based on the information of the candidate selected by the user. In the voting data generation process, the generation unit provides a mechanism that allows users to re-vote any number of times during the voting period. This makes it easy for users to change their vote if they wish to do so. The generation unit performs data integrity checks to ensure the consistency and accuracy of the voting data. For example, even if the same user votes multiple times, the final voting data is uniquely determined. Furthermore, the generation unit implements security measures to prevent fraudulent operations and data tampering during the voting data generation process. As a result, the generation unit can generate highly reliable voting data and improve the reliability of the entire system.

[0075] The encryption unit encrypts the voting data generated by the generation unit. Specifically, it encrypts the generated voting data using advanced encryption algorithms to ensure data confidentiality. The encryption unit uses encryption technologies such as AES (Advanced Encryption Standard) and RSA (Rivest-Shamir-Adleman) to encrypt the voting data. This prevents unauthorized access to the voting data by third parties. The encryption unit strictly manages encryption keys during the encryption process and takes measures to prevent the leakage of encryption keys. The encrypted voting data remains encrypted and protected until it is sent to the central tabulation server by the transmission unit. This allows the encryption unit to ensure the confidentiality and security of the voting data and improve the overall system security.

[0076] The transmission unit sends the encrypted voting data, encrypted by the encryption unit, to the central tabulation server. Specifically, it transmits the encrypted voting data using a secure communication protocol to ensure data integrity and confidentiality. The transmission unit uses secure communication protocols such as TLS (Transport Layer Security) or SSL (Secure Sockets Layer) to transmit the voting data. This prevents the data from being intercepted or tampered with by third parties during transmission. The transmission unit has a function to monitor the data transmission status in real time during the transmission process and to retransmit the data if a transmission error or communication failure occurs. This ensures that the voting data reliably reaches the central tabulation server, improving the reliability of the entire system. Furthermore, the transmission unit improves the transparency and auditability of the system by recording logs of transmitted data, allowing for later review of the transmission history.

[0077] The reception desk can verify the identity of a person by comparing their face with the image on their My Number Card using their smartphone camera and then requiring them to enter their My Number PIN. For example, the reception desk can verify the identity by comparing their face with the image on their My Number Card using their smartphone camera and then requiring them to enter their My Number PIN. This makes it possible to verify identity using both the smartphone camera and the My Number Card.

[0078] The selection unit can present candidates that match the user's preferences. For example, the selection unit can present candidates that match the user's preferences. This makes it possible to present candidates based on the user's preferences.

[0079] The generation unit can generate voting data based on the candidate selected by the user. For example, the generation unit generates voting data based on the candidate selected by the user. This makes it possible to generate voting data based on the candidate selected by the user.

[0080] The encryption unit can encrypt the generated voting data. For example, the encryption unit encrypts the generated voting data. This makes it possible to encrypt the generated voting data.

[0081] The transmission unit can send encrypted voting data to a central counting server. For example, the transmission unit sends encrypted voting data to a central counting server. This enables the transmission of encrypted voting data.

[0082] The selection section can create a table showing the position of each candidate on major policy issues for both constituency and proportional representation, and then present candidates that match the user's views. For example, the selection section can create a table showing the position of each candidate on major policy issues for both constituency and proportional representation, and then present candidates that match the user's views. This makes it possible to present candidates that match the user's views by creating a table showing the position of each candidate on major policy issues for both constituency and proportional representation.

[0083] The generation unit can generate voting data that allows for multiple re-votes during the voting period. For example, the generation unit generates voting data that allows for multiple re-votes during the voting period. This makes it possible to generate voting data that allows for multiple re-votes during the voting period.

[0084] The reception desk can estimate the user's emotions and adjust the identity verification procedure based on those emotions. For example, if the user is nervous, the reception desk can provide a calming voice guide and explain the procedure slowly. If the user is relaxed, the reception desk can also simplify the procedure and perform identity verification quickly. If the user is anxious, the reception desk can simplify the procedure and complete identity verification quickly. This allows for more appropriate identity verification by adjusting the procedure based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The reception desk can analyze a user's past voting history and select the most suitable identity verification method. For example, the reception desk may prioritize suggesting identity verification methods the user has used in the past. The reception desk can also select the fastest and most reliable identity verification method based on the user's past voting history. The reception desk can also analyze a user's past voting history and optimize the identity verification procedure. This makes it possible to select the most suitable identity verification method by analyzing the user's past voting history.

[0086] The reception desk can filter users based on their current lifestyle and areas of interest during identity verification. For example, the reception desk can suggest the most suitable identity verification method based on the user's current lifestyle. The reception desk can also customize the identity verification procedure based on the user's areas of interest. The reception desk can also optimize the identity verification procedure considering the user's lifestyle and areas of interest. This allows for more appropriate identity verification by filtering based on the user's current lifestyle and areas of interest.

[0087] The reception desk can estimate the user's emotions and determine the priority of identity verification based on those emotions. For example, if the user is nervous, the reception desk can prioritize identity verification to provide reassurance. If the user is relaxed, the reception desk can also prioritize identity verification for other users. If the user is anxious, the reception desk can perform identity verification quickly to reduce stress. This allows for more appropriate identity verification by determining the priority of identity verification based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The reception desk can prioritize the most relevant verification method during identity verification, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can suggest an identity verification method appropriate for that region. The reception desk can also select the optimal identity verification method based on the user's geographical location. The reception desk can also optimize the identity verification procedure, taking into account the user's geographical location. This allows for more appropriate identity verification by prioritizing the most relevant verification method based on the user's geographical location.

[0089] The reception desk can analyze a user's social media activity during identity verification and implement relevant verification methods. For example, the reception desk can analyze a user's social media activity and propose the most suitable identity verification method. The reception desk can also customize the identity verification procedure based on the user's social media activity. Furthermore, the reception desk can optimize the identity verification procedure by considering the user's social media activity. This allows for the implementation of relevant verification methods by analyzing the user's social media activity.

[0090] The selection unit can estimate the user's emotions and adjust how candidates are presented based on those emotions. For example, if the user is relaxed, the selection unit can provide detailed candidate information. If the user is in a hurry, the selection unit can also provide concise candidate information. If the user is excited, the selection unit can also provide visually appealing candidate information. By adjusting how candidates are presented based on the user's emotions, it becomes possible to present more appropriate candidates. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The selection unit can select the most suitable candidate by referring to the user's past voting history when presenting candidates. For example, the selection unit can suggest the most suitable candidate based on the candidates the user has voted for in the past. The selection unit can also select the candidate of the user's greatest interest from their past voting history. The selection unit can also analyze the user's past voting history and suggest the most suitable candidate. This makes it possible to select the most suitable candidate by referring to the user's past voting history.

[0092] The selection unit can filter candidates based on the user's current living situation and areas of interest when presenting them. For example, the selection unit can suggest the most suitable candidate based on the user's current living situation. The selection unit can also filter candidate information based on the user's areas of interest. The selection unit can also optimize candidate information considering the user's living situation and areas of interest. This allows for the presentation of more appropriate candidates by filtering based on the user's current living situation and areas of interest.

[0093] The selection unit can estimate the user's emotions and prioritize candidates based on those emotions. For example, if the user is relaxed, the selection unit can provide detailed candidate information. If the user is in a hurry, the selection unit can also provide concise candidate information. If the user is excited, the selection unit can also provide visually appealing candidate information. This allows for the presentation of more appropriate candidates by prioritizing candidates based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The selection function can prioritize presenting highly relevant candidates by considering the user's geographical location when displaying candidates. For example, if the user is in a specific region, the selection function will suggest candidates suitable for that region. The selection function can also select the most suitable candidate based on the user's geographical location. The selection function can also optimize candidate information by considering the user's geographical location. This allows for the presentation of more appropriate candidates by prioritizing highly relevant candidates while considering the user's geographical location.

[0095] The selection unit can analyze the user's social media activity when presenting candidates and suggest relevant candidates. For example, the selection unit can analyze the user's social media activity and suggest the most suitable candidates. The selection unit can also filter candidate information based on the user's social media activity. The selection unit can also optimize candidate information considering the user's social media activity. This makes it possible to present relevant candidates by analyzing the user's social media activity.

[0096] The generation unit can estimate the user's emotions and adjust the method of generating voting data based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate detailed voting data. If the user is in a hurry, the generation unit can also generate concise voting data. If the user is excited, the generation unit can also generate visually appealing voting data. This allows for the generation of more appropriate voting data by adjusting the method of generating voting data 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 is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The generation unit can generate optimal data by referring to the user's past voting history when generating voting data. For example, the generation unit can generate optimal voting data based on the candidates the user has voted for in the past. The generation unit can also generate the most relevant voting data from the user's past voting history. The generation unit can also analyze the user's past voting history and generate optimal voting data. This makes it possible to generate optimal voting data by referring to the user's past voting history.

[0098] The generation unit can filter voting data based on the user's current lifestyle and areas of interest during generation. For example, the generation unit generates optimal voting data according to the user's current lifestyle. The generation unit can also filter voting data based on the user's areas of interest. The generation unit can also optimize voting data by considering the user's lifestyle and areas of interest. This makes it possible to generate more appropriate voting data by filtering based on the user's current lifestyle and areas of interest.

[0099] The generation unit can estimate the user's emotions and prioritize voting data based on those emotions. For example, if the user is relaxed, the generation unit can generate detailed voting data. If the user is in a hurry, the generation unit can also generate concise voting data. If the user is excited, the generation unit can also generate visually appealing voting data. This allows for the generation of more appropriate voting data by prioritizing voting data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The generation unit can prioritize generating highly relevant data by considering the user's geographical location when generating voting data. For example, if a user is in a specific region, the generation unit will generate voting data appropriate for that region. The generation unit can also generate optimal voting data based on the user's geographical location. The generation unit can also optimize voting data by considering the user's geographical location. This makes it possible to generate more appropriate voting data by prioritizing the generation of highly relevant data by considering the user's geographical location.

[0101] The generation unit can analyze users' social media activity and generate relevant data when generating voting data. For example, the generation unit can analyze users' social media activity and generate optimal voting data. The generation unit can also filter voting data based on users' social media activity. The generation unit can also optimize voting data by taking users' social media activity into consideration. This makes it possible to generate relevant data by analyzing users' social media activity.

[0102] The encryption unit can estimate the user's emotions and adjust the encryption procedure based on the estimated emotions. For example, if the user is relaxed, the encryption unit can provide a detailed encryption procedure. If the user is in a hurry, the encryption unit can also provide a simplified encryption procedure. If the user is excited, the encryption unit can also provide a visually appealing encryption procedure. This allows for more appropriate encryption by adjusting the encryption procedure based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0103] The encryption unit can select the optimal encryption method by referring to the user's past voting history during encryption. For example, the encryption unit may prioritize suggesting encryption methods that the user has used in the past. The encryption unit can also select the most secure encryption method from the user's past voting history. The encryption unit can also analyze the user's past voting history and suggest the optimal encryption method. This makes it possible to select the optimal encryption method by referring to the user's past voting history.

[0104] The encryption unit can filter data based on the user's current lifestyle and areas of interest during encryption. For example, it can suggest the optimal encryption method based on the user's current lifestyle. The encryption unit can also customize the encryption procedure based on the user's areas of interest. Furthermore, it can optimize the encryption procedure by considering the user's lifestyle and areas of interest. This allows for more appropriate encryption by filtering based on the user's current lifestyle and areas of interest.

[0105] The encryption unit can estimate the user's emotions and determine encryption priorities based on those emotions. For example, if the user is relaxed, the encryption unit can provide detailed encryption steps. If the user is in a hurry, it can also provide simplified encryption steps. If the user is excited, it can even provide visually appealing encryption steps. This allows for more appropriate encryption by determining encryption priorities based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The encryption unit can prioritize the most relevant encryption method during encryption, taking into account the user's geographical location. For example, if the user is in a specific region, the encryption unit will suggest an encryption method suitable for that region. The encryption unit can also select the optimal encryption method based on the user's geographical location. The encryption unit can also optimize the encryption procedure, taking the user's geographical location into consideration. This allows for more appropriate encryption by prioritizing the most relevant encryption method based on the user's geographical location.

[0107] The encryption unit can analyze the user's social media activity during encryption and implement the relevant encryption method. For example, the encryption unit can analyze the user's social media activity and propose the optimal encryption method. The encryption unit can also customize the encryption procedure based on the user's social media activity. Furthermore, the encryption unit can optimize the encryption procedure by considering the user's social media activity. This makes it possible to implement the relevant encryption method by analyzing the user's social media activity.

[0108] The sending unit can estimate the user's emotions and adjust the sending procedure based on the estimated emotions. For example, if the user is relaxed, the sending unit can provide a detailed sending procedure. If the user is in a hurry, the sending unit can also provide a simplified sending procedure. If the user is excited, the sending unit can also provide a visually appealing sending procedure. This allows for more appropriate sending by adjusting the sending procedure based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The sending unit can select the optimal sending method by referring to the user's past voting history at the time of sending. For example, the sending unit may prioritize suggesting sending methods that the user has used in the past. The sending unit can also select the fastest and most reliable sending method from the user's past voting history. The sending unit can also analyze the user's past voting history and suggest the optimal sending method. This makes it possible to select the optimal sending method by referring to the user's past voting history.

[0110] The transmission unit can filter messages based on the user's current lifestyle and areas of interest during transmission. For example, the transmission unit can suggest the optimal transmission method based on the user's current lifestyle. The transmission unit can also customize the transmission procedure based on the user's areas of interest. Furthermore, the transmission unit can optimize the transmission procedure by considering the user's lifestyle and areas of interest. This allows for more appropriate transmissions by filtering messages based on the user's current lifestyle and areas of interest.

[0111] The sending unit can estimate the user's emotions and determine the priority of sending based on the estimated emotions. For example, if the user is relaxed, the sending unit can provide detailed sending instructions. If the user is in a hurry, the sending unit can also provide simplified sending instructions. If the user is excited, the sending unit can also provide visually appealing sending instructions. This allows for more appropriate sending by determining the priority of sending based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0112] The transmission unit can prioritize the most relevant transmission method by considering the user's geographical location during transmission. For example, if the user is in a specific region, the transmission unit will suggest a transmission method suitable for that region. The transmission unit can also select the optimal transmission method based on the user's geographical location. The transmission unit can also optimize the transmission procedure by considering the user's geographical location. This allows for more appropriate transmission by prioritizing the most relevant transmission method by considering the user's geographical location.

[0113] The sending unit can analyze the user's social media activity and implement the appropriate sending method during transmission. For example, the sending unit can analyze the user's social media activity and suggest the optimal sending method. The sending unit can also customize the sending procedure based on the user's social media activity. The sending unit can also optimize the sending procedure considering the user's social media activity. This makes it possible to implement the appropriate sending method by analyzing the user's social media activity.

[0114] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0115] The reception desk can verify the user's identity using their biometric authentication information. For example, using fingerprint or iris authentication can ensure higher security. If the user chooses fingerprint authentication, the reception desk can verify their identity using the fingerprint sensor on their smartphone. If the user chooses iris authentication, the reception desk can verify their identity by scanning their iris using the smartphone's camera. This allows users to choose from multiple biometric authentication methods, improving both security and convenience.

[0116] The selection function can analyze the user's voting history and prioritize displaying information on candidates they have voted for in the past. For example, by displaying the policies and activities of candidates the user has previously supported, it makes it easier for the user to decide whether to vote for that candidate again. Based on the information of candidates the user has previously voted for, the selection function can also suggest new candidates with similar policies. This allows the user to choose a more appropriate candidate while referring to their past voting history.

[0117] The generation unit can include additional information to ensure voting transparency when generating user voting data. For example, the voting data can include information such as the date, time, and location of the vote, and the device used. The generation unit can also include a summary of the user's voting history in the voting data. This can improve the transparency and reliability of voting.

[0118] The encryption unit can use different encryption algorithms depending on the user's choice when encrypting the generated voting data. For example, if the user requires high security, a stronger encryption algorithm can be used. The encryption unit can also use a lightweight encryption algorithm if the user requires fast processing. This allows for flexible encryption tailored to the user's needs.

[0119] The transmission unit can adjust the transmission method according to the user's network conditions when sending encrypted voting data. For example, if the user has a high-speed internet connection, a large amount of data can be sent at once. Conversely, if the user has a slow internet connection, the data can be sent in smaller chunks. This improves the reliability and efficiency of the transmission.

[0120] The reception desk can estimate the user's emotions and adjust the identity verification procedure based on those emotions. For example, if the user is nervous, it can provide a calming voice guide and explain the procedure slowly. If the user is relaxed, the procedure can be simplified and identity verification can be completed quickly. If the user is anxious, the procedure can be simplified and identity verification can be completed quickly. In this way, by adjusting the identity verification procedure based on the user's emotions, more appropriate identity verification becomes possible.

[0121] The selection function can estimate the user's emotions and adjust how candidates are presented based on those emotions. For example, if the user is relaxed, detailed candidate information can be provided. If the user is in a hurry, concise candidate information can be provided. If the user is excited, visually appealing candidate information can be provided. By adjusting the candidate presentation method based on the user's emotions, it becomes possible to present more appropriate candidates.

[0122] The generation unit can estimate the user's emotions and adjust the method of generating voting data based on those emotions. For example, if the user is relaxed, it can generate detailed voting data. If the user is in a hurry, it can generate concise voting data. If the user is excited, it can generate visually appealing voting data. By adjusting the method of generating voting data based on the user's emotions, it becomes possible to generate more appropriate voting data.

[0123] The encryption unit can estimate the user's emotions and adjust the encryption procedure based on those emotions. For example, if the user is relaxed, it can provide a detailed encryption procedure. If the user is in a hurry, it can provide a simplified encryption procedure. If the user is excited, it can provide a visually appealing encryption procedure. By adjusting the encryption procedure based on the user's emotions, more appropriate encryption becomes possible.

[0124] The transmission unit can estimate the user's emotions and adjust the transmission procedure based on those emotions. For example, if the user is relaxed, it can provide detailed transmission instructions. If the user is in a hurry, it can provide simplified instructions. If the user is excited, it can provide visually appealing instructions. By adjusting the transmission procedure based on the user's emotions, more appropriate transmissions become possible.

[0125] The following briefly describes the processing flow for example form 2.

[0126] Step 1: The reception desk receives voting information from users. For example, a user downloads the app and registers their personal information using their My Number Card information. The reception desk can verify the user's identity by comparing their face with the My Number Card using the smartphone camera and by having them enter their My Number PIN. Step 2: The selection unit selects candidates based on the information received by the reception unit. For example, it may present candidates that match the user's views. The selection unit can also create a table showing the positions of each candidate on major policy issues for each constituency and proportional representation candidate, and then present candidates that match the user's views. Step 3: The generation unit generates voting data based on the candidates selected by the selection unit. For example, it generates voting data based on the candidates selected by the user. The generation unit can also generate voting data that allows for multiple re-votes during the voting period. Step 4: The encryption unit encrypts the voting data generated by the generation unit. For example, it encrypts the generated voting data. Step 5: The transmission unit sends the encrypted voting data, which has been encrypted by the encryption unit, to the central counting server. For example, it sends encrypted voting data to the central counting server.

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0129] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0130] Each of the multiple elements described above, including the reception unit, selection unit, generation unit, encryption unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user downloads an app and registers personal information using information from their My Number Card. The selection unit is implemented by the control unit 46A of the smart device 14, where it presents candidates that match the user's preferences. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, where it generates voting data based on the selected candidate. The encryption unit is implemented by the identification processing unit 290 of the data processing unit 12, where it encrypts the generated voting data. The transmission unit is implemented by the control unit 46A of the smart device 14, where it transmits the encrypted voting data to a central aggregation server. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0132] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the reception unit, selection unit, generation unit, encryption unit, and transmission unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user downloads an app and registers personal information using information from their My Number Card. The selection unit is implemented by the control unit 46A of the smart glasses 214, where candidates that match the user's preferences are presented. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, where voting data is generated based on the selected candidates. The encryption unit is implemented by the identification processing unit 290 of the data processing unit 12, where the generated voting data is encrypted. The transmission unit is implemented by the control unit 46A of the smart glasses 214, where the encrypted voting data is transmitted to a central aggregation server. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0148] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0155] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0156] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0157] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0158] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0159] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0160] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0161] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0162] Each of the multiple elements described above, including the reception unit, selection unit, generation unit, encryption unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user downloads an app and registers personal information using information from their My Number Card. The selection unit is implemented by the control unit 46A of the headset terminal 314, where candidates that match the user's preferences are presented. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, where voting data is generated based on the selected candidates. The encryption unit is implemented by the identification processing unit 290 of the data processing unit 12, where the generated voting data is encrypted. The transmission unit is implemented by the control unit 46A of the headset terminal 314, where the encrypted voting data is transmitted to a central aggregation server. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0163] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0164] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0165] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0166] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0167] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0169] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0170] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0171] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0172] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0173] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0174] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0175] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0176] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0177] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0178] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0179] Each of the multiple elements described above, including the reception unit, selection unit, generation unit, encryption unit, and transmission unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user downloads an app and registers personal information using information from their My Number Card. The selection unit is implemented by, for example, the control unit 46A of the robot 414, and presents candidates that match the user's preferences. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and generates voting data based on the selected candidate. The encryption unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and encrypts the generated voting data. The transmission unit is implemented by, for example, the control unit 46A of the robot 414, and transmits the encrypted voting data to a central aggregation server. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0180] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0181] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0182] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0183] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0184] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0185] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0186] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0187] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0188] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0189] 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.

[0190] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0191] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0192] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0193] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0194] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0195] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0196] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0197] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0198] (Note 1) A reception desk that receives voting information from users, A selection unit that selects candidates based on the information received by the reception unit, A generation unit that generates voting data based on the candidate selected by the selection unit, An encryption unit that encrypts the voting data generated by the generation unit, The system includes a transmission unit that transmits the voting data encrypted by the encryption unit to a central tabulation server. A system characterized by the following features. (Note 2) The aforementioned reception unit is Identity verification is performed by comparing the face on the smartphone camera with the face on the My Number Card and entering the My Number PIN. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned selection unit is Present candidates that match the user's preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate voting data based on the candidate selected by the user. The system described in Appendix 1, characterized by the features described herein. (Note 5) The encryption unit is Encrypt the generated voting data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned transmitting unit Encrypted voting data is sent to a central counting server. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned selection unit is The system creates a table showing the position of each candidate on major policy issues for both electoral districts and proportional representation, and then presents candidates that match the user's views. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Generate voting data that allows for multiple re-votes during the voting period. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and adjusts the identity verification process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Analyze the user's past voting history and select the most suitable identity verification method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During identity verification, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the user's emotions and determines the priority of identity verification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During identity verification, the system prioritizes the use of highly relevant verification methods, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is During identity verification, we analyze the user's social media activity and implement relevant verification methods. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is It estimates the user's emotions and adjusts how candidates are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is When presenting candidates, the system selects the most suitable candidate by referring to the user's past voting history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When presenting candidates, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is It estimates the user's emotions and determines candidate priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned selection unit is When presenting candidates, the system prioritizes showing highly relevant candidates by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned selection unit is When presenting candidates, the system analyzes the user's social media activity and suggests relevant candidates. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is We estimate user sentiment and adjust how voting data is generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating voting data, the system references the user's past voting history to generate the most optimal data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating voting data, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is The system estimates user sentiment and prioritizes voting data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating voting data, the system prioritizes generating highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating voting data, the system analyzes users' social media activity and generates relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The encryption unit is It estimates the user's emotions and adjusts the encryption procedure based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The encryption unit is During encryption, the system selects the optimal encryption method by referring to the user's past voting history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The encryption unit is During encryption, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 30) The encryption unit is It estimates the user's emotions and determines encryption priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The encryption unit is During encryption, the system prioritizes the most relevant encryption method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The encryption unit is During encryption, the system analyzes the user's social media activity and implements the appropriate encryption method. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned transmitting unit It estimates the user's emotions and adjusts the sending procedure based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned transmitting unit When sending a vote, the system will refer to the user's past voting history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned transmitting unit When sending, filtering is performed based on the user's current lifestyle and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned transmitting unit It estimates the user's emotions and determines the priority of messages based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned transmitting unit When sending data, the system prioritizes the most relevant sending method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned transmitting unit When sending a message, we analyze the user's social media activity and implement appropriate sending methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0199] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that receives voting information from users, A selection unit that selects candidates based on the information received by the reception unit, A generation unit that generates voting data based on the candidate selected by the selection unit, An encryption unit that encrypts the voting data generated by the generation unit, The system includes a transmission unit that transmits the voting data encrypted by the encryption unit to a central tabulation server. A system characterized by the following features.

2. The aforementioned reception unit is Identity verification is performed by comparing the face on the smartphone camera with the face on the My Number Card and entering the My Number PIN. The system according to feature 1.

3. The aforementioned selection unit is Present candidates that match the user's preferences. The system according to feature 1.

4. The generating unit is Generate voting data based on the candidate selected by the user. The system according to feature 1.

5. The encryption unit is Encrypt the generated voting data. The system according to feature 1.

6. The aforementioned transmitting unit Encrypted voting data is sent to a central counting server. The system according to feature 1.

7. The aforementioned selection unit is The system creates a table showing the position of each candidate on major policy issues for both electoral districts and proportional representation, and then presents candidates that match the user's views. The system according to feature 1.

8. The generating unit is Generate voting data that allows for multiple re-votes during the voting period. The system according to feature 1.

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