System

Abstract

An object of a system according to an embodiment is to efficiently process personal information included in voice data while safely handling the personal information.SOLUTION: A system includes a collection unit, an analysis unit, an encryption unit, a storage unit, and a decryption unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and specifies personal information. The encryption unit encrypts the personal information specified by the analysis unit. The storage unit stores the data encrypted by the encryption unit. The decryption unit decrypts the data stored in the storage unit as necessary.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to achieve both security and efficiency when handling personal information contained in voice data.

[0005] The system according to the embodiment aims to efficiently process personal information contained in voice data while handling it safely. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an encryption unit, a storage unit, and a decryption unit. The collection unit collects voice data. The analysis unit analyzes the voice data collected by the collection unit and identifies personal information. The encryption unit encrypts the personal information identified by the analysis unit. The storage unit stores the data encrypted by the encryption unit. The decryption unit decrypts the data stored in the storage unit as needed. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently process personal information contained in voice data while safely handling it. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention is a system for improving efficiency while paying careful attention to the handling of personal information when introducing new external SaaS products or cloud services within a specific company. This system collects voice data, and a generation AI identifies personal information and encrypts the resulting text. For example, the system collects voice data from customer interactions at a call center, and the generation AI identifies and encrypts the personal information. The encrypted data is stored in an internal database and decrypted as needed. This allows companies to utilize new products and cloud services while avoiding the risk of personal information leaking outside the company. This system can promote the use of generation AI and digital transformation within a specific company. For example, this system improves call center operational efficiency and improves the quality of customer service. It also reduces risks related to the handling of personal information and strengthens security.

[0029] The information processing system according to the embodiment includes a collection unit, an analysis unit, an encryption unit, a storage unit, and a decryption unit. The collection unit collects voice data. The voice data may include, but is not limited to, various audio file formats, sampling rates, and bit rates. The collection unit may collect, for example, voice data of customer interactions at a call center. The collection unit may also collect voice data in real time. The analysis unit uses a generation AI to analyze the voice data collected by the collection unit and identify personal information. The generation AI may analyze the voice data using technology such as Transformer. The analysis unit may identify personal information such as name, address, and phone number from the voice data. The analysis unit may also use the generation AI to analyze the content of the voice data and extract personal information. The encryption unit encrypts the personal information identified by the analysis unit. For example, algorithms such as AES, RSA, and DES are used for encryption, but are not limited to these examples. The encryption unit may encrypt the personal information using, for example, AES. The encryption unit may also encrypt the personal information using RSA. The encryption unit may also encrypt personal information using DES. The storage unit stores the encrypted data in an internal database. Examples of databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premises server. The storage unit stores the encrypted data in, for example, an SQL database. The storage unit may also store the encrypted data in a NoSQL database. The storage unit may also store the encrypted data in an on-premises server. The decryption unit decrypts the data stored in the storage unit as needed. Examples of algorithms used for decryption include, but are not limited to, AES, RSA, and DES. The decryption unit decrypts data encrypted using, for example, AES. The decryption unit may also decrypt data encrypted using RSA. The decryption unit may also decrypt data encrypted using DES.As a result, the information processing system according to the embodiment can efficiently perform a series of processes from collecting, analyzing, encrypting, storing, and decrypting audio data.

[0030] The collection unit can collect voice data of interactions with customers at a call center. Examples of call centers include, but are not limited to, customer support centers and telemarketing centers. The collection unit, for example, collects voice data of interactions with customers at the call center in real time. The collection unit can also collect call records from the call center. For example, the collection unit can automatically collect call records and save them as voice data. The collection unit can also manually collect voice data by call center operators. This allows for efficient collection of voice data at the call center.

[0031] The analysis unit can analyze the voice data using a generation AI to identify personal information. Examples of the generation AI include, but are not limited to, Transformer. The analysis unit can analyze the voice data using, for example, a multimodal generation AI (e.g., a Transformer-based model) to identify personal information. The analysis unit can also analyze the voice data using Transformer to identify personal information. For example, the analysis unit can identify personal information such as name, address, and phone number from the voice data. The analysis unit can also analyze the content of the voice data and extract personal information using the generation AI. As a result, by using the generation AI, personal information can be identified from the voice data with high accuracy.

[0032] The encryption unit can encrypt the identified personal information. For example, an algorithm such as AES, RSA, or DES is used for encryption, but is not limited to these examples. For example, the encryption unit encrypts the personal information using AES. The encryption unit can also encrypt the personal information using RSA. The encryption unit can also encrypt the personal information using DES. For example, the encryption unit can encrypt the personal information using AES to enhance security. The encryption unit can also encrypt the personal information using RSA to enhance security. The encryption unit can also encrypt the personal information using DES to enhance security. In this way, security can be enhanced by encrypting the personal information.

[0033] The storage unit may store the encrypted data in an internal database. Examples of internal databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premises server. The storage unit may store the encrypted data in, for example, an SQL database. Alternatively, the storage unit may store the encrypted data in a NoSQL database. Alternatively, the storage unit may store the encrypted data in an on-premises server. For example, the storage unit may store the encrypted data in an SQL database and securely manage it. Alternatively, the storage unit may store the encrypted data in a NoSQL database and securely manage it. Alternatively, the storage unit may store the encrypted data in an on-premises server and securely manage it. This allows the encrypted data to be stored securely.

[0034] The decryption unit can decrypt encrypted data as needed. For example, an algorithm such as AES, RSA, or DES is used for the decryption, but is not limited to these examples. For example, the decryption unit decrypts data encrypted using AES. The decryption unit can also decrypt data encrypted using RSA. The decryption unit can also decrypt data encrypted using DES. For example, the decryption unit decrypts data encrypted using AES to make it reusable. The decryption unit can also decrypt data encrypted using RSA to make it reusable. The decryption unit can also decrypt data encrypted using DES to make it reusable. In this way, encrypted data can be decrypted as needed to make it reusable.

[0035] The information processing system includes a collection unit that adjusts the collection method based on the skill level of the call center operator. For example, if the operator is new, the collection unit simplifies the collection method and collects only basic information. On the other hand, if the operator is experienced, the collection unit can collect detailed information so that the operator can respond to complex inquiries. The collection unit can also adjust the range and depth of the information to be collected according to the operator's skill level. This enables efficient data collection by optimizing the collection method according to the operator's skill level.

[0036] The information processing system includes a collection unit that applies a noise removal filter to improve sound quality when collecting voice data. The collection unit, for example, applies a filter that removes background noise in real time when collecting voice data. The collection unit can also apply a noise removal filter to the collected voice data in post-processing to improve sound quality. The collection unit can also apply a filter that removes noise in a specific frequency band to collect clear voice data. In this way, applying the noise removal filter makes it possible to collect clear voice data.

[0037] The information processing system includes a collection unit that, when collecting voice data, customizes the content of the collection by referring to the user's past inquiry history. For example, the collection unit refers to the user's past inquiry history and prioritizes the collection of related information. The collection unit can also ask additional questions based on the content of the user's past inquiries to collect more detailed information. The collection unit can also adjust the range of information to be collected based on the user's past inquiry history. This makes it possible to collect more relevant information by referring to the user's past inquiry history.

[0038] The information processing system includes a collection unit that, when collecting voice data, prioritizes collection of highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collection of information related to that area. The collection unit can also collect information on issues specific to the area based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect information related to the user's current location in real time. This makes it possible to efficiently collect information on issues specific to the area by considering the user's geographical location information.

[0039] The information processing system includes a collection unit that analyzes the user's social media activities and collects related data when collecting voice data. The collection unit, for example, analyzes the content posted by the user on social media to collect related information. The collection unit can also collect information about places where the user has checked in on social media. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0040] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0041] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the audio data during analysis. The analysis unit performs a detailed analysis of audio data that contains important information, for example. The analysis unit can also perform a basic analysis of audio data that contains general information. The analysis unit can also adjust the depth of the analysis based on the importance of the audio data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio data.

[0042] The information processing system includes an analysis unit that applies different analysis algorithms depending on the category of voice data during analysis. The analysis unit selects an appropriate analysis algorithm depending on, for example, the content of a customer inquiry. The analysis unit can also apply different analysis algorithms depending on the category of voice data (technical support, sales, complaints, etc.). The analysis unit can also select the optimal analysis algorithm based on the category of voice data. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of voice data.

[0043] The information processing system includes an analysis unit that, during analysis, improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0044] The information processing system includes an analysis unit that determines the analysis priority based on the time when the voice data was collected during analysis. The analysis unit, for example, prioritizes analysis of the most recent voice data and responds quickly. The analysis unit can also complement the current analysis results by referring to past voice data. The analysis unit can also adjust the analysis priority based on the time when the voice data was collected. This enables a quick response by determining the analysis priority based on the time when the voice data was collected.

[0045] The information processing system includes an analysis unit that adjusts the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant voice data to quickly extract important information. The analysis unit can also postpone analysis of less relevant voice data for later analysis, thereby performing efficient analysis. The analysis unit can also optimize the order of analysis based on the relevance of the voice data. As a result, important information can be quickly extracted by adjusting the order of analysis based on the relevance of the voice data.

[0046] The information processing system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides the analysis results using detailed technical terms. Alternatively, if the user has only general knowledge, the analysis unit can provide the analysis results in simple terms. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0047] The information processing system includes an encryption unit that adjusts the encryption strength based on the importance of personal information during encryption. The encryption unit performs strong encryption on important personal information, for example. The encryption unit can also perform basic encryption on general personal information. The encryption unit can also adjust the encryption strength based on the importance of the personal information. This makes it possible to strengthen security by adjusting the encryption strength based on the importance of the personal information.

[0048] The information processing system includes an encryption unit that applies different encryption algorithms depending on the category of personal information during encryption. The encryption unit selects an appropriate encryption algorithm for personal information such as name and address. The encryption unit can also apply different encryption algorithms to personal information such as phone number and email address. The encryption unit can also select the most appropriate encryption algorithm based on the category of personal information. This improves the accuracy of encryption by applying the most appropriate encryption algorithm depending on the category of personal information.

[0049] The information processing system includes an encryption unit that, during encryption, improves the accuracy of encryption by referring to the user's past encryption results. The encryption unit, for example, corrects the current encryption method based on the user's past encryption results. The encryption unit can also adjust the encryption algorithm by referring to the user's past encryption results. The encryption unit can also improve the accuracy of encryption by using the user's past encryption results. In this way, the accuracy of encryption can be improved by referring to the user's past encryption results.

[0050] The information processing system includes an encryption unit that determines the encryption priority based on when personal information was collected during encryption. The encryption unit, for example, prioritizes encrypting the most recent personal information, enabling a prompt response. The encryption unit can also complement the current encryption method by referring to past personal information. The encryption unit can also adjust the encryption priority based on when the personal information was collected. This allows for a prompt response by determining the encryption priority based on when the personal information was collected.

[0051] The information processing system includes an encryption unit that adjusts the encryption order based on the relevance of personal information during encryption. The encryption unit, for example, prioritizes encryption of highly relevant personal information to quickly protect important information. The encryption unit can also perform efficient encryption by deferring less relevant personal information. The encryption unit can also optimize the encryption order based on the relevance of personal information. As a result, important information can be quickly protected by adjusting the encryption order based on the relevance of personal information.

[0052] The information processing system includes an encryption unit that adjusts the encryption method according to the user's level of expertise during encryption. For example, if the user has specialized knowledge, the encryption unit uses a detailed encryption method. Alternatively, if the user only has general knowledge, the encryption unit can use a simple encryption method. The encryption unit can also adjust the encryption method according to the user's level of expertise. This allows for efficient and appropriate encryption by adjusting the encryption method according to the user's level of expertise.

[0053] The information processing system includes a storage unit that adjusts the storage method based on the importance of the data when saving. For example, the storage unit uses a highly redundant storage method for important data. The storage unit can also use a basic storage method for general data. The storage unit can also adjust the storage method based on the importance of the data. This allows for efficient and safe data storage by adjusting the storage method based on the importance of the data.

[0054] The information processing system includes a storage unit that applies different storage algorithms depending on the data category when storing data. The storage unit selects an appropriate storage algorithm for personal information, for example. The storage unit can also apply a different storage algorithm to business data. The storage unit can also select the optimal storage algorithm based on the data category. This improves storage accuracy by applying the optimal storage algorithm depending on the data category.

[0055] The information processing system includes a storage unit that determines storage priorities based on the access frequency of data when saving. The storage unit, for example, prioritizes saving data with high access frequency, enabling quick access. The storage unit can also postpone saving data with low access frequency, enabling efficient saving. The storage unit can also adjust the storage priorities based on the access frequency of data. This allows quick access by determining storage priorities based on the access frequency of data.

[0056] The information processing system includes a storage unit that adjusts the backup frequency of data when saving the data. The storage unit, for example, frequently backs up important data. The storage unit can also set a basic backup frequency for general data. The storage unit can also adjust the backup frequency according to the importance of the data. This makes it possible to ensure the safety of the data by adjusting the backup frequency according to the importance of the data.

[0057] The information processing system includes a storage unit that determines storage priorities based on the time when data was collected. The storage unit, for example, prioritizes storing the most recent data, enabling quick access. The storage unit can also complement the current storage method by referring to past data. The storage unit can also adjust the storage priorities based on the time when the data was collected. This allows quick access by determining storage priorities based on the time when the data was collected.

[0058] The information processing system includes a storage unit that adjusts the order of storage based on the relevance of data when storing the data. The storage unit, for example, prioritizes storing highly relevant data to quickly protect important information. The storage unit can also postpone storing less relevant data for later storage, enabling efficient storage. The storage unit can also optimize the order of storage based on the relevance of data. This allows important information to be quickly protected by adjusting the order of storage based on the relevance of data.

[0059] The information processing system includes a storage unit that sets access permissions for data when the data is saved. The storage unit sets strict access permissions for important data, for example. The storage unit can also set basic access permissions for general data. The storage unit can also adjust the access permissions according to the importance of the data. This makes it possible to strengthen data security by setting access permissions according to the importance of the data.

[0060] The information processing system includes a storage unit that optimizes the storage location of data when it is saved. The storage unit, for example, stores important data in a safe location to enhance security. The storage unit can also efficiently store general data to speed up access. The storage unit can also optimize the storage location according to the importance of the data. In this way, by optimizing the storage location according to the importance of the data, it is possible to improve security and speed up access.

[0061] The information processing system includes a decryption unit that adjusts the strength of decryption based on the importance of personal information during decryption. The decryption unit performs strong decryption on important personal information, for example. The decryption unit can also perform basic decryption on general personal information. The decryption unit can also adjust the strength of decryption based on the importance of the personal information. This allows security to be enhanced by adjusting the strength of decryption based on the importance of the personal information.

[0062] The information processing system includes a decoding unit that applies different decoding algorithms depending on the category of personal information during decoding. The decoding unit selects an appropriate decoding algorithm for personal information such as name and address. The decoding unit can also apply different decoding algorithms to personal information such as phone number and email address. The decoding unit can also select the optimal decoding algorithm based on the category of personal information. This improves the accuracy of decoding by applying the optimal decoding algorithm depending on the category of personal information.

[0063] The information processing system includes a decoding unit that, during decoding, improves the accuracy of decoding by referring to the user's past decoding results. The decoding unit, for example, corrects the current decoding method based on the user's past decoding results. The decoding unit can also adjust the decoding algorithm by referring to the user's past decoding results. The decoding unit can also improve the accuracy of decoding by using the user's past decoding results. In this way, the accuracy of decoding can be improved by referring to the user's past decoding results.

[0064] The information processing system includes a decoding unit that determines the priority of decryption based on the time when personal information was collected during decryption. The decoding unit, for example, prioritizes decryption of the most recent personal information, enabling a prompt response. The decoding unit can also complement the current decoding method by referring to past personal information. The decoding unit can also adjust the priority of decryption based on the time when personal information was collected. This allows a prompt response by determining the priority of decryption based on the time when personal information was collected.

[0065] The information processing system includes a decoding unit that adjusts the decoding order based on the relevance of personal information during decoding. The decoding unit, for example, prioritizes decryption of highly relevant personal information, thereby quickly protecting important information. The decoding unit can also defer decoding of less relevant personal information for later execution, thereby enabling efficient decoding. The decoding unit can also optimize the decoding order based on the relevance of personal information. As a result, important information can be quickly protected by adjusting the decoding order based on the relevance of personal information.

[0066] The information processing system includes a decoding unit that adjusts the decoding method according to the user's level of expertise during decoding. For example, if the user has specialized knowledge, the decoding unit uses a detailed decoding method. Alternatively, if the user only has general knowledge, the decoding unit can use a simple decoding method. The decoding unit can also adjust the decoding method according to the user's level of expertise. This allows for efficient and appropriate decoding by adjusting the decoding method according to the user's level of expertise.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The information processing system includes a collection unit that analyzes a user's past behavioral patterns and determines the optimal collection timing when collecting voice data. For example, if a user has made many inquiries during a specific time period in the past, the collection unit prioritizes collecting voice data during that time period. Furthermore, if a user has made many inquiries on a specific day of the week in the past, the collection unit can also prioritize collecting voice data on that day. Furthermore, the collection unit can optimize the collection timing based on the user's past behavioral patterns. This enables efficient collection of voice data by taking into account the user's past behavioral patterns.

[0069] The information processing system includes a collection unit that monitors the status of a user's device and selects the optimal collection method when collecting voice data. For example, if the user's device has low battery power, the collection unit simplifies the collection method to reduce battery consumption. Furthermore, if the user's device has high performance, the collection unit can collect detailed voice data and perform highly accurate analysis. Furthermore, the collection unit can adjust the collection method depending on the network connection status of the user's device. This enables efficient collection of voice data by taking the status of the user's device into consideration.

[0070] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0071] The information processing system includes a collection unit that, when collecting voice data, prioritizes collection of highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collection of information related to that area. The collection unit can also collect information on issues specific to the area based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect information related to the user's current location in real time. This makes it possible to efficiently collect information on issues specific to the area by considering the user's geographical location information.

[0072] The information processing system includes a collection unit that analyzes the user's social media activities and collects related data when collecting voice data. The collection unit, for example, analyzes the content posted by the user on social media to collect related information. The collection unit can also collect information about places where the user has checked in on social media. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0073] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0074] The processing flow of the first embodiment will be briefly explained below.

[0075] Step 1: The collection unit collects voice data. The voice data includes, but is not limited to, for example, an audio file format, a sampling rate, and a bit rate. For example, the collection unit collects voice data of interactions with customers at a call center. The collection unit can also collect voice data in real time. Step 2: The analysis unit uses the generation AI to analyze the voice data collected by the collection unit and identify personal information. The generation AI analyzes the voice data using technology such as Transformer. The analysis unit identifies personal information such as name, address, and phone number from the voice data. The analysis unit can also use the generation AI to analyze the content of the voice data and extract personal information. Step 3: The encryption unit encrypts the personal information identified by the analysis unit. For example, an algorithm such as AES, RSA, or DES is used for encryption, but is not limited to these examples. For example, the encryption unit encrypts the personal information using AES. The encryption unit can also encrypt the personal information using RSA. The encryption unit can also encrypt the personal information using DES. Step 4: The storage unit stores the encrypted data in an internal database. Examples of databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premise server. The storage unit stores the encrypted data in, for example, an SQL database. Alternatively, the storage unit may store the encrypted data in a NoSQL database. Alternatively, the storage unit may store the encrypted data in an on-premise server. Step 5: The decryption unit decrypts the data stored in the storage unit as necessary. For example, an algorithm such as AES, RSA, or DES is used for decryption, but is not limited to these examples. For example, the decryption unit decrypts data encrypted using AES. The decryption unit can also decrypt data encrypted using RSA. The decryption unit can also decrypt data encrypted using DES.

[0076] (Example 2) A system according to an embodiment of the present invention is a system for improving efficiency while paying careful attention to the handling of personal information when introducing new external SaaS products or cloud services within a specific company. This system collects voice data, and a generation AI identifies personal information and encrypts the resulting text. For example, the system collects voice data from customer interactions at a call center, and the generation AI identifies and encrypts the personal information. The encrypted data is stored in an internal database and decrypted as needed. This allows companies to utilize new products and cloud services while avoiding the risk of personal information leaking outside the company. This system can promote the use of generation AI and digital transformation within a specific company. For example, this system improves call center operational efficiency and improves the quality of customer service. It also reduces risks related to the handling of personal information and strengthens security.

[0077] The information processing system according to the embodiment includes a collection unit, an analysis unit, an encryption unit, a storage unit, and a decryption unit. The collection unit collects voice data. The voice data may include, but is not limited to, various audio file formats, sampling rates, and bit rates. The collection unit may collect, for example, voice data of customer interactions at a call center. The collection unit may also collect voice data in real time. The analysis unit uses a generation AI to analyze the voice data collected by the collection unit and identify personal information. The generation AI may analyze the voice data using technology such as Transformer. The analysis unit may identify personal information such as name, address, and phone number from the voice data. The analysis unit may also use the generation AI to analyze the content of the voice data and extract personal information. The encryption unit encrypts the personal information identified by the analysis unit. For example, algorithms such as AES, RSA, and DES are used for encryption, but are not limited to these examples. The encryption unit may encrypt the personal information using, for example, AES. The encryption unit may also encrypt the personal information using RSA. The encryption unit may also encrypt personal information using DES. The storage unit stores the encrypted data in an internal database. Examples of databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premises server. The storage unit stores the encrypted data in, for example, an SQL database. The storage unit may also store the encrypted data in a NoSQL database. The storage unit may also store the encrypted data in an on-premises server. The decryption unit decrypts the data stored in the storage unit as needed. Examples of algorithms used for decryption include, but are not limited to, AES, RSA, and DES. The decryption unit decrypts data encrypted using, for example, AES. The decryption unit may also decrypt data encrypted using RSA. The decryption unit may also decrypt data encrypted using DES.As a result, the information processing system according to the embodiment can efficiently perform a series of processes from collecting, analyzing, encrypting, storing, and decrypting audio data.

[0078] The collection unit can collect voice data of interactions with customers at a call center. Examples of call centers include, but are not limited to, customer support centers and telemarketing centers. The collection unit, for example, collects voice data of interactions with customers at the call center in real time. The collection unit can also collect call records from the call center. For example, the collection unit can automatically collect call records and save them as voice data. The collection unit can also manually collect voice data by call center operators. This allows for efficient collection of voice data at the call center.

[0079] The analysis unit can analyze the voice data using a generation AI to identify personal information. Examples of the generation AI include, but are not limited to, Transformer. The analysis unit can analyze the voice data using, for example, a multimodal generation AI (e.g., a Transformer-based model) to identify personal information. The analysis unit can also analyze the voice data using Transformer to identify personal information. For example, the analysis unit can identify personal information such as name, address, and phone number from the voice data. The analysis unit can also analyze the content of the voice data and extract personal information using the generation AI. As a result, by using the generation AI, personal information can be identified from the voice data with high accuracy.

[0080] The encryption unit can encrypt the identified personal information. For example, an algorithm such as AES, RSA, or DES is used for encryption, but is not limited to these examples. For example, the encryption unit encrypts the personal information using AES. The encryption unit can also encrypt the personal information using RSA. The encryption unit can also encrypt the personal information using DES. For example, the encryption unit can encrypt the personal information using AES to enhance security. The encryption unit can also encrypt the personal information using RSA to enhance security. The encryption unit can also encrypt the personal information using DES to enhance security. In this way, security can be enhanced by encrypting the personal information.

[0081] The storage unit may store the encrypted data in an internal database. Examples of internal databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premises server. The storage unit may store the encrypted data in, for example, an SQL database. Alternatively, the storage unit may store the encrypted data in a NoSQL database. Alternatively, the storage unit may store the encrypted data in an on-premises server. For example, the storage unit may store the encrypted data in an SQL database and securely manage it. Alternatively, the storage unit may store the encrypted data in a NoSQL database and securely manage it. Alternatively, the storage unit may store the encrypted data in an on-premises server and securely manage it. This allows the encrypted data to be stored securely.

[0082] The decryption unit can decrypt encrypted data as needed. For example, an algorithm such as AES, RSA, or DES is used for the decryption, but is not limited to these examples. For example, the decryption unit decrypts data encrypted using AES. The decryption unit can also decrypt data encrypted using RSA. The decryption unit can also decrypt data encrypted using DES. For example, the decryption unit decrypts data encrypted using AES to make it reusable. The decryption unit can also decrypt data encrypted using RSA to make it reusable. The decryption unit can also decrypt data encrypted using DES to make it reusable. In this way, encrypted data can be decrypted as needed to make it reusable.

[0083] The information processing system includes a collection unit that estimates a user's emotion and adjusts the timing of collecting voice data based on the estimated user emotion. For example, if the user is feeling stressed, the collection unit delays the collection timing and waits until the user is relaxed. Furthermore, if the user is relaxed, the collection unit can also immediately collect voice data and record natural conversation. Furthermore, if the user is in a hurry, the collection unit can also optimize the collection timing to collect necessary information in a short time. Thus, by adjusting the collection timing according to the user's emotion, more natural voice data can be collected. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0084] The information processing system includes a collection unit that adjusts the collection method based on the skill level of the call center operator. For example, if the operator is new, the collection unit simplifies the collection method and collects only basic information. On the other hand, if the operator is experienced, the collection unit can collect detailed information so that the operator can respond to complex inquiries. The collection unit can also adjust the range and depth of the information to be collected according to the operator's skill level. This enables efficient data collection by optimizing the collection method according to the operator's skill level.

[0085] The information processing system includes a collection unit that applies a noise removal filter to improve sound quality when collecting voice data. The collection unit, for example, applies a filter that removes background noise in real time when collecting voice data. The collection unit can also apply a noise removal filter to the collected voice data in post-processing to improve sound quality. The collection unit can also apply a filter that removes noise in a specific frequency band to collect clear voice data. In this way, applying the noise removal filter makes it possible to collect clear voice data.

[0086] The information processing system includes a collection unit that, when collecting voice data, customizes the content of the collection by referring to the user's past inquiry history. For example, the collection unit refers to the user's past inquiry history and prioritizes the collection of related information. The collection unit can also ask additional questions based on the content of the user's past inquiries to collect more detailed information. The collection unit can also adjust the range of information to be collected based on the user's past inquiry history. This makes it possible to collect more relevant information by referring to the user's past inquiry history.

[0087] The information processing system includes a collection unit that estimates a user's emotion and determines the priority of voice data to be collected based on the estimated user emotion. For example, when the user is angry, the collection unit prioritizes collecting important information and responds quickly. Furthermore, when the user is relaxed, the collection unit can also collect detailed information and respond comprehensively. Furthermore, when the user is feeling anxious, the collection unit can prioritize collecting necessary information to provide a sense of security. In this way, by determining the priority of voice data to be collected according to the user's emotion, important information can be collected quickly. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0088] The information processing system includes a collection unit that, when collecting voice data, prioritizes collection of highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collection of information related to that area. The collection unit can also collect information on issues specific to the area based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect information related to the user's current location in real time. This makes it possible to efficiently collect information on issues specific to the area by considering the user's geographical location information.

[0089] The information processing system includes a collection unit that analyzes the user's social media activities and collects related data when collecting voice data. The collection unit, for example, analyzes the content posted by the user on social media to collect related information. The collection unit can also collect information about places where the user has checked in on social media. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0090] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0091] The information processing system includes an analysis unit that estimates a user's emotions and adjusts the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a summary analysis result if the user is in a hurry. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide an analysis result that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0092] The information processing system includes an analysis unit that adjusts the level of detail of the analysis based on the importance of the audio data during analysis. The analysis unit performs a detailed analysis of audio data that contains important information, for example. The analysis unit can also perform a basic analysis of audio data that contains general information. The analysis unit can also adjust the depth of the analysis based on the importance of the audio data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the audio data.

[0093] The information processing system includes an analysis unit that applies different analysis algorithms depending on the category of voice data during analysis. The analysis unit selects an appropriate analysis algorithm depending on, for example, the content of a customer inquiry. The analysis unit can also apply different analysis algorithms depending on the category of voice data (technical support, sales, complaints, etc.). The analysis unit can also select the optimal analysis algorithm based on the category of voice data. This improves analysis accuracy by applying the optimal analysis algorithm depending on the category of voice data.

[0094] The information processing system includes an analysis unit that, during analysis, improves the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results.

[0095] The information processing system includes an analysis unit that estimates a user's emotion and adjusts the length of the analysis based on the estimated user emotion. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a visually stimulating analysis result if the user is excited. By adjusting the length of the analysis according to the user's emotion, it is possible to provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0096] The information processing system includes an analysis unit that determines the analysis priority based on the time when the voice data was collected during analysis. The analysis unit, for example, prioritizes analysis of the most recent voice data and responds quickly. The analysis unit can also complement the current analysis results by referring to past voice data. The analysis unit can also adjust the analysis priority based on the time when the voice data was collected. This enables a quick response by determining the analysis priority based on the time when the voice data was collected.

[0097] The information processing system includes an analysis unit that adjusts the order of analysis based on the relevance of the voice data during analysis. The analysis unit, for example, prioritizes analysis of highly relevant voice data to quickly extract important information. The analysis unit can also postpone analysis of less relevant voice data for later analysis, thereby performing efficient analysis. The analysis unit can also optimize the order of analysis based on the relevance of the voice data. As a result, important information can be quickly extracted by adjusting the order of analysis based on the relevance of the voice data.

[0098] The information processing system includes an analysis unit that adjusts the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, if the user has technical expertise, the analysis unit provides the analysis results using detailed technical terms. Alternatively, if the user has only general knowledge, the analysis unit can provide the analysis results in simple terms. The analysis unit can also adjust the way the analysis results are expressed according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0099] The information processing system includes an encryption unit that estimates a user's emotion and adjusts the encryption method based on the estimated user emotion. For example, if the user is nervous, the encryption unit uses a simple and quick encryption method. Alternatively, if the user is relaxed, the encryption unit can use a detailed encryption method. Alternatively, if the user is in a hurry, the encryption unit can use a quick encryption method. This allows for efficient and appropriate encryption by adjusting the encryption method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0100] The information processing system includes an encryption unit that adjusts the encryption strength based on the importance of personal information during encryption. The encryption unit performs strong encryption on important personal information, for example. The encryption unit can also perform basic encryption on general personal information. The encryption unit can also adjust the encryption strength based on the importance of the personal information. This makes it possible to strengthen security by adjusting the encryption strength based on the importance of the personal information.

[0101] The information processing system includes an encryption unit that applies different encryption algorithms depending on the category of personal information during encryption. The encryption unit selects an appropriate encryption algorithm for personal information such as name and address. The encryption unit can also apply different encryption algorithms to personal information such as phone number and email address. The encryption unit can also select the most appropriate encryption algorithm based on the category of personal information. This improves the accuracy of encryption by applying the most appropriate encryption algorithm depending on the category of personal information.

[0102] The information processing system includes an encryption unit that, during encryption, improves the accuracy of encryption by referring to the user's past encryption results. The encryption unit, for example, corrects the current encryption method based on the user's past encryption results. The encryption unit can also adjust the encryption algorithm by referring to the user's past encryption results. The encryption unit can also improve the accuracy of encryption by using the user's past encryption results. In this way, the accuracy of encryption can be improved by referring to the user's past encryption results.

[0103] The information processing system includes an encryption unit that estimates a user's emotions and determines encryption priorities based on the estimated user emotions. For example, if the user is nervous, the encryption unit prioritizes encryption of important personal information. The encryption unit can also encrypt detailed information if the user is relaxed. The encryption unit can also prioritize information that needs to be encrypted quickly if the user is in a hurry. This allows important information to be encrypted quickly by determining encryption priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0104] The information processing system includes an encryption unit that determines the encryption priority based on when personal information was collected during encryption. The encryption unit, for example, prioritizes encrypting the most recent personal information, enabling a prompt response. The encryption unit can also complement the current encryption method by referring to past personal information. The encryption unit can also adjust the encryption priority based on when the personal information was collected. This allows for a prompt response by determining the encryption priority based on when the personal information was collected.

[0105] The information processing system includes an encryption unit that adjusts the encryption order based on the relevance of personal information during encryption. The encryption unit, for example, prioritizes encryption of highly relevant personal information to quickly protect important information. The encryption unit can also perform efficient encryption by deferring less relevant personal information. The encryption unit can also optimize the encryption order based on the relevance of personal information. As a result, important information can be quickly protected by adjusting the encryption order based on the relevance of personal information.

[0106] The information processing system includes an encryption unit that adjusts the encryption method according to the user's level of expertise during encryption. For example, if the user has specialized knowledge, the encryption unit uses a detailed encryption method. Alternatively, if the user only has general knowledge, the encryption unit can use a simple encryption method. The encryption unit can also adjust the encryption method according to the user's level of expertise. This allows for efficient and appropriate encryption by adjusting the encryption method according to the user's level of expertise.

[0107] The information processing system includes a storage unit that adjusts the storage method based on the importance of the data when saving. For example, the storage unit uses a highly redundant storage method for important data. The storage unit can also use a basic storage method for general data. The storage unit can also adjust the storage method based on the importance of the data. This allows for efficient and safe data storage by adjusting the storage method based on the importance of the data.

[0108] The information processing system includes a storage unit that applies different storage algorithms depending on the data category when storing data. The storage unit selects an appropriate storage algorithm for personal information, for example. The storage unit can also apply a different storage algorithm to business data. The storage unit can also select the optimal storage algorithm based on the data category. This improves storage accuracy by applying the optimal storage algorithm depending on the data category.

[0109] The information processing system includes a storage unit that determines storage priorities based on the access frequency of data when saving. The storage unit, for example, prioritizes saving data with high access frequency, enabling quick access. The storage unit can also postpone saving data with low access frequency, enabling efficient saving. The storage unit can also adjust the storage priorities based on the access frequency of data. This allows quick access by determining storage priorities based on the access frequency of data.

[0110] The information processing system includes a storage unit that adjusts the backup frequency of data when saving the data. The storage unit, for example, frequently backs up important data. The storage unit can also set a basic backup frequency for general data. The storage unit can also adjust the backup frequency according to the importance of the data. This makes it possible to ensure the safety of the data by adjusting the backup frequency according to the importance of the data.

[0111] The information processing system includes a storage unit that determines storage priorities based on the time when data was collected. The storage unit, for example, prioritizes storing the most recent data, enabling quick access. The storage unit can also complement the current storage method by referring to past data. The storage unit can also adjust the storage priorities based on the time when the data was collected. This allows quick access by determining storage priorities based on the time when the data was collected.

[0112] The information processing system includes a storage unit that adjusts the order of storage based on the relevance of data when storing the data. The storage unit, for example, prioritizes storing highly relevant data to quickly protect important information. The storage unit can also postpone storing less relevant data for later storage, enabling efficient storage. The storage unit can also optimize the order of storage based on the relevance of data. This allows important information to be quickly protected by adjusting the order of storage based on the relevance of data.

[0113] The information processing system includes a storage unit that sets access permissions for data when the data is saved. The storage unit sets strict access permissions for important data, for example. The storage unit can also set basic access permissions for general data. The storage unit can also adjust the access permissions according to the importance of the data. This makes it possible to strengthen data security by setting access permissions according to the importance of the data.

[0114] The information processing system includes a storage unit that optimizes the storage location of data when it is saved. The storage unit, for example, stores important data in a safe location to enhance security. The storage unit can also efficiently store general data to speed up access. The storage unit can also optimize the storage location according to the importance of the data. In this way, by optimizing the storage location according to the importance of the data, it is possible to improve security and speed up access.

[0115] The information processing system includes a decoding unit that estimates a user's emotion and adjusts a decoding method based on the estimated user's emotion. For example, if the user is nervous, the decoding unit uses a simple and quick decoding method. Alternatively, if the user is relaxed, the decoding unit can use a detailed decoding method. Alternatively, if the user is in a hurry, the decoding unit can use a quick decoding method. This allows for efficient and appropriate decoding by adjusting the decoding method according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0116] The information processing system includes a decryption unit that adjusts the strength of decryption based on the importance of personal information during decryption. The decryption unit performs strong decryption on important personal information, for example. The decryption unit can also perform basic decryption on general personal information. The decryption unit can also adjust the strength of decryption based on the importance of the personal information. This allows security to be enhanced by adjusting the strength of decryption based on the importance of the personal information.

[0117] The information processing system includes a decoding unit that applies different decoding algorithms depending on the category of personal information during decoding. The decoding unit selects an appropriate decoding algorithm for personal information such as name and address. The decoding unit can also apply different decoding algorithms to personal information such as phone number and email address. The decoding unit can also select the optimal decoding algorithm based on the category of personal information. This improves the accuracy of decoding by applying the optimal decoding algorithm depending on the category of personal information.

[0118] The information processing system includes a decoding unit that, during decoding, improves the accuracy of decoding by referring to the user's past decoding results. The decoding unit, for example, corrects the current decoding method based on the user's past decoding results. The decoding unit can also adjust the decoding algorithm by referring to the user's past decoding results. The decoding unit can also improve the accuracy of decoding by using the user's past decoding results. In this way, the accuracy of decoding can be improved by referring to the user's past decoding results.

[0119] The information processing system includes a decoding unit that estimates a user's emotion and determines a decoding priority based on the estimated user's emotion. For example, if the user is nervous, the decoding unit prioritizes decoding of important personal information. The decoding unit can also decode detailed information if the user is relaxed. The decoding unit can also prioritize information to be decoded quickly if the user is in a hurry. In this way, by determining the decoding priority according to the user's emotion, important information can be decoded quickly. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The information processing system includes a decoding unit that determines the priority of decryption based on the time when personal information was collected during decryption. The decoding unit, for example, prioritizes decryption of the most recent personal information, enabling a prompt response. The decoding unit can also complement the current decoding method by referring to past personal information. The decoding unit can also adjust the priority of decryption based on the time when personal information was collected. This allows a prompt response by determining the priority of decryption based on the time when personal information was collected.

[0121] The information processing system includes a decoding unit that adjusts the decoding order based on the relevance of personal information during decoding. The decoding unit, for example, prioritizes decryption of highly relevant personal information, thereby quickly protecting important information. The decoding unit can also defer decoding of less relevant personal information for later execution, thereby enabling efficient decoding. The decoding unit can also optimize the decoding order based on the relevance of personal information. As a result, important information can be quickly protected by adjusting the decoding order based on the relevance of personal information.

[0122] The information processing system includes a decoding unit that adjusts the decoding method according to the user's level of expertise during decoding. For example, if the user has specialized knowledge, the decoding unit uses a detailed decoding method. Alternatively, if the user only has general knowledge, the decoding unit can use a simple decoding method. The decoding unit can also adjust the decoding method according to the user's level of expertise. This allows for efficient and appropriate decoding by adjusting the decoding method according to the user's level of expertise. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, encryption unit, storage unit, and decryption unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect audio data using the camera 42 or microphone 38B of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the audio data using a generation AI to identify personal information. The encryption unit is realized by the specific processing unit 290 of the data processing device 12 and encrypts the identified personal information. The storage unit stores the encrypted data in the database 24 of the data processing device 12. The decryption unit is realized by the specific processing unit 290 of the data processing device 12 and decrypts the stored data as needed. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, encryption unit, storage unit, and decryption unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect voice data using the camera 42 or microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice data using a generation AI to identify personal information. The encryption unit is realized by the specific processing unit 290 of the data processing device 12 and encrypts the identified personal information. The storage unit stores the encrypted data in the database 24 of the data processing device 12. The decryption unit is realized by the specific processing unit 290 of the data processing device 12 and decrypts the stored data as needed. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, encryption unit, storage unit, and decryption unit is realized, for example, in at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit can collect voice data using the camera 42 or microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the voice data using a generation AI to identify personal information. The encryption unit is realized by the specific processing unit 290 of the data processing device 12 and encrypts the identified personal information. The storage unit stores the encrypted data in the database 24 of the data processing device 12. The decryption unit is realized by the specific processing unit 290 of the data processing device 12 and decrypts the stored data as necessary. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, encryption unit, storage unit, and decryption unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect audio data using the camera 42 or microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the audio data using a generation AI to identify personal information. The encryption unit is realized by the specific processing unit 290 of the data processing device 12 and encrypts the identified personal information. The storage unit stores the encrypted data in the database 24 of the data processing device 12. The decryption unit is realized by the specific processing unit 290 of the data processing device 12 and decrypts the stored data as necessary.

[0123] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0124] The information processing system includes a collection unit that analyzes a user's past behavioral patterns and determines the optimal collection timing when collecting voice data. For example, if a user has made many inquiries during a specific time period in the past, the collection unit prioritizes collecting voice data during that time period. Furthermore, if a user has made many inquiries on a specific day of the week in the past, the collection unit can also prioritize collecting voice data on that day. Furthermore, the collection unit can optimize the collection timing based on the user's past behavioral patterns. This enables efficient collection of voice data by taking into account the user's past behavioral patterns.

[0125] The information processing system includes a collection unit that estimates a user's emotions when collecting voice data and adjusts the content of the collected voice data based on the estimated user emotions. For example, if the user is angry, the collection unit prioritizes collecting important information and responds quickly. Also, if the user is relaxed, the collection unit can collect detailed information and respond comprehensively. Also, if the user is feeling anxious, the collection unit can prioritize collecting necessary information to provide a sense of security. Thus, by adjusting the content of the collected voice data according to the user's emotions, important information can be collected quickly.

[0126] The information processing system includes a collection unit that monitors the status of a user's device and selects the optimal collection method when collecting voice data. For example, if the user's device has low battery power, the collection unit simplifies the collection method to reduce battery consumption. Furthermore, if the user's device has high performance, the collection unit can collect detailed voice data and perform highly accurate analysis. Furthermore, the collection unit can adjust the collection method depending on the network connection status of the user's device. This enables efficient collection of voice data by taking the status of the user's device into consideration.

[0127] The information processing system includes a collection unit that estimates a user's emotions when collecting voice data and adjusts the format of the collected voice data based on the estimated user emotions. For example, if the user is nervous, the collection unit uses a simple, highly visible format. If the user is relaxed, the collection unit can also use a detailed format. If the user is in a hurry, the collection unit can also use a format that focuses on the main points. In this way, by adjusting the format of the collected voice data according to the user's emotions, it is possible to collect data that is easy for the user to understand.

[0128] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0129] The information processing system includes a collection unit that estimates a user's emotions when collecting voice data and determines the priority of the voice data to be collected based on the estimated user emotions. For example, if the user is angry, the collection unit prioritizes collecting important information and responds quickly. Also, if the user is relaxed, the collection unit can collect detailed information and respond comprehensively. Also, if the user is feeling anxious, the collection unit can prioritize collecting necessary information to provide a sense of security. Thus, by determining the priority of the voice data to be collected according to the user's emotions, important information can be collected quickly.

[0130] The information processing system includes a collection unit that, when collecting voice data, prioritizes collection of highly relevant data in consideration of the user's geographical location information. For example, when the user is in a specific area, the collection unit prioritizes collection of information related to that area. The collection unit can also collect information on issues specific to the area based on the user's geographical location information. Furthermore, when the user is moving, the collection unit can also collect information related to the user's current location in real time. This makes it possible to efficiently collect information on issues specific to the area by considering the user's geographical location information.

[0131] The information processing system includes a collection unit that analyzes the user's social media activities and collects related data when collecting voice data. The collection unit, for example, analyzes the content posted by the user on social media to collect related information. The collection unit can also collect information about places where the user has checked in on social media. The collection unit can also collect related information by referring to the activities of the user's friends on social media. In this way, highly relevant information can be collected by analyzing the user's social media activities.

[0132] The information processing system includes a collection unit that estimates a user's emotions when collecting voice data and determines the priority of the voice data to be collected based on the estimated user emotions. For example, if the user is angry, the collection unit prioritizes collecting important information and responds quickly. Also, if the user is relaxed, the collection unit can collect detailed information and respond comprehensively. Also, if the user is feeling anxious, the collection unit can prioritize collecting necessary information to provide a sense of security. Thus, by determining the priority of the voice data to be collected according to the user's emotions, important information can be collected quickly.

[0133] The information processing system includes a collection unit that customizes a collection method by reflecting a user's past feedback when collecting voice data. The collection unit adjusts the collection method based on, for example, feedback provided by the user in the past. The collection unit can also customize the range of information to be collected based on the user's past feedback. The collection unit can also optimize the collection method by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0134] The processing flow of the second embodiment will be briefly explained below.

[0135] Step 1: The collection unit collects voice data. The voice data includes, but is not limited to, for example, an audio file format, a sampling rate, and a bit rate. For example, the collection unit collects voice data of interactions with customers at a call center. The collection unit can also collect voice data in real time. Step 2: The analysis unit uses the generation AI to analyze the voice data collected by the collection unit and identify personal information. The generation AI analyzes the voice data using technology such as Transformer. The analysis unit identifies personal information such as name, address, and phone number from the voice data. The analysis unit can also use the generation AI to analyze the content of the voice data and extract personal information. Step 3: The encryption unit encrypts the personal information identified by the analysis unit. For example, an algorithm such as AES, RSA, or DES is used for encryption, but is not limited to these examples. For example, the encryption unit encrypts the personal information using AES. The encryption unit can also encrypt the personal information using RSA. The encryption unit can also encrypt the personal information using DES. Step 4: The storage unit stores the encrypted data in an internal database. Examples of databases include, but are not limited to, an SQL database, a NoSQL database, and an on-premise server. The storage unit stores the encrypted data in, for example, an SQL database. Alternatively, the storage unit may store the encrypted data in a NoSQL database. Alternatively, the storage unit may store the encrypted data in an on-premise server. Step 5: The decryption unit decrypts the data stored in the storage unit as necessary. For example, an algorithm such as AES, RSA, or DES is used for decryption, but is not limited to these examples. For example, the decryption unit decrypts data encrypted using AES. The decryption unit can also decrypt data encrypted using RSA. The decryption unit can also decrypt data encrypted using DES.

[0136] 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.

[0137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). 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 speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0141] 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.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] 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.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 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.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] 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.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] 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.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0173] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0174] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0175] 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.

[0176] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0177] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0178] 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.

[0179] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0180] 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.

[0181] 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.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0185] 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.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] 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.

[0190] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0191] 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.

[0192] 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).

[0193] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0194] 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."

[0195] 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.

[0196] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0201] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0202] 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.

[0203] 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.

[0204] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0205] 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.

[0206] 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.

[0207] [Explanation of symbols]

[0208] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects voice data; an analysis unit that analyzes the voice data collected by the collection unit and identifies personal information; an encryption unit that encrypts the personal information identified by the analysis unit; a storage unit for storing the data encrypted by the encryption unit; a decoding unit that decodes the data stored in the storage unit as needed; Equipped with A system characterized by:

2. The collecting unit Collecting voice data from customer interactions in our call centre 2. The system of claim 1.

3. The analysis unit Analyze voice data using generative AI to identify personal information 2. The system of claim 1.

4. The encryption unit Encrypt identified personal information 2. The system of claim 1.

5. The storage unit Store encrypted data in an internal database 2. The system of claim 1.

6. The decoding unit Decrypting encrypted data as needed 2. The system of claim 1.

7. The collecting unit The user's emotions are estimated, and the timing of collecting voice data is adjusted based on the estimated user's emotions.

2. The system of claim 1.

8. The collecting unit Tailor collection methods based on the skill level of call center agents 2. The system of claim 1.

Images