System

Abstract

A system is provided.SOLUTION: A system comprising: a noise addition means for adding noise to data collected from a plurality of information providers to realize differential privacy; a data transmission means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit; a secure computation means for securely integrating a plurality of pieces of the encrypted data and performing statistical computation; and a result return means for encrypting a result of the statistical computation and returning the encrypted result to each information provider.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In the logistics industry, resource shortages due to restrictions on truck driver overtime work have become a serious problem. To solve this problem, it is necessary for logistics companies to share the data they hold and allocate resources efficiently. However, the data held by each company contains confidential information about individual drivers and vehicles, and companies are reluctant to share this information directly with other companies. The purpose of this invention is to enable each company's data to be shared safely while protecting privacy, thereby enabling the effective use of logistics resources. [Means for solving the problem]

[0005] This invention uses a noise adding means that adds noise to data collected from multiple information providers to achieve differential privacy. Next, a data transmission means is used that encrypts the noise-added data and transmits it to a central processing unit. The central processing unit has a secret calculation means that securely integrates the multiple encrypted data and performs statistical calculations. Finally, a result return means is used that encrypts the results of the statistical calculations and returns them to each information provider. This invention allows each company to obtain useful integrated statistical information without disclosing confidential information to other companies.

[0006] An "information provider" is an entity that collects and provides data, such as a logistics company or an individual.

[0007] "Data" refers to information collected from each information provider, such as truck driver and vehicle operating status, driving routes, and working hours.

[0008] "Noise" is a random value added to data and is a means of achieving differential privacy.

[0009] "Differential privacy" is a privacy-preserving technique that adds appropriate noise to data to ensure that similar statistical results are obtained regardless of whether a particular individual is included in the dataset.

[0010] A "noise-adding means" is a device or program that adds random noise to collected data in order to achieve differential privacy.

[0011] The "data transmission means" refers to a device or program for encrypting the data to which noise has been added by the noise adding means and transmitting the data safely to the central processing unit.

[0012] The "central processing unit" is a device that receives the encrypted data sent from each information provider, integrates it, and performs secret calculations.

[0013] "Secure computing" is a technology that allows multiple participants to perform calculations collaboratively without disclosing their data to others.

[0014] The "secret calculation means" is a device or program that integrates received data and performs statistical calculations in the central processing unit.

[0015] The "result return means" is a device or program for encrypting the statistical results obtained by the secure calculation means and returning them to each information provider. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] System configuration

[0038] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0039] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0040] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0041] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0042] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0043] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0044] Data collection

[0045] User: Each logistics company's terminal collects data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0046] Example: For example, a terminal of a logistics company A collects the following data:

[0047] Driver ID: 12345

[0048] Vehicle ID: 67890

[0049] Operating time: 8 hours

[0050] Overtime work hours: 2 hours

[0051] Data noise addition

[0052] Terminal: Noise is added to the collected data based on differential privacy technology to reduce the risk of specific personal or vehicle information being directly leaked.

[0053] Example: Data showing an operating time of 8 hours is converted to 8.3 hours by adding noise of ±0.3 hours.

[0054] Data transmission

[0055] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0056] Example: The transmitted data is driver ID 12345, vehicle ID 67890, working hours 8.3 hours, overtime hours 2.1 hours (after adding noise).

[0057] Data Integration and Secure Computing

[0058] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0059] Example: Aggregating data from each company and calculating, for example, total working hours and average overtime hours.

[0060] Generate and return statistical results

[0061] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0062] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0063] Usage example

[0064] For example, logistics company A collects data internally, adds noise, and sends it in encrypted form to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to allocate resources efficiently.

[0065] The present invention allows logistics companies to obtain statistically valuable information without leaking their data to other companies, thereby helping to solve the problem of resource shortages.

[0066] The processing flow will be explained below.

[0067] Step 1:

[0068] User: Logistics company terminals collect operational data on truck drivers and vehicles, such as driver ID, vehicle ID, operating hours, and overtime hours.

[0069] Step 2:

[0070] Terminal: Enters collected data into the system and standardizes the data format. Converts collected data into a standard format such as JSON. Also, performs data consistency checks and corrects errors if necessary.

[0071] Step 3:

[0072] Terminal: Noise is added to the data using differential privacy techniques. A noise generation function is applied to add noise to each data point. For example, if data shows an operating time of 8 hours, a noise of ±0.3 hours is added to convert it to 8.3 hours.

[0073] Step 4:

[0074] Terminal: The data with noise added is encrypted and sent to the central processing unit (server). The data is encrypted using AES encryption technology. After encryption, the data is sent via the HTTPS protocol.

[0075] Step 5:

[0076] Server: Decrypts the noise-added data received from each device, for example, using AES encryption to restore the original data format.

[0077] Step 6:

[0078] Server: Integrates the received data and performs statistical calculations using secure computing technology. For example, it calculates the overall average working hours, average overtime hours, etc. The noisy data sent from each logistics company is unified as data with random noise added.

[0079] Step 7:

[0080] Server: The statistical calculation results are encrypted before being sent back to each logistics company. The calculated statistical results are converted into JSON format and then encrypted again using AES.

[0081] Step 8:

[0082] Server: Encrypted statistical results are returned to each logistics company via HTTPS. Again, encrypted communication is used when sending data, ensuring secure information sharing.

[0083] Step 9:

[0084] User: Receives the returned statistical results on their own device and decrypts them. Based on the decrypted statistical results, they develop strategies for resource allocation, scheduling, etc.

[0085] In this way, this system allows each logistics company to obtain statistical information safely without disclosing the data.

[0086] Example 1

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

[0088] In today's logistics industry, multiple companies need to cooperate and share overall statistical information in order to operate efficiently. However, because data sharing carries the risk of individual information being leaked, there is a need for a system that allows each company to access statistical information while keeping its own data secure.

[0089] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0090] In this invention, the server includes a noise adding means for adding noise to data collected from multiple data collection devices to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret computation means for securely integrating multiple encrypted data and performing statistical computations, a result return means for encrypting the statistical computation results and returning them to each data collection device, and a system in which each data collection device adds noise to the data to be identified and then transmits the data using encrypted communication means, and the central processing unit integrates the multiple data using secret computation technology to generate and securely return the statistical computation results.This allows each company to safely share overall statistical information without leaking its own data, enabling efficient business operations.

[0091] A "data collection device" is a device for collecting data from multiple information providers.

[0092] The "noise adding means" is a means for adding noise to collected data to achieve differential privacy.

[0093] The "data transmission means" is a means for encrypting the data to which noise has been added and transmitting the data to the central processing unit.

[0094] The "central processing unit" is a device that receives data sent from multiple information providers and integrates it using secure computing technology.

[0095] A "secret computation means" is a means for securely integrating multiple encrypted data and performing statistical computations.

[0096] The "result return means" is a means for encrypting the statistical calculation results and returning them to each information provider.

[0097] "Differential privacy" is a technology that adds noise to data to make individual data unidentifiable and protect the privacy of that data.

[0098] "Encrypted communication means" refers to a means for encrypting data and communicating it securely.

[0099] "Secure computing technology" is a technology that securely integrates multiple data sets and calculates statistical information without disclosing the individual data.

[0100] "Statistical calculation" refers to the calculation of aggregating received data and generating statistical information.

[0101] "Encryption" is a technology that converts data using a certain algorithm to prevent it from being read by third parties.

[0102] This invention is a data sharing system for the logistics industry, designed to securely collect, process, and share specific data. The system includes the following components:

[0103] System configuration

[0104] 1. Data collection device (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0105] 2. Noise injection means (terminal): Noise is injected into the collected data based on differential privacy technology. Noise injection reduces the risk of direct leakage of specific personal or vehicle information. This technology is realized, for example, using a Laplace noise generation algorithm.

[0106] 3. Data transmission means (terminal): The data with noise added is encrypted and sent to the central processing unit. This transmission uses an encryption communication protocol such as AES encryption to ensure security.

[0107] 4. Secure computing means (server): The data sent from each logistics company is integrated and statistical calculations are performed using secure computing technology, such as Secure Multi-party Computation (SMPC).

[0108] 5. Result return method (server): The calculation results are encrypted and returned to each company. This return also uses an encrypted communication protocol.

[0109] Data collection

[0110] User: Personnel at each logistics company use terminals to input and collect data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0111] Example: When a terminal of logistics company A collects the following data, "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," "Overtime hours 2 hours," etc. are entered into the data collection device.

[0112] Data noise addition

[0113] Terminal: Noise is added to the collected data using differential privacy techniques. For example, noise of ±0.3 hours can be added using a Laplace noise generation algorithm.

[0114] Example: Add noise of ±0.3 hours to the data "8 hours of operation time" to convert it to "8.3 hours of operation time."

[0115] Data transmission

[0116] Terminal: The noise-added data is AES-encrypted and sent to the central processing unit. An encryption protocol (e.g., AES-256) is used to ensure secure data transmission.

[0117] Example: Encrypt and send the data "Driver ID 12345," "Vehicle ID 67890," "Working hours 8.3 hours," and "Overtime hours 2.1 hours (after adding noise)."

[0118] Data Integration and Secure Computing

[0119] Server: The central processing unit integrates the noise-added data received from each logistics company and calculates statistical information using Secure Multi-party Computation (SMPC) technology.

[0120] Example: Integrate operating time data collected from multiple logistics companies to calculate the overall average operating time and overtime hours.

[0121] Generate and return statistical results

[0122] Server: Returns the calculation results to each logistics company using encrypted communication. The encryption protocol is used to ensure the secure transfer of the results.

[0123] Example: Statistical results such as "total average working hours: 8.2 hours" and "total average overtime hours: 2.3 hours" are generated, encrypted, and sent back to each logistics company.

[0124] Usage example

[0125] For example, logistics company A collects data in-house, adds noise to it, encrypts the data, and sends it to a central server. The central server also receives data from multiple logistics companies (B and C), consolidates it, and generates overall statistical information. This statistical information is then re-encrypted and securely sent back to each company. Each company can use this information to improve their own operating conditions and working hours.

[0126] Example prompts for generative AI models

[0127] "Please explain the process by which a logistics company's terminal collects truck driver working time data, adds noise to it, encrypts it, and then transmits it to a central server. The server then aggregates this data, generates statistical results using secure computing technology, and securely returns them to each company. Please provide a concrete example of such a scenario."

[0128] This system allows each logistics company to safely obtain overall statistical information without leaking their own data to other companies, thereby improving operational efficiency and optimizing resources.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] User: Personnel at each logistics company use terminals to input and collect data on truck drivers and vehicles, including details such as working hours, routes, and overtime hours.

[0132] Input: Data entered into the terminal by the logistics company staff (e.g., driver ID, vehicle ID, working hours, overtime hours).

[0133] Output: Raw data saved to the device.

[0134] Specific operation: The following information is entered into the terminal of logistics company A: "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," and "Overtime hours 2 hours."

[0135] Step 2:

[0136] Terminal: Noise is added to the collected data based on differential privacy technology. The level of noise is set randomly to prevent individual data from being identified.

[0137] Input: Raw data (e.g., operating time 8 hours).

[0138] Output: Noisy data (e.g., operating time 8.3 hours).

[0139] Specific operation: Add noise of ±0.3 hours to the data of "8 hours of operation time" and convert it to "8.3 hours of operation time."

[0140] Step 3:

[0141] Terminal: The noise-added data is encrypted and sent to the central processing unit. To ensure security, the AES encryption protocol is used.

[0142] Input: Noisy data (e.g., operating time 8.3 hours).

[0143] Output: The encrypted data.

[0144] Specific operation: The noise-added data "8.3 hours of operation time" is AES encrypted and the encrypted data is sent to the central processing unit.

[0145] Step 4:

[0146] Server: The central processing unit aggregates the noise-added data received from each logistics company and calculates statistical information using secret computing technology. This allows the extraction of overall statistical information without disclosing individual data.

[0147] Input: Encrypted data (noisy data received from multiple logistics companies).

[0148] Output: Consolidated statistical data.

[0149] Specific operation: Data received from each logistics company is integrated using Secure Multi-party Computation (SMPC) technology to calculate the overall average operating hours and overtime hours.

[0150] Step 5:

[0151] Server: The calculation results are encrypted and sent back to each logistics company. To ensure secure communication, an encryption protocol is again used.

[0152] Input: Consolidated statistical data.

[0153] Output: Encrypted statistical calculation results.

[0154] Specific operations: Generate statistical results such as overall average operating hours and overtime hours, encrypt them, and send them back to each logistics company.

[0155] Through the above processing steps, this system enables logistics companies to safely share overall statistical information without leaking their own data to other companies, thereby improving business efficiency and optimizing resources.

[0156] (Application example 1)

[0157] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0158] In the logistics industry, each logistics company needs to securely share its data with other companies while obtaining statistically valuable information. However, information sharing is not easy unless privacy protection and data confidentiality are ensured. In particular, advanced privacy protection and data encryption technologies are required to securely collect and integrate data such as driver operation information, driving routes, and working hours to obtain statistical information. Against this background, a method is needed for logistics companies to securely share data while improving overall operational efficiency.

[0159] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0160] In this invention, the server includes a noise adding means for adding noise to data collected from multiple information providers to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations, a result return means for encrypting the statistical calculation results and returning them to each information provider, a data collection means for collecting driver operation information, driving routes, and working hours for managing transportation operations, and an encryption communication means for using an encryption communication protocol when encrypting the data and transmitting it to the central processing unit. This allows each logistics company to safely share data and obtain statistically valuable information while protecting the privacy of their individual data.

[0161] "Data collection means" refers to a device or method for collecting data such as driver operating information, driving routes, and working hours for the purpose of managing transportation operations.

[0162] A "noise-adding means" is a device or method that achieves differential privacy by adding random noise to collected data, making individual data unidentifiable.

[0163] The "data transmission means" refers to a device or method for encrypting the data to which noise has been added and transmitting the encrypted data to the central processing unit.

[0164] "Encrypted communication means" refers to a device or method that uses protocols or techniques to encrypt data and communicate securely.

[0165] A "secret computation method" is a technology or method for securely integrating multiple encrypted data sets and performing statistical calculations.

[0166] The "result return means" is a device or method for encrypting the statistical calculation results and returning them to each information provider.

[0167] In the present invention, a data sharing system in the logistics industry is composed of the following steps.

[0168] 1. Data Collection

[0169] The terminals of each logistics company collect data such as driver operating information, driving routes, and working hours. This allows each logistics company to obtain detailed driving information for its drivers. As a specific example, the terminal of a certain logistics company collects data such as driver ID "12345," vehicle ID "67890," working hours "8 hours," and overtime hours "2 hours."

[0170] 2. Adding noise

[0171] The noise-adding means adds random noise to the data collected within the device to achieve differential privacy. This makes individual data unidentifiable. For example, data showing an operating time of "8 hours" is converted to "8.3 hours" by adding noise of ±0.3 hours.

[0172] 3. Data Transmission

[0173] The data transmission means encrypts the noise-added data and transmits it securely to the central server. At this time, the encryption communication means ensures the security of the data using an encryption communication protocol. As a specific example, the encrypted data is transmitted as driver ID "12345," vehicle ID "67890," operating hours "8.3 hours," and overtime hours "2.1 hours."

[0174] 4. Data Integration and Secure Computing

[0175] A central server receives the encrypted data with noise added from each logistics company, and a secret computation means securely integrates the data and performs statistical calculations.Secure computation technology makes it possible to obtain overall statistical information without disclosing the data of each logistics company.As a specific example, data from each company can be aggregated to calculate total operating hours, average overtime hours, etc.

[0176] 5. Generating and returning statistical results

[0177] The central server encrypts the statistical calculation results and returns them to each information provider. The statistical results are then securely transmitted to each logistics company by the result return means. For example, statistical results such as the overall average working hours of "8.2 hours" and the overall average overtime hours of "2.3 hours" are returned.

[0178] Data transmission and encryption protocols use cryptography libraries and specific encryption protocols (e.g., AES encryption).

[0179] Here are some example prompts to input to a generative AI model:

[0180] "Please tell me how to implement a smartphone application that collects operational data from truck drivers working at a logistics center, adds noise using differential privacy techniques, and then securely transmits the data to a central server. Specifically, please explain in detail each stage of data collection, noise addition, and encrypted transmission."

[0181] As described above, logistics companies can safely share data while protecting the privacy of their individual data, and obtain statistically valuable information, which will improve operational efficiency across the logistics industry.

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The user (the terminal of each logistics company) collects data such as driver operation information, driving routes, and working hours. Specifically, the terminal acquires each driver's GPS data and operation log in real time. Inputs include driver ID, vehicle ID, operating hours, and overtime hours, which the terminal acquires from sensing devices and logging apps. The collected raw data is generated as output.

[0185] Step 2:

[0186] The device adds noise to the collected data. Specifically, a program that adds random noise to the data is executed within the device. The raw data collected in step 1 is used as input. To achieve differential privacy, data processing involves adding or subtracting noise to, for example, working hours or overtime hours. Partially anonymized data with added noise is generated as output.

[0187] Step 3:

[0188] The device encrypts the noise-added data and sends it to the central server. Specifically, the device uses a cryptography library to perform AES encryption and sends the encrypted data to the server via a secure communication protocol (e.g., HTTPS). The noise-added data generated in step 2 is used as input. Data transformation involves encryption. The encrypted data is sent to the server as output.

[0189] Step 4:

[0190] The server receives encrypted data sent from multiple logistics companies and securely integrates it. Specifically, the server saves the received encrypted data and stores it in a database for subsequent integration calculations. The input is encrypted data sent from the terminals of each logistics company. The data calculation involves virtually integrating this data into a single dataset. The output is an integrated dataset.

[0191] Step 5:

[0192] The server performs secure computation on the integrated dataset to generate statistical information. Specifically, it uses secure computation means to analyze the data and calculate statistical information such as the overall average working hours and average overtime hours. The input is the integrated dataset created in step 4. Data operations such as addition and averaging are performed. The output is the required statistical information.

[0193] Step 6:

[0194] The server encrypts the generated statistical information and returns it to each logistics company. Specifically, the server encrypts the statistical information using encryption means and sends it to each logistics company's terminal using a secure communication protocol. The input is the statistical information generated in step 5. Data conversion involves re-encryption. The encrypted statistical information is returned to each logistics company as output.

[0195] The above is a detailed flow of data processing in the embodiment of the present invention.

[0196] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0197] System configuration

[0198] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0199] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0200] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0201] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0202] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0203] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0204] 6. Emotion engine (terminal): A module that recognizes the user's emotional state from their voice, facial expressions, text input, etc.

[0205] Data collection and emotion recognition

[0206] User: Each logistics company's terminal collects data on truck drivers, vehicle operation status, driving routes, overtime hours, etc. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[0207] Example: For example, a terminal at a logistics company A collects data such as driver ID, vehicle ID, working hours, and overtime hours, while analyzing the user's voice to determine their stress level.

[0208] Data preprocessing and noise addition

[0209] Terminal: Inputs collected data into the system and standardizes the data format. It also adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[0210] Example: Convert collected data into a unified format such as JSON, and adjust the intensity of noise if the user is in a high stress state.

[0211] Data Encryption and Transmission

[0212] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0213] Example: Noise-added uptime data is AES encrypted and sent to a central server via HTTPS protocol.

[0214] Data Integration and Secure Computing

[0215] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0216] Example: Calculate the overall average working hours and average overtime hours based on the noisy data received from each company.

[0217] Generate and return statistical results

[0218] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0219] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0220] Usage example

[0221] For example, logistics company A collects data in-house, recognizes the user's emotional state using an emotion engine, adds noise to the data, encrypts it, and sends it to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to efficiently allocate resources.

[0222] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[0223] The processing flow will be explained below.

[0224] Step 1:

[0225] User: A logistics company employee collects data on each truck driver and vehicle's operating status, driving routes, overtime hours, etc. At the same time, the emotion engine recognizes the driver's emotional state from their voice and facial expressions.

[0226] Example: Collect data for driver ID 12345, vehicle ID 67890, 8 hours of working time, and 2 hours of overtime work, and determine the driver's stress level as "high."

[0227] Step 2:

[0228] Terminal: Inputs the collected data and emotional state into the system, standardizes the data format (for example, converts it to JSON format), checks the data for consistency, and corrects any errors.

[0229] Example: Convert the collected data as follows: {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8, "overtime_hours": 2, "stress_level": "high"}

[0230] Step 3:

[0231] Device: The emotion engine recognizes the collected data and adjusts the noise addition method based on the emotional state. For example, if the stress level is "high," the noise intensity is set lower than normal.

[0232] Example: If the operating time before noise is added is 8 hours, the normal noise range is adjusted from ±0.5 hours to ±0.3 hours, converting it to 8.3 hours.

[0233] Step 4:

[0234] Terminal: The noise-added data is AES encrypted and securely sent to a central server using the HTTPS protocol.

[0235] Example: Send the noisy and encrypted data to a central server.

[0236] Step 5:

[0237] Server: Decrypts the encrypted data received from each device and converts it back to JSON format.

[0238] Example: Decrypt the encrypted data {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8.3, "overtime_hours": 2.1} and return it to its original form.

[0239] Step 6:

[0240] Server: Integrates the received data and performs overall statistical calculations using secure computing technology. Collects noise-added data provided by each company and calculates statistics such as average working hours and average overtime hours.

[0241] Example: Based on noisy data from each company, the average working hours are calculated to be 8.2 hours and the average overtime hours to be 2.3 hours.

[0242] Step 7:

[0243] Server: The statistical calculation results are encrypted and sent back to each logistics company. To ensure the results are sent securely, AES encryption and HTTPS protocol are used.

[0244] Example: The statistical calculation results {"average_working_hours": 8.2, "average_overtime_hours": 2.3} are AES encrypted and sent to each company via HTTPS protocol.

[0245] Step 8:

[0246] User: Each logistics company receives the returned encrypted data on their own terminal and decrypts it. Based on the decrypted statistical results, they create resource allocation and scheduling strategies.

[0247] Example: Analyze the statistical results after decoding and consider and implement measures to improve your company's operational status.

[0248] The system allows logistics companies to optimize resource management by leveraging statistical information and emotional states while preserving privacy.

[0249] Example 2

[0250] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0251] In the logistics industry, there is a need for multiple companies to share data to improve overall efficiency. However, when individual companies directly share their own data with other companies, there is a risk of privacy violations and data leaks. Furthermore, efficient data management while understanding the emotional state of drivers requires complex information processing, which is not easy to achieve. To solve these issues, a safe and efficient data sharing system is required.

[0252] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a noise-adding means for adding noise to data collected from multiple information providers to achieve differential privacy; a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit; a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations; a result return means for encrypting the statistical calculation results and returning them to each information provider; an emotion recognition means for recognizing the emotional state of a user from their voice or facial expression and processing the data in conjunction with data collection; and a data preprocessing means for converting collected data into a unified format and adjusting noise-adding parameters based on the emotional state. This enables individual companies to safely and efficiently obtain statistical information and improve resource management without directly sharing their own data.

[0253] "Information provider" refers to a company or individual that collects and provides data in the logistics industry.

[0254] "Adding noise to data" refers to a technique of making individual data indistinguishable by adding random variations to the original data.

[0255] "Differential privacy" refers to a technology that protects the privacy of individual data by ensuring that adding or removing a single piece of data from a dataset being analyzed does not significantly change the results.

[0256] "Noise injection means" means a technique or device for achieving differential privacy by adding random noise to collected data.

[0257] "Data transmission means" refers to the technology or device that encrypts collected data and transmits it securely to the central processing unit.

[0258] "Central processing unit" refers to a server or computer device that integrates data sent from each information provider and performs statistical calculations using secret computation.

[0259] "Secret computation means" refers to technologies and methods that securely integrate multiple encrypted data sets and perform statistical calculations while protecting the privacy of each information provider.

[0260] "Result return means" refers to a technology or device that encrypts the statistical calculation results and returns them securely to each information provider.

[0261] "Emotion recognition means" refers to technology or devices that analyze the user's voice and facial expressions and recognize their emotional state.

[0262] "Data pre-processing means" refers to techniques and methods for converting collected data into a unified format and adjusting noise-adding parameters based on emotional state.

[0263] "Integrated format" means a format that converts data collected from different sources into a unified format.

[0264] "Noise addition parameters" refer to parameters for setting the intensity and distribution of noise to be added to collected data.

[0265] "AES encryption" stands for Advanced Encryption Standard, a standard encryption method for securely encrypting data.

[0266] "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and refers to a protocol for providing secure communications.

[0267] This invention is a data sharing system for the logistics industry, which aims to collect, process, transmit, integrate, and return data safely and efficiently. This system consists of multiple components, including a data collection terminal, a noise addition module, a data transmission module, a secure computation module, and a result return module. The data collection terminal is also equipped with an emotion engine that recognizes the user's emotional state.

[0268] Data collection and emotion recognition

[0269] User: A logistics company employee operates a data collection terminal to input information such as the truck driver and vehicle operating status, route, and overtime hours. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[0270] Example: A logistics company driver logs into a terminal and inputs driving data, while his stress level is determined in real time through voice analysis.

[0271] Data preprocessing and noise addition

[0272] Terminal: Converts collected data into a unified format such as JSON, and adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[0273] Example: The device converts collected data into JSON format and automatically increases the noise intensity if the user is in a high stress state.

[0274] Data Encryption and Transmission

[0275] Terminal: The noisy data is AES encrypted and sent to a central server using the HTTPS protocol.

[0276] Example: Noise-added uptime data is AES encrypted and sent to a server using a secure communication protocol.

[0277] Data Integration and Secure Computing

[0278] Server: The central server receives the noise-added data sent by each logistics company and performs integrated calculations using secret computing technology. This allows individual companies to obtain overall statistical information without directly sharing their own data with other companies.

[0279] Example: The server calculates the overall average working hours and average overtime hours based on the noisy data received from each company.

[0280] Generate and return statistical results

[0281] Server: Securely returns the statistical calculation results to each company. This return also uses encrypted communication.

[0282] Example: Statistical results such as the overall average working hours being 8.2 hours and the overall average overtime hours being 2.3 hours are sent back to each company.

[0283] Usage example

[0284] For example, a logistics company could collect data in-house, use an emotion engine to recognize the user's emotional state, add noise to the data, AES encrypt it, and send it to a central server. The central server then receives data from multiple logistics companies, integrates it, and performs overall statistical calculations. The calculated statistical results are then securely returned to each company, allowing each company to obtain valuable statistical information without directly sharing their own data with other companies.

[0285] Example prompt sentence:

[0286] "As a logistics company, please create a program for a data collection system that uses data collection terminals to collect driver working hours and emotional states. Please also create a program that adds noise to the collected data, securely transmits it to a server, and calculates the overall average working hours."

[0287] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[0288] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0289] Step 1: Data collection

[0290] User: A logistics company employee operates a data collection terminal to input information such as the truck driver's and vehicle's operating status, route, and overtime hours.

[0291] Input: Driver ID, vehicle ID, operating hours, route, overtime hours, etc.

[0292] Output: Collected data.

[0293] Specific operation: Drivers log in to the terminal at the start of their shift and record their route and working hours in real time.

[0294] Step 2: Emotion Recognition

[0295] On the device: The emotion engine recognizes the user's emotional state from their voice and facial expressions. This information is collected simultaneously.

[0296] Input: User's voice and facial expression data.

[0297] Output: Perceived emotional state.

[0298] Specific operation: The device uses voice input and the camera to analyze the user's voice and facial expressions and evaluate their stress level in real time.

[0299] Step 3: Data Preprocessing

[0300] Terminal: Converts collected data into a unified format (e.g., JSON format) and adjusts noise-adding parameters based on the emotional state recognized by the emotion engine.

[0301] Input: Collected data, perceived emotional state.

[0302] Output: Data converted to a unified format, adjusted noise addition parameters.

[0303] Specific operation: The device converts data into JSON format and automatically increases the noise intensity if the user is in a state of high stress.

[0304] Step 4: Add noise

[0305] Terminal: Add noise to the transformed data to achieve differential privacy.

[0306] Input: Data converted to unified format, adjusted noise addition parameters.

[0307] Output: Noise-augmented data.

[0308] Specific behavior: For example, adding random noise to uptime data to prevent accurate data from being sent directly.

[0309] Step 5: Data Encryption

[0310] Terminal: The noise-added data is AES encrypted.

[0311] Input: Noisy data.

[0312] Output: The encrypted data.

[0313] Specific operation: Using an encryption algorithm, the added noise data is securely encrypted to protect it from unauthorized access.

[0314] Step 6: Send data

[0315] Terminal: Sends encrypted data to a central server using the HTTPS protocol.

[0316] Input: Encrypted data.

[0317] Output: Data sent to the central server.

[0318] Specific operation: The device sends encrypted data to a central server using a secure communication protocol (e.g., HTTPS).

[0319] Step 7: Data reception and integration

[0320] Server: The central server receives the noise-added data sent from each logistics company and integrates the data using secure computing technology.

[0321] Input: Noisy data received from multiple logistics companies.

[0322] Output: Consolidated data.

[0323] Specific operation: The server decodes the received data and calculates the overall statistics using a secret calculation algorithm.

[0324] Step 8: Generate statistical results

[0325] Server: Generates statistical calculation results, such as the overall average working hours and average overtime hours.

[0326] Input: Consolidated data.

[0327] Output: Statistical calculation results.

[0328] Specific operation: The server processes the data and generates overall statistics (e.g., average working hours: 8.2 hours, average overtime hours: 2.3 hours).

[0329] Step 9: Encrypting the statistical results

[0330] Server: Encrypts the generated statistical results.

[0331] Input: Statistical calculation results.

[0332] Output: Encrypted statistical results.

[0333] What it does: Securely encrypts the statistical results using the AES encryption algorithm.

[0334] Step 10: Returning results

[0335] Server: Returns the encrypted statistical results to each logistics company.

[0336] Input: Encrypted statistical results.

[0337] Output: Encryption statistics results sent.

[0338] Specific operation: The server uses the HTTPS protocol to send encrypted statistical results to each logistics company.

[0339] Step 11: Interpret and display the results

[0340] Terminal: Decodes the received statistical results and displays them to the user.

[0341] Input: Encrypted statistical results.

[0342] Output: Display of the interpreted statistical results.

[0343] Specific operations: Company personnel use a terminal to decipher the statistical results and visually confirm them on the management screen.

[0344] (Application example 2)

[0345] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0346] While conventional data sharing systems are required to collect and safely share large amounts of data in the logistics industry, there is a risk that individual data may be identified and privacy may be violated.In addition, there is no data collection method that takes into account the stress and emotional state of workers, making it difficult to improve work efficiency and the working environment.

[0347] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a noise adding means that adds noise to data collected from multiple information providers to achieve differential privacy, a data transmission means that encrypts the noise-added data and transmits it to a central processing unit, a secret calculation means that securely integrates multiple pieces of the encrypted data and performs statistical calculations, and an emotion recognition means that recognizes the emotional state of the worker and adjusts the intensity of noise addition to the collected data. This allows individual data to be shared safely while protecting privacy, and enables data collection according to the emotional state of the worker.

[0348] An "information provider" is an entity that collects data and provides it to the system.

[0349] The "noise adding means" is a technique that makes it difficult to distinguish the collected data by adding random noise to the data.

[0350] The "data transmission means" is a technology for encrypting data to which noise has been added and transmitting the data securely to a central processing unit or other system.

[0351] A "central processing unit" is a device that receives multiple data sets and performs statistical calculations and data integration.

[0352] "Secure computation" is a technology that aggregates multiple encrypted data and performs statistical calculations while protecting privacy.

[0353] The "result return means" is a technique for safely returning the results of statistical calculations to each information provider.

[0354] The "emotion recognition means" is a technology that recognizes the emotional state of the worker and adjusts the intensity of noise added to the collected data.

[0355] "Emotional state" refers to data that indicates the psychological and physiological state of the worker, including stress level.

[0356] "Differential privacy" is a technique for collecting and analyzing statistical information while preserving the privacy of individual data.

[0357] "Noise intensity" refers to the amount of noise added to the data, and is adjusted according to the emotional state.

[0358] "AES encryption" is a highly secure encryption algorithm used to encrypt data.

[0359] System configuration

[0360] This invention is a system that monitors the work status and emotional state of factory workers in real time, and collects, adds noise to, encrypts, transmits, and processes statistical data.

[0361] Data collection and emotion recognition

[0362] Device: Each worker wears smart glasses (or a smartphone) equipped with a camera and microphone to collect work status and voice data. An emotion recognition module is used to recognize the user's emotional state (e.g., stress level) from their voice and facial expressions. This emotion recognition module uses a machine learning model to analyze the user's emotions in real time.

[0363] Data preprocessing and noise addition

[0364] Terminal: Collected operational data (e.g., work hours, work content) and emotional data are first converted into a unified data format. Next, a noise-adding means adds random noise to the data according to the emotional state. For example, the noise intensity increases when the subject is in a high stress state, and decreases when the subject is in a low stress state.

[0365] Data Encryption and Transmission

[0366] Terminal: The noisy data is encrypted using the AES encryption algorithm. The encrypted data is sent to a central server using a secure communication protocol (e.g., HTTPS).

[0367] Data Integration and Secure Computing

[0368] Server: The central server receives the encrypted data sent from each device. The received data is aggregated using secret calculation techniques to generate overall statistical information (e.g., average work time, average stress level). This statistical calculation provides aggregated information without disclosing individual data.

[0369] Generate and return statistical results

[0370] Server: The statistical calculation results are again encrypted and securely sent back to each worker's device. This allows each worker and manager to refer to the statistical information in real time, which can be used for appropriate resource management and improving the working environment.

[0371] Specific examples

[0372] For example, worker A at a factory is wearing smart glasses while working. The smart glasses' camera collects the work situation, the microphone collects audio data, and the emotion recognition module determines the stress level. If worker A is in a high stress state, the noise intensity of the data is increased and the encrypted noise data is sent to a central server via HTTPS. The central server combines data from other workers to generate statistical information such as the average stress level, and then re-encrypts the results and sends them back to each worker's device.

[0373] Example of a generated AI prompt

[0374] "Collect the user's work situation and emotional state in real time from the camera footage. If the stress level is high, add high-intensity noise to the data, protect it with AES encryption, and send it to a central server using a secure communication protocol. If the stress level is low, add low-intensity noise."

[0375] This will enable companies to allocate resources efficiently and improve working conditions while protecting transparency of the work environment and workers' privacy.

[0376] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0377] Step 1: Data collection

[0378] The terminal collects work situation and emotion data through smart glasses worn by each worker. Specifically, a camera captures the worker's movements and a microphone records audio data. The collected data includes frames of image data and audio samples of audio data. The input data are image frames and audio data, and the output is digital data in a unified format.

[0379] Step 2: Emotion Recognition

[0380] The emotion recognition module in the device analyzes the collected image frames and audio data to recognize the worker's emotional state. Specifically, the machine learning model performs facial expression and audio analysis to determine the stress level. The input data are the image frames and audio data collected in step 1, and the output is the emotional state, such as "high stress" or "low stress."

[0381] Step 3: Add noise

[0382] The device adds random noise to the collected data based on the emotion recognition results. Specifically, if the emotional state is "high stress," the noise intensity is increased, and if it is "low stress," the noise intensity is decreased. The noise is generated using statistical methods such as Gaussian noise. The input data is the emotional state obtained in step 2 and the data collected in step 1, and the output is the noise-added data.

[0383] Step 4: Data Encryption

[0384] The device encrypts the noise-added data using the AES encryption algorithm. Specifically, the data is block-encrypted and securely stored along with the encryption key. The input data is the noise-added data obtained in step 3, and the output is the encrypted data.

[0385] Step 5: Send data

[0386] The terminal sends the encrypted data to the central server using the HTTPS protocol. Specifically, the terminal sends data using HTTPS and monitors the transmission status. The input data is the data encrypted in step 4, and the output is the transmission result to the central server.

[0387] Step 6: Data Integration and Secure Computing

[0388] The server receives the encrypted data sent from each device and integrates it using secure computing technology. Specifically, it performs statistical calculations without decrypting the encrypted data to calculate the overall average work time and stress level. The input data is the encrypted data, and the output is the statistical calculation results.

[0389] Step 7: Returning statistical results

[0390] The server encrypts the statistical calculation results and returns them to each worker's terminal. Specifically, it uses encryption technology to block-encrypt the calculation results and returns them using the HTTPS protocol. The input data is the statistical calculation results obtained in step 6, and the output is the return of the encrypted calculation results.

[0391] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0392] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0393] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0394] [Second embodiment]

[0395] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0396] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0397] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0398] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0399] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0400] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0401] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0402] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0403] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0404] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0405] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0406] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0407] System configuration

[0408] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0409] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0410] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0411] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0412] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0413] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0414] Data collection

[0415] User: Each logistics company's terminal collects data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0416] Example: For example, a terminal of a logistics company A collects the following data:

[0417] Driver ID: 12345

[0418] Vehicle ID: 67890

[0419] Operating time: 8 hours

[0420] Overtime work hours: 2 hours

[0421] Data noise addition

[0422] Terminal: Noise is added to the collected data based on differential privacy technology to reduce the risk of specific personal or vehicle information being directly leaked.

[0423] Example: Data showing an operating time of 8 hours is converted to 8.3 hours by adding noise of ±0.3 hours.

[0424] Data transmission

[0425] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0426] Example: The transmitted data is driver ID 12345, vehicle ID 67890, working hours 8.3 hours, overtime hours 2.1 hours (after adding noise).

[0427] Data Integration and Secure Computing

[0428] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0429] Example: Aggregating data from each company and calculating, for example, total working hours and average overtime hours.

[0430] Generate and return statistical results

[0431] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0432] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0433] Usage example

[0434] For example, logistics company A collects data internally, adds noise, and sends it in encrypted form to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to allocate resources efficiently.

[0435] The present invention allows logistics companies to obtain statistically valuable information without leaking their data to other companies, thereby helping to solve the problem of resource shortages.

[0436] The processing flow will be explained below.

[0437] Step 1:

[0438] User: Logistics company terminals collect operational data on truck drivers and vehicles, such as driver ID, vehicle ID, operating hours, and overtime hours.

[0439] Step 2:

[0440] Terminal: Enters collected data into the system and standardizes the data format. Converts collected data into a standard format such as JSON. Also, performs data consistency checks and corrects errors if necessary.

[0441] Step 3:

[0442] Terminal: Noise is added to the data using differential privacy techniques. A noise generation function is applied to add noise to each data point. For example, if data shows an operating time of 8 hours, a noise of ±0.3 hours is added to convert it to 8.3 hours.

[0443] Step 4:

[0444] Terminal: The data with noise added is encrypted and sent to the central processing unit (server). The data is encrypted using AES encryption technology. After encryption, the data is sent via the HTTPS protocol.

[0445] Step 5:

[0446] Server: Decrypts the noise-added data received from each device, for example, using AES encryption to restore the original data format.

[0447] Step 6:

[0448] Server: Integrates the received data and performs statistical calculations using secure computing technology. For example, it calculates the overall average working hours, average overtime hours, etc. The noisy data sent from each logistics company is unified as data with random noise added.

[0449] Step 7:

[0450] Server: The statistical calculation results are encrypted before being sent back to each logistics company. The calculated statistical results are converted into JSON format and then encrypted again using AES.

[0451] Step 8:

[0452] Server: Encrypted statistical results are returned to each logistics company via HTTPS. Again, encrypted communication is used when sending data, ensuring secure information sharing.

[0453] Step 9:

[0454] User: Receives the returned statistical results on their own device and decrypts them. Based on the decrypted statistical results, they develop strategies for resource allocation, scheduling, etc.

[0455] In this way, this system allows each logistics company to obtain statistical information safely without disclosing the data.

[0456] Example 1

[0457] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0458] In today's logistics industry, multiple companies need to cooperate and share overall statistical information in order to operate efficiently. However, because data sharing carries the risk of individual information being leaked, there is a need for a system that allows each company to access statistical information while keeping its own data secure.

[0459] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0460] In this invention, the server includes a noise adding means for adding noise to data collected from multiple data collection devices to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret computation means for securely integrating multiple encrypted data and performing statistical computations, a result return means for encrypting the statistical computation results and returning them to each data collection device, and a system in which each data collection device adds noise to the data to be identified and then transmits the data using encrypted communication means, and the central processing unit integrates the multiple data using secret computation technology to generate and securely return the statistical computation results.This allows each company to safely share overall statistical information without leaking its own data, enabling efficient business operations.

[0461] A "data collection device" is a device for collecting data from multiple information providers.

[0462] The "noise adding means" is a means for adding noise to collected data to achieve differential privacy.

[0463] The "data transmission means" is a means for encrypting the data to which noise has been added and transmitting the data to the central processing unit.

[0464] The "central processing unit" is a device that receives data sent from multiple information providers and integrates it using secure computing technology.

[0465] A "secret computation means" is a means for securely integrating multiple encrypted data and performing statistical computations.

[0466] The "result return means" is a means for encrypting the statistical calculation results and returning them to each information provider.

[0467] "Differential privacy" is a technology that adds noise to data to make individual data unidentifiable and protect the privacy of that data.

[0468] "Encrypted communication means" refers to a means for encrypting data and communicating it securely.

[0469] "Secure computing technology" is a technology that securely integrates multiple data sets and calculates statistical information without disclosing the individual data.

[0470] "Statistical calculation" refers to the calculation of aggregating received data and generating statistical information.

[0471] "Encryption" is a technology that converts data using a certain algorithm to prevent it from being read by third parties.

[0472] This invention is a data sharing system for the logistics industry, designed to securely collect, process, and share specific data. The system includes the following components:

[0473] System configuration

[0474] 1. Data collection device (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0475] 2. Noise injection means (terminal): Noise is injected into the collected data based on differential privacy technology. Noise injection reduces the risk of direct leakage of specific personal or vehicle information. This technology is realized, for example, using a Laplace noise generation algorithm.

[0476] 3. Data transmission means (terminal): The data with noise added is encrypted and sent to the central processing unit. This transmission uses an encryption communication protocol such as AES encryption to ensure security.

[0477] 4. Secure computing means (server): The data sent from each logistics company is integrated and statistical calculations are performed using secure computing technology, such as Secure Multi-party Computation (SMPC).

[0478] 5. Result return method (server): The calculation results are encrypted and returned to each company. This return also uses an encrypted communication protocol.

[0479] Data collection

[0480] User: Personnel at each logistics company use terminals to input and collect data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0481] Example: When a terminal of logistics company A collects the following data, "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," "Overtime hours 2 hours," etc. are entered into the data collection device.

[0482] Data noise addition

[0483] Terminal: Noise is added to the collected data using differential privacy techniques. For example, noise of ±0.3 hours can be added using a Laplace noise generation algorithm.

[0484] Example: Add noise of ±0.3 hours to the data "8 hours of operation time" to convert it to "8.3 hours of operation time."

[0485] Data transmission

[0486] Terminal: The noise-added data is AES-encrypted and sent to the central processing unit. An encryption protocol (e.g., AES-256) is used to ensure secure data transmission.

[0487] Example: Encrypt and send the data "Driver ID 12345," "Vehicle ID 67890," "Working hours 8.3 hours," and "Overtime hours 2.1 hours (after adding noise)."

[0488] Data Integration and Secure Computing

[0489] Server: The central processing unit integrates the noise-added data received from each logistics company and calculates statistical information using Secure Multi-party Computation (SMPC) technology.

[0490] Example: Integrate operating time data collected from multiple logistics companies to calculate the overall average operating time and overtime hours.

[0491] Generate and return statistical results

[0492] Server: Returns the calculation results to each logistics company using encrypted communication. The encryption protocol is used to ensure the secure transfer of the results.

[0493] Example: Statistical results such as "total average working hours: 8.2 hours" and "total average overtime hours: 2.3 hours" are generated, encrypted, and sent back to each logistics company.

[0494] Usage example

[0495] For example, logistics company A collects data in-house, adds noise to it, encrypts the data, and sends it to a central server. The central server also receives data from multiple logistics companies (B and C), consolidates it, and generates overall statistical information. This statistical information is then re-encrypted and securely sent back to each company. Each company can use this information to improve their own operating conditions and working hours.

[0496] Example prompts for generative AI models

[0497] "Please explain the process by which a logistics company's terminal collects truck driver working time data, adds noise to it, encrypts it, and then transmits it to a central server. The server then aggregates this data, generates statistical results using secure computing technology, and securely returns them to each company. Please provide a concrete example of such a scenario."

[0498] This system allows each logistics company to safely obtain overall statistical information without leaking their own data to other companies, thereby improving operational efficiency and optimizing resources.

[0499] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0500] Step 1:

[0501] User: Personnel at each logistics company use terminals to input and collect data on truck drivers and vehicles, including details such as working hours, routes, and overtime hours.

[0502] Input: Data entered into the terminal by the logistics company staff (e.g., driver ID, vehicle ID, working hours, overtime hours).

[0503] Output: Raw data saved to the device.

[0504] Specific operation: The following information is entered into the terminal of logistics company A: "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," and "Overtime hours 2 hours."

[0505] Step 2:

[0506] Terminal: Noise is added to the collected data based on differential privacy technology. The level of noise is set randomly to prevent individual data from being identified.

[0507] Input: Raw data (e.g., operating time 8 hours).

[0508] Output: Noisy data (e.g., operating time 8.3 hours).

[0509] Specific operation: Add noise of ±0.3 hours to the data of "8 hours of operation time" and convert it to "8.3 hours of operation time."

[0510] Step 3:

[0511] Terminal: The noise-added data is encrypted and sent to the central processing unit. To ensure security, the AES encryption protocol is used.

[0512] Input: Noisy data (e.g., operating time 8.3 hours).

[0513] Output: The encrypted data.

[0514] Specific operation: The noise-added data "8.3 hours of operation time" is AES encrypted and the encrypted data is sent to the central processing unit.

[0515] Step 4:

[0516] Server: The central processing unit aggregates the noise-added data received from each logistics company and calculates statistical information using secret computing technology. This allows the extraction of overall statistical information without disclosing individual data.

[0517] Input: Encrypted data (noisy data received from multiple logistics companies).

[0518] Output: Consolidated statistical data.

[0519] Specific operation: Data received from each logistics company is integrated using Secure Multi-party Computation (SMPC) technology to calculate the overall average operating hours and overtime hours.

[0520] Step 5:

[0521] Server: The calculation results are encrypted and sent back to each logistics company. To ensure secure communication, an encryption protocol is again used.

[0522] Input: Consolidated statistical data.

[0523] Output: Encrypted statistical calculation results.

[0524] Specific operations: Generate statistical results such as overall average operating hours and overtime hours, encrypt them, and send them back to each logistics company.

[0525] Through the above processing steps, this system enables logistics companies to safely share overall statistical information without leaking their own data to other companies, thereby improving business efficiency and optimizing resources.

[0526] (Application example 1)

[0527] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0528] In the logistics industry, each logistics company needs to securely share its data with other companies while obtaining statistically valuable information. However, information sharing is not easy unless privacy protection and data confidentiality are ensured. In particular, advanced privacy protection and data encryption technologies are required to securely collect and integrate data such as driver operation information, driving routes, and working hours to obtain statistical information. Against this background, a method is needed for logistics companies to securely share data while improving overall operational efficiency.

[0529] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0530] In this invention, the server includes a noise adding means for adding noise to data collected from multiple information providers to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations, a result return means for encrypting the statistical calculation results and returning them to each information provider, a data collection means for collecting driver operation information, driving routes, and working hours for managing transportation operations, and an encryption communication means for using an encryption communication protocol when encrypting the data and transmitting it to the central processing unit. This allows each logistics company to safely share data and obtain statistically valuable information while protecting the privacy of their individual data.

[0531] "Data collection means" refers to a device or method for collecting data such as driver operating information, driving routes, and working hours for the purpose of managing transportation operations.

[0532] A "noise-adding means" is a device or method that achieves differential privacy by adding random noise to collected data, making individual data unidentifiable.

[0533] The "data transmission means" refers to a device or method for encrypting the data to which noise has been added and transmitting the encrypted data to the central processing unit.

[0534] "Encrypted communication means" refers to a device or method that uses protocols or techniques to encrypt data and communicate securely.

[0535] A "secret computation method" is a technology or method for securely integrating multiple encrypted data sets and performing statistical calculations.

[0536] The "result return means" is a device or method for encrypting the statistical calculation results and returning them to each information provider.

[0537] In the present invention, a data sharing system in the logistics industry is composed of the following steps.

[0538] 1. Data Collection

[0539] The terminals of each logistics company collect data such as driver operating information, driving routes, and working hours. This allows each logistics company to obtain detailed driving information for its drivers. As a specific example, the terminal of a certain logistics company collects data such as driver ID "12345," vehicle ID "67890," working hours "8 hours," and overtime hours "2 hours."

[0540] 2. Adding noise

[0541] The noise-adding means adds random noise to the data collected within the device to achieve differential privacy. This makes individual data unidentifiable. For example, data showing an operating time of "8 hours" is converted to "8.3 hours" by adding noise of ±0.3 hours.

[0542] 3. Data Transmission

[0543] The data transmission means encrypts the noise-added data and transmits it securely to the central server. At this time, the encryption communication means ensures the security of the data using an encryption communication protocol. As a specific example, the encrypted data is transmitted as driver ID "12345," vehicle ID "67890," operating hours "8.3 hours," and overtime hours "2.1 hours."

[0544] 4. Data Integration and Secure Computing

[0545] A central server receives the encrypted data with noise added from each logistics company, and a secret computation means securely integrates the data and performs statistical calculations.Secure computation technology makes it possible to obtain overall statistical information without disclosing the data of each logistics company.As a specific example, data from each company can be aggregated to calculate total operating hours, average overtime hours, etc.

[0546] 5. Generating and returning statistical results

[0547] The central server encrypts the statistical calculation results and returns them to each information provider. The statistical results are then securely transmitted to each logistics company by the result return means. For example, statistical results such as the overall average working hours of "8.2 hours" and the overall average overtime hours of "2.3 hours" are returned.

[0548] Data transmission and encryption protocols use cryptography libraries and specific encryption protocols (e.g., AES encryption).

[0549] Here are some example prompts to input to a generative AI model:

[0550] "Please tell me how to implement a smartphone application that collects operational data from truck drivers working at a logistics center, adds noise using differential privacy techniques, and then securely transmits the data to a central server. Specifically, please explain in detail each stage of data collection, noise addition, and encrypted transmission."

[0551] As described above, logistics companies can safely share data while protecting the privacy of their individual data, and obtain statistically valuable information, which will improve operational efficiency across the logistics industry.

[0552] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0553] Step 1:

[0554] The user (the terminal of each logistics company) collects data such as driver operation information, driving routes, and working hours. Specifically, the terminal acquires each driver's GPS data and operation log in real time. Inputs include driver ID, vehicle ID, operating hours, and overtime hours, which the terminal acquires from sensing devices and logging apps. The collected raw data is generated as output.

[0555] Step 2:

[0556] The device adds noise to the collected data. Specifically, a program that adds random noise to the data is executed within the device. The raw data collected in step 1 is used as input. To achieve differential privacy, data processing involves adding or subtracting noise to, for example, working hours or overtime hours. Partially anonymized data with added noise is generated as output.

[0557] Step 3:

[0558] The device encrypts the noise-added data and sends it to the central server. Specifically, the device uses a cryptography library to perform AES encryption and sends the encrypted data to the server via a secure communication protocol (e.g., HTTPS). The noise-added data generated in step 2 is used as input. Data transformation involves encryption. The encrypted data is sent to the server as output.

[0559] Step 4:

[0560] The server receives encrypted data sent from multiple logistics companies and securely integrates it. Specifically, the server saves the received encrypted data and stores it in a database for subsequent integration calculations. The input is encrypted data sent from the terminals of each logistics company. The data calculation involves virtually integrating this data into a single dataset. The output is an integrated dataset.

[0561] Step 5:

[0562] The server performs secure computation on the integrated dataset to generate statistical information. Specifically, it uses secure computation means to analyze the data and calculate statistical information such as the overall average working hours and average overtime hours. The input is the integrated dataset created in step 4. Data operations such as addition and averaging are performed. The output is the required statistical information.

[0563] Step 6:

[0564] The server encrypts the generated statistical information and returns it to each logistics company. Specifically, the server encrypts the statistical information using encryption means and sends it to each logistics company's terminal using a secure communication protocol. The input is the statistical information generated in step 5. Data conversion involves re-encryption. The encrypted statistical information is returned to each logistics company as output.

[0565] The above is a detailed flow of data processing in the embodiment of the present invention.

[0566] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0567] System configuration

[0568] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0569] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0570] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0571] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0572] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0573] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0574] 6. Emotion engine (terminal): A module that recognizes the user's emotional state from their voice, facial expressions, text input, etc.

[0575] Data collection and emotion recognition

[0576] User: Each logistics company's terminal collects data on truck drivers, vehicle operation status, driving routes, overtime hours, etc. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[0577] Example: For example, a terminal at a logistics company A collects data such as driver ID, vehicle ID, working hours, and overtime hours, while analyzing the user's voice to determine their stress level.

[0578] Data preprocessing and noise addition

[0579] Terminal: Inputs collected data into the system and standardizes the data format. It also adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[0580] Example: Convert collected data into a unified format such as JSON, and adjust the intensity of noise if the user is in a high stress state.

[0581] Data Encryption and Transmission

[0582] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0583] Example: Noise-added uptime data is AES encrypted and sent to a central server via HTTPS protocol.

[0584] Data Integration and Secure Computing

[0585] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0586] Example: Calculate the overall average working hours and average overtime hours based on the noisy data received from each company.

[0587] Generate and return statistical results

[0588] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0589] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0590] Usage example

[0591] For example, logistics company A collects data in-house, recognizes the user's emotional state using an emotion engine, adds noise to the data, encrypts it, and sends it to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to efficiently allocate resources.

[0592] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[0593] The processing flow will be explained below.

[0594] Step 1:

[0595] User: A logistics company employee collects data on each truck driver and vehicle's operating status, driving routes, overtime hours, etc. At the same time, the emotion engine recognizes the driver's emotional state from their voice and facial expressions.

[0596] Example: Collect data for driver ID 12345, vehicle ID 67890, 8 hours of working time, and 2 hours of overtime work, and determine the driver's stress level as "high."

[0597] Step 2:

[0598] Terminal: Inputs the collected data and emotional state into the system, standardizes the data format (for example, converts it to JSON format), checks the data for consistency, and corrects any errors.

[0599] Example: Convert the collected data as follows: {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8, "overtime_hours": 2, "stress_level": "high"}

[0600] Step 3:

[0601] Device: The emotion engine recognizes the collected data and adjusts the noise addition method based on the emotional state. For example, if the stress level is "high," the noise intensity is set lower than normal.

[0602] Example: If the operating time before noise is added is 8 hours, the normal noise range is adjusted from ±0.5 hours to ±0.3 hours, converting it to 8.3 hours.

[0603] Step 4:

[0604] Terminal: The noise-added data is AES encrypted and securely sent to a central server using the HTTPS protocol.

[0605] Example: Send the noisy and encrypted data to a central server.

[0606] Step 5:

[0607] Server: Decrypts the encrypted data received from each device and converts it back to JSON format.

[0608] Example: Decrypt the encrypted data {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8.3, "overtime_hours": 2.1} and return it to its original form.

[0609] Step 6:

[0610] Server: Integrates the received data and performs overall statistical calculations using secure computing technology. Collects noise-added data provided by each company and calculates statistics such as average working hours and average overtime hours.

[0611] Example: Based on noisy data from each company, the average working hours are calculated to be 8.2 hours and the average overtime hours to be 2.3 hours.

[0612] Step 7:

[0613] Server: The statistical calculation results are encrypted and sent back to each logistics company. To ensure the results are sent securely, AES encryption and HTTPS protocol are used.

[0614] Example: The statistical calculation results {"average_working_hours": 8.2, "average_overtime_hours": 2.3} are AES encrypted and sent to each company via HTTPS protocol.

[0615] Step 8:

[0616] User: Each logistics company receives the returned encrypted data on their own terminal and decrypts it. Based on the decrypted statistical results, they create resource allocation and scheduling strategies.

[0617] Example: Analyze the statistical results after decoding and consider and implement measures to improve your company's operational status.

[0618] The system allows logistics companies to optimize resource management by leveraging statistical information and emotional states while preserving privacy.

[0619] Example 2

[0620] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0621] In the logistics industry, there is a need for multiple companies to share data to improve overall efficiency. However, when individual companies directly share their own data with other companies, there is a risk of privacy violations and data leaks. Furthermore, efficient data management while understanding the emotional state of drivers requires complex information processing, which is not easy to achieve. To solve these issues, a safe and efficient data sharing system is required.

[0622] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a noise-adding means for adding noise to data collected from multiple information providers to achieve differential privacy; a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit; a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations; a result return means for encrypting the statistical calculation results and returning them to each information provider; an emotion recognition means for recognizing the emotional state of a user from their voice or facial expression and processing the data in conjunction with data collection; and a data preprocessing means for converting collected data into a unified format and adjusting noise-adding parameters based on the emotional state. This enables individual companies to safely and efficiently obtain statistical information and improve resource management without directly sharing their own data.

[0623] "Information provider" refers to a company or individual that collects and provides data in the logistics industry.

[0624] "Adding noise to data" refers to a technique of making individual data indistinguishable by adding random variations to the original data.

[0625] "Differential privacy" refers to a technology that protects the privacy of individual data by ensuring that adding or removing a single piece of data from a dataset being analyzed does not significantly change the results.

[0626] "Noise injection means" means a technique or device for achieving differential privacy by adding random noise to collected data.

[0627] "Data transmission means" refers to the technology or device that encrypts collected data and transmits it securely to the central processing unit.

[0628] "Central processing unit" refers to a server or computer device that integrates data sent from each information provider and performs statistical calculations using secret computation.

[0629] "Secret computation means" refers to technologies and methods that securely integrate multiple encrypted data sets and perform statistical calculations while protecting the privacy of each information provider.

[0630] "Result return means" refers to a technology or device that encrypts the statistical calculation results and returns them securely to each information provider.

[0631] "Emotion recognition means" refers to technology or devices that analyze the user's voice and facial expressions and recognize their emotional state.

[0632] "Data pre-processing means" refers to techniques and methods for converting collected data into a unified format and adjusting noise-adding parameters based on emotional state.

[0633] "Integrated format" means a format that converts data collected from different sources into a unified format.

[0634] "Noise addition parameters" refer to parameters for setting the intensity and distribution of noise to be added to collected data.

[0635] "AES encryption" stands for Advanced Encryption Standard, a standard encryption method for securely encrypting data.

[0636] "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and refers to a protocol for providing secure communications.

[0637] This invention is a data sharing system for the logistics industry, which aims to collect, process, transmit, integrate, and return data safely and efficiently. This system consists of multiple components, including a data collection terminal, a noise addition module, a data transmission module, a secure computation module, and a result return module. The data collection terminal is also equipped with an emotion engine that recognizes the user's emotional state.

[0638] Data collection and emotion recognition

[0639] User: A logistics company employee operates a data collection terminal to input information such as the truck driver and vehicle operating status, route, and overtime hours. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[0640] Example: A logistics company driver logs into a terminal and inputs driving data, while his stress level is determined in real time through voice analysis.

[0641] Data preprocessing and noise addition

[0642] Terminal: Converts collected data into a unified format such as JSON, and adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[0643] Example: The device converts collected data into JSON format and automatically increases the noise intensity if the user is in a high stress state.

[0644] Data Encryption and Transmission

[0645] Terminal: The noisy data is AES encrypted and sent to a central server using the HTTPS protocol.

[0646] Example: Noise-added uptime data is AES encrypted and sent to a server using a secure communication protocol.

[0647] Data Integration and Secure Computing

[0648] Server: The central server receives the noise-added data sent by each logistics company and performs integrated calculations using secret computing technology. This allows individual companies to obtain overall statistical information without directly sharing their own data with other companies.

[0649] Example: The server calculates the overall average working hours and average overtime hours based on the noisy data received from each company.

[0650] Generate and return statistical results

[0651] Server: Securely returns the statistical calculation results to each company. This return also uses encrypted communication.

[0652] Example: Statistical results such as the overall average working hours being 8.2 hours and the overall average overtime hours being 2.3 hours are sent back to each company.

[0653] Usage example

[0654] For example, a logistics company could collect data in-house, use an emotion engine to recognize the user's emotional state, add noise to the data, AES encrypt it, and send it to a central server. The central server then receives data from multiple logistics companies, integrates it, and performs overall statistical calculations. The calculated statistical results are then securely returned to each company, allowing each company to obtain valuable statistical information without directly sharing their own data with other companies.

[0655] Example prompt sentence:

[0656] "As a logistics company, please create a program for a data collection system that uses data collection terminals to collect driver working hours and emotional states. Please also create a program that adds noise to the collected data, securely transmits it to a server, and calculates the overall average working hours."

[0657] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[0658] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0659] Step 1: Data collection

[0660] User: A logistics company employee operates a data collection terminal to input information such as the truck driver's and vehicle's operating status, route, and overtime hours.

[0661] Input: Driver ID, vehicle ID, operating hours, route, overtime hours, etc.

[0662] Output: Collected data.

[0663] Specific operation: Drivers log in to the terminal at the start of their shift and record their route and working hours in real time.

[0664] Step 2: Emotion Recognition

[0665] On the device: The emotion engine recognizes the user's emotional state from their voice and facial expressions. This information is collected simultaneously.

[0666] Input: User's voice and facial expression data.

[0667] Output: Perceived emotional state.

[0668] Specific operation: The device uses voice input and the camera to analyze the user's voice and facial expressions and evaluate their stress level in real time.

[0669] Step 3: Data Preprocessing

[0670] Terminal: Converts collected data into a unified format (e.g., JSON format) and adjusts noise-adding parameters based on the emotional state recognized by the emotion engine.

[0671] Input: Collected data, perceived emotional state.

[0672] Output: Data converted to a unified format, adjusted noise addition parameters.

[0673] Specific operation: The device converts data into JSON format and automatically increases the noise intensity if the user is in a state of high stress.

[0674] Step 4: Add noise

[0675] Terminal: Add noise to the transformed data to achieve differential privacy.

[0676] Input: Data converted to unified format, adjusted noise addition parameters.

[0677] Output: Noise-augmented data.

[0678] Specific behavior: For example, adding random noise to uptime data to prevent accurate data from being sent directly.

[0679] Step 5: Data Encryption

[0680] Terminal: The noise-added data is AES encrypted.

[0681] Input: Noisy data.

[0682] Output: The encrypted data.

[0683] Specific operation: Using an encryption algorithm, the added noise data is securely encrypted to protect it from unauthorized access.

[0684] Step 6: Send data

[0685] Terminal: Sends encrypted data to a central server using the HTTPS protocol.

[0686] Input: Encrypted data.

[0687] Output: Data sent to the central server.

[0688] Specific operation: The device sends encrypted data to a central server using a secure communication protocol (e.g., HTTPS).

[0689] Step 7: Data reception and integration

[0690] Server: The central server receives the noise-added data sent from each logistics company and integrates the data using secure computing technology.

[0691] Input: Noisy data received from multiple logistics companies.

[0692] Output: Consolidated data.

[0693] Specific operation: The server decodes the received data and calculates the overall statistics using a secret calculation algorithm.

[0694] Step 8: Generate statistical results

[0695] Server: Generates statistical calculation results, such as the overall average working hours and average overtime hours.

[0696] Input: Consolidated data.

[0697] Output: Statistical calculation results.

[0698] Specific operation: The server processes the data and generates overall statistics (e.g., average working hours: 8.2 hours, average overtime hours: 2.3 hours).

[0699] Step 9: Encrypting the statistical results

[0700] Server: Encrypts the generated statistical results.

[0701] Input: Statistical calculation results.

[0702] Output: Encrypted statistical results.

[0703] What it does: Securely encrypts the statistical results using the AES encryption algorithm.

[0704] Step 10: Returning results

[0705] Server: Returns the encrypted statistical results to each logistics company.

[0706] Input: Encrypted statistical results.

[0707] Output: Encryption statistics results sent.

[0708] Specific operation: The server uses the HTTPS protocol to send encrypted statistical results to each logistics company.

[0709] Step 11: Interpret and display the results

[0710] Terminal: Decodes the received statistical results and displays them to the user.

[0711] Input: Encrypted statistical results.

[0712] Output: Display of the interpreted statistical results.

[0713] Specific operations: Company personnel use a terminal to decipher the statistical results and visually confirm them on the management screen.

[0714] (Application example 2)

[0715] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0716] While conventional data sharing systems are required to collect and safely share large amounts of data in the logistics industry, there is a risk that individual data may be identified and privacy may be violated.In addition, there is no data collection method that takes into account the stress and emotional state of workers, making it difficult to improve work efficiency and the working environment.

[0717] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a noise adding means that adds noise to data collected from multiple information providers to achieve differential privacy, a data transmission means that encrypts the noise-added data and transmits it to a central processing unit, a secret calculation means that securely integrates multiple pieces of the encrypted data and performs statistical calculations, and an emotion recognition means that recognizes the emotional state of the worker and adjusts the intensity of noise addition to the collected data. This allows individual data to be shared safely while protecting privacy, and enables data collection according to the emotional state of the worker.

[0718] An "information provider" is an entity that collects data and provides it to the system.

[0719] The "noise adding means" is a technique that makes it difficult to distinguish the collected data by adding random noise to the data.

[0720] The "data transmission means" is a technology for encrypting data to which noise has been added and transmitting the data securely to a central processing unit or other system.

[0721] A "central processing unit" is a device that receives multiple data sets and performs statistical calculations and data integration.

[0722] "Secure computation" is a technology that aggregates multiple encrypted data and performs statistical calculations while protecting privacy.

[0723] The "result return means" is a technique for safely returning the results of statistical calculations to each information provider.

[0724] The "emotion recognition means" is a technology that recognizes the emotional state of the worker and adjusts the intensity of noise added to the collected data.

[0725] "Emotional state" refers to data that indicates the psychological and physiological state of the worker, including stress level.

[0726] "Differential privacy" is a technique for collecting and analyzing statistical information while preserving the privacy of individual data.

[0727] "Noise intensity" refers to the amount of noise added to the data, and is adjusted according to the emotional state.

[0728] "AES encryption" is a highly secure encryption algorithm used to encrypt data.

[0729] System configuration

[0730] This invention is a system that monitors the work status and emotional state of factory workers in real time, and collects, adds noise to, encrypts, transmits, and processes statistical data.

[0731] Data collection and emotion recognition

[0732] Device: Each worker wears smart glasses (or a smartphone) equipped with a camera and microphone to collect work status and voice data. An emotion recognition module is used to recognize the user's emotional state (e.g., stress level) from their voice and facial expressions. This emotion recognition module uses a machine learning model to analyze the user's emotions in real time.

[0733] Data preprocessing and noise addition

[0734] Terminal: Collected operational data (e.g., work hours, work content) and emotional data are first converted into a unified data format. Next, a noise-adding means adds random noise to the data according to the emotional state. For example, the noise intensity increases when the subject is in a high stress state, and decreases when the subject is in a low stress state.

[0735] Data Encryption and Transmission

[0736] Terminal: The noisy data is encrypted using the AES encryption algorithm. The encrypted data is sent to a central server using a secure communication protocol (e.g., HTTPS).

[0737] Data Integration and Secure Computing

[0738] Server: The central server receives the encrypted data sent from each device. The received data is aggregated using secret calculation techniques to generate overall statistical information (e.g., average work time, average stress level). This statistical calculation provides aggregated information without disclosing individual data.

[0739] Generate and return statistical results

[0740] Server: The statistical calculation results are again encrypted and securely sent back to each worker's device. This allows each worker and manager to refer to the statistical information in real time, which can be used for appropriate resource management and improving the working environment.

[0741] Specific examples

[0742] For example, worker A at a factory is wearing smart glasses while working. The smart glasses' camera collects the work situation, the microphone collects audio data, and the emotion recognition module determines the stress level. If worker A is in a high stress state, the noise intensity of the data is increased and the encrypted noise data is sent to a central server via HTTPS. The central server combines data from other workers to generate statistical information such as the average stress level, and then re-encrypts the results and sends them back to each worker's device.

[0743] Example of a generated AI prompt

[0744] "Collect the user's work situation and emotional state in real time from the camera footage. If the stress level is high, add high-intensity noise to the data, protect it with AES encryption, and send it to a central server using a secure communication protocol. If the stress level is low, add low-intensity noise."

[0745] This will enable companies to allocate resources efficiently and improve working conditions while protecting transparency of the work environment and workers' privacy.

[0746] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0747] Step 1: Data collection

[0748] The terminal collects work situation and emotion data through smart glasses worn by each worker. Specifically, a camera captures the worker's movements and a microphone records audio data. The collected data includes frames of image data and audio samples of audio data. The input data are image frames and audio data, and the output is digital data in a unified format.

[0749] Step 2: Emotion Recognition

[0750] The emotion recognition module in the device analyzes the collected image frames and audio data to recognize the worker's emotional state. Specifically, the machine learning model performs facial expression and audio analysis to determine the stress level. The input data are the image frames and audio data collected in step 1, and the output is the emotional state, such as "high stress" or "low stress."

[0751] Step 3: Add noise

[0752] The device adds random noise to the collected data based on the emotion recognition results. Specifically, if the emotional state is "high stress," the noise intensity is increased, and if it is "low stress," the noise intensity is decreased. The noise is generated using statistical methods such as Gaussian noise. The input data is the emotional state obtained in step 2 and the data collected in step 1, and the output is the noise-added data.

[0753] Step 4: Data Encryption

[0754] The device encrypts the noise-added data using the AES encryption algorithm. Specifically, the data is block-encrypted and securely stored along with the encryption key. The input data is the noise-added data obtained in step 3, and the output is the encrypted data.

[0755] Step 5: Send data

[0756] The terminal sends the encrypted data to the central server using the HTTPS protocol. Specifically, the terminal sends data using HTTPS and monitors the transmission status. The input data is the data encrypted in step 4, and the output is the transmission result to the central server.

[0757] Step 6: Data Integration and Secure Computing

[0758] The server receives the encrypted data sent from each device and integrates it using secure computing technology. Specifically, it performs statistical calculations without decrypting the encrypted data to calculate the overall average work time and stress level. The input data is the encrypted data, and the output is the statistical calculation results.

[0759] Step 7: Returning statistical results

[0760] The server encrypts the statistical calculation results and returns them to each worker's terminal. Specifically, it uses encryption technology to block-encrypt the calculation results and returns them using the HTTPS protocol. The input data is the statistical calculation results obtained in step 6, and the output is the return of the encrypted calculation results.

[0761] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0762] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0763] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0764] [Third embodiment]

[0765] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0766] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0767] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0768] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0769] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0770] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0771] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0772] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0773] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0774] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0775] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0776] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0777] System configuration

[0778] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0779] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0780] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0781] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0782] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0783] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0784] Data collection

[0785] User: Each logistics company's terminal collects data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0786] Example: For example, a terminal of a logistics company A collects the following data:

[0787] Driver ID: 12345

[0788] Vehicle ID: 67890

[0789] Operating time: 8 hours

[0790] Overtime work hours: 2 hours

[0791] Data noise addition

[0792] Terminal: Noise is added to the collected data based on differential privacy technology to reduce the risk of specific personal or vehicle information being directly leaked.

[0793] Example: Data showing an operating time of 8 hours is converted to 8.3 hours by adding noise of ±0.3 hours.

[0794] Data transmission

[0795] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0796] Example: The transmitted data is driver ID 12345, vehicle ID 67890, working hours 8.3 hours, overtime hours 2.1 hours (after adding noise).

[0797] Data Integration and Secure Computing

[0798] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0799] Example: Aggregating data from each company and calculating, for example, total working hours and average overtime hours.

[0800] Generate and return statistical results

[0801] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0802] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0803] Usage example

[0804] For example, logistics company A collects data internally, adds noise, and sends it in encrypted form to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to allocate resources efficiently.

[0805] The present invention allows logistics companies to obtain statistically valuable information without leaking their data to other companies, thereby helping to solve the problem of resource shortages.

[0806] The processing flow will be explained below.

[0807] Step 1:

[0808] User: Logistics company terminals collect operational data on truck drivers and vehicles, such as driver ID, vehicle ID, operating hours, and overtime hours.

[0809] Step 2:

[0810] Terminal: Enters collected data into the system and standardizes the data format. Converts collected data into a standard format such as JSON. Also, performs data consistency checks and corrects errors if necessary.

[0811] Step 3:

[0812] Terminal: Noise is added to the data using differential privacy techniques. A noise generation function is applied to add noise to each data point. For example, if data shows an operating time of 8 hours, a noise of ±0.3 hours is added to convert it to 8.3 hours.

[0813] Step 4:

[0814] Terminal: The data with noise added is encrypted and sent to the central processing unit (server). The data is encrypted using AES encryption technology. After encryption, the data is sent via the HTTPS protocol.

[0815] Step 5:

[0816] Server: Decrypts the noise-added data received from each device, for example, using AES encryption to restore the original data format.

[0817] Step 6:

[0818] Server: Integrates the received data and performs statistical calculations using secure computing technology. For example, it calculates the overall average working hours, average overtime hours, etc. The noisy data sent from each logistics company is unified as data with random noise added.

[0819] Step 7:

[0820] Server: The statistical calculation results are encrypted before being sent back to each logistics company. The calculated statistical results are converted into JSON format and then encrypted again using AES.

[0821] Step 8:

[0822] Server: Encrypted statistical results are returned to each logistics company via HTTPS. Again, encrypted communication is used when sending data, ensuring secure information sharing.

[0823] Step 9:

[0824] User: Receives the returned statistical results on their own device and decrypts them. Based on the decrypted statistical results, they develop strategies for resource allocation, scheduling, etc.

[0825] In this way, this system allows each logistics company to obtain statistical information safely without disclosing the data.

[0826] Example 1

[0827] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0828] In today's logistics industry, multiple companies need to cooperate and share overall statistical information in order to operate efficiently. However, because data sharing carries the risk of individual information being leaked, there is a need for a system that allows each company to access statistical information while keeping its own data secure.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0830] In this invention, the server includes a noise adding means for adding noise to data collected from multiple data collection devices to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret computation means for securely integrating multiple encrypted data and performing statistical computations, a result return means for encrypting the statistical computation results and returning them to each data collection device, and a system in which each data collection device adds noise to the data to be identified and then transmits the data using encrypted communication means, and the central processing unit integrates the multiple data using secret computation technology to generate and securely return the statistical computation results.This allows each company to safely share overall statistical information without leaking its own data, enabling efficient business operations.

[0831] A "data collection device" is a device for collecting data from multiple information providers.

[0832] The "noise adding means" is a means for adding noise to collected data to achieve differential privacy.

[0833] The "data transmission means" is a means for encrypting the data to which noise has been added and transmitting the data to the central processing unit.

[0834] The "central processing unit" is a device that receives data sent from multiple information providers and integrates it using secure computing technology.

[0835] A "secret computation means" is a means for securely integrating multiple encrypted data and performing statistical computations.

[0836] The "result return means" is a means for encrypting the statistical calculation results and returning them to each information provider.

[0837] "Differential privacy" is a technology that adds noise to data to make individual data unidentifiable and protect the privacy of that data.

[0838] "Encrypted communication means" refers to a means for encrypting data and communicating it securely.

[0839] "Secure computing technology" is a technology that securely integrates multiple data sets and calculates statistical information without disclosing the individual data.

[0840] "Statistical calculation" refers to the calculation of aggregating received data and generating statistical information.

[0841] "Encryption" is a technology that converts data using a certain algorithm to prevent it from being read by third parties.

[0842] This invention is a data sharing system for the logistics industry, designed to securely collect, process, and share specific data. The system includes the following components:

[0843] System configuration

[0844] 1. Data collection device (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0845] 2. Noise injection means (terminal): Noise is injected into the collected data based on differential privacy technology. Noise injection reduces the risk of direct leakage of specific personal or vehicle information. This technology is realized, for example, using a Laplace noise generation algorithm.

[0846] 3. Data transmission means (terminal): The data with noise added is encrypted and sent to the central processing unit. This transmission uses an encryption communication protocol such as AES encryption to ensure security.

[0847] 4. Secure computing means (server): The data sent from each logistics company is integrated and statistical calculations are performed using secure computing technology, such as Secure Multi-party Computation (SMPC).

[0848] 5. Result return method (server): The calculation results are encrypted and returned to each company. This return also uses an encrypted communication protocol.

[0849] Data collection

[0850] User: Personnel at each logistics company use terminals to input and collect data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[0851] Example: When a terminal of logistics company A collects the following data, "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," "Overtime hours 2 hours," etc. are entered into the data collection device.

[0852] Data noise addition

[0853] Terminal: Noise is added to the collected data using differential privacy techniques. For example, noise of ±0.3 hours can be added using a Laplace noise generation algorithm.

[0854] Example: Add noise of ±0.3 hours to the data "8 hours of operation time" to convert it to "8.3 hours of operation time."

[0855] Data transmission

[0856] Terminal: The noise-added data is AES-encrypted and sent to the central processing unit. An encryption protocol (e.g., AES-256) is used to ensure secure data transmission.

[0857] Example: Encrypt and send the data "Driver ID 12345," "Vehicle ID 67890," "Working hours 8.3 hours," and "Overtime hours 2.1 hours (after adding noise)."

[0858] Data Integration and Secure Computing

[0859] Server: The central processing unit integrates the noise-added data received from each logistics company and calculates statistical information using Secure Multi-party Computation (SMPC) technology.

[0860] Example: Integrate operating time data collected from multiple logistics companies to calculate the overall average operating time and overtime hours.

[0861] Generate and return statistical results

[0862] Server: Returns the calculation results to each logistics company using encrypted communication. The encryption protocol is used to ensure the secure transfer of the results.

[0863] Example: Statistical results such as "total average working hours: 8.2 hours" and "total average overtime hours: 2.3 hours" are generated, encrypted, and sent back to each logistics company.

[0864] Usage example

[0865] For example, logistics company A collects data in-house, adds noise to it, encrypts the data, and sends it to a central server. The central server also receives data from multiple logistics companies (B and C), consolidates it, and generates overall statistical information. This statistical information is then re-encrypted and securely sent back to each company. Each company can use this information to improve their own operating conditions and working hours.

[0866] Example prompts for generative AI models

[0867] "Please explain the process by which a logistics company's terminal collects truck driver working time data, adds noise to it, encrypts it, and then transmits it to a central server. The server then aggregates this data, generates statistical results using secure computing technology, and securely returns them to each company. Please provide a concrete example of such a scenario."

[0868] This system allows each logistics company to safely obtain overall statistical information without leaking their own data to other companies, thereby improving operational efficiency and optimizing resources.

[0869] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0870] Step 1:

[0871] User: Personnel at each logistics company use terminals to input and collect data on truck drivers and vehicles, including details such as working hours, routes, and overtime hours.

[0872] Input: Data entered into the terminal by the logistics company staff (e.g., driver ID, vehicle ID, working hours, overtime hours).

[0873] Output: Raw data saved to the device.

[0874] Specific operation: The following information is entered into the terminal of logistics company A: "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," and "Overtime hours 2 hours."

[0875] Step 2:

[0876] Terminal: Noise is added to the collected data based on differential privacy technology. The level of noise is set randomly to prevent individual data from being identified.

[0877] Input: Raw data (e.g., operating time 8 hours).

[0878] Output: Noisy data (e.g., operating time 8.3 hours).

[0879] Specific operation: Add noise of ±0.3 hours to the data of "8 hours of operation time" and convert it to "8.3 hours of operation time."

[0880] Step 3:

[0881] Terminal: The noise-added data is encrypted and sent to the central processing unit. To ensure security, the AES encryption protocol is used.

[0882] Input: Noisy data (e.g., operating time 8.3 hours).

[0883] Output: The encrypted data.

[0884] Specific operation: The noise-added data "8.3 hours of operation time" is AES encrypted and the encrypted data is sent to the central processing unit.

[0885] Step 4:

[0886] Server: The central processing unit aggregates the noise-added data received from each logistics company and calculates statistical information using secret computing technology. This allows the extraction of overall statistical information without disclosing individual data.

[0887] Input: Encrypted data (noisy data received from multiple logistics companies).

[0888] Output: Consolidated statistical data.

[0889] Specific operation: Data received from each logistics company is integrated using Secure Multi-party Computation (SMPC) technology to calculate the overall average operating hours and overtime hours.

[0890] Step 5:

[0891] Server: The calculation results are encrypted and sent back to each logistics company. To ensure secure communication, an encryption protocol is again used.

[0892] Input: Consolidated statistical data.

[0893] Output: Encrypted statistical calculation results.

[0894] Specific operations: Generate statistical results such as overall average operating hours and overtime hours, encrypt them, and send them back to each logistics company.

[0895] Through the above processing steps, this system enables logistics companies to safely share overall statistical information without leaking their own data to other companies, thereby improving business efficiency and optimizing resources.

[0896] (Application example 1)

[0897] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0898] In the logistics industry, each logistics company needs to securely share its data with other companies while obtaining statistically valuable information. However, information sharing is not easy unless privacy protection and data confidentiality are ensured. In particular, advanced privacy protection and data encryption technologies are required to securely collect and integrate data such as driver operation information, driving routes, and working hours to obtain statistical information. Against this background, a method is needed for logistics companies to securely share data while improving overall operational efficiency.

[0899] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0900] In this invention, the server includes a noise adding means for adding noise to data collected from multiple information providers to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations, a result return means for encrypting the statistical calculation results and returning them to each information provider, a data collection means for collecting driver operation information, driving routes, and working hours for managing transportation operations, and an encryption communication means for using an encryption communication protocol when encrypting the data and transmitting it to the central processing unit. This allows each logistics company to safely share data and obtain statistically valuable information while protecting the privacy of their individual data.

[0901] "Data collection means" refers to a device or method for collecting data such as driver operating information, driving routes, and working hours for the purpose of managing transportation operations.

[0902] A "noise-adding means" is a device or method that achieves differential privacy by adding random noise to collected data, making individual data unidentifiable.

[0903] The "data transmission means" refers to a device or method for encrypting the data to which noise has been added and transmitting the encrypted data to the central processing unit.

[0904] "Encrypted communication means" refers to a device or method that uses protocols or techniques to encrypt data and communicate securely.

[0905] A "secret computation method" is a technology or method for securely integrating multiple encrypted data sets and performing statistical calculations.

[0906] The "result return means" is a device or method for encrypting the statistical calculation results and returning them to each information provider.

[0907] In the present invention, a data sharing system in the logistics industry is composed of the following steps.

[0908] 1. Data Collection

[0909] The terminals of each logistics company collect data such as driver operating information, driving routes, and working hours. This allows each logistics company to obtain detailed driving information for its drivers. As a specific example, the terminal of a certain logistics company collects data such as driver ID "12345," vehicle ID "67890," working hours "8 hours," and overtime hours "2 hours."

[0910] 2. Adding noise

[0911] The noise-adding means adds random noise to the data collected within the device to achieve differential privacy. This makes individual data unidentifiable. For example, data showing an operating time of "8 hours" is converted to "8.3 hours" by adding noise of ±0.3 hours.

[0912] 3. Data Transmission

[0913] The data transmission means encrypts the noise-added data and transmits it securely to the central server. At this time, the encryption communication means ensures the security of the data using an encryption communication protocol. As a specific example, the encrypted data is transmitted as driver ID "12345," vehicle ID "67890," operating hours "8.3 hours," and overtime hours "2.1 hours."

[0914] 4. Data Integration and Secure Computing

[0915] A central server receives the encrypted data with noise added from each logistics company, and a secret computation means securely integrates the data and performs statistical calculations.Secure computation technology makes it possible to obtain overall statistical information without disclosing the data of each logistics company.As a specific example, data from each company can be aggregated to calculate total operating hours, average overtime hours, etc.

[0916] 5. Generating and returning statistical results

[0917] The central server encrypts the statistical calculation results and returns them to each information provider. The statistical results are then securely transmitted to each logistics company by the result return means. For example, statistical results such as the overall average working hours of "8.2 hours" and the overall average overtime hours of "2.3 hours" are returned.

[0918] Data transmission and encryption protocols use cryptography libraries and specific encryption protocols (e.g., AES encryption).

[0919] Here are some example prompts to input to a generative AI model:

[0920] "Please tell me how to implement a smartphone application that collects operational data from truck drivers working at a logistics center, adds noise using differential privacy techniques, and then securely transmits the data to a central server. Specifically, please explain in detail each stage of data collection, noise addition, and encrypted transmission."

[0921] As described above, logistics companies can safely share data while protecting the privacy of their individual data, and obtain statistically valuable information, which will improve operational efficiency across the logistics industry.

[0922] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0923] Step 1:

[0924] The user (the terminal of each logistics company) collects data such as driver operation information, driving routes, and working hours. Specifically, the terminal acquires each driver's GPS data and operation log in real time. Inputs include driver ID, vehicle ID, operating hours, and overtime hours, which the terminal acquires from sensing devices and logging apps. The collected raw data is generated as output.

[0925] Step 2:

[0926] The device adds noise to the collected data. Specifically, a program that adds random noise to the data is executed within the device. The raw data collected in step 1 is used as input. To achieve differential privacy, data processing involves adding or subtracting noise to, for example, working hours or overtime hours. Partially anonymized data with added noise is generated as output.

[0927] Step 3:

[0928] The device encrypts the noise-added data and sends it to the central server. Specifically, the device uses a cryptography library to perform AES encryption and sends the encrypted data to the server via a secure communication protocol (e.g., HTTPS). The noise-added data generated in step 2 is used as input. Data transformation involves encryption. The encrypted data is sent to the server as output.

[0929] Step 4:

[0930] The server receives encrypted data sent from multiple logistics companies and securely integrates it. Specifically, the server saves the received encrypted data and stores it in a database for subsequent integration calculations. The input is encrypted data sent from the terminals of each logistics company. The data calculation involves virtually integrating this data into a single dataset. The output is an integrated dataset.

[0931] Step 5:

[0932] The server performs secure computation on the integrated dataset to generate statistical information. Specifically, it uses secure computation means to analyze the data and calculate statistical information such as the overall average working hours and average overtime hours. The input is the integrated dataset created in step 4. Data operations such as addition and averaging are performed. The output is the required statistical information.

[0933] Step 6:

[0934] The server encrypts the generated statistical information and returns it to each logistics company. Specifically, the server encrypts the statistical information using encryption means and sends it to each logistics company's terminal using a secure communication protocol. The input is the statistical information generated in step 5. Data conversion involves re-encryption. The encrypted statistical information is returned to each logistics company as output.

[0935] The above is a detailed flow of data processing in the embodiment of the present invention.

[0936] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0937] System configuration

[0938] The present invention is a data sharing system in the logistics industry, and includes the following components:

[0939] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[0940] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[0941] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[0942] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[0943] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[0944] 6. Emotion engine (terminal): A module that recognizes the user's emotional state from their voice, facial expressions, text input, etc.

[0945] Data collection and emotion recognition

[0946] User: Each logistics company's terminal collects data on truck drivers, vehicle operation status, driving routes, overtime hours, etc. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[0947] Example: For example, a terminal at a logistics company A collects data such as driver ID, vehicle ID, working hours, and overtime hours, while analyzing the user's voice to determine their stress level.

[0948] Data preprocessing and noise addition

[0949] Terminal: Inputs collected data into the system and standardizes the data format. It also adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[0950] Example: Convert collected data into a unified format such as JSON, and adjust the intensity of noise if the user is in a high stress state.

[0951] Data Encryption and Transmission

[0952] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[0953] Example: Noise-added uptime data is AES encrypted and sent to a central server via HTTPS protocol.

[0954] Data Integration and Secure Computing

[0955] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[0956] Example: Calculate the overall average working hours and average overtime hours based on the noisy data received from each company.

[0957] Generate and return statistical results

[0958] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[0959] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[0960] Usage example

[0961] For example, logistics company A collects data in-house, recognizes the user's emotional state using an emotion engine, adds noise to the data, encrypts it, and sends it to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to efficiently allocate resources.

[0962] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[0963] The processing flow will be explained below.

[0964] Step 1:

[0965] User: A logistics company employee collects data on each truck driver and vehicle's operating status, driving routes, overtime hours, etc. At the same time, the emotion engine recognizes the driver's emotional state from their voice and facial expressions.

[0966] Example: Collect data for driver ID 12345, vehicle ID 67890, 8 hours of working time, and 2 hours of overtime work, and determine the driver's stress level as "high."

[0967] Step 2:

[0968] Terminal: Inputs the collected data and emotional state into the system, standardizes the data format (for example, converts it to JSON format), checks the data for consistency, and corrects any errors.

[0969] Example: Convert the collected data as follows: {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8, "overtime_hours": 2, "stress_level": "high"}

[0970] Step 3:

[0971] Device: The emotion engine recognizes the collected data and adjusts the noise addition method based on the emotional state. For example, if the stress level is "high," the noise intensity is set lower than normal.

[0972] Example: If the operating time before noise is added is 8 hours, the normal noise range is adjusted from ±0.5 hours to ±0.3 hours, converting it to 8.3 hours.

[0973] Step 4:

[0974] Terminal: The noise-added data is AES encrypted and securely sent to a central server using the HTTPS protocol.

[0975] Example: Send the noisy and encrypted data to a central server.

[0976] Step 5:

[0977] Server: Decrypts the encrypted data received from each device and converts it back to JSON format.

[0978] Example: Decrypt the encrypted data {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8.3, "overtime_hours": 2.1} and return it to its original form.

[0979] Step 6:

[0980] Server: Integrates the received data and performs overall statistical calculations using secure computing technology. Collects noise-added data provided by each company and calculates statistics such as average working hours and average overtime hours.

[0981] Example: Based on noisy data from each company, the average working hours are calculated to be 8.2 hours and the average overtime hours to be 2.3 hours.

[0982] Step 7:

[0983] Server: The statistical calculation results are encrypted and sent back to each logistics company. To ensure the results are sent securely, AES encryption and HTTPS protocol are used.

[0984] Example: The statistical calculation results {"average_working_hours": 8.2, "average_overtime_hours": 2.3} are AES encrypted and sent to each company via HTTPS protocol.

[0985] Step 8:

[0986] User: Each logistics company receives the returned encrypted data on their own terminal and decrypts it. Based on the decrypted statistical results, they create resource allocation and scheduling strategies.

[0987] Example: Analyze the statistical results after decoding and consider and implement measures to improve your company's operational status.

[0988] The system allows logistics companies to optimize resource management by leveraging statistical information and emotional states while preserving privacy.

[0989] Example 2

[0990] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0991] In the logistics industry, there is a need for multiple companies to share data to improve overall efficiency. However, when individual companies directly share their own data with other companies, there is a risk of privacy violations and data leaks. Furthermore, efficient data management while understanding the emotional state of drivers requires complex information processing, which is not easy to achieve. To solve these issues, a safe and efficient data sharing system is required.

[0992] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a noise-adding means for adding noise to data collected from multiple information providers to achieve differential privacy; a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit; a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations; a result return means for encrypting the statistical calculation results and returning them to each information provider; an emotion recognition means for recognizing the emotional state of a user from their voice or facial expression and processing the data in conjunction with data collection; and a data preprocessing means for converting collected data into a unified format and adjusting noise-adding parameters based on the emotional state. This enables individual companies to safely and efficiently obtain statistical information and improve resource management without directly sharing their own data.

[0993] "Information provider" refers to a company or individual that collects and provides data in the logistics industry.

[0994] "Adding noise to data" refers to a technique of making individual data indistinguishable by adding random variations to the original data.

[0995] "Differential privacy" refers to a technology that protects the privacy of individual data by ensuring that adding or removing a single piece of data from a dataset being analyzed does not significantly change the results.

[0996] "Noise injection means" means a technique or device for achieving differential privacy by adding random noise to collected data.

[0997] "Data transmission means" refers to the technology or device that encrypts collected data and transmits it securely to the central processing unit.

[0998] "Central processing unit" refers to a server or computer device that integrates data sent from each information provider and performs statistical calculations using secret computation.

[0999] "Secret computation means" refers to technologies and methods that securely integrate multiple encrypted data sets and perform statistical calculations while protecting the privacy of each information provider.

[1000] "Result return means" refers to a technology or device that encrypts the statistical calculation results and returns them securely to each information provider.

[1001] "Emotion recognition means" refers to technology or devices that analyze the user's voice and facial expressions and recognize their emotional state.

[1002] "Data pre-processing means" refers to techniques and methods for converting collected data into a unified format and adjusting noise-adding parameters based on emotional state.

[1003] "Integrated format" means a format that converts data collected from different sources into a unified format.

[1004] "Noise addition parameters" refer to parameters for setting the intensity and distribution of noise to be added to collected data.

[1005] "AES encryption" stands for Advanced Encryption Standard, a standard encryption method for securely encrypting data.

[1006] "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and refers to a protocol for providing secure communications.

[1007] This invention is a data sharing system for the logistics industry, which aims to collect, process, transmit, integrate, and return data safely and efficiently. This system consists of multiple components, including a data collection terminal, a noise addition module, a data transmission module, a secure computation module, and a result return module. The data collection terminal is also equipped with an emotion engine that recognizes the user's emotional state.

[1008] Data collection and emotion recognition

[1009] User: A logistics company employee operates a data collection terminal to input information such as the truck driver and vehicle operating status, route, and overtime hours. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[1010] Example: A logistics company driver logs into a terminal and inputs driving data, while his stress level is determined in real time through voice analysis.

[1011] Data preprocessing and noise addition

[1012] Terminal: Converts collected data into a unified format such as JSON, and adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[1013] Example: The device converts collected data into JSON format and automatically increases the noise intensity if the user is in a high stress state.

[1014] Data Encryption and Transmission

[1015] Terminal: The noisy data is AES encrypted and sent to a central server using the HTTPS protocol.

[1016] Example: Noise-added uptime data is AES encrypted and sent to a server using a secure communication protocol.

[1017] Data Integration and Secure Computing

[1018] Server: The central server receives the noise-added data sent by each logistics company and performs integrated calculations using secret computing technology. This allows individual companies to obtain overall statistical information without directly sharing their own data with other companies.

[1019] Example: The server calculates the overall average working hours and average overtime hours based on the noisy data received from each company.

[1020] Generate and return statistical results

[1021] Server: Securely returns the statistical calculation results to each company. This return also uses encrypted communication.

[1022] Example: Statistical results such as the overall average working hours being 8.2 hours and the overall average overtime hours being 2.3 hours are sent back to each company.

[1023] Usage example

[1024] For example, a logistics company could collect data in-house, use an emotion engine to recognize the user's emotional state, add noise to the data, AES encrypt it, and send it to a central server. The central server then receives data from multiple logistics companies, integrates it, and performs overall statistical calculations. The calculated statistical results are then securely returned to each company, allowing each company to obtain valuable statistical information without directly sharing their own data with other companies.

[1025] Example prompt sentence:

[1026] "As a logistics company, please create a program for a data collection system that uses data collection terminals to collect driver working hours and emotional states. Please also create a program that adds noise to the collected data, securely transmits it to a server, and calculates the overall average working hours."

[1027] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[1028] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1029] Step 1: Data collection

[1030] User: A logistics company employee operates a data collection terminal to input information such as the truck driver's and vehicle's operating status, route, and overtime hours.

[1031] Input: Driver ID, vehicle ID, operating hours, route, overtime hours, etc.

[1032] Output: Collected data.

[1033] Specific operation: Drivers log in to the terminal at the start of their shift and record their route and working hours in real time.

[1034] Step 2: Emotion Recognition

[1035] On the device: The emotion engine recognizes the user's emotional state from their voice and facial expressions. This information is collected simultaneously.

[1036] Input: User's voice and facial expression data.

[1037] Output: Perceived emotional state.

[1038] Specific operation: The device uses voice input and the camera to analyze the user's voice and facial expressions and evaluate their stress level in real time.

[1039] Step 3: Data Preprocessing

[1040] Terminal: Converts collected data into a unified format (e.g., JSON format) and adjusts noise-adding parameters based on the emotional state recognized by the emotion engine.

[1041] Input: Collected data, perceived emotional state.

[1042] Output: Data converted to a unified format, adjusted noise addition parameters.

[1043] Specific operation: The device converts data into JSON format and automatically increases the noise intensity if the user is in a state of high stress.

[1044] Step 4: Add noise

[1045] Terminal: Add noise to the transformed data to achieve differential privacy.

[1046] Input: Data converted to unified format, adjusted noise addition parameters.

[1047] Output: Noise-augmented data.

[1048] Specific behavior: For example, adding random noise to uptime data to prevent accurate data from being sent directly.

[1049] Step 5: Data Encryption

[1050] Terminal: The noise-added data is AES encrypted.

[1051] Input: Noisy data.

[1052] Output: The encrypted data.

[1053] Specific operation: Using an encryption algorithm, the added noise data is securely encrypted to protect it from unauthorized access.

[1054] Step 6: Send data

[1055] Terminal: Sends encrypted data to a central server using the HTTPS protocol.

[1056] Input: Encrypted data.

[1057] Output: Data sent to the central server.

[1058] Specific operation: The device sends encrypted data to a central server using a secure communication protocol (e.g., HTTPS).

[1059] Step 7: Data reception and integration

[1060] Server: The central server receives the noise-added data sent from each logistics company and integrates the data using secure computing technology.

[1061] Input: Noisy data received from multiple logistics companies.

[1062] Output: Consolidated data.

[1063] Specific operation: The server decodes the received data and calculates the overall statistics using a secret calculation algorithm.

[1064] Step 8: Generate statistical results

[1065] Server: Generates statistical calculation results, such as the overall average working hours and average overtime hours.

[1066] Input: Consolidated data.

[1067] Output: Statistical calculation results.

[1068] Specific operation: The server processes the data and generates overall statistics (e.g., average working hours: 8.2 hours, average overtime hours: 2.3 hours).

[1069] Step 9: Encrypting the statistical results

[1070] Server: Encrypts the generated statistical results.

[1071] Input: Statistical calculation results.

[1072] Output: Encrypted statistical results.

[1073] What it does: Securely encrypts the statistical results using the AES encryption algorithm.

[1074] Step 10: Returning results

[1075] Server: Returns the encrypted statistical results to each logistics company.

[1076] Input: Encrypted statistical results.

[1077] Output: Encryption statistics results sent.

[1078] Specific operation: The server uses the HTTPS protocol to send encrypted statistical results to each logistics company.

[1079] Step 11: Interpret and display the results

[1080] Terminal: Decodes the received statistical results and displays them to the user.

[1081] Input: Encrypted statistical results.

[1082] Output: Display of the interpreted statistical results.

[1083] Specific operations: Company personnel use a terminal to decipher the statistical results and visually confirm them on the management screen.

[1084] (Application example 2)

[1085] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1086] While conventional data sharing systems are required to collect and safely share large amounts of data in the logistics industry, there is a risk that individual data may be identified and privacy may be violated.In addition, there is no data collection method that takes into account the stress and emotional state of workers, making it difficult to improve work efficiency and the working environment.

[1087] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a noise adding means that adds noise to data collected from multiple information providers to achieve differential privacy, a data transmission means that encrypts the noise-added data and transmits it to a central processing unit, a secret calculation means that securely integrates multiple pieces of the encrypted data and performs statistical calculations, and an emotion recognition means that recognizes the emotional state of the worker and adjusts the intensity of noise addition to the collected data. This allows individual data to be shared safely while protecting privacy, and enables data collection according to the emotional state of the worker.

[1088] An "information provider" is an entity that collects data and provides it to the system.

[1089] The "noise adding means" is a technique that makes it difficult to distinguish the collected data by adding random noise to the data.

[1090] The "data transmission means" is a technology for encrypting data to which noise has been added and transmitting the data securely to a central processing unit or other system.

[1091] A "central processing unit" is a device that receives multiple data sets and performs statistical calculations and data integration.

[1092] "Secure computation" is a technology that aggregates multiple encrypted data and performs statistical calculations while protecting privacy.

[1093] The "result return means" is a technique for safely returning the results of statistical calculations to each information provider.

[1094] The "emotion recognition means" is a technology that recognizes the emotional state of the worker and adjusts the intensity of noise added to the collected data.

[1095] "Emotional state" refers to data that indicates the psychological and physiological state of the worker, including stress level.

[1096] "Differential privacy" is a technique for collecting and analyzing statistical information while preserving the privacy of individual data.

[1097] "Noise intensity" refers to the amount of noise added to the data, and is adjusted according to the emotional state.

[1098] "AES encryption" is a highly secure encryption algorithm used to encrypt data.

[1099] System configuration

[1100] This invention is a system that monitors the work status and emotional state of factory workers in real time, and collects, adds noise to, encrypts, transmits, and processes statistical data.

[1101] Data collection and emotion recognition

[1102] Device: Each worker wears smart glasses (or a smartphone) equipped with a camera and microphone to collect work status and voice data. An emotion recognition module is used to recognize the user's emotional state (e.g., stress level) from their voice and facial expressions. This emotion recognition module uses a machine learning model to analyze the user's emotions in real time.

[1103] Data preprocessing and noise addition

[1104] Terminal: Collected operational data (e.g., work hours, work content) and emotional data are first converted into a unified data format. Next, a noise-adding means adds random noise to the data according to the emotional state. For example, the noise intensity increases when the subject is in a high stress state, and decreases when the subject is in a low stress state.

[1105] Data Encryption and Transmission

[1106] Terminal: The noisy data is encrypted using the AES encryption algorithm. The encrypted data is sent to a central server using a secure communication protocol (e.g., HTTPS).

[1107] Data Integration and Secure Computing

[1108] Server: The central server receives the encrypted data sent from each device. The received data is aggregated using secret calculation techniques to generate overall statistical information (e.g., average work time, average stress level). This statistical calculation provides aggregated information without disclosing individual data.

[1109] Generate and return statistical results

[1110] Server: The statistical calculation results are again encrypted and securely sent back to each worker's device. This allows each worker and manager to refer to the statistical information in real time, which can be used for appropriate resource management and improving the working environment.

[1111] Specific examples

[1112] For example, worker A at a factory is wearing smart glasses while working. The smart glasses' camera collects the work situation, the microphone collects audio data, and the emotion recognition module determines the stress level. If worker A is in a high stress state, the noise intensity of the data is increased and the encrypted noise data is sent to a central server via HTTPS. The central server combines data from other workers to generate statistical information such as the average stress level, and then re-encrypts the results and sends them back to each worker's device.

[1113] Example of a generated AI prompt

[1114] "Collect the user's work situation and emotional state in real time from the camera footage. If the stress level is high, add high-intensity noise to the data, protect it with AES encryption, and send it to a central server using a secure communication protocol. If the stress level is low, add low-intensity noise."

[1115] This will enable companies to allocate resources efficiently and improve working conditions while protecting transparency of the work environment and workers' privacy.

[1116] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1117] Step 1: Data collection

[1118] The terminal collects work situation and emotion data through smart glasses worn by each worker. Specifically, a camera captures the worker's movements and a microphone records audio data. The collected data includes frames of image data and audio samples of audio data. The input data are image frames and audio data, and the output is digital data in a unified format.

[1119] Step 2: Emotion Recognition

[1120] The emotion recognition module in the device analyzes the collected image frames and audio data to recognize the worker's emotional state. Specifically, the machine learning model performs facial expression and audio analysis to determine the stress level. The input data are the image frames and audio data collected in step 1, and the output is the emotional state, such as "high stress" or "low stress."

[1121] Step 3: Add noise

[1122] The device adds random noise to the collected data based on the emotion recognition results. Specifically, if the emotional state is "high stress," the noise intensity is increased, and if it is "low stress," the noise intensity is decreased. The noise is generated using statistical methods such as Gaussian noise. The input data is the emotional state obtained in step 2 and the data collected in step 1, and the output is the noise-added data.

[1123] Step 4: Data Encryption

[1124] The device encrypts the noise-added data using the AES encryption algorithm. Specifically, the data is block-encrypted and securely stored along with the encryption key. The input data is the noise-added data obtained in step 3, and the output is the encrypted data.

[1125] Step 5: Send data

[1126] The terminal sends the encrypted data to the central server using the HTTPS protocol. Specifically, the terminal sends data using HTTPS and monitors the transmission status. The input data is the data encrypted in step 4, and the output is the transmission result to the central server.

[1127] Step 6: Data Integration and Secure Computing

[1128] The server receives the encrypted data sent from each device and integrates it using secure computing technology. Specifically, it performs statistical calculations without decrypting the encrypted data to calculate the overall average work time and stress level. The input data is the encrypted data, and the output is the statistical calculation results.

[1129] Step 7: Returning statistical results

[1130] The server encrypts the statistical calculation results and returns them to each worker's terminal. Specifically, it uses encryption technology to block-encrypt the calculation results and returns them using the HTTPS protocol. The input data is the statistical calculation results obtained in step 6, and the output is the return of the encrypted calculation results.

[1131] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1132] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1133] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1134] [Fourth embodiment]

[1135] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1139] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1144] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1146] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1147] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1148] System configuration

[1149] The present invention is a data sharing system in the logistics industry, and includes the following components:

[1150] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[1151] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[1152] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[1153] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[1154] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[1155] Data collection

[1156] User: Each logistics company's terminal collects data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[1157] Example: For example, a terminal of a logistics company A collects the following data:

[1158] Driver ID: 12345

[1159] Vehicle ID: 67890

[1160] Operating time: 8 hours

[1161] Overtime work hours: 2 hours

[1162] Data noise addition

[1163] Terminal: Noise is added to the collected data based on differential privacy technology to reduce the risk of specific personal or vehicle information being directly leaked.

[1164] Example: Data showing an operating time of 8 hours is converted to 8.3 hours by adding noise of ±0.3 hours.

[1165] Data transmission

[1166] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[1167] Example: The transmitted data is driver ID 12345, vehicle ID 67890, working hours 8.3 hours, overtime hours 2.1 hours (after adding noise).

[1168] Data Integration and Secure Computing

[1169] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[1170] Example: Aggregating data from each company and calculating, for example, total working hours and average overtime hours.

[1171] Generate and return statistical results

[1172] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[1173] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[1174] Usage example

[1175] For example, logistics company A collects data internally, adds noise, and sends it in encrypted form to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to allocate resources efficiently.

[1176] The present invention allows logistics companies to obtain statistically valuable information without leaking their data to other companies, thereby helping to solve the problem of resource shortages.

[1177] The processing flow will be explained below.

[1178] Step 1:

[1179] User: Logistics company terminals collect operational data on truck drivers and vehicles, such as driver ID, vehicle ID, operating hours, and overtime hours.

[1180] Step 2:

[1181] Terminal: Enters collected data into the system and standardizes the data format. Converts collected data into a standard format such as JSON. Also, performs data consistency checks and corrects errors if necessary.

[1182] Step 3:

[1183] Terminal: Noise is added to the data using differential privacy techniques. A noise generation function is applied to add noise to each data point. For example, if data shows an operating time of 8 hours, a noise of ±0.3 hours is added to convert it to 8.3 hours.

[1184] Step 4:

[1185] Terminal: The data with noise added is encrypted and sent to the central processing unit (server). The data is encrypted using AES encryption technology. After encryption, the data is sent via the HTTPS protocol.

[1186] Step 5:

[1187] Server: Decrypts the noise-added data received from each device, for example, using AES encryption to restore the original data format.

[1188] Step 6:

[1189] Server: Integrates the received data and performs statistical calculations using secure computing technology. For example, it calculates the overall average working hours, average overtime hours, etc. The noisy data sent from each logistics company is unified as data with random noise added.

[1190] Step 7:

[1191] Server: The statistical calculation results are encrypted before being sent back to each logistics company. The calculated statistical results are converted into JSON format and then encrypted again using AES.

[1192] Step 8:

[1193] Server: Encrypted statistical results are returned to each logistics company via HTTPS. Again, encrypted communication is used when sending data, ensuring secure information sharing.

[1194] Step 9:

[1195] User: Receives the returned statistical results on their own device and decrypts them. Based on the decrypted statistical results, they develop strategies for resource allocation, scheduling, etc.

[1196] In this way, this system allows each logistics company to obtain statistical information safely without disclosing the data.

[1197] Example 1

[1198] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1199] In today's logistics industry, multiple companies need to cooperate and share overall statistical information in order to operate efficiently. However, because data sharing carries the risk of individual information being leaked, there is a need for a system that allows each company to access statistical information while keeping its own data secure.

[1200] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1201] In this invention, the server includes a noise adding means for adding noise to data collected from multiple data collection devices to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret computation means for securely integrating multiple encrypted data and performing statistical computations, a result return means for encrypting the statistical computation results and returning them to each data collection device, and a system in which each data collection device adds noise to the data to be identified and then transmits the data using encrypted communication means, and the central processing unit integrates the multiple data using secret computation technology to generate and securely return the statistical computation results.This allows each company to safely share overall statistical information without leaking its own data, enabling efficient business operations.

[1202] A "data collection device" is a device for collecting data from multiple information providers.

[1203] The "noise adding means" is a means for adding noise to collected data to achieve differential privacy.

[1204] The "data transmission means" is a means for encrypting the data to which noise has been added and transmitting the data to the central processing unit.

[1205] The "central processing unit" is a device that receives data sent from multiple information providers and integrates it using secure computing technology.

[1206] A "secret computation means" is a means for securely integrating multiple encrypted data and performing statistical computations.

[1207] The "result return means" is a means for encrypting the statistical calculation results and returning them to each information provider.

[1208] "Differential privacy" is a technology that adds noise to data to make individual data unidentifiable and protect the privacy of that data.

[1209] "Encrypted communication means" refers to a means for encrypting data and communicating it securely.

[1210] "Secure computing technology" is a technology that securely integrates multiple data sets and calculates statistical information without disclosing the individual data.

[1211] "Statistical calculation" refers to the calculation of aggregating received data and generating statistical information.

[1212] "Encryption" is a technology that converts data using a certain algorithm to prevent it from being read by third parties.

[1213] This invention is a data sharing system for the logistics industry, designed to securely collect, process, and share specific data. The system includes the following components:

[1214] System configuration

[1215] 1. Data collection device (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[1216] 2. Noise injection means (terminal): Noise is injected into the collected data based on differential privacy technology. Noise injection reduces the risk of direct leakage of specific personal or vehicle information. This technology is realized, for example, using a Laplace noise generation algorithm.

[1217] 3. Data transmission means (terminal): The data with noise added is encrypted and sent to the central processing unit. This transmission uses an encryption communication protocol such as AES encryption to ensure security.

[1218] 4. Secure computing means (server): The data sent from each logistics company is integrated and statistical calculations are performed using secure computing technology, such as Secure Multi-party Computation (SMPC).

[1219] 5. Result return method (server): The calculation results are encrypted and returned to each company. This return also uses an encrypted communication protocol.

[1220] Data collection

[1221] User: Personnel at each logistics company use terminals to input and collect data such as truck driver and vehicle operating status, driving routes, and overtime hours.

[1222] Example: When a terminal of logistics company A collects the following data, "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," "Overtime hours 2 hours," etc. are entered into the data collection device.

[1223] Data noise addition

[1224] Terminal: Noise is added to the collected data using differential privacy techniques. For example, noise of ±0.3 hours can be added using a Laplace noise generation algorithm.

[1225] Example: Add noise of ±0.3 hours to the data "8 hours of operation time" to convert it to "8.3 hours of operation time."

[1226] Data transmission

[1227] Terminal: The noise-added data is AES-encrypted and sent to the central processing unit. An encryption protocol (e.g., AES-256) is used to ensure secure data transmission.

[1228] Example: Encrypt and send the data "Driver ID 12345," "Vehicle ID 67890," "Working hours 8.3 hours," and "Overtime hours 2.1 hours (after adding noise)."

[1229] Data Integration and Secure Computing

[1230] Server: The central processing unit integrates the noise-added data received from each logistics company and calculates statistical information using Secure Multi-party Computation (SMPC) technology.

[1231] Example: Integrate operating time data collected from multiple logistics companies to calculate the overall average operating time and overtime hours.

[1232] Generate and return statistical results

[1233] Server: Returns the calculation results to each logistics company using encrypted communication. The encryption protocol is used to ensure the secure transfer of the results.

[1234] Example: Statistical results such as "total average working hours: 8.2 hours" and "total average overtime hours: 2.3 hours" are generated, encrypted, and sent back to each logistics company.

[1235] Usage example

[1236] For example, logistics company A collects data in-house, adds noise to it, encrypts the data, and sends it to a central server. The central server also receives data from multiple logistics companies (B and C), consolidates it, and generates overall statistical information. This statistical information is then re-encrypted and securely sent back to each company. Each company can use this information to improve their own operating conditions and working hours.

[1237] Example prompts for generative AI models

[1238] "Please explain the process by which a logistics company's terminal collects truck driver working time data, adds noise to it, encrypts it, and then transmits it to a central server. The server then aggregates this data, generates statistical results using secure computing technology, and securely returns them to each company. Please provide a concrete example of such a scenario."

[1239] This system allows each logistics company to safely obtain overall statistical information without leaking their own data to other companies, thereby improving operational efficiency and optimizing resources.

[1240] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1241] Step 1:

[1242] User: Personnel at each logistics company use terminals to input and collect data on truck drivers and vehicles, including details such as working hours, routes, and overtime hours.

[1243] Input: Data entered into the terminal by the logistics company staff (e.g., driver ID, vehicle ID, working hours, overtime hours).

[1244] Output: Raw data saved to the device.

[1245] Specific operation: The following information is entered into the terminal of logistics company A: "Driver ID 12345," "Vehicle ID 67890," "Working hours 8 hours," and "Overtime hours 2 hours."

[1246] Step 2:

[1247] Terminal: Noise is added to the collected data based on differential privacy technology. The level of noise is set randomly to prevent individual data from being identified.

[1248] Input: Raw data (e.g., operating time 8 hours).

[1249] Output: Noisy data (e.g., operating time 8.3 hours).

[1250] Specific operation: Add noise of ±0.3 hours to the data of "8 hours of operation time" and convert it to "8.3 hours of operation time."

[1251] Step 3:

[1252] Terminal: The noise-added data is encrypted and sent to the central processing unit. To ensure security, the AES encryption protocol is used.

[1253] Input: Noisy data (e.g., operating time 8.3 hours).

[1254] Output: The encrypted data.

[1255] Specific operation: The noise-added data "8.3 hours of operation time" is AES encrypted and the encrypted data is sent to the central processing unit.

[1256] Step 4:

[1257] Server: The central processing unit aggregates the noise-added data received from each logistics company and calculates statistical information using secret computing technology. This allows the extraction of overall statistical information without disclosing individual data.

[1258] Input: Encrypted data (noisy data received from multiple logistics companies).

[1259] Output: Consolidated statistical data.

[1260] Specific operation: Data received from each logistics company is integrated using Secure Multi-party Computation (SMPC) technology to calculate the overall average operating hours and overtime hours.

[1261] Step 5:

[1262] Server: The calculation results are encrypted and sent back to each logistics company. To ensure secure communication, an encryption protocol is again used.

[1263] Input: Consolidated statistical data.

[1264] Output: Encrypted statistical calculation results.

[1265] Specific operations: Generate statistical results such as overall average operating hours and overtime hours, encrypt them, and send them back to each logistics company.

[1266] Through the above processing steps, this system enables logistics companies to safely share overall statistical information without leaking their own data to other companies, thereby improving business efficiency and optimizing resources.

[1267] (Application example 1)

[1268] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1269] In the logistics industry, each logistics company needs to securely share its data with other companies while obtaining statistically valuable information. However, information sharing is not easy unless privacy protection and data confidentiality are ensured. In particular, advanced privacy protection and data encryption technologies are required to securely collect and integrate data such as driver operation information, driving routes, and working hours to obtain statistical information. Against this background, a method is needed for logistics companies to securely share data while improving overall operational efficiency.

[1270] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1271] In this invention, the server includes a noise adding means for adding noise to data collected from multiple information providers to achieve differential privacy, a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit, a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations, a result return means for encrypting the statistical calculation results and returning them to each information provider, a data collection means for collecting driver operation information, driving routes, and working hours for managing transportation operations, and an encryption communication means for using an encryption communication protocol when encrypting the data and transmitting it to the central processing unit. This allows each logistics company to safely share data and obtain statistically valuable information while protecting the privacy of their individual data.

[1272] "Data collection means" refers to a device or method for collecting data such as driver operating information, driving routes, and working hours for the purpose of managing transportation operations.

[1273] A "noise-adding means" is a device or method that achieves differential privacy by adding random noise to collected data, making individual data unidentifiable.

[1274] The "data transmission means" refers to a device or method for encrypting the data to which noise has been added and transmitting the encrypted data to the central processing unit.

[1275] "Encrypted communication means" refers to a device or method that uses protocols or techniques to encrypt data and communicate securely.

[1276] A "secret computation method" is a technology or method for securely integrating multiple encrypted data sets and performing statistical calculations.

[1277] The "result return means" is a device or method for encrypting the statistical calculation results and returning them to each information provider.

[1278] In the present invention, a data sharing system in the logistics industry is composed of the following steps.

[1279] 1. Data Collection

[1280] The terminals of each logistics company collect data such as driver operating information, driving routes, and working hours. This allows each logistics company to obtain detailed driving information for its drivers. As a specific example, the terminal of a certain logistics company collects data such as driver ID "12345," vehicle ID "67890," working hours "8 hours," and overtime hours "2 hours."

[1281] 2. Adding noise

[1282] The noise-adding means adds random noise to the data collected within the device to achieve differential privacy. This makes individual data unidentifiable. For example, data showing an operating time of "8 hours" is converted to "8.3 hours" by adding noise of ±0.3 hours.

[1283] 3. Data Transmission

[1284] The data transmission means encrypts the noise-added data and transmits it securely to the central server. At this time, the encryption communication means ensures the security of the data using an encryption communication protocol. As a specific example, the encrypted data is transmitted as driver ID "12345," vehicle ID "67890," operating hours "8.3 hours," and overtime hours "2.1 hours."

[1285] 4. Data Integration and Secure Computing

[1286] A central server receives the encrypted data with noise added from each logistics company, and a secret computation means securely integrates the data and performs statistical calculations.Secure computation technology makes it possible to obtain overall statistical information without disclosing the data of each logistics company.As a specific example, data from each company can be aggregated to calculate total operating hours, average overtime hours, etc.

[1287] 5. Generating and returning statistical results

[1288] The central server encrypts the statistical calculation results and returns them to each information provider. The statistical results are then securely transmitted to each logistics company by the result return means. For example, statistical results such as the overall average working hours of "8.2 hours" and the overall average overtime hours of "2.3 hours" are returned.

[1289] Data transmission and encryption protocols use cryptography libraries and specific encryption protocols (e.g., AES encryption).

[1290] Here are some example prompts to input to a generative AI model:

[1291] "Please tell me how to implement a smartphone application that collects operational data from truck drivers working at a logistics center, adds noise using differential privacy techniques, and then securely transmits the data to a central server. Specifically, please explain in detail each stage of data collection, noise addition, and encrypted transmission."

[1292] As described above, logistics companies can safely share data while protecting the privacy of their individual data, and obtain statistically valuable information, which will improve operational efficiency across the logistics industry.

[1293] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1294] Step 1:

[1295] The user (the terminal of each logistics company) collects data such as driver operation information, driving routes, and working hours. Specifically, the terminal acquires each driver's GPS data and operation log in real time. Inputs include driver ID, vehicle ID, operating hours, and overtime hours, which the terminal acquires from sensing devices and logging apps. The collected raw data is generated as output.

[1296] Step 2:

[1297] The device adds noise to the collected data. Specifically, a program that adds random noise to the data is executed within the device. The raw data collected in step 1 is used as input. To achieve differential privacy, data processing involves adding or subtracting noise to, for example, working hours or overtime hours. Partially anonymized data with added noise is generated as output.

[1298] Step 3:

[1299] The device encrypts the noise-added data and sends it to the central server. Specifically, the device uses a cryptography library to perform AES encryption and sends the encrypted data to the server via a secure communication protocol (e.g., HTTPS). The noise-added data generated in step 2 is used as input. Data transformation involves encryption. The encrypted data is sent to the server as output.

[1300] Step 4:

[1301] The server receives encrypted data sent from multiple logistics companies and securely integrates it. Specifically, the server saves the received encrypted data and stores it in a database for subsequent integration calculations. The input is encrypted data sent from the terminals of each logistics company. The data calculation involves virtually integrating this data into a single dataset. The output is an integrated dataset.

[1302] Step 5:

[1303] The server performs secure computation on the integrated dataset to generate statistical information. Specifically, it uses secure computation means to analyze the data and calculate statistical information such as the overall average working hours and average overtime hours. The input is the integrated dataset created in step 4. Data operations such as addition and averaging are performed. The output is the required statistical information.

[1304] Step 6:

[1305] The server encrypts the generated statistical information and returns it to each logistics company. Specifically, the server encrypts the statistical information using encryption means and sends it to each logistics company's terminal using a secure communication protocol. The input is the statistical information generated in step 5. Data conversion involves re-encryption. The encrypted statistical information is returned to each logistics company as output.

[1306] The above is a detailed flow of data processing in the embodiment of the present invention.

[1307] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1308] System configuration

[1309] The present invention is a data sharing system in the logistics industry, and includes the following components:

[1310] 1. Data collection terminal (user): A terminal owned by each logistics company that collects information on truck drivers and vehicles.

[1311] 2. Data noise addition module (terminal): A module that achieves differential privacy by adding noise to collected data.

[1312] 3. Data transmission module (terminal): Securely transmits the noise-added data to the central server.

[1313] 4. Secure computing module (server): The data sent from each company is integrated using secure computing technology to generate the computation results.

[1314] 5. Result return module (server): Securely returns the integrated calculation results to each company.

[1315] 6. Emotion engine (terminal): A module that recognizes the user's emotional state from their voice, facial expressions, text input, etc.

[1316] Data collection and emotion recognition

[1317] User: Each logistics company's terminal collects data on truck drivers, vehicle operation status, driving routes, overtime hours, etc. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[1318] Example: For example, a terminal at a logistics company A collects data such as driver ID, vehicle ID, working hours, and overtime hours, while analyzing the user's voice to determine their stress level.

[1319] Data preprocessing and noise addition

[1320] Terminal: Inputs collected data into the system and standardizes the data format. It also adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[1321] Example: Convert collected data into a unified format such as JSON, and adjust the intensity of noise if the user is in a high stress state.

[1322] Data Encryption and Transmission

[1323] Terminal: The noise-added data is encrypted and sent to a central server using an encrypted communication protocol to ensure security.

[1324] Example: Noise-added uptime data is AES encrypted and sent to a central server via HTTPS protocol.

[1325] Data Integration and Secure Computing

[1326] Server: The central server receives the noise-added data sent from each logistics company and performs an integrated calculation using secure computation technology. Through secure computation, the overall statistical information is obtained without disclosing it to individual companies.

[1327] Example: Calculate the overall average working hours and average overtime hours based on the noisy data received from each company.

[1328] Generate and return statistical results

[1329] Server: Securely returns the calculation results to each company. This return of the results also uses encrypted communication.

[1330] For example, statistical results are returned, such as the overall average working hours being 8.2 hours, the overall average overtime hours being 2.3 hours, etc. This allows each logistics company to obtain statistically valuable information without directly sharing their own data with other companies.

[1331] Usage example

[1332] For example, logistics company A collects data in-house, recognizes the user's emotional state using an emotion engine, adds noise to the data, encrypts it, and sends it to a central server. The central server receives similar data from logistics companies B and C, integrates this data, and calculates statistics such as the overall average operating hours and overtime hours. The calculated statistical results are encrypted and securely returned to each company, which then uses this information to efficiently allocate resources.

[1333] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[1334] The processing flow will be explained below.

[1335] Step 1:

[1336] User: A logistics company employee collects data on each truck driver and vehicle's operating status, driving routes, overtime hours, etc. At the same time, the emotion engine recognizes the driver's emotional state from their voice and facial expressions.

[1337] Example: Collect data for driver ID 12345, vehicle ID 67890, 8 hours of working time, and 2 hours of overtime work, and determine the driver's stress level as "high."

[1338] Step 2:

[1339] Terminal: Inputs the collected data and emotional state into the system, standardizes the data format (for example, converts it to JSON format), checks the data for consistency, and corrects any errors.

[1340] Example: Convert the collected data as follows: {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8, "overtime_hours": 2, "stress_level": "high"}

[1341] Step 3:

[1342] Device: The emotion engine recognizes the collected data and adjusts the noise addition method based on the emotional state. For example, if the stress level is "high," the noise intensity is set lower than normal.

[1343] Example: If the operating time before noise is added is 8 hours, the normal noise range is adjusted from ±0.5 hours to ±0.3 hours, converting it to 8.3 hours.

[1344] Step 4:

[1345] Terminal: The noise-added data is AES encrypted and securely sent to a central server using the HTTPS protocol.

[1346] Example: Send the noisy and encrypted data to a central server.

[1347] Step 5:

[1348] Server: Decrypts the encrypted data received from each device and converts it back to JSON format.

[1349] Example: Decrypt the encrypted data {"driver_id": 12345, "vehicle_id": 67890, "working_hours": 8.3, "overtime_hours": 2.1} and return it to its original form.

[1350] Step 6:

[1351] Server: Integrates the received data and performs overall statistical calculations using secure computing technology. Collects noise-added data provided by each company and calculates statistics such as average working hours and average overtime hours.

[1352] Example: Based on noisy data from each company, the average working hours are calculated to be 8.2 hours and the average overtime hours to be 2.3 hours.

[1353] Step 7:

[1354] Server: The statistical calculation results are encrypted and sent back to each logistics company. To ensure the results are sent securely, AES encryption and HTTPS protocol are used.

[1355] Example: The statistical calculation results {"average_working_hours": 8.2, "average_overtime_hours": 2.3} are AES encrypted and sent to each company via HTTPS protocol.

[1356] Step 8:

[1357] User: Each logistics company receives the returned encrypted data on their own terminal and decrypts it. Based on the decrypted statistical results, they create resource allocation and scheduling strategies.

[1358] Example: Analyze the statistical results after decoding and consider and implement measures to improve your company's operational status.

[1359] The system allows logistics companies to optimize resource management by leveraging statistical information and emotional states while preserving privacy.

[1360] Example 2

[1361] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1362] In the logistics industry, there is a need for multiple companies to share data to improve overall efficiency. However, when individual companies directly share their own data with other companies, there is a risk of privacy violations and data leaks. Furthermore, efficient data management while understanding the emotional state of drivers requires complex information processing, which is not easy to achieve. To solve these issues, a safe and efficient data sharing system is required.

[1363] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a noise-adding means for adding noise to data collected from multiple information providers to achieve differential privacy; a data transmission means for encrypting the noise-added data and transmitting it to a central processing unit; a secret calculation means for securely integrating multiple encrypted data and performing statistical calculations; a result return means for encrypting the statistical calculation results and returning them to each information provider; an emotion recognition means for recognizing the emotional state of a user from their voice or facial expression and processing the data in conjunction with data collection; and a data preprocessing means for converting collected data into a unified format and adjusting noise-adding parameters based on the emotional state. This enables individual companies to safely and efficiently obtain statistical information and improve resource management without directly sharing their own data.

[1364] "Information provider" refers to a company or individual that collects and provides data in the logistics industry.

[1365] "Adding noise to data" refers to a technique of making individual data indistinguishable by adding random variations to the original data.

[1366] "Differential privacy" refers to a technology that protects the privacy of individual data by ensuring that adding or removing a single piece of data from a dataset being analyzed does not significantly change the results.

[1367] "Noise injection means" means a technique or device for achieving differential privacy by adding random noise to collected data.

[1368] "Data transmission means" refers to the technology or device that encrypts collected data and transmits it securely to the central processing unit.

[1369] "Central processing unit" refers to a server or computer device that integrates data sent from each information provider and performs statistical calculations using secret computation.

[1370] "Secret computation means" refers to technologies and methods that securely integrate multiple encrypted data sets and perform statistical calculations while protecting the privacy of each information provider.

[1371] "Result return means" refers to a technology or device that encrypts the statistical calculation results and returns them securely to each information provider.

[1372] "Emotion recognition means" refers to technology or devices that analyze the user's voice and facial expressions and recognize their emotional state.

[1373] "Data pre-processing means" refers to techniques and methods for converting collected data into a unified format and adjusting noise-adding parameters based on emotional state.

[1374] "Integrated format" means a format that converts data collected from different sources into a unified format.

[1375] "Noise addition parameters" refer to parameters for setting the intensity and distribution of noise to be added to collected data.

[1376] "AES encryption" stands for Advanced Encryption Standard, a standard encryption method for securely encrypting data.

[1377] "HTTPS protocol" is an abbreviation for Hypertext Transfer Protocol Secure, and refers to a protocol for providing secure communications.

[1378] This invention is a data sharing system for the logistics industry, which aims to collect, process, transmit, integrate, and return data safely and efficiently. This system consists of multiple components, including a data collection terminal, a noise addition module, a data transmission module, a secure computation module, and a result return module. The data collection terminal is also equipped with an emotion engine that recognizes the user's emotional state.

[1379] Data collection and emotion recognition

[1380] User: A logistics company employee operates a data collection terminal to input information such as the truck driver and vehicle operating status, route, and overtime hours. At the same time, the emotion engine analyzes the user's voice and facial expressions to recognize their emotional state.

[1381] Example: A logistics company driver logs into a terminal and inputs driving data, while his stress level is determined in real time through voice analysis.

[1382] Data preprocessing and noise addition

[1383] Terminal: Converts collected data into a unified format such as JSON, and adjusts the parameters of the noise addition method for differential privacy based on the emotional state recognized by the emotion engine.

[1384] Example: The device converts collected data into JSON format and automatically increases the noise intensity if the user is in a high stress state.

[1385] Data Encryption and Transmission

[1386] Terminal: The noisy data is AES encrypted and sent to a central server using the HTTPS protocol.

[1387] Example: Noise-added uptime data is AES encrypted and sent to a server using a secure communication protocol.

[1388] Data Integration and Secure Computing

[1389] Server: The central server receives the noise-added data sent by each logistics company and performs integrated calculations using secret computing technology. This allows individual companies to obtain overall statistical information without directly sharing their own data with other companies.

[1390] Example: The server calculates the overall average working hours and average overtime hours based on the noisy data received from each company.

[1391] Generate and return statistical results

[1392] Server: Securely returns the statistical calculation results to each company. This return also uses encrypted communication.

[1393] Example: Statistical results such as the overall average working hours being 8.2 hours and the overall average overtime hours being 2.3 hours are sent back to each company.

[1394] Usage example

[1395] For example, a logistics company could collect data in-house, use an emotion engine to recognize the user's emotional state, add noise to the data, AES encrypt it, and send it to a central server. The central server then receives data from multiple logistics companies, integrates it, and performs overall statistical calculations. The calculated statistical results are then securely returned to each company, allowing each company to obtain valuable statistical information without directly sharing their own data with other companies.

[1396] Example prompt sentence:

[1397] "As a logistics company, please create a program for a data collection system that uses data collection terminals to collect driver working hours and emotional states. Please also create a program that adds noise to the collected data, securely transmits it to a server, and calculates the overall average working hours."

[1398] The present invention allows logistics companies to provide data and user emotional states to obtain more precise and safe statistical information and improve resource management.

[1399] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1400] Step 1: Data collection

[1401] User: A logistics company employee operates a data collection terminal to input information such as the truck driver's and vehicle's operating status, route, and overtime hours.

[1402] Input: Driver ID, vehicle ID, operating hours, route, overtime hours, etc.

[1403] Output: Collected data.

[1404] Specific operation: Drivers log in to the terminal at the start of their shift and record their route and working hours in real time.

[1405] Step 2: Emotion Recognition

[1406] On the device: The emotion engine recognizes the user's emotional state from their voice and facial expressions. This information is collected simultaneously.

[1407] Input: User's voice and facial expression data.

[1408] Output: Perceived emotional state.

[1409] Specific operation: The device uses voice input and the camera to analyze the user's voice and facial expressions and evaluate their stress level in real time.

[1410] Step 3: Data Preprocessing

[1411] Terminal: Converts collected data into a unified format (e.g., JSON format) and adjusts noise-adding parameters based on the emotional state recognized by the emotion engine.

[1412] Input: Collected data, perceived emotional state.

[1413] Output: Data converted to a unified format, adjusted noise addition parameters.

[1414] Specific operation: The device converts data into JSON format and automatically increases the noise intensity if the user is in a state of high stress.

[1415] Step 4: Add noise

[1416] Terminal: Add noise to the transformed data to achieve differential privacy.

[1417] Input: Data converted to unified format, adjusted noise addition parameters.

[1418] Output: Noise-augmented data.

[1419] Specific behavior: For example, adding random noise to uptime data to prevent accurate data from being sent directly.

[1420] Step 5: Data Encryption

[1421] Terminal: The noise-added data is AES encrypted.

[1422] Input: Noisy data.

[1423] Output: The encrypted data.

[1424] Specific operation: Using an encryption algorithm, the added noise data is securely encrypted to protect it from unauthorized access.

[1425] Step 6: Send data

[1426] Terminal: Sends encrypted data to a central server using the HTTPS protocol.

[1427] Input: Encrypted data.

[1428] Output: Data sent to the central server.

[1429] Specific operation: The device sends encrypted data to a central server using a secure communication protocol (e.g., HTTPS).

[1430] Step 7: Data reception and integration

[1431] Server: The central server receives the noise-added data sent from each logistics company and integrates the data using secure computing technology.

[1432] Input: Noisy data received from multiple logistics companies.

[1433] Output: Consolidated data.

[1434] Specific operation: The server decodes the received data and calculates the overall statistics using a secret calculation algorithm.

[1435] Step 8: Generate statistical results

[1436] Server: Generates statistical calculation results, such as the overall average working hours and average overtime hours.

[1437] Input: Consolidated data.

[1438] Output: Statistical calculation results.

[1439] Specific operation: The server processes the data and generates overall statistics (e.g., average working hours: 8.2 hours, average overtime hours: 2.3 hours).

[1440] Step 9: Encrypting the statistical results

[1441] Server: Encrypts the generated statistical results.

[1442] Input: Statistical calculation results.

[1443] Output: Encrypted statistical results.

[1444] What it does: Securely encrypts the statistical results using the AES encryption algorithm.

[1445] Step 10: Returning results

[1446] Server: Returns the encrypted statistical results to each logistics company.

[1447] Input: Encrypted statistical results.

[1448] Output: Encryption statistics results sent.

[1449] Specific operation: The server uses the HTTPS protocol to send encrypted statistical results to each logistics company.

[1450] Step 11: Interpret and display the results

[1451] Terminal: Decodes the received statistical results and displays them to the user.

[1452] Input: Encrypted statistical results.

[1453] Output: Display of the interpreted statistical results.

[1454] Specific operations: Company personnel use a terminal to decipher the statistical results and visually confirm them on the management screen.

[1455] (Application example 2)

[1456] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1457] While conventional data sharing systems are required to collect and safely share large amounts of data in the logistics industry, there is a risk that individual data may be identified and privacy may be violated.In addition, there is no data collection method that takes into account the stress and emotional state of workers, making it difficult to improve work efficiency and the working environment.

[1458] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a noise adding means that adds noise to data collected from multiple information providers to achieve differential privacy, a data transmission means that encrypts the noise-added data and transmits it to a central processing unit, a secret calculation means that securely integrates multiple pieces of the encrypted data and performs statistical calculations, and an emotion recognition means that recognizes the emotional state of the worker and adjusts the intensity of noise addition to the collected data. This allows individual data to be shared safely while protecting privacy, and enables data collection according to the emotional state of the worker.

[1459] An "information provider" is an entity that collects data and provides it to the system.

[1460] The "noise adding means" is a technique that makes it difficult to distinguish the collected data by adding random noise to the data.

[1461] The "data transmission means" is a technology for encrypting data to which noise has been added and transmitting the data securely to a central processing unit or other system.

[1462] A "central processing unit" is a device that receives multiple data sets and performs statistical calculations and data integration.

[1463] "Secure computation" is a technology that aggregates multiple encrypted data and performs statistical calculations while protecting privacy.

[1464] The "result return means" is a technique for safely returning the results of statistical calculations to each information provider.

[1465] The "emotion recognition means" is a technology that recognizes the emotional state of the worker and adjusts the intensity of noise added to the collected data.

[1466] "Emotional state" refers to data that indicates the psychological and physiological state of the worker, including stress level.

[1467] "Differential privacy" is a technique for collecting and analyzing statistical information while preserving the privacy of individual data.

[1468] "Noise intensity" refers to the amount of noise added to the data, and is adjusted according to the emotional state.

[1469] "AES encryption" is a highly secure encryption algorithm used to encrypt data.

[1470] System configuration

[1471] This invention is a system that monitors the work status and emotional state of factory workers in real time, and collects, adds noise to, encrypts, transmits, and processes statistical data.

[1472] Data collection and emotion recognition

[1473] Device: Each worker wears smart glasses (or a smartphone) equipped with a camera and microphone to collect work status and voice data. An emotion recognition module is used to recognize the user's emotional state (e.g., stress level) from their voice and facial expressions. This emotion recognition module uses a machine learning model to analyze the user's emotions in real time.

[1474] Data preprocessing and noise addition

[1475] Terminal: Collected operational data (e.g., work hours, work content) and emotional data are first converted into a unified data format. Next, a noise-adding means adds random noise to the data according to the emotional state. For example, the noise intensity increases when the subject is in a high stress state, and decreases when the subject is in a low stress state.

[1476] Data Encryption and Transmission

[1477] Terminal: The noisy data is encrypted using the AES encryption algorithm. The encrypted data is sent to a central server using a secure communication protocol (e.g., HTTPS).

[1478] Data Integration and Secure Computing

[1479] Server: The central server receives the encrypted data sent from each device. The received data is aggregated using secret calculation techniques to generate overall statistical information (e.g., average work time, average stress level). This statistical calculation provides aggregated information without disclosing individual data.

[1480] Generate and return statistical results

[1481] Server: The statistical calculation results are again encrypted and securely sent back to each worker's device. This allows each worker and manager to refer to the statistical information in real time, which can be used for appropriate resource management and improving the working environment.

[1482] Specific examples

[1483] For example, worker A at a factory is wearing smart glasses while working. The smart glasses' camera collects the work situation, the microphone collects audio data, and the emotion recognition module determines the stress level. If worker A is in a high stress state, the noise intensity of the data is increased and the encrypted noise data is sent to a central server via HTTPS. The central server combines data from other workers to generate statistical information such as the average stress level, and then re-encrypts the results and sends them back to each worker's device.

[1484] Example of a generated AI prompt

[1485] "Collect the user's work situation and emotional state in real time from the camera footage. If the stress level is high, add high-intensity noise to the data, protect it with AES encryption, and send it to a central server using a secure communication protocol. If the stress level is low, add low-intensity noise."

[1486] This will enable companies to allocate resources efficiently and improve working conditions while protecting transparency of the work environment and workers' privacy.

[1487] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1488] Step 1: Data collection

[1489] The terminal collects work situation and emotion data through smart glasses worn by each worker. Specifically, a camera captures the worker's movements and a microphone records audio data. The collected data includes frames of image data and audio samples of audio data. The input data are image frames and audio data, and the output is digital data in a unified format.

[1490] Step 2: Emotion Recognition

[1491] The emotion recognition module in the device analyzes the collected image frames and audio data to recognize the worker's emotional state. Specifically, the machine learning model performs facial expression and audio analysis to determine the stress level. The input data are the image frames and audio data collected in step 1, and the output is the emotional state, such as "high stress" or "low stress."

[1492] Step 3: Add noise

[1493] The device adds random noise to the collected data based on the emotion recognition results. Specifically, if the emotional state is "high stress," the noise intensity is increased, and if it is "low stress," the noise intensity is decreased. The noise is generated using statistical methods such as Gaussian noise. The input data is the emotional state obtained in step 2 and the data collected in step 1, and the output is the noise-added data.

[1494] Step 4: Data Encryption

[1495] The device encrypts the noise-added data using the AES encryption algorithm. Specifically, the data is block-encrypted and securely stored along with the encryption key. The input data is the noise-added data obtained in step 3, and the output is the encrypted data.

[1496] Step 5: Send data

[1497] The terminal sends the encrypted data to the central server using the HTTPS protocol. Specifically, the terminal sends data using HTTPS and monitors the transmission status. The input data is the data encrypted in step 4, and the output is the transmission result to the central server.

[1498] Step 6: Data Integration and Secure Computing

[1499] The server receives the encrypted data sent from each device and integrates it using secure computing technology. Specifically, it performs statistical calculations without decrypting the encrypted data to calculate the overall average work time and stress level. The input data is the encrypted data, and the output is the statistical calculation results.

[1500] Step 7: Returning statistical results

[1501] The server encrypts the statistical calculation results and returns them to each worker's terminal. Specifically, it uses encryption technology to block-encrypt the calculation results and returns them using the HTTPS protocol. The input data is the statistical calculation results obtained in step 6, and the output is the return of the encrypted calculation results.

[1502] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1503] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1504] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1505] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1506] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1507] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1508] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1509] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1510] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1511] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1512] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1513] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1514] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1515] 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.

[1516] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1517] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1518] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1519] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1520] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1521] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1522] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1523] The following is further disclosed regarding the above embodiment.

[1524] (Claim 1)

[1525] a noise adding means for adding noise to data collected from a plurality of information providers to realize differential privacy;

[1526] a data transmitting means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit;

[1527] a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations;

[1528] a result return means for encrypting the statistical calculation results and returning them to each information provider;

[1529] A system including:

[1530] (Claim 2)

[1531] 2. The system of claim 1, wherein the noise adding means adds random noise to the collected data, making individual data unidentifiable.

[1532] (Claim 3)

[1533] 10. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data.

[1534] "Example 1"

[1535] (Claim 1)

[1536] a noise adding means for adding noise to data collected from a plurality of data collection devices to realize differential privacy;

[1537] a data transmitting means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit;

[1538] a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations;

[1539] a result return means for encrypting the statistical calculation results and returning them to each data collection device;

[1540] Each data collection device adds noise to the data of the specified target, and then transmits the data using an encrypted communication means;

[1541] A system in which a central processing unit uses secure computing technology to integrate multiple data sets, generate statistical calculation results, and securely return them.

[1542] (Claim 2)

[1543] 2. The system of claim 1, wherein the noise adding means adds random noise to the collected data, making individual data unidentifiable.

[1544] (Claim 3)

[1545] 10. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data.

[1546] "Application Example 1"

[1547] (Claim 1)

[1548] a noise adding means for adding noise to data collected from a plurality of information providers to realize differential privacy;

[1549] a data transmitting means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit;

[1550] a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations;

[1551] a result return means for encrypting the statistical calculation results and returning them to each information provider;

[1552] A data collection means for collecting driver operation information, driving routes, and working hours for the management of transportation operations;

[1553] an encryption communication means that uses an encryption communication protocol when encrypting data and transmitting the data to the central processing unit;

[1554] A system including:

[1555] (Claim 2)

[1556] 2. The system of claim 1, wherein the noise adding means adds random noise to the collected data, making individual data unidentifiable.

[1557] (Claim 3)

[1558] 10. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data.

[1559] "Example 2: Combining Emotion Engines"

[1560] (Claim 1)

[1561] a noise adding means for adding noise to data collected from a plurality of information providers to realize differential privacy;

[1562] a data transmitting means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit;

[1563] a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations;

[1564] a result return means for encrypting the statistical calculation results and returning them to each information provider;

[1565] An emotion recognition means for recognizing the user's emotional state from their voice and facial expression and processing the data in conjunction with the data collection;

[1566] a data pre-processing means for converting the collected data into a unified format and adjusting noise-adding parameters based on the emotional state;

[1567] A system including:

[1568] (Claim 2)

[1569] 2. The system of claim 1, wherein the noise adding means adds random noise to the collected data, making individual data unidentifiable.

[1570] (Claim 3)

[1571] 10. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data.

[1572] "Application example 2 when combining emotion engines"

[1573] (Claim 1)

[1574] a noise adding means for adding noise to data collected from a plurality of information providers to realize differential privacy;

[1575] a data transmitting means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit;

[1576] a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations;

[1577] a result return means for encrypting the statistical calculation results and returning them to each information provider;

[1578] an emotion recognition means for recognizing an emotional state of a worker and adjusting the intensity of noise addition to collected data;

[1579] A system including:

[1580] (Claim 2)

[1581] the noise adding means adds random noise to the collected data to make individual data unidentifiable;

[1582] 2. The system according to claim 1, wherein the emotion recognition means uses a camera and a microphone to determine the emotional state of the worker.

[1583] (Claim 3)

[1584] 10. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data. [Explanation of symbols]

[1585] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a noise adding means for adding noise to data collected from a plurality of information providers to realize differential privacy; a data transmission means for encrypting the noise-added data and transmitting the encrypted data to a central processing unit; a secret calculation means for securely integrating a plurality of the encrypted data and performing statistical calculations; a result return means for encrypting the statistical calculation results and returning them to each information provider; A system including:

2. 2. The system of claim 1, wherein said noise applying means applies random noise to the collected data, thereby making individual data unidentifiable.

3. 2. The system of claim 1, wherein the data transmission means uses AES encryption to encrypt and securely communicate data.

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