system

Abstract

The system according to the embodiment aims to acquire data from multiple databases in real time and unify them. According to an embodiment, the system includes an acquisition unit, a conversion unit, and a unification unit. The acquisition unit acquires data in real time. The conversion unit converts the data acquired by the acquisition unit into a unified format. The unification unit centralizes the data converted by the conversion unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult to retrieve data from multiple databases and unify different data formats, leading to issues such as complicated business flows and high costs associated with system updates.

[0005] The system according to the embodiment aims to acquire data from multiple databases in real time and unify them. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a conversion unit, and a unification unit. The acquisition unit acquires data in real time. The conversion unit converts the data acquired by the acquisition unit into a unified format. The unification unit unifies the data converted by the conversion unit. [Effects of the Invention]

[0007] The system according to the embodiment can acquire and unify data from multiple databases in real time. [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) An AI system according to an embodiment of the present invention uses generative AI technology to acquire and centralize data stored in multiple databases in real time. The AI ​​system acquires data from multiple databases in real time, converts the data from the different databases into a unified format, and centralizes it, thereby providing companies with the information they need to make business decisions in real time. For example, the AI ​​system acquires sales information from Database A and inventory information from Database B. The AI ​​system then uses generative AI to convert the data from the different databases into a unified format. For example, if sales information in Database A is stored in numeric format and inventory information in Database B is stored in string format, the generative AI converts them into a unified numeric format. The AI ​​system then centralizes the unified data. This allows companies to obtain the information they need in real time from a single interface. For example, by centralizing sales information and inventory information, inventory shortages can be identified in real time. This allows companies to minimize the need for business system development and the user interface (UI). It also solves problems such as complex business flows, siloed systems caused by system updates, and the enormous costs and human resource consumption associated with system updates. For example, when migrating data from an old system to a new system during a system update, generation AI can standardize and centralize the data format, making the migration process more efficient.

[0029] The AI ​​system according to the embodiment includes an acquisition unit, a conversion unit, and a unification unit. The acquisition unit acquires data in real time. For example, the acquisition unit acquires data from multiple databases in real time. The acquisition unit can also generate appropriate queries for each database to acquire necessary data. For example, the acquisition unit acquires sales information from Database A and inventory information from Database B. The conversion unit converts the data acquired by the acquisition unit into a unified format. For example, the conversion unit converts data between different databases into a unified format. The conversion unit can also convert data using a data format conversion algorithm. For example, if sales information in Database A is stored in a numeric format and inventory information in Database B is stored in a string format, the conversion unit converts them into a unified numeric format. The unification unit centralizes the data converted by the conversion unit. For example, the unification unit centralizes the unified data. The unification unit can also provide information necessary for companies to make business decisions in real time. For example, by centralizing sales information and inventory information, inventory shortages can be identified in real time. As a result, the AI ​​system according to the embodiment can provide information necessary for companies to make business decisions in real time. Some or all of the above-described processes in the acquisition unit, conversion unit, and unification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can generate appropriate queries for each database and input prompts to the generation AI to acquire the necessary data, causing the generation AI to generate the queries. The conversion unit can input an algorithm to the generation AI to convert the acquired data into a unified format, causing the generation AI to convert the data. The unification unit can input prompts to the generation AI to centralize the unified data, causing the generation AI to centralize the data.

[0030] The acquisition unit can acquire data from multiple databases in real time. For example, the acquisition unit acquires data from multiple databases in real time. The acquisition unit can acquire data from multiple types of databases, such as SQL databases and NoSQL databases. For example, the acquisition unit acquires sales information from database A and inventory information from database B. This allows the acquisition of data from multiple databases in real time, thereby providing the latest information. Some or all of the above-mentioned processing in the acquisition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input prompts to the generation AI to generate appropriate queries for each database, and the generation AI can generate the queries.

[0031] The conversion unit can convert data between different databases into a unified format. For example, the conversion unit converts data between different databases into a unified format. The conversion unit can use an algorithm for converting data formats, data types, encoding methods, etc. into a unified format. For example, if sales information in database A is stored in a numeric format and inventory information in database B is stored in a character string format, the conversion unit converts them into a unified numeric format. By converting data between different databases into a unified format, data consistency can be maintained. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input an algorithm for converting acquired data into a unified format into the generation AI, and the generation AI can convert the data.

[0032] The centralization unit can centralize the unified data. For example, the centralization unit can centralize the unified data. The centralization unit can centralize data based on specific methods and criteria for centralization, such as the database integration method and data storage location. For example, sales information and inventory information can be centralized, allowing inventory shortages to be identified in real time. By centralizing the unified data, companies can obtain the information they need from a single interface. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to the generation AI to centralize the unified data, and the generation AI can centralize the data.

[0033] The acquisition unit can generate appropriate queries for each database and acquire the necessary data. For example, the acquisition unit can generate appropriate queries for each database and acquire the necessary data. The acquisition unit can generate queries based on criteria and methods for generating appropriate queries, such as a query optimization method and query structure. For example, the acquisition unit can generate a query to acquire sales information from database A and a query to acquire inventory information from database B. In this way, by generating appropriate queries for each database, the necessary data can be efficiently acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input prompts to the generation AI to generate appropriate queries for each database, and the generation AI can generate the queries.

[0034] The conversion unit can convert data using a data format conversion algorithm. The conversion unit converts data using, for example, a data format conversion algorithm. The conversion unit can convert data based on a specific type and implementation method of the format conversion algorithm, such as data mapping or normalization techniques. For example, if sales information in database A is stored in a numeric format and inventory information in database B is stored in a character string format, the conversion unit converts them into a unified numeric format. By using a data format conversion algorithm, the accuracy of data conversion is improved. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input an algorithm for converting acquired data into a unified format into the generation AI, and the generation AI can convert the data.

[0035] The centralization unit can provide information necessary for a company to make business decisions in real time. For example, the centralization unit can provide information necessary for a company to make business decisions in real time. The centralization unit can provide information based on specific content and criteria of the necessary information, such as business indicators and real-time data. For example, sales information and inventory information can be centralized to identify inventory shortages in real time. This allows for quick decision-making by providing information necessary for a company to make business decisions in real time. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input prompts to the generation AI to centralize unified data, and the generation AI can centralize the data.

[0036] The acquisition unit can analyze past query histories of each database and select an optimal query generation method. The acquisition unit, for example, extracts frequently used query patterns from the past query histories and generates an optimal query. The acquisition unit can, for example, analyze the query histories and select a query generation method that minimizes the load on the database. The acquisition unit can, for example, generate an optimal query for a specific time period based on the query histories and perform efficient data acquisition. This enables the analysis of past query histories to select an optimal query generation method and perform efficient data acquisition. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to analyze past query histories to the generation AI, which can then analyze the query history and select an optimal query generation method.

[0037] When acquiring data, the acquisition unit can determine the acquisition priority based on the importance of the data. For example, the acquisition unit can prioritize acquisition of highly important data and provide it in real time. For example, the acquisition unit can acquire less important data during times when the system load is low. For example, the acquisition unit can adjust the acquisition frequency according to the importance of the data to perform efficient data acquisition. As a result, important data can be acquired preferentially by determining the acquisition priority based on the importance of the data. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to evaluate the importance of the data to the generation AI, and the generation AI can evaluate the importance of the data and determine the acquisition priority.

[0038] The acquisition unit can adjust the acquisition method during data acquisition, taking into account the load status of the database. For example, when the load on the database is high, the acquisition unit can reduce the acquisition frequency to maintain system stability. For example, when the load on the database is low, the acquisition unit can increase the acquisition frequency to improve real-time performance. For example, the acquisition unit can dynamically adjust the acquisition method according to the load status of the database to perform efficient data acquisition. As a result, system stability can be maintained by adjusting the acquisition method taking into account the load status of the database. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to the generation AI to evaluate the load status of the database, and the generation AI can evaluate the load status of the database and adjust the acquisition method.

[0039] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring relevant data based on the user's current location. The acquisition unit can prioritize acquiring highly relevant data by taking into account the user's movement history, for example. The acquisition unit can select and acquire optimal data based on the user's geographical location information, for example. This makes it possible to prioritize acquiring highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to the generation AI to evaluate the user's geographical location information, and the generation AI can evaluate the geographical location information and select highly relevant data.

[0040] The acquisition unit can analyze the user's social media activity and acquire related data when acquiring data. For example, the acquisition unit can analyze the content of the user's social media posts and acquire related data. For example, the acquisition unit can acquire related data by referring to the activities of the user's friends on social media. For example, the acquisition unit can acquire related data based on the user's check-in information on social media. This makes it possible to efficiently acquire related data by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to analyze the user's social media activity to the generation AI, which can then analyze the social media activity and acquire related data.

[0041] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit can, for example, optimize the acquisition method based on the user's past feedback. The acquisition unit can, for example, preferentially acquire specific data from the user's past feedback. The acquisition unit can, for example, adjust the acquisition frequency and timing by referring to the user's past feedback. This makes it possible to optimize the acquisition method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt for analyzing the user's past feedback to the generation AI, which can then analyze the feedback and customize the acquisition method.

[0042] The conversion unit can adjust the level of detail of the conversion based on the importance of the data during data conversion. For example, the conversion unit performs a detailed conversion on data of high importance to increase the accuracy of the information. For example, the conversion unit can perform a simplified conversion on data of low importance to quickly provide information. For example, the conversion unit can dynamically adjust the level of detail of the conversion according to the importance of the data. This makes it possible to increase the accuracy of the information by adjusting the level of detail of the conversion based on the importance of the data. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the importance of the data, and the generation AI can evaluate the importance of the data and adjust the level of detail of the conversion.

[0043] During data conversion, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply a numerical conversion algorithm to numerical data to increase accuracy. For example, the conversion unit can apply a string conversion algorithm to string data to maintain consistency of information. For example, the conversion unit can apply an image conversion algorithm to image data to preserve visual information. In this way, by applying different conversion algorithms depending on the category of data, consistency of information can be maintained. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to evaluate the category of data to the generation AI, and the generation AI can evaluate the category of the data and apply an appropriate conversion algorithm.

[0044] When converting data, the conversion unit can improve the accuracy of the conversion by referring to past conversion results. For example, the conversion unit analyzes past conversion results and optimizes the conversion algorithm. For example, the conversion unit can correct errors from past conversion results and improve accuracy. For example, the conversion unit can learn conversion patterns based on past conversion results and improve accuracy. In this way, the accuracy of the conversion can be improved by referring to past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversion unit can input a prompt to the generation AI to analyze past conversion results, and the generation AI can analyze the conversion results and optimize the conversion algorithm.

[0045] During data conversion, the conversion unit can determine the conversion priority based on the time of data submission. For example, the conversion unit can prioritize converting the most recent data, maintaining real-time performance. For example, the conversion unit can convert old data during a time period when the system load is low. For example, the conversion unit can dynamically adjust the conversion priority according to the time of data submission. This allows real-time performance to be maintained by determining the conversion priority based on the time of data submission. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the time of data submission, and the generation AI can evaluate the submission time and determine the conversion priority.

[0046] The conversion unit can adjust the order of conversion based on the relevance of the data when converting data. For example, the conversion unit can prioritize converting highly relevant data to maintain consistency of information. For example, the conversion unit can convert less relevant data during a time period with low system load. For example, the conversion unit can dynamically adjust the order of conversion according to the relevance of the data. As a result, consistency of information can be maintained by adjusting the order of conversion based on the relevance of the data. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the relevance of the data, and the generation AI can evaluate the relevance and adjust the order of conversion.

[0047] During data conversion, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise. For example, the conversion unit can use detailed technical terminology for a user with high technical expertise in the conversion. For example, the conversion unit can use simpler terminology for a user with low technical expertise in the conversion. For example, the conversion unit can dynamically adjust the use of technical terminology in the conversion according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the user's level of expertise, and the generation AI can evaluate the expertise level and adjust the use of technical terminology.

[0048] The centralization unit can adjust the level of detail of the centralization based on the importance of the data during centralization. For example, the centralization unit can perform detailed centralization on data of high importance to increase the accuracy of the information. For example, the centralization unit can perform simplified centralization on data of low importance to quickly provide information. For example, the centralization unit can dynamically adjust the level of detail of the centralization according to the importance of the data. As a result, the accuracy of the information can be increased by adjusting the level of detail of the centralization based on the importance of the data. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the importance of the data to the generation AI, which can evaluate the importance of the data and adjust the level of detail of the centralization.

[0049] During centralization, the centralization unit can apply different centralization algorithms depending on the category of data. For example, the centralization unit can apply a numeric centralization algorithm to numerical data to increase accuracy. For example, the centralization unit can apply a string centralization algorithm to string data to maintain consistency of information. For example, the centralization unit can apply an image centralization algorithm to image data to preserve visual information. In this way, by applying different centralization algorithms depending on the category of data, consistency of information can be maintained. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the category of data to the generation AI, which can evaluate the category of the data and apply an appropriate centralization algorithm.

[0050] During centralization, the centralization unit can improve the accuracy of the centralization by referring to past centralization results. The centralization unit, for example, analyzes past centralization results and optimizes the centralization algorithm. The centralization unit, for example, can correct errors from past centralization results and improve accuracy. The centralization unit, for example, can learn centralization patterns based on past centralization results and improve accuracy. In this way, the accuracy of centralization can be improved by referring to past centralization results. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to analyze past centralization results to the generation AI, which can analyze the centralization results and optimize the centralization algorithm.

[0051] During centralization, the centralization unit can determine the priority of centralization based on the time of data submission. For example, the centralization unit prioritizes centralization of the latest data, maintaining real-time performance. For example, the centralization unit can centralize old data during times of low system load. For example, the centralization unit can dynamically adjust the priority of centralization according to the time of data submission. This allows real-time performance to be maintained by determining the priority of centralization based on the time of data submission. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to the generation AI to evaluate the time of data submission, and the generation AI can evaluate the submission time and determine the priority of centralization.

[0052] The centralization unit can adjust the order of centralization based on the relevance of the data during centralization. For example, the centralization unit can prioritize centralizing highly relevant data to maintain consistency of information. For example, the centralization unit can centralize less relevant data during times when the system load is low. For example, the centralization unit can dynamically adjust the order of centralization according to the relevance of the data. As a result, consistency of information can be maintained by adjusting the order of centralization based on the relevance of the data. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the relevance of the data to the generation AI, which can evaluate the relevance and adjust the order of centralization.

[0053] During centralization, the unifying unit can adjust the use of technical terminology in the centralization according to the user's level of expertise. For example, the unifying unit can centralize by using detailed technical terminology for a user with high expertise. For example, the unifying unit can centralize by using simple terminology for a user with low expertise. For example, the unifying unit can dynamically adjust the use of technical terminology in the centralization according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the unifying unit may be performed using or without the generation AI. For example, the unifying unit can input a prompt to the generation AI to evaluate the user's level of expertise, and the generation AI can evaluate the expertise level and adjust the use of technical terminology.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The acquisition unit can analyze the user's past behavioral history and predict the optimal timing for data acquisition. For example, if the user frequently checks data during a specific time period, data acquisition can be scheduled to coincide with that time period. Also, if the user needs specific data on a specific day of the week, data acquisition can be scheduled to coincide with that day of the week. Furthermore, if the user tends to check data before and after a specific event, data acquisition can be scheduled to coincide with that event. This makes it possible to improve user convenience by optimizing the timing of data acquisition based on the user's behavioral patterns.

[0056] The conversion unit can evaluate the reliability of data and convert highly reliable data preferentially. For example, data acquired from a highly reliable data source can be converted preferentially, thereby improving the accuracy of information. Data acquired from a less reliable data source can be converted after detailed verification. Furthermore, the conversion algorithm can be adjusted according to the reliability of the data, and detailed conversion can be performed on highly reliable data and simplified conversion can be performed on less reliable data. This allows the accuracy of information to be improved by determining the priority of conversion based on the reliability of the data.

[0057] The centralization unit can adjust the centralization method depending on the purpose of data use. For example, detailed centralization can be performed on data for business analysis, while simplified centralization can be performed on data for marketing. The frequency and timing of centralization can also be adjusted depending on the purpose of data use. Furthermore, the form and format of centralization can be changed depending on the purpose of data use. In this way, by adjusting the centralization method depending on the purpose of data use, the utility value of information can be maximized.

[0058] When acquiring data, the acquisition unit can determine the acquisition priority based on the importance of the data. For example, data with high importance can be acquired preferentially and provided in real time. Data with low importance can be acquired during times when the system load is low. Furthermore, the acquisition frequency can be adjusted according to the importance of the data, allowing for efficient data acquisition. As a result, by determining the acquisition priority based on the importance of the data, important data can be acquired preferentially.

[0059] When converting data, the conversion unit can apply different conversion algorithms depending on the category of the data. For example, a numeric conversion algorithm can be applied to numeric data to increase accuracy. A character string conversion algorithm can be applied to character string data to maintain consistency of the information. Furthermore, an image conversion algorithm can be applied to image data to preserve visual information. In this way, by applying different conversion algorithms depending on the category of data, consistency of the information can be maintained.

[0060] During centralization, the centralization unit can improve the accuracy of centralization by referring to past centralization results. For example, it can analyze past centralization results and optimize the centralization algorithm. It can also correct errors from past centralization results and improve accuracy. It can also learn centralization patterns based on past centralization results and improve accuracy. In this way, it is possible to improve the accuracy of centralization by referring to past centralization results.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The acquisition unit acquires data in real time. For example, it acquires data from multiple databases in real time and generates appropriate queries for each database to acquire the necessary data. Specifically, it acquires sales information from database A and inventory information from database B. Step 2: The conversion unit converts the data acquired by the acquisition unit into a unified format. For example, data from different databases is converted into a unified format, and the data is converted using a data format conversion algorithm. Specifically, if the sales information in database A is stored in numeric format and the inventory information in database B is stored in character string format, the conversion unit converts them into a unified numeric format. Step 3: The centralization unit centralizes the data converted by the conversion unit. For example, it centralizes unified data and provides the information necessary for companies to make business decisions in real time. Specifically, it centralizes sales information and inventory information, making it possible to grasp inventory shortages in real time.

[0063] (Example 2) An AI system according to an embodiment of the present invention uses generative AI technology to acquire and centralize data stored in multiple databases in real time. The AI ​​system acquires data from multiple databases in real time, converts the data from the different databases into a unified format, and centralizes it, thereby providing companies with the information they need to make business decisions in real time. For example, the AI ​​system acquires sales information from Database A and inventory information from Database B. The AI ​​system then uses generative AI to convert the data from the different databases into a unified format. For example, if sales information in Database A is stored in numeric format and inventory information in Database B is stored in string format, the generative AI converts them into a unified numeric format. The AI ​​system then centralizes the unified data. This allows companies to obtain the information they need in real time from a single interface. For example, by centralizing sales information and inventory information, inventory shortages can be identified in real time. This allows companies to minimize the need for business system development and the user interface (UI). It also solves problems such as complex business flows, siloed systems caused by system updates, and the enormous costs and human resource consumption associated with system updates. For example, when migrating data from an old system to a new system during a system update, generation AI can standardize and centralize the data format, making the migration process more efficient.

[0064] The AI ​​system according to the embodiment includes an acquisition unit, a conversion unit, and a unification unit. The acquisition unit acquires data in real time. For example, the acquisition unit acquires data from multiple databases in real time. The acquisition unit can also generate appropriate queries for each database to acquire necessary data. For example, the acquisition unit acquires sales information from Database A and inventory information from Database B. The conversion unit converts the data acquired by the acquisition unit into a unified format. For example, the conversion unit converts data between different databases into a unified format. The conversion unit can also convert data using a data format conversion algorithm. For example, if sales information in Database A is stored in a numeric format and inventory information in Database B is stored in a string format, the conversion unit converts them into a unified numeric format. The unification unit centralizes the data converted by the conversion unit. For example, the unification unit centralizes the unified data. The unification unit can also provide information necessary for companies to make business decisions in real time. For example, by centralizing sales information and inventory information, inventory shortages can be identified in real time. As a result, the AI ​​system according to the embodiment can provide information necessary for companies to make business decisions in real time. Some or all of the above-described processes in the acquisition unit, conversion unit, and unification unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can generate appropriate queries for each database and input prompts to the generation AI to acquire the necessary data, causing the generation AI to generate the queries. The conversion unit can input an algorithm to the generation AI to convert the acquired data into a unified format, causing the generation AI to convert the data. The unification unit can input prompts to the generation AI to centralize the unified data, causing the generation AI to centralize the data.

[0065] The acquisition unit can acquire data from multiple databases in real time. For example, the acquisition unit acquires data from multiple databases in real time. The acquisition unit can acquire data from multiple types of databases, such as SQL databases and NoSQL databases. For example, the acquisition unit acquires sales information from database A and inventory information from database B. This allows the acquisition of data from multiple databases in real time, thereby providing the latest information. Some or all of the above-mentioned processing in the acquisition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input prompts to the generation AI to generate appropriate queries for each database, and the generation AI can generate the queries.

[0066] The conversion unit can convert data between different databases into a unified format. For example, the conversion unit converts data between different databases into a unified format. The conversion unit can use an algorithm for converting data formats, data types, encoding methods, etc. into a unified format. For example, if sales information in database A is stored in a numeric format and inventory information in database B is stored in a character string format, the conversion unit converts them into a unified numeric format. By converting data between different databases into a unified format, data consistency can be maintained. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input an algorithm for converting acquired data into a unified format into the generation AI, and the generation AI can convert the data.

[0067] The centralization unit can centralize the unified data. For example, the centralization unit can centralize the unified data. The centralization unit can centralize data based on specific methods and criteria for centralization, such as the database integration method and data storage location. For example, sales information and inventory information can be centralized, allowing inventory shortages to be identified in real time. By centralizing the unified data, companies can obtain the information they need from a single interface. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to the generation AI to centralize the unified data, and the generation AI can centralize the data.

[0068] The acquisition unit can generate appropriate queries for each database and acquire the necessary data. For example, the acquisition unit can generate appropriate queries for each database and acquire the necessary data. The acquisition unit can generate queries based on criteria and methods for generating appropriate queries, such as a query optimization method and query structure. For example, the acquisition unit can generate a query to acquire sales information from database A and a query to acquire inventory information from database B. In this way, by generating appropriate queries for each database, the necessary data can be efficiently acquired. Some or all of the above-mentioned processing in the acquisition unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the acquisition unit can input prompts to the generation AI to generate appropriate queries for each database, and the generation AI can generate the queries.

[0069] The conversion unit can convert data using a data format conversion algorithm. The conversion unit converts data using, for example, a data format conversion algorithm. The conversion unit can convert data based on a specific type and implementation method of the format conversion algorithm, such as data mapping or normalization techniques. For example, if sales information in database A is stored in a numeric format and inventory information in database B is stored in a character string format, the conversion unit converts them into a unified numeric format. By using a data format conversion algorithm, the accuracy of data conversion is improved. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input an algorithm for converting acquired data into a unified format into the generation AI, and the generation AI can convert the data.

[0070] The centralization unit can provide information necessary for a company to make business decisions in real time. For example, the centralization unit can provide information necessary for a company to make business decisions in real time. The centralization unit can provide information based on specific content and criteria of the necessary information, such as business indicators and real-time data. For example, sales information and inventory information can be centralized to identify inventory shortages in real time. This allows for quick decision-making by providing information necessary for a company to make business decisions in real time. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input prompts to the generation AI to centralize unified data, and the generation AI can centralize the data.

[0071] The acquisition unit can estimate the user's emotions and adjust the timing of data acquisition based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit can reduce the frequency of data acquisition to reduce the load on the system. For example, when the user is relaxed, the acquisition unit can increase the frequency of data acquisition to improve real-time performance. For example, when the user is in a hurry, the acquisition unit can prioritize the acquisition of important data and provide it quickly. This allows the system load to be optimized by adjusting the timing of data acquisition according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the acquisition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input a prompt to the generation AI to estimate the user's emotions, and the generation AI can estimate the emotions.

[0072] The acquisition unit can analyze past query histories of each database and select an optimal query generation method. The acquisition unit, for example, extracts frequently used query patterns from the past query histories and generates an optimal query. The acquisition unit can, for example, analyze the query histories and select a query generation method that minimizes the load on the database. The acquisition unit can, for example, generate an optimal query for a specific time period based on the query histories and perform efficient data acquisition. This enables the analysis of past query histories to select an optimal query generation method and perform efficient data acquisition. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to analyze past query histories to the generation AI, which can then analyze the query history and select an optimal query generation method.

[0073] When acquiring data, the acquisition unit can determine the acquisition priority based on the importance of the data. For example, the acquisition unit can prioritize acquisition of highly important data and provide it in real time. For example, the acquisition unit can acquire less important data during times when the system load is low. For example, the acquisition unit can adjust the acquisition frequency according to the importance of the data to perform efficient data acquisition. As a result, important data can be acquired preferentially by determining the acquisition priority based on the importance of the data. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to evaluate the importance of the data to the generation AI, and the generation AI can evaluate the importance of the data and determine the acquisition priority.

[0074] The acquisition unit can adjust the acquisition method during data acquisition, taking into account the load status of the database. For example, when the load on the database is high, the acquisition unit can reduce the acquisition frequency to maintain system stability. For example, when the load on the database is low, the acquisition unit can increase the acquisition frequency to improve real-time performance. For example, the acquisition unit can dynamically adjust the acquisition method according to the load status of the database to perform efficient data acquisition. As a result, system stability can be maintained by adjusting the acquisition method taking into account the load status of the database. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to the generation AI to evaluate the load status of the database, and the generation AI can evaluate the load status of the database and adjust the acquisition method.

[0075] The acquisition unit can estimate the user's emotions and determine the priority of data to be acquired based on the estimated user emotions. For example, when the user is feeling stressed, the acquisition unit can prioritize acquiring important data and provide it quickly. For example, when the user is relaxed, the acquisition unit can acquire all data evenly and provide balanced information. For example, when the user is in a hurry, the acquisition unit can prioritize acquiring the most important data and provide it quickly. This allows important data to be provided quickly by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, 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. Some or all of the above-mentioned processing in the acquisition unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the acquisition unit can input a prompt to estimate the user's emotions to the generation AI, which then estimates the emotions and determines the priority of the data.

[0076] When acquiring data, the acquisition unit can prioritize acquiring highly relevant data by taking into account the user's geographical location information. The acquisition unit, for example, prioritizes acquiring relevant data based on the user's current location. The acquisition unit can prioritize acquiring highly relevant data by taking into account the user's movement history, for example. The acquisition unit can select and acquire optimal data based on the user's geographical location information, for example. This makes it possible to prioritize acquiring highly relevant data by taking into account the user's geographical location information. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to the generation AI to evaluate the user's geographical location information, and the generation AI can evaluate the geographical location information and select highly relevant data.

[0077] The acquisition unit can analyze the user's social media activity and acquire related data when acquiring data. For example, the acquisition unit can analyze the content of the user's social media posts and acquire related data. For example, the acquisition unit can acquire related data by referring to the activities of the user's friends on social media. For example, the acquisition unit can acquire related data based on the user's check-in information on social media. This makes it possible to efficiently acquire related data by analyzing the user's social media activity. Some or all of the above-described processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt to analyze the user's social media activity to the generation AI, which can then analyze the social media activity and acquire related data.

[0078] The acquisition unit can customize the acquisition method by reflecting the user's past feedback when acquiring data. The acquisition unit can, for example, optimize the acquisition method based on the user's past feedback. The acquisition unit can, for example, preferentially acquire specific data from the user's past feedback. The acquisition unit can, for example, adjust the acquisition frequency and timing by referring to the user's past feedback. This makes it possible to optimize the acquisition method by reflecting the user's past feedback. Some or all of the above-mentioned processing in the acquisition unit may be performed using or without the generation AI. For example, the acquisition unit can input a prompt for analyzing the user's past feedback to the generation AI, which can then analyze the feedback and customize the acquisition method.

[0079] The conversion unit can estimate the user's emotions and adjust the data conversion method based on the estimated user emotions. For example, if the user is relaxed, the conversion unit can perform detailed conversion to increase the accuracy of the information. For example, if the user is in a hurry, the conversion unit can perform simplified conversion to quickly provide information. For example, if the user is stressed, the conversion unit can perform simple conversion to make the information easier to understand. This can increase the accuracy of the information by adjusting the data conversion method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the conversion unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the conversion unit can input a prompt to estimate the user's emotions to the generation AI, which can then estimate the emotions and adjust the data conversion method.

[0080] The conversion unit can adjust the level of detail of the conversion based on the importance of the data during data conversion. For example, the conversion unit performs a detailed conversion on data of high importance to increase the accuracy of the information. For example, the conversion unit can perform a simplified conversion on data of low importance to quickly provide information. For example, the conversion unit can dynamically adjust the level of detail of the conversion according to the importance of the data. This makes it possible to increase the accuracy of the information by adjusting the level of detail of the conversion based on the importance of the data. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the importance of the data, and the generation AI can evaluate the importance of the data and adjust the level of detail of the conversion.

[0081] During data conversion, the conversion unit can apply different conversion algorithms depending on the category of data. For example, the conversion unit can apply a numerical conversion algorithm to numerical data to increase accuracy. For example, the conversion unit can apply a string conversion algorithm to string data to maintain consistency of information. For example, the conversion unit can apply an image conversion algorithm to image data to preserve visual information. In this way, by applying different conversion algorithms depending on the category of data, consistency of information can be maintained. Some or all of the above-mentioned processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to evaluate the category of data to the generation AI, and the generation AI can evaluate the category of the data and apply an appropriate conversion algorithm.

[0082] When converting data, the conversion unit can improve the accuracy of the conversion by referring to past conversion results. For example, the conversion unit analyzes past conversion results and optimizes the conversion algorithm. For example, the conversion unit can correct errors from past conversion results and improve accuracy. For example, the conversion unit can learn conversion patterns based on past conversion results and improve accuracy. In this way, the accuracy of the conversion can be improved by referring to past conversion results. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversion unit can input a prompt to the generation AI to analyze past conversion results, and the generation AI can analyze the conversion results and optimize the conversion algorithm.

[0083] The conversion unit can estimate the user's emotions and determine the priority of data to be converted based on the estimated user emotions. For example, when the user is feeling stressed, the conversion unit can prioritize converting important data and providing it quickly. For example, when the user is relaxed, the conversion unit can convert all data evenly to provide balanced information. For example, when the user is in a hurry, the conversion unit can prioritize converting the most important data and providing it quickly. This allows important data to be provided quickly by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 such examples. Some or all of the above-mentioned processing in the conversion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the conversion unit can input a prompt to estimate the user's emotions to the generation AI, which can then estimate the emotions and determine the priority of the data.

[0084] During data conversion, the conversion unit can determine the conversion priority based on the time of data submission. For example, the conversion unit can prioritize converting the most recent data, maintaining real-time performance. For example, the conversion unit can convert old data during a time period when the system load is low. For example, the conversion unit can dynamically adjust the conversion priority according to the time of data submission. This allows real-time performance to be maintained by determining the conversion priority based on the time of data submission. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the time of data submission, and the generation AI can evaluate the submission time and determine the conversion priority.

[0085] The conversion unit can adjust the order of conversion based on the relevance of the data when converting data. For example, the conversion unit can prioritize converting highly relevant data to maintain consistency of information. For example, the conversion unit can convert less relevant data during a time period with low system load. For example, the conversion unit can dynamically adjust the order of conversion according to the relevance of the data. As a result, consistency of information can be maintained by adjusting the order of conversion based on the relevance of the data. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the relevance of the data, and the generation AI can evaluate the relevance and adjust the order of conversion.

[0086] During data conversion, the conversion unit can adjust the use of technical terminology in the conversion according to the user's level of expertise. For example, the conversion unit can use detailed technical terminology for a user with high technical expertise in the conversion. For example, the conversion unit can use simpler terminology for a user with low technical expertise in the conversion. For example, the conversion unit can dynamically adjust the use of technical terminology in the conversion according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the conversion unit may be performed using or without the generation AI. For example, the conversion unit can input a prompt to the generation AI to evaluate the user's level of expertise, and the generation AI can evaluate the expertise level and adjust the use of technical terminology.

[0087] The unifying unit can estimate the user's emotions and adjust the unification method based on the estimated user emotions. For example, if the user is relaxed, the unifying unit can perform detailed unification to increase the accuracy of the information. For example, if the user is in a hurry, the unifying unit can perform simplified unification to quickly provide information. For example, if the user is stressed, the unifying unit can perform simple unification to make the information easier to understand. This can increase the accuracy of the information by adjusting the unification method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the unifying unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the unifying unit can input a prompt to estimate the user's emotions to the generation AI, which can then estimate the emotions and adjust the unification method.

[0088] The centralization unit can adjust the level of detail of the centralization based on the importance of the data during centralization. For example, the centralization unit can perform detailed centralization on data of high importance to increase the accuracy of the information. For example, the centralization unit can perform simplified centralization on data of low importance to quickly provide information. For example, the centralization unit can dynamically adjust the level of detail of the centralization according to the importance of the data. As a result, the accuracy of the information can be increased by adjusting the level of detail of the centralization based on the importance of the data. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the importance of the data to the generation AI, which can evaluate the importance of the data and adjust the level of detail of the centralization.

[0089] During centralization, the centralization unit can apply different centralization algorithms depending on the category of data. For example, the centralization unit can apply a numeric centralization algorithm to numerical data to increase accuracy. For example, the centralization unit can apply a string centralization algorithm to string data to maintain consistency of information. For example, the centralization unit can apply an image centralization algorithm to image data to preserve visual information. In this way, by applying different centralization algorithms depending on the category of data, consistency of information can be maintained. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the category of data to the generation AI, which can evaluate the category of the data and apply an appropriate centralization algorithm.

[0090] During centralization, the centralization unit can improve the accuracy of the centralization by referring to past centralization results. The centralization unit, for example, analyzes past centralization results and optimizes the centralization algorithm. The centralization unit, for example, can correct errors from past centralization results and improve accuracy. The centralization unit, for example, can learn centralization patterns based on past centralization results and improve accuracy. In this way, the accuracy of centralization can be improved by referring to past centralization results. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to analyze past centralization results to the generation AI, which can analyze the centralization results and optimize the centralization algorithm.

[0091] The centralization unit can estimate the user's emotions and determine the priority of data to be centralized based on the estimated user emotions. For example, when the user is feeling stressed, the centralization unit can prioritize important data and provide it quickly. For example, when the user is relaxed, the centralization unit can evenly centralize all data and provide balanced information. For example, when the user is in a hurry, the centralization unit can prioritize the most important data and provide it quickly. This allows important data to be provided quickly by determining the priority of data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the centralization unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the centralization unit can input a prompt to estimate the user's emotions to the generation AI, which can then estimate the emotions and determine the priority of the data.

[0092] During centralization, the centralization unit can determine the priority of centralization based on the time of data submission. For example, the centralization unit prioritizes centralization of the latest data, maintaining real-time performance. For example, the centralization unit can centralize old data during times of low system load. For example, the centralization unit can dynamically adjust the priority of centralization according to the time of data submission. This allows real-time performance to be maintained by determining the priority of centralization based on the time of data submission. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to the generation AI to evaluate the time of data submission, and the generation AI can evaluate the submission time and determine the priority of centralization.

[0093] The centralization unit can adjust the order of centralization based on the relevance of the data during centralization. For example, the centralization unit can prioritize centralizing highly relevant data to maintain consistency of information. For example, the centralization unit can centralize less relevant data during times when the system load is low. For example, the centralization unit can dynamically adjust the order of centralization according to the relevance of the data. As a result, consistency of information can be maintained by adjusting the order of centralization based on the relevance of the data. Some or all of the above-mentioned processing in the centralization unit may be performed using or without the generation AI. For example, the centralization unit can input a prompt to evaluate the relevance of the data to the generation AI, which can evaluate the relevance and adjust the order of centralization.

[0094] During centralization, the unifying unit can adjust the use of technical terminology in the centralization according to the user's level of expertise. For example, the unifying unit can centralize by using detailed technical terminology for a user with high expertise. For example, the unifying unit can centralize by using simple terminology for a user with low expertise. For example, the unifying unit can dynamically adjust the use of technical terminology in the centralization according to the user's level of expertise. This makes it easier to understand the information by adjusting the use of technical terminology according to the user's level of expertise. Some or all of the above-described processing in the unifying unit may be performed using or without the generation AI. For example, the unifying unit can input a prompt to the generation AI to evaluate the user's level of expertise, and the generation AI can evaluate the expertise level and adjust the use of technical terminology. === Hard Collateral 1-1 === Each of the multiple elements, including the acquisition unit, conversion unit, and unification unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and acquires data from multiple databases in real time. The conversion unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and converts the acquired data into a unified format. The unification unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, and centralizes the unified data. The acquisition unit can, for example, estimate a user's emotion and adjust the timing of data acquisition based on the estimated user's emotion. Emotion estimation is implemented, for example, using an emotion engine or a generative AI. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, conversion unit, and unification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and acquires data from multiple databases in real time. The conversion unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and converts the acquired data into a unified format. The unification unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, and centralizes the unified data. The acquisition unit can, for example, estimate a user's emotion and adjust the timing of data acquisition based on the estimated user's emotion. Emotion estimation is realized, for example, using an emotion engine or a generative AI. === Hard Collateral 1-3 === Each of the multiple elements, including the acquisition unit, conversion unit, and unification unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and acquires data from multiple databases in real time. The conversion unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and converts the acquired data into a unified format. The unification unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12, and centralizes the unified data. The acquisition unit can, for example, estimate the user's emotion and adjust the timing of data acquisition based on the estimated user emotion. Emotion estimation is realized, for example, using an emotion engine or a generative AI. === Hard Collateral 1-4 === Each of the multiple elements, including the acquisition unit, conversion unit, and unification unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and acquires data from multiple databases in real time. The conversion unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and converts the acquired data into a unified format. The unification unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and centralizes the unified data. The acquisition unit can, for example, estimate a user's emotion and adjust the timing of data acquisition based on the estimated user's emotion. Emotion estimation is realized, for example, using an emotion engine or a generative AI.

[0095] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0096] The acquisition unit can analyze the user's past behavioral history and predict the optimal timing for data acquisition. For example, if the user frequently checks data during a specific time period, data acquisition can be scheduled to coincide with that time period. Also, if the user needs specific data on a specific day of the week, data acquisition can be scheduled to coincide with that day of the week. Furthermore, if the user tends to check data before and after a specific event, data acquisition can be scheduled to coincide with that event. This makes it possible to improve user convenience by optimizing the timing of data acquisition based on the user's behavioral patterns.

[0097] The conversion unit can evaluate the reliability of data and convert highly reliable data preferentially. For example, data acquired from a highly reliable data source can be converted preferentially, thereby improving the accuracy of information. Data acquired from a less reliable data source can be converted after detailed verification. Furthermore, the conversion algorithm can be adjusted according to the reliability of the data, and detailed conversion can be performed on highly reliable data and simplified conversion can be performed on less reliable data. This allows the accuracy of information to be improved by determining the priority of conversion based on the reliability of the data.

[0098] The centralization unit can adjust the centralization method depending on the purpose of data use. For example, detailed centralization can be performed on data for business analysis, while simplified centralization can be performed on data for marketing. The frequency and timing of centralization can also be adjusted depending on the purpose of data use. Furthermore, the form and format of centralization can be changed depending on the purpose of data use. In this way, by adjusting the centralization method depending on the purpose of data use, the utility value of information can be maximized.

[0099] The acquisition unit can estimate the user's emotions and adjust the frequency of data acquisition based on the estimated user emotions. For example, if the user is feeling stressed, the frequency of data acquisition can be reduced to reduce the load on the system. Also, if the user is relaxed, the frequency of data acquisition can be increased to improve real-time performance. Furthermore, if the user is in a hurry, important data can be acquired with priority and provided quickly. In this way, the load on the system can be optimized by adjusting the frequency of data acquisition according to the user's emotions.

[0100] The conversion unit can estimate the user's emotions and adjust the accuracy of data conversion based on the estimated user emotions. For example, if the user is relaxed, detailed conversion can be performed to increase the accuracy of the information. If the user is in a hurry, simplified conversion can be performed to quickly provide information. Furthermore, if the user is stressed, simple conversion can be performed to make the information easier to understand. In this way, the accuracy of the information can be increased by adjusting the accuracy of data conversion according to the user's emotions.

[0101] The centralization unit can estimate the user's emotions and determine the priority of centralization based on the estimated user's emotions. For example, if the user is feeling stressed, important data can be prioritized and centralized quickly. If the user is relaxed, all data can be centralized equally to provide balanced information. If the user is in a hurry, the most important data can be prioritized and centralized quickly. Thus, by determining the priority of data according to the user's emotions, important data can be provided quickly.

[0102] When acquiring data, the acquisition unit can determine the acquisition priority based on the importance of the data. For example, data with high importance can be acquired preferentially and provided in real time. Data with low importance can be acquired during times when the system load is low. Furthermore, the acquisition frequency can be adjusted according to the importance of the data, allowing for efficient data acquisition. As a result, by determining the acquisition priority based on the importance of the data, important data can be acquired preferentially.

[0103] When converting data, the conversion unit can apply different conversion algorithms depending on the category of the data. For example, a numeric conversion algorithm can be applied to numeric data to increase accuracy. A character string conversion algorithm can be applied to character string data to maintain consistency of the information. Furthermore, an image conversion algorithm can be applied to image data to preserve visual information. In this way, by applying different conversion algorithms depending on the category of data, consistency of the information can be maintained.

[0104] During centralization, the centralization unit can improve the accuracy of centralization by referring to past centralization results. For example, it can analyze past centralization results and optimize the centralization algorithm. It can also correct errors from past centralization results and improve accuracy. It can also learn centralization patterns based on past centralization results and improve accuracy. In this way, it is possible to improve the accuracy of centralization by referring to past centralization results.

[0105] The acquisition unit can estimate the user's emotions and determine the priority of data to be acquired based on the estimated user's emotions. For example, if the user is feeling stressed, important data can be acquired with priority and provided promptly. Also, if the user is relaxed, all data can be acquired equally to provide balanced information. Furthermore, if the user is in a hurry, the most important data can be acquired with priority and provided promptly. Thus, by determining the priority of data according to the user's emotions, important data can be provided promptly.

[0106] The processing flow of the second embodiment will be briefly explained below.

[0107] Step 1: The acquisition unit acquires data in real time. For example, it acquires data from multiple databases in real time and generates appropriate queries for each database to acquire the necessary data. Specifically, it acquires sales information from database A and inventory information from database B. Step 2: The conversion unit converts the data acquired by the acquisition unit into a unified format. For example, data from different databases is converted into a unified format, and the data is converted using a data format conversion algorithm. Specifically, if the sales information in database A is stored in numeric format and the inventory information in database B is stored in character string format, the conversion unit converts them into a unified numeric format. Step 3: The centralization unit centralizes the data converted by the conversion unit. For example, it centralizes unified data and provides the information necessary for companies to make business decisions in real time. Specifically, it centralizes sales information and inventory information, making it possible to grasp inventory shortages in real time.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0128] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the 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.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0142] 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.

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

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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."

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] [Explanation of symbols]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an acquisition unit that acquires data in real time; a conversion unit that converts the data acquired by the acquisition unit into a unified format; a unifying unit that unifies the data converted by the converting unit; Equipped with A system characterized by:

2. The acquisition unit Retrieving data from multiple databases in real time 2. The system of claim 1.

3. The conversion unit Convert data between different databases into a unified format 2. The system of claim 1.

4. The unification unit Centralize a unified source of truth 2. The system of claim 1.

5. The acquisition unit Generates the appropriate queries for each database to retrieve the required data 2. The system of claim 1.

6. The conversion unit Convert data using a data format conversion algorithm 2. The system of claim 1.

7. The unification unit Providing companies with the real-time information they need to make business decisions 2. The system of claim 1.

8. The acquisition unit Estimate user emotions and adjust the timing of data acquisition based on the estimated user emotions 2. The system of claim 1.

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