Information processing apparatus, information processing method, and program

Abstract

To perform accurate analysis on data in every category.SOLUTION: An information processing apparatus has: identification means that identifies a category of input data with a category identification model; and control means that, on the basis of the degree of adaption of data belonging to the N-th category to analysis models for the first to the M-th categories for performing analysis on data in the first to the M-th categories, selects, from analysis models for the first to the M-th categories, an analysis model to be applied to the input data identified to be in the N-th category by the identification means.SELECTED DRAWING: Figure 6

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] In recent years, techniques have been proposed for extracting and analyzing useful information from various data such as images, text, and time-series data, and in particular, highly accurate analysis has become possible by using machine learning techniques. Patent Document 1 describes a system that uses a machine learning model to identify to which category unknown data belongs, and executes an analysis task suitable for data in the identified category (hereinafter, "specific category"). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2020-512631 [Non-patent literature]

[0004] [Non-Patent Document 1] Jiankang Deng, Jia Guo, Evangelos Ververas, Irene Kotsia, Stefanos Zafeiriou, “RetinaFace: Single-stage Dense Face Localization in the Wild”, CVPR 2020. [Non-Patent Document 2] Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding”, arXiv:1810.04805. Summary of the Invention [Problem to be solved by the invention]

[0005] When executing an analysis task suitable for each specific category as in Patent Document 1, it is necessary to prepare an analysis model for each specific category to execute the analysis task. In this case, if it is attempted to increase the number of types of specific categories, a new analysis model is required, but it may be difficult to generate a new analysis model due to, for example, restrictions on hardware resources.

[0006] An object of the present invention is to perform highly accurate analysis of data for each category. [Means for solving the problem]

[0007] The present invention is characterized by comprising an identification means for identifying a category of input data, and a control means for selecting an analytical model to be applied to the input data identified as being in the first category by the identification means from among the analytical models for each category, based on the degree of suitability of data belonging to a first category to the analytical model for each category for analyzing data of each category. Effect of the Invention

[0008] According to the present invention, highly accurate analysis can be performed on data for each category. [Brief description of the drawings]

[0009] [Figure 1] FIG. 2 is a diagram illustrating an example of a hardware configuration of an information processing device. [Diagram 2] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing device. [Diagram 3] 13 is a flowchart showing a process for calculating the conformity of an Nth category. [Figure 4] 13 is a flowchart showing a process for generating teacher data for the Nth category. [Diagram 5] 13 is a flowchart showing a learning process of a category discrimination model. [Figure 6]13 is a flowchart showing a detection model application process. [Figure 7] FIG. 2 is a diagram illustrating an example of a functional configuration of an information processing device. [Figure 8] 13 is a flowchart showing a process for generating teacher data for the Nth category. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments will be described with reference to the accompanying drawings.

[0011] [Embodiment 1] In this embodiment, a system is described that identifies the category of an object in an input image and detects a specific part (e.g., a face) from the image by applying a detection model that detects a specific part of the identified category. The process of detecting a specific part of a category from an image is an example of an analysis process. Note that the input data is not limited to images, but is also not limited to documents, time-series data, etc.

[0012] 1 shows an example of the hardware configuration of an information processing device according to this embodiment. The information processing device 1 includes a CPU 11, a ROM 12, a RAM 13, a secondary storage device 14, an input device 15, and a display device 16. These components are connected to each other via a connection bus 17. The CPU (Central Processing Unit) 11 controls the entire information processing device 1. The CPU 11 executes a control program stored in the ROM 12 or the like to execute the processes of each flowchart described below. Note that a GPU (Graphics Processing Unit) may be used instead of or together with the CPU.

[0013] The ROM 12 is a non-volatile memory that stores control programs and various parameter data. The RAM 13 is a volatile memory that temporarily stores images, control programs, and the results of their execution. The secondary storage device 14 is a rewritable secondary storage device such as a hard disk or flash memory, and stores various data used in each flowchart described below. For example, it stores input data, a learning data set, and processing results. This information is output to the RAM 13 and used by the CPU 11 to execute the control program. The input device 15 is a keyboard, mouse, touch panel device, etc., and inputs various user instructions. The display device 16 is a monitor that displays processing results, images, etc.

[0014] In this embodiment, the processes described below are realized by software using the CPU 11, but some or all of the processes described below may be realized by hardware. A dedicated circuit (ASIC) or a processor (reconfigurable processor, DSP), etc., may be used as the hardware. The information processing device 1 also has a communication unit for communicating with an external device, and may acquire input data, a control program, a learning data set, etc. from the external device via the communication unit, and may output processing results, etc. to the external device via the communication unit.

[0015] Fig. 2 shows an example of the functional configuration of an information processing device according to this embodiment. The information processing device 1 is configured to have the functions of the functional units shown in Fig. 2 by the CPU 11 executing a control program stored in the ROM 12 or the like. The information processing device 1 has an input data acquisition unit 202, a category identification unit 203, and an analysis unit 206.

[0016] The input data acquisition unit 202 acquires an image 201 as input data from an external device or the secondary storage device 14 . The category identification unit 203 identifies the category of an image. In this embodiment, consecutive numbers are assigned to the categories from 1st to Nth, and N categories (first category, second category, . . . , Mth category, and Nth category (N>M)) are identified. The category identification unit 203 includes a category identification model for identifying the category of an object in an image.

[0017] The analysis unit 206 has a detection model for detecting an area of ​​a specific part of a category (hereinafter, a specific area) from an image. In this embodiment, M detection models (a first category detection model 207, a second category detection model 208, ..., an Mth category detection model 209) are provided for each of M categories. On the other hand, the analysis unit 206 does not have a detection model for an Nth category (N>M).

[0018] Each detection model is a machine learning model trained using a teacher data set belonging to each category. The first category detection model 207 is a machine learning model trained using a teacher data set belonging to the first category. Similarly, the detection models for the second to Mth categories are machine learning models trained using teacher data sets belonging to each category. The machine learning model is, for example, a model trained based on correct answer information of a specific area of ​​a category using a multi-layer neural network. A known method such as Non-Patent Document 1 is applied as the training method.

[0019] <Calculation of the conformance of the Nth category> The information processing device 1 has a compatibility calculation unit 214. The process executed by the compatibility calculation unit 214 will be described below. In this embodiment, the compatibility calculation unit 214 generates a compatibility calculation result 205 using the detection models for the 1st to Mth categories held by the analysis unit 206 and a compatibility calculation data set 217 for the Nth category. The generated compatibility calculation result 205 is stored in the secondary storage device 14 or the like.

[0020] 3 is a flowchart showing a process for calculating the fitness of the Nth category. The fitness of the data in the Nth category to the detection model for the mth category is an index of whether the detection model for the mth category is applicable to detecting a specific area in the Nth category. The fitness of the data in the Nth category to the detection model for the mth category is represented as fitness A(N:m). In the following description, each process (step) is represented by adding an S to the beginning, and the notation of the process (step) is omitted.

[0021] In S301, the compatibility calculation unit 214 acquires the compatibility calculation data set 217 for the Nth category. The compatibility calculation data set 217 for the Nth category includes one or more pairs of data each consisting of an image of an object in the Nth category and information (correct answer information) indicating a specific area within the image. The compatibility calculation data set 217 for the Nth category may be stored in the secondary storage device 14 or the like. The compatibility calculation unit 214 may acquire the compatibility calculation data set 217 for the Nth category from the secondary storage device 14 or from an external device.

[0022] In S302, the conformance calculation unit 214 stores 1 in m, which indicates the category number. In S303, the suitability calculation unit 214 determines whether or not a detection model for the m-th category exists in the analysis unit 206. If the suitability calculation unit 214 determines that a detection model for the m-th category exists (Yes in S303), it acquires the detection model for the m-th category from the analysis unit 206 and proceeds to S304, and if it determines that a detection model for the m-th category does not exist (No in S303), it proceeds to S307.

[0023] In S304, the compatibility calculation unit 214 applies the m-th category detection model to the N-th category compatibility calculation dataset 217 acquired in S301. Specifically, an image in the N-th category compatibility calculation dataset 217 is input to the m-th category detection model to detect an area from the image.

[0024] In S305, the compatibility calculation unit 214 calculates the compatibility A(N:m) based on the comparison result between the detection area detected by the detection model for the m-th category in S304 and the specific area represented by the correct answer information of the compatibility calculation dataset 217 for the N-th category. Here, the difference between the areas is obtained as the comparison result. The difference between the areas is quantitatively given, for example, by the difference in the position of the area, the difference in the size or area, or a combination thereof. If the difference between the areas is large, the detection model for the m-th category cannot detect the specific area of ​​the N-th category, so that the compatibility A(N:m) can be determined to be low. On the other hand, if the difference between the areas is small, the detection model for the m-th category can detect the specific area of ​​the N-th category, so that the compatibility A(N:m) can be determined to be high. The compatibility A(N:m) may be expressed as the reciprocal of the difference between the detection area of ​​the detection model for the m-th category and the specific area of ​​the N-th category, or may be expressed as the magnitude of the IoU (Intersection over Union) between the detection area and the specific area.

[0025] In S306, the compatibility calculation section 214 increments m, which represents the category number, and proceeds to S303. The fitness calculation unit 214 calculates the fitness A(N:m) of each of the M detection models including the first category detection model 207 to the Mth category detection model 209 by repeating the processes of S303 to S306. Finally, in S307, the compatibility calculation unit 214 stores the M compatibility scores A(N:m) (m=1, 2, . . . , M) in the secondary storage device 14 or the like as compatibility calculation results 205. After that, a series of processes in the flowchart ends.

[0026] <Learning process for category discrimination model> The information processing device 1 has a teacher dataset generation unit 215 and a learning unit 213. Hereinafter, the processing executed by the teacher dataset generation unit 215 and the learning unit 213 will be described. The learning unit 213 learns a category discrimination model used in the category discrimination unit 203. The teacher dataset for learning the category discrimination model is composed of teacher datasets 211 of the first to Mth categories and a teacher dataset 212 of the Nth category. The teacher dataset 211 is stored in the secondary storage device 14 or the like. The teacher dataset 212 is generated by the teacher dataset generation unit 215. The teacher datasets 211 and 212 include a plurality of datasets each consisting of a pair of an image of an object and label information indicating which category the object in the image belongs to.

[0027] In this embodiment, a case will be described where only the fitness A(N:1) is high among the M fitnesses A(N:m). In this case, the teacher data set 212 for the Nth category may be labeled with a subcategory of the first category and used for training the category identification model. Alternatively, the teacher data set 212 for the Nth category may be labeled with the Nth category as the M+1th category and used for training the category identification model. In the former case, the learning unit 213 learns to identify M categories from the first category to the Mth category, and in the latter case, learns to identify (M+1) categories including the Nth category.

[0028] The following describes the above-mentioned former case, and the processing executed by the teacher dataset generation unit 215. In this embodiment, the teacher dataset generation unit 215 generates the teacher dataset 212 of the Nth category from the dataset 216 of the Nth category based on the compatibility calculation result 205.

[0029] FIG. 4 is a flowchart showing the processing for generating teacher data for the Nth category. In S401, the teacher dataset generation unit 215 acquires the Nth category dataset 216. The Nth category dataset 216 may be stored in the secondary storage device 14 or the like. The teacher dataset generation unit 215 may acquire the Nth category dataset 216 from the secondary storage device 14, or may acquire it from an external device. In S402, the teacher dataset generation unit 215 acquires the fitness A(N:m) of the data of the Nth category to the detection model for the mth category from the fitness calculation result 205. As described above, it is assumed that only the fitness A(N:1) is high among the M fitnesses A(N:m), and therefore only the fitness A(N:1) is acquired in S402. In S403, the teacher data set generation unit 215 determines whether the compatibility A(N:1) is equal to or greater than a predetermined threshold. If the teacher data set generation unit 215 determines that the compatibility A(N:1) is equal to or greater than the threshold (Yes in S403), the process proceeds to S404, and if the teacher data set generation unit 215 determines that the compatibility A(N:1) is less than the threshold (No in S403), the process proceeds to S405.

[0030] In S404, the teacher dataset generation unit 215 labels the first category as a subcategory for the dataset 216 of the Nth category to generate a teacher dataset 212 of the Nth category. This makes it possible to proceed with learning of the category identification model so that the Nth category approaches the first category. The generated teacher dataset 212 is stored in the secondary storage device 14 or the like. Thereafter, the processing of this flowchart ends. In S405, the teacher dataset generation unit 215 labels the Nth category dataset 216 with a category different from the first category, and generates it as the Nth category teacher dataset 212. This makes it possible to proceed with learning of the category identification model so that the Nth category moves away from the first category. The generated teacher dataset 212 is stored in the secondary storage device 14 or the like. Thereafter, the processing of this flowchart ends.

[0031] 5 is a flowchart showing a learning process of a category discrimination model. Learning unit 213 performs learning of the category discrimination model using teacher datasets 211 of the first to Mth categories prepared in advance and teacher dataset 212 of the Nth category generated by teacher dataset generation unit 215.

[0032] In S501, the learning unit 213 acquires the teacher data set 211 of the first category to the Mth category. In S502, the learning unit 213 acquires the teacher data set 212 for the Nth category.

[0033] In S503, the learning unit 213 acquires a category classification model to be used in the category classification unit 203. In this embodiment, the category classification model is a machine learning model for classifying the category of an object in an image. The machine learning model is a multi-layer neural network model. However, the machine learning model is not limited to the multi-layer neural network model, and a known machine learning model such as a random forest or AdaBoost may be used. In S504, the learning unit 213 acquires mini-batch images as input images from the teacher dataset acquired in S501 and S502.

[0034] In S505, the learning unit 213 performs inference processing on the image acquired in S504 using the category discrimination model acquired in S503. In S506, the learning unit 213 acquires label information corresponding to the image acquired in S504. In S507, the learning unit 213 calculates a value of a loss (loss function). In S508, the learning unit 213 applies the backpropagation algorithm to the loss calculated in S507 to calculate a gradient, and obtains an update amount of the weight of the category classification model.

[0035] In S509, the learning unit 213 updates the weights of the category classification model. Specifically, a known learning method for a multilayer neural network may be applied, and a detailed description thereof will be omitted. In S510 , the learning unit 213 outputs the category classification model with updated weights to the category classification unit 203 . The learning unit 213 determines the weights of the category classification model by repeatedly executing the processes of S501 to S510 until the loss value and classification accuracy converge, and then ends the series of processes in the flowchart.

[0036] <Detection model application process> FIG. 6 is a flowchart showing the detection model application process. The detection model application process is a process of identifying the category of input data of an unknown category, selecting a detection model according to the identified category, and applying it to the input data. The information processing device 1 has an analysis application unit 704. Hereinafter, the process executed by the analysis application unit 704 will be described with reference to the flowchart of FIG. 6. It is also assumed that the category identification unit 203 uses a category identification model capable of identifying the first to Nth categories. It is also assumed that the analysis unit 206 has the first to Mth category detection models. It is also assumed that the compatibility calculation result 205 has M compatibility A(N:m) (m=1, 2, . . . , M) for each of the first to Mth category detection models for data of the Nth category.

[0037] In S601, the input data acquisition unit 202 acquires an image 201 of an unknown category. In S602, the analysis application unit 204 acquires the goodness of fit calculation result 205. In S603, the category classification unit 203 performs category classification of the image 201 using the category classification model, and outputs the classification result.

[0038] In S604, the analysis application unit 204 determines whether the classification result is one of categories 1 to M. If the analysis application unit 204 determines that the classification result is one of categories 1 to M (Yes in S604), the process proceeds to S605, and if the analysis application unit 204 determines that the classification result is a category other than categories 1 to M (No in S604), the process proceeds to S606. In S605, the analysis application unit 204 selects a detection model for the category output as the classification result. Then, the process proceeds to S608. In this way, when the classification result represents a first to Mth category (specific category), the analysis application unit 204 selects a detection model for that category.

[0039] In S606, the analysis application unit 204 determines whether the classification result is the Nth category. If the analysis application unit 204 determines that the classification result is not the Nth category (No in S606), the series of processes in this flowchart is terminated, and if the classification result is the Nth category (Yes in S606), the process proceeds to S607. In S607, the analysis application unit 204 searches for the category n having the highest degree of suitability A(N:n) (1≦n≦M) for the Nth category from the degree of suitability calculation result 205 acquired in S602, and selects a detection model for the nth category. In this way, when the classification result represents the Nth category (a category other than the specific category), the analysis application unit 204 selects a detection model for the category from the detection models for the 1st to Mth categories based on the degree of suitability with the Nth category. In S608, the analysis unit 206 applies the detection model selected in S605 or S608 to the image 201 acquired in S601, and outputs the detection result 210. After that, the processing of this flowchart ends.

[0040] In the present embodiment, when the degree of suitability A(N:1) is high, and a category identification model is trained by labeling the subcategories of the first category in the teacher data set 212 of the Nth category, the training proceeds so that the Nth category approaches the first category. Therefore, in S603 of Fig. 6, the Nth category is identified as the first category, and in S605, the detection model for the first category is selected. On the other hand, when the Nth category is labeled to the teacher dataset 212 for the Nth category and a category identification model is trained, the training proceeds so that the Nth category is identified as the Nth category. Therefore, in S603 of Fig. 6, the Nth category is identified as the Nth category, and in S607, a detection model is selected from the detection models for the 1st to Mth categories based on the compatibility. In this embodiment, since the compatibility A(N:1) is high, the detection model for the first category is selected.

[0041] In this embodiment, when only a detection model for a specific category exists, even if an image of a category other than the specific category is acquired, a detection model for the specific category according to the degree of compatibility can be selected to perform a high-quality detection task. In addition, by using images of a category other than the specific category as teacher data for identifying a specific category with a high degree of compatibility with the category in learning the category identification model, a category suitable for applying the detection task can be easily identified. In other words, by effectively utilizing an existing analysis model, it becomes possible to perform a high-precision analysis task for data of a category other than the specific category.

[0042] [Embodiment 2] In the first embodiment, a configuration is described in which an input image is identified as a category of an object in the image, and a detection model is applied to detect a specific part of the identified category. In the present embodiment, a configuration is described in which an input document is classified into categories such as news (e.g., business, science and technology, entertainment), and a summary model is applied to summarize documents of the classified categories. The process of summarizing documents of a category is an example of an analysis process. Note that the document summarization task is assumed to be an abstract summarization. Abstract summarization is a task that is considered to be difficult to summarize with good quality compared to extraction summarization, because it involves document generation. As a method of improving quality, it is considered to use a machine learning model that has been trained specially for a category. Hereinafter, the explanation of the parts common to the first embodiment will be omitted, and the differences from the first embodiment will be mainly explained.

[0043] Fig. 7 shows an example of the functional configuration of an information processing device according to this embodiment. The functional configuration of Fig. 7 differs from the functional configuration of Fig. 2 in that the input data acquisition unit 202 acquires a document 701 as input data, and the analysis unit 706 has a summary model specialized for each category. Also, the analysis application unit 704 selects a summary model, and a summary result 710 is output as a result of applying the summary model. In this embodiment, the analysis unit 706 has M summary models (a first category summary model 707, a second category summary model 708, ..., an Mth category summary model 709) for each of the M categories. On the other hand, the analysis unit 706 does not have a summary model for the Nth category (N>M).

[0044] The category identification unit 203 identifies the category of a document. The category identification model of the category identification unit 203 is, for example, a machine learning model that is fine-tuned by a known category identification learning method based on a model in a non-patent document. Each summarization model held by the analysis unit 706 is, for example, a machine learning model that is fine-tuned based on the model of Non-Patent Document 2 using a known learning method for abstract summarization.

[0045] For example, the relevance A(N:m) is calculated using the ROUGE score, which is a type of evaluation index for document summarization. There are several variations of ROUGE, but in the case of ROUGE-L, for example, a document summarized using a summarization model is compared with a summary document represented by the correct answer information for the input document, and a score is calculated based on the maximum sequence (number of words) that matches between the two documents. The higher the ROUGE score, the higher the relevance.

[0046] In this embodiment, a case will be described in which, of the M degrees of suitability A(N:m), the degree of suitability A(N:1) for the first category summary model 707 of the Nth category and the degree of suitability A(N:2) for the second category summary model 708 of the Nth category are equal to or greater than a threshold. In this case, both the summary models for the first category and the second category are applicable to the document of the Nth category. On the other hand, the degrees of suitability A(N:3) to A(N:M) for the data of the Nth category for the summary models for the third to Mth categories are less than the threshold, and none of the summary models for the third to Mth categories are applicable to the document of the Nth category. Hereinafter, a process executed by the teacher dataset generation unit 215 in this embodiment will be described.

[0047] FIG. 8 is a flowchart showing the teacher data generation process for the Nth category. In S801, the teacher dataset generation unit 215 acquires the dataset 216 of the Nth category. In S802, the teacher data set generation unit 215 obtains the degrees of suitability A(N:m) (m=1, 2, . . . , M) of the Nth category data from the suitability calculation result 205 for each of the 1st to Mth category summary models.

[0048] In S803, the teacher dataset generation unit 215 stores 1 in m, which represents the category number. In S804, the teacher dataset generation unit 215 determines whether the goodness of fit A(N:m) is equal to or greater than a predetermined threshold. If the teacher dataset generation unit 215 determines that the goodness of fit A(N:m) is equal to or greater than the threshold (Yes in S804), the process proceeds to S805, and if the teacher dataset generation unit 215 determines that the goodness of fit A(N:1) is less than the threshold (No in S804), the process proceeds to S806.

[0049] In S805, the teacher dataset generation unit 215 labels the mth category as a subcategory for the Nth category dataset 216 to generate the Nth category teacher dataset 212. The generated teacher dataset 212 is stored in the secondary storage device 14 or the like. The process then proceeds to S807. In S806, the teacher dataset generation unit 215 labels the Nth category dataset 216 with a category different from the mth category to generate the Nth category teacher dataset 212. The generated teacher dataset 212 is stored in the secondary storage device 14 or the like. The process then proceeds to S807.

[0050] In S807, the teacher dataset generation unit 215 increments m, which represents the category number, and proceeds to S808. In S808, the teacher dataset generation unit 215 proceeds to S804 if m≦M, and ends the series of processes in this flowchart if m>M. In this embodiment, data of the Nth category is treated as teacher data for both a subcategory of the first category and a subcategory of the second category.

[0051] In this embodiment, the Nth category is treated as a subcategory of the first and second categories, but if there are two or more summary models whose fitness A(N:m) is equal to or greater than a threshold, the category of one of the summary models may be selected as the subcategory. For example, the categories of the top n summary models with the highest fitness A(N:m) may be selected as the subcategories. In this case, if n=1, the category of the summary model with the highest fitness is selected as the subcategory.

[0052] Next, the learning process of the category identification model of this embodiment will be described with reference to Fig. 5. Here, the difference from the first embodiment will be described. In S506, the learning unit 213 treats the Nth category as a subcategory of the first category and the second category. Therefore, in S507, the learning unit 213 calculates a loss by treating the data of the Nth category as either the first category or the second category in S505 as a correct answer. On the other hand, when the data of the Nth category is classified as any of the third category to the Mth category, the learning unit 213 calculates a loss so as to correct the loss to be large, since an error in applying summarization is not acceptable, so that a misclassification is not made. For example, the learning unit 213 may multiply the calculated loss by a correction coefficient of 1 or more to increase it, or may correct the loss so that the smaller the degree of conformance, the larger the loss becomes according to the degree of conformance.

[0053] In this embodiment, the category identification unit 203 uses the category identification model obtained by learning in this manner to identify the category of the input document 701. If the document is identified as the first category or first subcategory, the analysis application unit 704 controls so as to apply a first category summary model 707 to the input document 701. If the document is identified as the second category or second subcategory, the analysis application unit 704 controls so as to apply a second category summary model 708 to the input document 701.

[0054] In this embodiment, when only a summary model for a specific category exists, even if a document of a category other than the specific category is acquired, a summary model for the specific category according to the degree of suitability can be selected to perform a high-quality summarization task. Furthermore, by using documents of categories other than the specific category as training data for identifying documents of the specific category that have a high degree of suitability with the category in learning the category identification model, it becomes easier to identify categories suitable for applying a summarization task. In other words, by effectively utilizing existing analysis models, it becomes possible to perform a high-precision analysis task on input data of categories other than the specific category.

[0055] In this manner, when only an analytical model for a specific category exists, it is possible to perform highly accurate analysis of categories other than the specific category without newly learning analytical models for categories other than the specific category, thereby effectively increasing the types of categories that can be applied to the analytical model.

[0056] [Modification of the second embodiment] In this embodiment, the learning unit 213 learned the category identification model by treating the Nth category as a subcategory of the first category and a subcategory of the second category. However, the category identification model learned in this way may have a reduced identification performance for the first or second category. In such a case, the teacher dataset generation unit 215 adds a category and labels the Nth category itself, rather than labeling the subcategory in the teacher data of the Nth category. The learning unit 213 learns using the teacher dataset 212 of the Nth category thus generated. For the document 701 identified as the Nth category by the category identification model, the analysis application unit 704 applies a summary model for the category (summary model of the first or second category) that has the highest compatibility with the Nth category to summarize the document of the Nth category.

[0057] Other embodiments Although the present invention has been described above with reference to the embodiments, the above embodiments are merely illustrative of the specific examples of the present invention, and the technical scope of the present invention should not be interpreted as being limited by these embodiments. In other words, the present invention can be embodied in various forms without departing from its technical concept or main features.

[0058] The present invention can also be realized by a process in which a program for implementing one or more of the functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and one or more processors in a computer of the system or device read and execute the program. The present invention can also be realized by a circuit (e.g., ASIC) that implements one or more of the functions.

[0059] The disclosure of each of the above-described embodiments includes the following configurations, methods, and programs. (Configuration 1) an identification means for identifying a category of input data; a control means for selecting an analytical model to be applied to the input data classified into the first category by the classification means from among analytical models for each category based on a degree of conformity of data belonging to a first category to the analytical model for each category for analyzing data of the respective category; 13. An information processing device comprising: (Configuration 2) The information processing device according to configuration 1, wherein the control means applies an analytical model for the first category to the input data identified as the first category when an analytical model for the first category is available, and applies a selected analytical model when an analytical model for the first category is not available. (Configuration 3) 3. The information processing apparatus according to claim 1, wherein the control means selects the analysis model having the highest degree of conformance from among the analysis models for each category. (Configuration 4) a matching degree calculation means for calculating the matching degree based on a comparison result between a result obtained by inputting the data belonging to the first category into an analysis model for each category and correct answer information of the data belonging to the first category; 4. The information processing device according to any one of configurations 1 to 3, further comprising: (Configuration 5) A learning means for learning a discrimination model used in the discrimination means; Further comprising: the learning means performs learning of the discrimination model using training data in which data belonging to the first category is labeled with the first category; The information processing device according to any one of configurations 1 to 4, characterized in that, when there is no analytical model for the first category, the control means applies a selected analytical model to the input data identified as the first category. (Configuration 6) The identification means identifies a category of an image; 6. The information processing device according to any one of configurations 1 to 5, wherein the analysis model is a model for detecting a specific part of a category from an image. (Configuration 7) The identification means identifies a category of the document; 7. The information processing device according to any one of configurations 1 to 6, wherein the analysis model is a model for summarizing documents of a category. (Configuration 8) 8. The information processing device according to any one of configurations 1 to 7, wherein the analysis model is a neural network. (Configuration 9) 6. The information processing device according to configuration 5, wherein the discrimination model is a neural network. (Configuration 10) training means for training a discriminative model for discriminating between categories of data; The information processing device is characterized in that the learning means uses teacher data in which data belonging to the first category is labeled with a category based on the degree of conformity of the data belonging to the first category to an analytical model for each category for analyzing data of each category, for learning the discrimination model. (Configuration 11) A classification means for classifying a category of input data using the classification model trained by the learning means; a control means for applying to the input data an analytical model for the categories identified by the identification means; 11. The information processing device according to configuration 10, comprising: (Configuration 12) The information processing device according to configuration 10 or 11, characterized in that the learning means uses teacher data in which data belonging to the first category is labeled with a second category, which is a category of an analytical model in which the degree of conformance is equal to or greater than a threshold, for learning the discrimination model. (Configuration 13) The information processing device according to any one of configurations 10 to 12, wherein the learning means uses teacher data in which data belonging to the first category is labeled with a category different from the category of the analytical model in which the degree of conformance is less than a threshold value, for learning the discrimination model. (Configuration 14) The information processing device according to configuration 12, characterized in that the learning means, when the discrimination performance of the discrimination model for data belonging to the second category decreases as a result of learning, trains the discrimination model using training data in which data belonging to the first category is labeled with the first category. (Configuration 15) The information processing device according to any one of configurations 10 to 14, wherein the learning means corrects the loss to be large when a category of an analytical model in which the goodness of fit is less than a threshold is output as a classification result by the discrimination model for data belonging to the first category. (Configuration 16) 13. The information processing apparatus according to claim 12, wherein the learning means, when there are a plurality of analytical models whose goodness of fit is equal to or greater than a threshold value, labels the category of the analytical model having a higher goodness of fit. (Configuration 17) 17. The information processing device according to any one of configurations 10 to 16, wherein the identification model is a neural network. (Configuration 18) the discriminative model identifies a category of an image; 18. The information processing device according to any one of configurations 10 to 17, wherein the analysis model is a model for detecting a specific part of a category from an image. (Configuration 19) the discriminative model identifies a category of documents; 19. The information processing device according to any one of configurations 10 to 18, wherein the analysis model is a model for summarizing documents of a category. (Configuration 20) 20. The information processing device according to any one of configurations 10 to 19, wherein the analysis model is a neural network. (method) an identification step for identifying a category of input data; a control step of selecting an analytical model to be applied to the input data identified as belonging to the first category in the identification step from among the analytical models for each category based on a degree of conformity of the data belonging to the first category to the analytical model for each category for analyzing the data of the each category; 13. An information processing method comprising: (program) The computer of the information processing device, an identification means for identifying a category of input data; a control means for selecting an analytical model to be applied to the input data classified into the first category by the classification means from among analytical models for each category based on a degree of conformity of data belonging to a first category to the analytical model for each category for analyzing data of the respective category; A program to function as a

Claims

1. an identification means for identifying a category of input data; a control means for selecting an analytical model to be applied to the input data classified into the first category by the classification means from among analytical models for each category based on a degree of conformity of data belonging to a first category to the analytical model for each category for analyzing data of the respective category; 13. An information processing device comprising:

2. 2. The information processing apparatus according to claim 1, wherein the control means applies an analytical model for the first category to the input data identified as the first category when an analytical model for the first category is available, and applies a selected analytical model when an analytical model for the first category is not available.

3. 2. The information processing apparatus according to claim 1, wherein said control means selects the analysis model having the highest degree of conformance from among the analysis models for each category.

4. a matching degree calculation means for calculating the matching degree based on a comparison result between a result obtained by inputting the data belonging to the first category into an analysis model for each category and correct answer information of the data belonging to the first category; 2. The information processing apparatus according to claim 1, further comprising:

5. A learning means for learning a discrimination model used in the discrimination means; Further comprising: the learning means performs learning of the discriminative model using training data in which data belonging to the first category is labeled with the first category; 2. The information processing apparatus according to claim 1, wherein the control means applies a selected analytical model to the input data identified as belonging to the first category when there is no analytical model for the first category.

6. The identification means identifies a category of an image; The information processing apparatus according to claim 1 , wherein the analysis model is a model for detecting a specific part of a category from an image.

7. The identification means identifies a category of the document; 2. The information processing apparatus according to claim 1, wherein the analysis model is a model for summarizing documents of a category.

8. 2. The information processing apparatus according to claim 1, wherein the analysis model is a neural network.

9. training means for training a discriminative model for discriminating between categories of data; The information processing device is characterized in that the learning means uses teacher data in which data belonging to the first category is labeled with a category based on the degree of suitability of the data belonging to the first category to an analytical model for each category for analyzing data of each category, for learning the discrimination model.

10. A classification means for classifying a category of input data using the classification model trained by the learning means; a control means for applying to the input data an analytical model for the categories identified by the identification means; 10. The information processing apparatus according to claim 9, further comprising:

11. The information processing device according to claim 9, characterized in that the learning means uses teacher data in which data belonging to the first category is labeled with a second category, which is a category of an analytical model in which the degree of conformance is equal to or greater than a threshold, for learning the discrimination model.

12. The information processing device according to claim 9, characterized in that the learning means uses training data in which data belonging to the first category is labeled with a category different from the category of the analytical model for which the degree of conformance is less than a threshold value, for training the discrimination model.

13. 12. The information processing device according to claim 11, wherein the learning means, when a classification performance of the discrimination model for data belonging to the second category has decreased as a result of learning, trains the discrimination model using training data in which data belonging to the first category is labeled with the first category.

14. 10. The information processing device according to claim 9, wherein the learning means corrects the loss so as to be large when a category of an analytical model in which the goodness of fit is less than a threshold is output as a classification result by the discrimination model for data belonging to the first category.

15. 12. The information processing apparatus according to claim 11, wherein, when there are a plurality of analytical models whose goodness of fit is equal to or greater than a threshold, the learning means labels the category of the analytical model having a higher goodness of fit.

16. 10. The information processing apparatus according to claim 9, wherein the discrimination model is a neural network.

17. an identification step for identifying a category of input data; a control step of selecting an analytical model to be applied to the input data identified as belonging to the first category in the identification step from among the analytical models for each category based on a degree of conformity of data belonging to a first category to the analytical model for each category for analyzing data of the respective category; 13. An information processing method comprising:

18. The computer of the information processing device, an identification means for identifying a category of input data; a control means for selecting an analytical model to be applied to the input data identified as belonging to the first category by the identification means from among analytical models for each category, based on a degree of conformity of data belonging to a first category to the analytical model for each category for analyzing data of the respective category; A program to function as a

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