Information Processing Apparatus, Information Processing Method, and Information Processing Program

Abstract

Provide only data with guaranteed reliability as learning data for the generation model. 【Solution means】The information processing server 10 includes a collection unit 131 that collects data, a determination unit 133 that determines the reliability of the data collected by the collection unit 131 using a determination model 134, and a transmission control unit 135 that transmits, as learning data for a generation model that generates data in a predetermined field, the data determined by the determination unit 133 to have a predetermined reliability.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] Generative AI (Artificial Intelligence) that generates text, images, audio, and video using a trained machine learning model has attracted attention (for example, Patent Documents 1 and 2).

[0003] For example, as a text generative AI, there is a large language model (LLM), which is a natural language processing model trained using a large amount of text data. In particular, GPT (Generative Pre-trained Transformer) (chatGPT) of OpenAI has attracted attention and has acquired a large number of users.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0005] Here, various data is used as learning data for generative AI. For example, chatGPT uses a large amount of text data publicly available on the Internet as learning data. Specifically, in chatGPT, various types of text data such as web pages, books, news articles, magazines, papers, and Wikipedia are used as learning data.

[0006] However, it is unclear whether the content of the large amount of text data publicly available on the Internet is truly correct. In other words, it is unknown whether the content of the data used as the training data for generative AI is correct.

[0007] And there are cases where the data generated by a generative AI (generative model) trained using data of unknown authenticity may not be correct.

[0008] The present invention has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program that can provide only data with guaranteed reliability as training data for a generative model.

Means for Solving the Problems

[0009] In order to solve the above-described problems and achieve the object, the information processing apparatus of the present invention includes a collection unit that collects data, a determination unit that determines the reliability of the data collected by the collection unit using a determination model, and a transmission control unit that transmits, as training data for a generative model that generates data in a predetermined field, the data determined by the determination unit to have a predetermined reliability.

Effects of the Invention

[0010] According to the present invention, it is possible to provide only data with guaranteed reliability as training data for a generative model.

Brief Description of the Drawings

[0011]

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[0012] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited by this embodiment. In the description of the drawings, the same parts are denoted by the same reference numerals.

[0013] [Embodiment] In the embodiment, using a determination model, the reliability of various collected data is determined, and only the data determined to have a predetermined reliability, that is, the data with the highest reliability and guaranteed reliability, is transmitted as learning data for a generation model that generates information in a predetermined field. As a result, in the embodiment, only data with guaranteed reliability is provided as learning data for the generation model (generative AI).

[0014] [Information Processing System] FIG. 1 is a block diagram showing an example of the configuration of an information processing system according to the embodiment.

[0015] As shown in FIG. 1, the information processing system 100 according to the embodiment is composed of an information processing server 10 and a generation device 20 having a generation model 21 for generating data.

[0016] The information processing server 10 communicates with external servers 30-1 and 30-2 via a network 40. When collectively referring to external servers, they are described as external server 30.

[0017] The information processing server 10 collects data from the external server 30 and evaluates the reliability of the collected data. The information processing server 10 transmits only the data determined to have a predetermined reliability, the data with the highest reliability, and the data with guaranteed reliability to the generation device 20 as learning data for the generation model 21 (described later) of the generation device 20.

[0018] The information processing server 10 classifies the fields of the collected data and determines the reliability only for the data in a predetermined field corresponding to the generation model 21. The information processing server 10 transmits only the data in a predetermined field corresponding to the generation model 21 and with guaranteed reliability to the generation device 20.

[0019] The generation device 20 generates data using the generation model 21. The generation model 21 is a machine learning model for generating data in a predetermined field. In other words, the generation model 21 is a generation model specialized in a predetermined field. The predetermined field is medical, legal, financial, smart city, factory management, agriculture, or chemistry.

[0020] The generation model 21 is, for example, a machine learning model for generating text data and is a natural language model pre-trained using a large amount of text data. Subsequently, the generation model 21 is fine-tuned to execute a predetermined task using only the data in a predetermined field with guaranteed reliability transmitted from the information processing server 10 as learning data. Also, the generation model 21 may be a machine learning model for generating image data.

[0021] [Information Processing Server] Next, the information processing server 10 shown in FIG. 2 will be described. FIG. 2 is a block diagram showing an example of the configuration of the information processing server 10 shown in FIG. 1.

[0022] As shown in FIG. 2, the information processing server 10 has, for example, a communication unit 11, a storage unit 12, and a control unit 13. Note that input devices such as a mouse and a keyboard, and output devices such as a display and a speaker may be connected to the information processing server 10.

[0023] The communication unit 11 controls communication regarding various information. For example, the communication unit 11 controls communication performed with an external server 30 and communication performed with the generation device 20. The communication unit 11 receives various information from the external server 30 and outputs it to the control unit 13. The communication unit 11 communicates with the generation device 20 and transmits data with guaranteed reliability to the generation device 20.

[0024] The storage unit 12 stores data and programs necessary for various processes by the control unit 13. For example, the storage unit 12 may be a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. For example, the storage unit 12 has generation model field data 121, collected data 122, and reliability - guaranteed data 123.

[0025] The generation model field data 121 is information indicating the field to which the generation model 21 of the generation device 20 for which the information processing server 10 provides data corresponds. FIG. 3 is a diagram showing an example of the data configuration of the generation model field data 121 shown in FIG. 2.

[0026] As shown in FIG. 3, the generation model field data 121 has, for example, identification information of the generation model of the generation device 20, the field to which the generation device corresponds, and items of the reliability of the data provided to the generation model. Information on these items is registered, for example, at the time of communication with the generation device 20 that provides the data. For example, it is registered that the generation model 21 corresponds to the medical field and transmits the most reliable data.

[0027] The collected data 122 is various data collected by the information processing server 10 from the external server 30.

[0028] The reliability - guaranteed data 123 is data whose reliability is guaranteed for transmission to the generation model 21 among the data collected by the information processing server 10.

[0029] The control unit 13 has an internal memory for storing programs that define various processing procedures and the required data, and executes various processes based on these. Here, the control unit 13 may be, for example, an electronic circuit such as a CPU (Central Processing Unit) or an MPU (Micro Processing Unit), or an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0030] The control unit 13 has a collection unit 131, a field classification unit 132, a determination unit 133, and a transmission control unit 135.

[0031] The collection unit 131 collects various data from the external server 30 via the communication unit 11. The collection unit 131 collects information publicly available on the network. The data collected by the collection unit 131 is stored in the storage unit 12 as the collected data 122.

[0032] The field classification unit 132 is provided in front of the determination unit 133. The field classification unit 132 classifies the fields of the data collected by the collection unit 131, and outputs only the data of a predetermined field among the classified data to the determination unit 133. When the generation model 21 corresponds to the medical field, the field classification unit 132 outputs only the medical field data among the classified data to the determination unit 133. The field classification unit 132 classifies to which field the data belongs based on the title, frequently used terms, drawings, the business type of the publisher, etc. The field classification unit 132 may classify the data using a learned classifier. Also, when it is possible to determine whether the data is medical field data during the reliability determination by the determination model 134 described later, the field classification unit 132 may be omitted.

[0033] The determination unit 133 determines the reliability of the data collected by the collection unit 131 using the determination model 134. Among each data, the determination unit 133 associates the determination result that the data with the highest reliability (which can be transmitted to the generation model 21) has the highest reliability, and stores it in the storage unit 12 as reliability-guaranteed data 123.

[0034] Here, the determination unit 133 may provide a summarization model for preprocessing in front of the determination model 134. For example, the summarization model is a natural language processing model (e.g., a large language model (LLM)) trained using a large amount of text data so as to output a summary of the input text. The determination unit 133 may generate a summary of each data collected by the collection unit 131 using the summarization model, and input the summary generated by the summarization model to the determination model 134.

[0035] The determination model 134 is a machine learning model that outputs the reliability of the input data when the data is input. In this case, a prompt for instructing a task of determining whether the reliability of the input health level is one of the highest, high, standard, and low and outputting it in a predetermined format is set in the determination model 134.

[0036] The determination model 134 is a model obtained by fine-tuning a natural language processing model (e.g., a large language model (LLM)) trained using a large amount of text data, with the teacher data T1 shown below. The determination model 134 is a machine learning model in which machine learning is performed using peer-reviewed papers in a predetermined field, academic books in a predetermined field, the content of national examinations in a predetermined field, and publications of administrative agencies that govern a predetermined field.

[0037] FIG. 4 is a diagram for explaining the learning of the determination model 134 shown in FIG. 2. When the field of the generation model 21 is medical (see FIG. 3), as shown in FIG. 4, the determination model 134 performs machine learning using peer-reviewed papers T11 in the medical field, academic books T12 in the medical field, publications T13 of administrative agencies that govern the medical field, and the content of national medical examinations as the teacher data T1.

[0038] When the peer-reviewed papers T11 in the medical field, academic books T12 in the medical field, publications T13 of administrative agencies that govern the medical field, and the content of national medical examinations are input to the determination model 134, the parameter update unit 50 updates the parameters of the determination model 134 so as to output the highest reliability.

[0039] The determination model 134 determines the reliability of the input data as, for example, the highest, high, standard, or low, by comparing the words and the surrounding context included in the peer-reviewed papers T11 in the medical field, academic books T12 in the medical field, publications T13 of administrative agencies that govern the medical field, and the content of national medical examinations, with the words and the surrounding context included in the input data. Specifically, the determination model 134 obtains the similarity between the words and the surrounding context included in the teacher data T1 and the words and the surrounding context included in the input data, based on the feature vectors of the teacher data T1 and the feature vectors of the input data, and determines the reliability of the input data according to the obtained similarity. For example, ranges of similarity are set corresponding to the four levels of data reliability, for example, the highest, high, standard, or low.

[0040] The parameter update unit 50 may be provided inside the information processing server 10, or may be provided in a device different from the information processing server 10. The determination model 134 with optimized parameters by the parameter update unit 50 is applied to the determination unit 133 of the information processing server 10.

[0041] The transmission control unit 135 transmits, as learning data for the generation model 21 that generates data in a predetermined field, the data determined by the determination unit 133 to have a predetermined reliability. When the field of the generation model 21 is medical (see FIG. 3), only the data determined by the determination unit 133 to have the highest reliability is transmitted to the generation device 20, for example, as the learning data for the generation model 21.

[0042] [Outline of processing] FIG. 5 is a diagram showing an outline of the processing of the information processing system 100 shown in FIG. 1. In the example of FIG. 5, the case where the field of the generation model 21 is medical will be described as an example.

[0043] In the information processing server 10, the field classification unit 132 classifies the fields of the collected data, and outputs only the data in the medical field, which is the field of the generation model 21, to the determination unit 133.

[0044] The determination model 134 of the determination unit 133 determines the reliability of the input medical field data, and outputs the medical field data with the highest reliability to the transmission control unit 135. The transmission control unit 135 transmits the medical field data with the highest reliability as the learning data for the generation model 21.

[0045] The parameter update unit 60 uses the medical field data with the highest reliability as teacher data to execute learning of the generation model 21 and updates the parameters of the generation model 21. The parameter update unit 60 may be provided inside the generation device 20, or may be provided in a device different from the generation device 20. The generation model 21 with optimized parameters by the parameter update unit 60 is applied to the generation device 20.

[0046] [Processing procedure] FIG. 6 is a sequence diagram showing the processing procedure of the information processing method according to the embodiment.

[0047] The information processing server 10 communicates with the external server 30 and collects data (step S1). The information processing server 10 classifies the fields of the collected data and outputs only the data in a predetermined field to the determination unit 133 (step S2). In the information processing server 10, the determination unit 133 uses the determination model 134 to determine the reliability of each data collected by the collection unit 131 (step S3).

[0048] In the information processing server 10, the transmission control unit 135 transmits, for example, the data determined to have a predetermined reliability by the determination unit 133 to the generation device 20 as learning data for a generation model that generates data in a predetermined field (step S4).

[0049] In the generation device 20, learning of the generation model 21 is executed so as to execute a predetermined task, using only the data in a predetermined field transmitted from the information processing server 10 and having guaranteed reliability as learning data (step S5).

[0050] [Effects of the Embodiment] The information processing server 10 according to the embodiment collects data and determines the reliability of the collected data using the determination model 134. Then, the information processing server 10 transmits the data determined to have a predetermined reliability as learning data for a generation model 21 that generates data in a predetermined field.

[0051] In this way, the information processing server 10 can provide only the data with guaranteed reliability as the learning data for the generation model 21. As a result, the generation model 21 is learned using only the data with guaranteed reliability as the learning data. As a result, the reliability of the data in a predetermined field generated by the generation model 21 can also be guaranteed.

[0052] In addition, the determination model 134 is a machine learning model obtained by performing machine learning using peer-reviewed papers in a predetermined field, academic books in a predetermined field, the content of national examinations in a predetermined field, and publications of administrative agencies that govern a predetermined field. By using this determination model 134, the information processing server 10 can appropriately evaluate the reliability of data.

[0053] In addition, the information processing server 10 further provides a classification unit for classifying the fields of the collected data in front of the determination unit 133 so that only data in a predetermined field can be output to the determination unit 133, thereby improving the accuracy of the reliability evaluation of the determination unit 133.

[0054] [Modification Example] Next, a modification example of the embodiment will be described. FIG. 7 is a block diagram showing an example of the configuration of an information processing system according to a modification example of the embodiment.

[0055] As shown in FIG. 7, the information processing system 200 includes an information processing server 210 instead of the information processing server 10 in FIG. 1. It is also composed of generation devices 20A, 20B, and 20C each having first to third generation models 21A, 21B, and 21C for generating data. The first to third generation models 21A, 21B, and 21C are specialized in fields such as medical, legal, financial, smart city, factory management, agriculture, or chemistry to generate data. The number of generation devices 20A, 20B, and 20C is an example and may be one or two, or four or more.

[0056] The information processing server 210 provides determination models according to the fields of the data generated by the first to third generation models 21A, 21B, and 21C, that is, the fields of medical, legal, financial, smart city, factory management, agriculture, or chemistry. Then, the information processing server 210 determines the reliability of the data sorted by field using the corresponding determination model.

[0057] The information processing server 210 transmits data (first to third reliability - guaranteed data) determined to have a predetermined reliability to the generation devices 20A, 20B, and 20C having the first to third generation models 21A, 21B, and 21C corresponding to the fields to which the data belongs.

[0058] FIG. 8 is a block diagram showing an example of the configuration of the information processing server 210 shown in FIG. 7. As shown in FIG. 8, the information processing server 210 has a storage unit 212 and a control unit 213 instead of the storage unit 12 and the control unit 13 shown in FIG. 2.

[0059] The storage unit 212 has the same functions as the storage unit 12. The storage unit 212 has generation - model field data 2121, collected data 122, field - classified data 2123, and reliability - guaranteed data 2124.

[0060] The generation - model field data 2121 is information indicating the fields corresponding to the first to third generation models 21A, 21B, and 21C of the generation devices 20A, 20B, and 20C to which the information processing server 210 provides data.

[0061] FIG. 9 is a diagram showing an example of the data configuration of the generation - model field data 2121 shown in FIG. 8. As shown in FIG. 9, the generation - model field data 2121 has, for example, identification information of the generation model, the field to which the generation device corresponds, and items of the reliability of the data provided to the generation model. The information of these items is registered, for example, at the time of communication with the generation devices 20A, 20B, and 20C that perform data provision.

[0062] For example, the first generation model 21A is registered to correspond to the medical field and transmit the most reliable data. The second generation model 21B is registered to correspond to the legal field and transmit the most reliable data. The third generation model 21C is registered to correspond to the smart city field and transmit the most reliable data. Also, a generation model 21D (not shown) is registered to correspond to the factory management field and transmit the most reliable data. For the generation models 12E (not shown) and later, the corresponding fields and the reliability of the data to be transmitted are registered.

[0063] The field-classified data 2123 is data collected by the information processing server 210, and data with information indicating the field classified by a field classification unit 2132 (described later) associated therewith.

[0064] The reliability-guaranteed data 2124 is data for which the reliability of transmission to the first to third generation models 21A, 21B, 21C among the data collected by the information processing server 210 is guaranteed, and is data with information indicating the classified field or identification information of the destination generation devices 20A, 20B, 20C associated therewith.

[0065] The control unit 213 has the same functions as the control unit 13. The control unit 213 includes a collection unit 131, a field classification unit 2132, a determination unit 2133, and a transmission control unit 2135.

[0066] The field classification unit 2132 classifies the fields of the data collected by the collection unit 131. The field classification unit 2132 associates information indicating the classified field with the data collected by the collection unit 131 and stores it in the storage unit 212.

[0067] The determination unit 2133 determines the reliability of each piece of data using the determination models provided respectively according to each field of medicine, law, finance, smart city, factory management, agriculture, or chemistry. The determination unit 2133 includes a distribution unit 2136, a first determination model 2134-1, a second determination model 2134-2, and a third determination model 2134-3. The first determination model 2134-1 corresponds to the medical field, the second determination model 2134-2 corresponds to the legal field, and the third determination model 2134-3 corresponds to the smart city field. Note that the determination unit 2133 has each determination model (not shown) specialized in the fields of finance, factory management, agriculture, or chemistry.

[0068] The distribution unit 2136 distributes the data collected by the collection unit 131 to each determination model (the first determination model 2134-1, the second determination model 2134-2, the third determination model 2134-3) according to the field classified by the field classification unit 2132.

[0069] The distribution unit 2136 inputs only the data classified as the medical field by the field classification unit 2132 into the first determination model 2134-1 corresponding to the medical field. The distribution unit 2136 inputs only the data classified as the legal field by the field classification unit 2132 into the second determination model 2134-2 corresponding to the legal field. The distribution unit 2136 inputs only the data classified as the smart city field by the field classification unit 2132 into the third determination model 2134-3 corresponding to the smart city field.

[0070] The first determination model 2134-1, the second determination model 2134-2, and the third determination model 2134-3 determine the reliability of the data distributed by the distribution unit 2136 as, for example, the highest, high, standard, or low. Note that the number of determination models is not limited to three, and may be one or two, or four or more, and is provided according to the number of fields to which the generation model of the data transmission destination corresponds.

[0071] The first determination model 2134-1, the second determination model 2134-2, and the third determination model 2134-3 are machine learning models that output the reliability of the input data when data is input. The first determination model 2134-1, the second determination model 2134-2, and the third determination model 2134-3 are machine learning models in which machine learning is executed using, respectively, peer-reviewed papers in a predetermined field, academic books in a predetermined field, the content of national examinations in a predetermined field, and publications of administrative agencies that govern a predetermined field.

[0072] The first determination model 2134-1 corresponds to, for example, the medical field of the first generation model 21A, and machine learning is executed using peer-reviewed papers in the medical field, academic books in the medical field, publications of administrative agencies that govern the medical field, and the content of medical national examinations as teacher data. When peer-reviewed papers in the medical field, academic books in the medical field, publications of administrative agencies that govern the medical field, and the content of medical national examinations are input into the first determination model 2134-1, the parameters are updated so as to output the highest reliability.

[0073] Also, the second determination model 2134-2 corresponds to, for example, the legal field of the second generation model 21B, and machine learning is executed using peer-reviewed papers in the legal field, academic books in the legal field, publications of administrative agencies that govern the legal field, and the content of legal national examinations as teacher data. When peer-reviewed papers in the legal field, academic books in the legal field, publications of administrative agencies that govern the legal field, and the content of legal national examinations are input into the second determination model 2134-2, the parameters are updated so as to output the highest reliability.

[0074] The third determination model 2134-3 corresponds to, for example, the smart city field of the third generation model 21C, and machine learning is executed using, as teacher data, peer-reviewed papers in the smart city field, academic books in the smart city field, publications of administrative agencies that govern the smart city field, and the content of qualification exams required of engineers when constructing a smart city. When peer-reviewed papers in the smart city field, academic books in the smart city field, publications of administrative agencies that govern the smart city field, and qualification exams required of engineers when constructing a smart city are input into the second determination model 2134-2, the parameters of the third determination model 2134-3 are updated so as to output the highest reliability.

[0075] The determination unit 2133 associates the determination result determined to have the highest reliability with only the data among the data that can be transmitted to the first to third generation models 21A, 21B, and 21C (the highest reliability), and the information indicating the classified field or the identification information of the destination generation devices 20A, 20B, and 20C, and stores them in the storage unit 12 as reliability-guaranteed data 2124. The information indicating the field corresponds to the field to which the determination model that is the output source of the reliability determination result corresponds. The identification information of the destination generation device is the identification information of the generation device corresponding to the field to which the determination model that is the output source of the reliability determination result corresponds.

[0076] The transmission control unit 2135 transmits, as learning data for the first to third generation models 21A, 21B, and 21C, the data determined by the determination unit 2133 to have a predetermined reliability.

[0077] The transmission control unit 2135 transmits, as first reliability-guaranteed data, the data among the reliability-guaranteed data 2124 in which the classified field is medical information or the destination generation device is the generation device 20A to the generation device 20A.

[0078] Also, the transmission control unit 2135 transmits, as second reliability-guaranteed data, the data among the reliability-guaranteed data 2124 in which the classified field is legal information or the destination generation device is the generation device 20B to the generation device 20B.

[0079] Also, among the reliability-guaranteed data 2124, the data whose classified field is the smart city or the data whose generating device of the transmission destination is the generating device 20C is transmitted to the generating device 20C as the second reliability-guaranteed data.

[0080] [Processing Procedure] FIG. 10 is a sequence diagram showing the processing procedure of the information processing method according to a modification of the embodiment.

[0081] The information processing server 210 communicates with the external server 30 and collects data (step S11). The information processing server 210 has the field classification unit 2132 classify the field of the collected data (step S12). The information processing server 210 has the distribution unit 2136 distribute the collected data to each determination model according to the field classified by the field classification unit 2132 (step S13).

[0082] The information processing server 210 has each determination model determine the reliability of the distributed data (step S14).

[0083] Then, the information processing server 210 transmits the data in the medical field determined to have a predetermined reliability (the highest) to the generating device as the learning data of the generating model corresponding to the field in which this data is classified.

[0084] The transmission control unit 2135 transmits the data determined to have a predetermined reliability by the determination unit 2133 as the learning data of the first to third generation models 21A, 21B, and 21C.

[0085] The transmission control unit 2135 transmits the first reliability-guaranteed data among the reliability-guaranteed data 2124 to the generation device 20A (step S15). The transmission control unit 2135 transmits the second reliability-guaranteed data among the reliability-guaranteed data 2124 to the generation device 20B (step S17). The transmission control unit 2135 transmits the third reliability-guaranteed data among the reliability-guaranteed data 2124 to the generation device 20C (step S19).

[0086] In the generation devices 20A, 20B, and 20C, learning of the first to third generation models 21A, 21B, and 21C is executed so as to execute a predetermined task using only the first to third reliability-guaranteed data transmitted from the information processing server 10 as learning data (steps S16, S18, and S20).

[0087] In this way, the information processing server 210 classifies the data fields and determines the reliability for each field using each determination model, so that the data with the highest reliability can also be provided as learning data for each generation model corresponding to a plurality of fields.

[0088] [Regarding the System Configuration of the Embodiment] The information processing servers 10 and 210 are conceptually functional and do not necessarily have to be physically configured as shown in the figure. That is, the specific forms of distribution and integration of the functions of the information processing servers 10 and 210 are not limited to those shown in the figure, and all or part of them can be functionally or physically distributed or integrated in arbitrary units according to various loads and usage situations.

[0089] Also, each process performed in the information processing servers 10 and 210 may be realized in whole or in any part by a program analyzed and executed by a CPU, a GPU (Graphics Processing Unit), and the CPU and GPU. Further, each process performed in the information processing servers 10 and 210 may be realized as hardware by wired logic.

[0090] In addition, among the processes described in the embodiments, all or part of the processes described as being automatically performed can also be performed manually. Or, all or part of the processes described as being performed manually can also be automatically performed by a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters described above and shown in the figures can be appropriately changed unless otherwise specified.

[0091] [Program] FIG. 11 is a diagram showing an example of a computer in which the information processing servers 10 and 210 are realized by executing a program. The computer 1000 has, for example, a memory 1010 and a CPU 1020. The computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0092] The memory 1010 includes a ROM 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System), for example. The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or an optical disk is inserted into the disk drive 1100, for example. The serial port interface 1050 is connected to, for example, a mouse 1110 and a keyboard 1120. The video adapter 1060 is connected to, for example, a display 1130.

[0093] The hard disk drive 1090 stores, for example, an OS (Operating System) 1091, application programs 1092, program modules 1093, and program data 1094. That is, the programs defining the respective processes of the information processing server 10,210 are implemented as program modules 1093 in which code executable by the computer 1000 is described. The program modules 1093 are stored, for example, in the hard disk drive 1090. For example, program modules 1093 for executing processes similar to the functional configuration in the information processing server 10,210 are stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced by an SSD (Solid State Drive).

[0094] In addition, the setting data used in the processes of the above-described embodiments is stored as program data 1094, for example, in the memory 1010 or the hard disk drive 1090. Then, the CPU 1020 reads out the program modules 1093 and the program data 1094 stored in the memory 1010 or the hard disk drive 1090 to the RAM 1012 and executes them as needed.

[0095] Note that the program modules 1093 and the program data 1094 are not limited to being stored in the hard disk drive 1090, and may be stored, for example, in a removable storage medium and read by the CPU 1020 via a disk drive 1100 or the like. Alternatively, the program modules 1093 and the program data 1094 may be stored in another computer connected via a network (LAN (Local Area Network), WAN (Wide Area Network), etc.). Then, the program modules 1093 and the program data 1094 may be read by the CPU 1020 from the other computer via the network interface 1070.

[0096] The embodiments to which the invention made by the present inventor has been applied have been described above. However, the present invention is not limited by the description and drawings that form part of the disclosure of the present invention according to this embodiment. That is, all other embodiments, examples, operation techniques, etc. made by those skilled in the art based on this embodiment are included in the scope of the present invention.

Explanation of Reference Numerals

[0097] 10,210 Information Processing Server 11 Communication Unit 12,212 Storage Unit 13,213 Control Unit 20,20A~20C Generation Device 21 Generation Model 30,30-1,30-2 External Server 100,200 Information Processing System 121,2121 Generation Model Field Data 122 Collected Data 123,2124 Reliability Guaranteed Data 131 Collection Unit 132,2132 Field Classification Unit 133,2133 Judgment Unit 134 Judgment Model 135,2135 Transmission Control Unit 2123 Field-Classified Data 2136 Distribution Unit

Claims

1. a collection unit that collects data; a determination unit that determines the reliability of the data collected by the collection unit using a determination model; a transmission control unit that transmits, as learning data for a generation model that generates data in a predetermined field, the data determined by the determination unit to have a predetermined reliability; characterized by comprising: wherein the determination model is a machine learning model that outputs the reliability of the input data when data is input, and is a machine learning model in which machine learning is performed using peer-reviewed papers in the predetermined field, academic books in the predetermined field, the content of national examinations in the predetermined field, and publications of administrative agencies that govern the predetermined field. An information processing apparatus.

2. The information processing apparatus according to claim 1, wherein the predetermined field is medical, legal, financial, smart city, factory management, agriculture, or chemistry.

3. The information processing apparatus according to claim 1, further comprising a classification unit that classifies the field of the data collected by the collection unit before the determination unit and outputs only the data in the predetermined field to the determination unit.

4. The determination models are respectively provided according to fields of medical, legal, financial, smart city, factory management, agriculture, or chemistry, further comprising a classification unit that classifies the field of the data collected by the collection unit before the determination unit, The information processing apparatus according to claim 1, wherein the determination unit has a distribution unit that distributes the data collected by the collection unit to each determination model according to the field classified by the classification unit.

5. An information processing method executed by an information processing apparatus, comprising: a step of collecting data; a step of determining the reliability of the data collected in the step of collecting using a determination model; a step of transmitting, as learning data for a generation model that generates data in a predetermined field, the data determined to have a predetermined reliability in the step of determining; characterized by comprising: wherein the determination model is a machine learning model that outputs the reliability of the input data when data is input, and is a machine learning model in which machine learning is performed using peer-reviewed papers in the predetermined field, academic books in the predetermined field, the content of national examinations in the predetermined field, and publications of administrative agencies that govern the predetermined field. An information processing method.

6. a step of collecting data; A step of determining the reliability of the data collected in the step of collecting using a determination model; A step of transmitting, as learning data for a generation model that generates data in a predetermined field, data determined to have a predetermined reliability in the step of determining; Causing a computer to execute; The determination model is a machine learning model that outputs the reliability of the input data when data is input, and is an information processing program that is a machine learning model in which machine learning is performed using peer-reviewed papers in the predetermined field, academic books in the predetermined field, the content of national examinations in the predetermined field, and publications of administrative agencies that govern the predetermined field.

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