Information processing device, information processing method, and recording medium

Abstract

An information processing device (10) comprises: an acquisition means (50) for acquiring a plurality of elements included in series data; a likelihood ratio calculation means (100) for inputting the plurality of elements and calculating the relevance of each element to calculate a likelihood ratio indicating the likelihood of a class to which the series data belongs; a score calculation means (150) for calculating a score by adding a value corresponding to an emergency signal to the likelihood ratio; and a classification means (200) for classifying, on the basis of the score, the series data into at least one class from among a plurality of classes that are classification candidates. This information processing device makes it possible to quickly classify series data into a class.

Description

Information processing device, information processing method, and recording medium

[0001] The present disclosure relates to the technical fields of an information processing device, an information processing method, and a recording medium.

[0002] Known examples of this type of device include one that classifies sequential data using likelihood ratios. For example, Patent Document 1 discloses a method for classifying sequential data into one of a plurality of predetermined classes by sequentially acquiring and analyzing multiple elements included in the sequential data. Patent Document 2 discloses a method for determining a threshold value for a score each time multiple elements are acquired.

[0003] International Publication No. 2020 / 194497 International Publication No. 2021 / 229654

[0004] This disclosure aims to improve upon the related art discussed above.

[0005] One aspect of the information processing device disclosed herein includes an acquisition means for acquiring multiple elements included in sequence data; a likelihood ratio calculation means for inputting the multiple elements and calculating the relationship between each element to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs; a score calculation means for adding a value corresponding to an urgency signal to the likelihood ratio to calculate a score; and a classification means for classifying the sequence data into at least one of multiple candidate classes based on the score.

[0006] One aspect of the information processing method disclosed herein involves using at least one computer to acquire multiple elements contained in sequence data, input the multiple elements, and calculate the relationships between the elements to calculate a likelihood ratio indicating the likelihood of the class to which the sequence data belongs, add a value corresponding to an urgency signal to the likelihood ratio to calculate a score, and classify the sequence data into at least one of multiple candidate classes based on the score.

[0007] One aspect of the recording medium of this disclosure is a computer program recorded on at least one computer that causes the computer to execute an information processing method, which includes acquiring multiple elements included in sequence data, inputting the multiple elements and calculating the relationships between the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs, calculating a score by adding a value corresponding to an urgency signal to the likelihood ratio, and classifying the sequence data into at least one of multiple candidate classes based on the score.

[0008] 1 is a block diagram showing a hardware configuration of an information processing device according to a first embodiment. FIG. 2 is a block diagram showing a functional configuration of the information processing device according to the first embodiment. FIG. 3 is a flowchart showing the flow of operation of the information processing device according to the first embodiment. FIG. 4 is a graph (part 1) showing an example of a score calculated by the information processing device according to the first embodiment together with a comparative example. FIG. 5 is a graph (part 2) showing an example of a score calculated by the information processing device according to the first embodiment together with a comparative example. FIG. 6 is a block diagram showing a functional configuration of an information processing device according to a second embodiment. FIG. 7 is a flowchart showing the flow of operation of the information processing device according to the second embodiment. FIG. 8 is a block diagram showing a functional configuration of an information processing device according to a third embodiment. FIG. 9 is a flowchart showing the flow of operation of the information processing device according to the third embodiment. FIG. 10 is a block diagram showing a functional configuration of an information processing device according to a fourth embodiment. FIG. 11 is a flowchart showing the flow of operation of the information processing device according to the fourth embodiment. FIG. 12 is a block diagram showing a functional configuration of an information processing device according to a fifth embodiment. FIG. 13 is a flowchart showing the flow of likelihood ratio calculation operation in an information processing device according to the fifth embodiment.

[0009] Hereinafter, embodiments of an information processing device, an information processing method, and a recording medium will be described with reference to the drawings.

[0010] First Embodiment An information processing apparatus according to a first embodiment will be described with reference to FIGS. 1 to 5. FIG.

[0011] (Hardware Configuration) First, the hardware configuration of the information processing apparatus according to the first embodiment will be described with reference to Fig. 1. Fig. 1 is a block diagram showing the hardware configuration of the information processing apparatus according to the first embodiment.

[0012] 1 , an information processing device 10 according to the first embodiment includes a processor 11, a RAM (Random Access Memory) 12, a ROM (Read Only Memory) 13, and a storage device 14. The information processing device 10 may further include an input device 15 and an output device 16. The processor 11, RAM 12, ROM 13, storage device 14, input device 15, and output device 16 are connected to each other via a data bus 17.

[0013] The processor 11 loads a computer program. For example, the processor 11 is configured to load a computer program stored in at least one of the RAM 12, the ROM 13, and the storage device 14. Alternatively, the processor 11 may load a computer program stored in a computer-readable storage medium using a storage medium reading device (not shown). The processor 11 may acquire (i.e., load) the computer program from a device (not shown) located outside the information processing device 10 via a network interface. The processor 11 controls the RAM 12, the storage device 14, the input device 15, and the output device 16 by executing the loaded computer program. In particular, in this embodiment, when the processor 11 executes the loaded computer program, a functional block that performs class classification based on likelihood ratios is realized within the processor 11. In other words, the processor 11 may function as a controller that executes each control in the information processing device 10.

[0014] The processor 11 may be configured as, for example, a central processing unit (CPU), a graphics processing unit (GPU), a field-programmable gate array (FPGA), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), or a quantum processor. The processor 11 may be configured as one of these, or may be configured to use multiple processors in parallel.

[0015] The RAM 12 temporarily stores computer programs executed by the processor 11. The RAM 12 temporarily stores data that the processor 11 temporarily uses while it is executing the computer programs. The RAM 12 may be, for example, a dynamic random access memory (D-RAM) or a static random access memory (SRAM). Alternatively, other types of volatile memory may be used instead of the RAM 12.

[0016] The ROM 13 stores computer programs executed by the processor 11. The ROM 13 may also store fixed data. The ROM 13 may be, for example, a programmable read-only memory (PROM) or an erasable read-only memory (EPROM). Alternatively, other types of non-volatile memory may be used instead of the ROM 13.

[0017] The storage device 14 stores data that is to be saved long-term by the information processing device 10. The storage device 14 may operate as a temporary storage device for the processor 11. The storage device 14 may include, for example, at least one of a hard disk device, a magneto-optical disk device, an SSD (Solid State Drive), and a disk array device.

[0018] The input device 15 is a device that receives input instructions from a user of the information processing device 10. The input device 15 may include, for example, at least one of a keyboard, a mouse, and a touch panel. The input device 15 may be configured as a mobile terminal such as a smartphone or a tablet. The input device 15 may also be, for example, a device that includes a microphone and is capable of voice input.

[0019] The output device 16 is a device that outputs information related to the information processing device 10 to the outside. For example, the output device 16 may be a display device (e.g., a display) that can display information related to the information processing device 10. The output device 16 may also be a speaker or the like that can output information related to the information processing device 10 as audio. The output device 16 may be configured as a mobile terminal such as a smartphone or a tablet.

[0020] 1 shows an example of an information processing device 10 including multiple devices, but all or some of the functions may be realized by a single device. Such an information processing device may be configured to include only the above-mentioned processor 11, RAM 12, and ROM 13, and the other components (i.e., the storage device 14, input device 15, output device 16, etc.) may be provided by an external device connected to the information processing device 10. Furthermore, some of the calculation functions of the information processing device 10 may be realized by an external device (e.g., an external server, a cloud, etc.).

[0021] (Functional Configuration) Next, the functional configuration of the information processing device 10 according to the first embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing the functional configuration of the information processing device according to the first embodiment.

[0022] 2, the information processing device 10 according to the first embodiment is a device that performs class classification of input sequence data, and is configured to include, as components for realizing its functions, a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, and a class classification unit 200. Each of the data acquisition unit 50, the likelihood ratio calculation unit 100, the score calculation unit 150, and the class classification unit 200 may be a processing block realized by, for example, the above-mentioned processor 11 (see FIG. 1).

[0023] The data acquisition unit 50 is configured to acquire multiple elements included in the sequence data. The data acquisition unit 50 may acquire data directly from any data acquisition device (e.g., a camera, a microphone, etc.), or may read data previously acquired by a data acquisition device and stored in storage, etc. When acquiring data from a camera, the data acquisition unit 50 may be configured to acquire data from each of multiple cameras. The elements of the sequence data acquired by the data acquisition unit 50 are configured to be output to the likelihood ratio calculation unit 100. Note that sequence data is data including multiple elements arranged in a predetermined order, and an example of this is time-series data. More specific examples of sequence data include, but are not limited to, video data, audio data, or subdivided image data.

[0024] The likelihood ratio calculation unit 100 is configured to be able to calculate a likelihood ratio based on the multiple elements acquired by the data acquisition unit 50. The likelihood ratio calculation unit 100 may be configured to be able to calculate a likelihood ratio based on the relationship between at least two consecutive elements among the multiple elements acquired. Note that the "likelihood ratio" here is an index indicating the likelihood of the class to which the sequence data belongs. Specific examples of likelihood ratios and specific calculation methods will be described in detail in other embodiments described later. The likelihood ratio calculated by the likelihood ratio calculation unit 100 is sent to the score calculation unit 150.

[0025] The score calculation unit 150 is configured to be able to calculate a score for class classification based on the likelihood ratio calculated by the likelihood ratio calculation unit 100. Specifically, the score calculation unit 150 is configured to be able to calculate a score by adding a value corresponding to an urgency signal to the likelihood ratio. Note that the "urgency signal" here is a signal that indicates the urgency of a response, and is used, for example, when making a decision early (see, for example, References 1 and 2 below).

[0026] [Reference 1]: Gain modulation by an urgency signal controls the speed accuracy trade off in a network model of a cortical decision circuit [Reference 2]: Evidence and Urgency Related EEG Signals during Dynamic Decision Making in Humans

[0027] By adding the above-described urgency signal to the likelihood ratio, the score calculation unit 150 calculates a score that is larger than when the score is calculated simply from the likelihood ratio. That is, the urgency signal according to this embodiment is used to increase the calculated score. The score calculation unit 150 may calculate the score, for example, by calculating the sum of the likelihood ratio and the urgency signal. Alternatively, the score calculation unit 150 may calculate the score by multiplying the likelihood ratio by a coefficient corresponding to the urgency signal. That is, the process of "adding" the urgency signal according to this embodiment may be any arithmetic process that uses the urgency signal to increase the score calculated from the likelihood ratio.

[0028] The value corresponding to the urgency signal may be calculated using, for example, a fixed function form. However, the function form in this case is not particularly limited and may be, for example, a constant, linear, or non-linear. The value corresponding to the urgency signal may be input from outside the information processing device 10, or may be stored inside the information processing device 10. Alternatively, the value corresponding to the urgency signal may be generated as appropriate in the information processing device 10. A configuration for generating a value corresponding to the urgency signal will be described in detail in another embodiment described later.

[0029] The classification unit 200 is configured to classify sequential data based on the score calculated by the score calculation unit 150 (i.e., a score obtained by adding an urgency signal to the likelihood ratio). The classification unit 200 selects at least one class to which the sequential data belongs from among multiple candidate classes. The candidate classes may be preset. Alternatively, the candidate classes may be appropriately set by a user or appropriately set based on the type of sequential data being handled. For example, the multiple classes may be set as a class indicating that a face included in video data is a real face (i.e., a biological face) and a class indicating that a face is a fake face (e.g., a face in a photograph or a 3D mask). In this manner, the information processing device 10 can be used as an impersonation detection device. Alternatively, the multiple classes may be set as a class indicating that an object included in a video captured by a moving body such as a car is an obstacle to be avoided and a class indicating that the object is an object other than an obstacle. In this case, the information processing device 10 can be used as an obstacle detection device. The number of classes that are classification candidates is not limited to two, but may be three or more classes.

[0030] (Flow of Operation) Next, the flow of operation of the information processing device 10 according to the first embodiment will be described with reference to Fig. 3. Fig. 3 is a flowchart showing the flow of operation of the information processing device according to the first embodiment.

[0031] 3, when the information processing device 10 starts operating, the data acquisition unit 50 first acquires elements included in the sequence data (step S11). The data acquisition unit 50 outputs the acquired sequence data elements to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates a likelihood ratio based on the relationship between the acquired elements (step S12).

[0032] Next, the score calculation unit 150 calculates a score to be used for classification by adding the urgency signal to the likelihood ratio calculated by the likelihood ratio calculation unit 100 (step S13).Then, the classification unit 200 performs classification based on the calculated score (step S14).The classification may determine a single class to which the sequence data belongs, or may determine multiple classes to which the sequence data is likely to belong.

[0033] The classification unit 200 may have a function of outputting the classification result. For example, the classification unit 200 may output the classification result to a display or the like. Alternatively, the classification unit 200 may output the classification result as sound via a speaker or the like.

[0034] If the calculated likelihood ratio does not exceed a predetermined threshold (i.e., a threshold for determining which class to classify), the classification unit 200 may calculate the likelihood ratio again without performing classification (i.e., without determining which class to classify into). In this case, the data acquisition unit 50 may acquire new elements included in the sequence data, and a new likelihood ratio may be calculated.

[0035] (Technical Effects) Next, technical effects obtained by the information processing device 10 according to the first embodiment will be described with reference to Fig. 4 and Fig. 5. Fig. 4 is a graph (part 1) showing an example of a score calculated by the information processing device according to the first embodiment together with a comparative example. Fig. 5 is a graph (part 2) showing an example of a score calculated by the information processing device according to the first embodiment together with a comparative example.

[0036] As shown in FIG. 4 , the classification unit 200 may determine a class to be classified into when the calculated score reaches a predetermined judgment threshold. Here, in the information processing device 10 according to the present embodiment, the score is calculated by adding an urgency signal to the likelihood ratio, so the score increases relatively rapidly. Therefore, the score calculated in this embodiment reaches the judgment threshold relatively early (time T1 in the figure). On the other hand, in the information processing device 10 according to the comparative example, the score is calculated without adding an urgency signal to the likelihood ratio. Therefore, the score increases relatively slowly. Therefore, the score calculated in the comparative example reaches the judgment threshold later (time T2 in the figure) than in this embodiment. In this way, the information processing device 10 according to the present embodiment can achieve early classification using the score to which the urgency signal is added.

[0037] As shown in FIG. 5 , the information processing device 10 may set a maximum time Tmax allowed for classification. For example, when performing classification using a video of a person passing in front of a camera, it is required to complete the classification before the person moves out of the camera's field of view. In such a case, in a comparative example in which an urgency signal is not added to the likelihood ratio, the score may not reach the determination threshold by the maximum time Tmax. That is, the information processing device 10 according to the comparative example may not be able to perform classification properly. However, in this embodiment, the score is calculated by adding an urgency signal to the likelihood ratio, so the score reaches the determination threshold at time T3, which is before the maximum time Tmax. As such, the information processing device 10 according to this embodiment can appropriately classify sequence data even when the time allowed for classification is limited.

[0038] A method for achieving similar results is to vary the determination threshold according to elapsed time (i.e., use a dynamic threshold). However, optimizing the dynamic threshold requires rigorous calculations (e.g., calculating likelihood ratios up to the maximum time Tmax and then performing back-calculation). In this case, the calculations for setting the optimal dynamic threshold take time, resulting in a longer time required for classification. However, in this embodiment, it is sufficient to add an urgency signal to the likelihood ratio, so classification can be achieved more quickly than when using a dynamic threshold.

[0039] Second Embodiment An information processing device 10 according to a second embodiment will be described with reference to Figures 6 and 7. The second embodiment differs from the first embodiment described above only in part of the configuration and operation, and other parts may be the same as those of the first embodiment. Therefore, the following will describe in detail the parts that differ from the first embodiment, and will omit a description of other overlapping parts as appropriate.

[0040] (Functional Configuration) First, the functional configuration of the information processing device 10 according to the second embodiment will be described with reference to Fig. 6. Fig. 6 is a block diagram showing the functional configuration of the information processing device according to the second embodiment. Note that in Fig. 6, the same elements as those shown in Fig. 2 are denoted by the same reference numerals.

[0041] 6, the information processing device 10 according to the second embodiment is configured to include, as components for realizing its functions, a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, a classification unit 200, and an urgency determination unit 310. That is, the information processing device 10 according to the second embodiment further includes, in addition to the configuration of the first embodiment (see FIG. 2), an urgency determination unit 310. The urgency determination unit 310 may be a processing block realized by, for example, the above-described processor 11 (see FIG. 1).

[0042] The urgency determination unit 310 is configured to be able to determine whether the current state is an emergency state. Here, an "emergency state" refers to a state in which it is possible that the sequence data cannot be classified (i.e., the score does not reach the determination threshold) before the maximum time Tmax has elapsed. Whether or not an emergency state exists may be determined, for example, using the likelihood ratio calculated by the likelihood ratio calculation unit 100. For example, the urgency determination unit 310 may determine whether or not an emergency state exists using the value of the likelihood ratio, a rate of change, or the like. The determination result by the urgency determination unit 310 is configured to be output to the score calculation unit 150. The score calculation unit 150 according to the second embodiment calculates a score according to the determination result of the urgency determination unit 310. The operation of the score calculation unit 150 at this time will be described in detail below.

[0043] (Operation Flow) Next, the operation flow of the information processing device 10 according to the second embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing the operation flow of the information processing device according to the second embodiment. Note that in Fig. 7, the same processes as those shown in Fig. 3 are denoted by the same reference numerals.

[0044] 7 , when the information processing device 10 starts operating, the data acquisition unit 50 first acquires elements included in the sequence data (step S11). The data acquisition unit 50 outputs the acquired sequence data elements to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates a likelihood ratio based on the relationship between the acquired elements (step S12).

[0045] Next, the urgency determination unit 310 determines whether or not an emergency state exists based on the calculated likelihood ratio, etc. (step S15). If it is determined that an emergency state exists (step S15: YES), the score calculation unit 150 adds an urgency signal to the likelihood ratio calculated by the likelihood ratio calculation unit 100 to calculate a score to be used for classification (step S13). On the other hand, if it is determined that an emergency state does not exist (step S15: NO), the score calculation unit 150 calculates a score to be used for classification from the likelihood ratio calculated by the likelihood ratio calculation unit 100 (without adding the urgency signal) (step S16).

[0046] Thereafter, the classification unit 200 performs classification based on the calculated score (step S14). That is, if there is an emergency state, the classification unit 200 performs classification using the score to which the urgency signal is added. On the other hand, if there is no emergency state, the classification unit 200 performs classification using the score to which the urgency signal is not added. Note that if there is no emergency state determined in the initial stage but an emergency state is determined partway through, the score to which the urgency signal is not added until partway through may be used, and the score to which the urgency signal is added after it is determined that there is an emergency state may be used.

[0047] (Technical Effects) Next, technical effects obtained by the information processing device 10 according to the second embodiment will be described.

[0048] 6 and 7, in the information processing device 10 according to the second embodiment, when it is determined that an emergency state exists, a value corresponding to the urgency signal is added to calculate the score, and when it is determined that no emergency state exists, a value corresponding to the urgency is not added to calculate the score. In this way, it is possible to prevent the urgency signal from being added unnecessarily, and more appropriate class classification can be achieved.

[0049] Third Embodiment An information processing device 10 according to a third embodiment will be described with reference to Figures 8 and 9. Note that the third embodiment differs only in part from the first and second embodiments in configuration and operation, and other parts may be the same as the first embodiment. Therefore, the following will describe in detail the parts that differ from the embodiments already described, and will omit a description of other overlapping parts as appropriate.

[0050] (Functional Configuration) First, the functional configuration of the information processing device 10 according to the third embodiment will be described with reference to Fig. 8. Fig. 8 is a block diagram showing the functional configuration of the information processing device according to the third embodiment. Note that in Fig. 8, the same elements as those shown in Fig. 2 are denoted by the same reference numerals.

[0051] As shown in Fig. 8, the information processing device 10 according to the third embodiment is configured to include, as components for realizing its functions, a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, a classification unit 200, and an urgency signal generation unit 320. That is, the information processing device 10 according to the third embodiment further includes, in addition to the configuration of the first embodiment (see Fig. 2), an urgency signal generation unit 320. The urgency signal generation unit 320 may be a processing block realized by, for example, the above-described processor 11 (see Fig. 1).

[0052] The urgency signal generation unit 320 is configured to be able to generate a value corresponding to the urgency signal using a trained model. That is, the urgency signal generation unit 320 is configured to be able to generate a value corresponding to the urgency that is used by the score calculation unit 150 when calculating the score. The trained model may include, for example, a neural network. The trained model may be trained as a model that receives a value such as a likelihood ratio as input and outputs a value corresponding to the urgency signal. The trained model may be generated by machine learning using training data prepared in advance.

[0053] For example, the trained model may be trained using a loss function plus a value corresponding to the time elapsed since the acquisition of multiple elements began. More specifically, the trained model may be trained using a loss function LOSS expressed by the following mathematical formula (1):

[0054]

[0055] Here, "t" is the current time (i.e., the time elapsed since the element acquisition started). "const." is a predetermined constant, which may be a value appropriately set by a user depending on how quickly the classification needs to be performed. By using the loss function LOSS as described above, it is possible to train a model that can appropriately generate a value corresponding to the urgency signal.

[0056] (Operation Flow) Next, the operation flow of the information processing device 10 according to the third embodiment will be described with reference to Fig. 9. Fig. 9 is a flowchart showing the operation flow of the information processing device according to the third embodiment. Note that in Fig. 9, the same processes as those shown in Fig. 3 are denoted by the same reference numerals.

[0057] 9 , when the information processing device 10 starts operating, the data acquisition unit 50 first acquires elements included in the sequence data (step S11). The data acquisition unit 50 outputs the acquired sequence data elements to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates a likelihood ratio based on the relationship between the acquired elements (step S12).

[0058] Next, the urgency signal generator 150 generates a value corresponding to the urgency signal using the trained model (step S17).Then, the score calculator 150 adds the value generated by the emergency mental synthesis calculator 150 to the likelihood ratio calculated by the likelihood ratio calculator 100 to calculate a score to be used for classification (step S13).

[0059] The classification unit 200 then performs classification based on the calculated scores (step S14). The classification may determine a single class to which the sequence data belongs, or may determine multiple classes to which the sequence data is likely to belong.

[0060] (Technical Effects) Next, technical effects obtained by the information processing device 10 according to the third embodiment will be described.

[0061] 8 and 9, in the information processing device 10 according to the third embodiment, a value corresponding to the urgency signal is generated using a trained model. By using the value generated in this manner, it is possible to calculate a score more appropriately, and it is possible to achieve early classification.

[0062] <Fourth embodiment> An information processing device 10 according to a fourth embodiment will be described with reference to Figures 10 and 11. Note that the fourth embodiment differs only in part of the configuration and operation from the first to third embodiments described above, and other parts may be similar to the first to third embodiments. Therefore, the following will describe in detail the parts that differ from the embodiments already described, and will omit a description of other overlapping parts as appropriate.

[0063] (Functional Configuration) First, the functional configuration of the information processing device 10 according to the fourth embodiment will be described with reference to Fig. 10. Fig. 10 is a block diagram showing the functional configuration of the information processing device according to the fourth embodiment. Note that in Fig. 10, the same elements as those shown in Fig. 2 are denoted by the same reference numerals.

[0064] 10 , the information processing device 10 according to the fourth embodiment is configured to include, as components for realizing its functions, a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, and a class classification unit 200. In particular, in the fourth embodiment, the likelihood ratio calculation unit 100 includes a first calculation unit 110 and a second calculation unit 120. Note that each of the first calculation unit 110 and the second calculation unit 120 may be realized by, for example, the above-mentioned processor 11 (see FIG. 1 ).

[0065] The first calculation unit 110 is configured to be able to calculate an individual likelihood ratio based on two consecutive elements included in the sequence data. The individual likelihood ratio is calculated as a likelihood ratio indicating the likelihood of a class to which the two consecutive elements belong. The first calculation unit 110 may, for example, sequentially acquire elements included in the sequence data from the data acquisition unit 50 and sequentially calculate individual likelihood ratios based on the two consecutive elements. The individual likelihood ratios calculated by the first calculation unit 110 are configured to be output to the second calculation unit 120.

[0066] The second calculation unit 120 is configured to calculate an integrated likelihood ratio based on the multiple individual likelihood ratios calculated by the first calculation unit 110. The integrated likelihood ratio is calculated as a likelihood ratio indicating the likelihood of a class to which multiple elements considered in each of the multiple individual likelihood ratios belong. In other words, the integrated likelihood ratio is calculated as a likelihood ratio indicating the likelihood of a class to which sequence data containing multiple elements belongs. The integrated likelihood ratio calculated by the second calculation unit 120 is output to the classification unit 200. The classification unit 200 classifies the sequence data based on the integrated likelihood ratio.

[0067] (Flow of Operation) Next, the flow of operation of the information processing device 10 according to the fourth embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the flow of operation of the information processing device according to the fourth embodiment.

[0068] 11 , when the information processing device 10 according to the fourth embodiment starts operating, the data acquisition unit 50 first acquires elements included in the sequence data (step S21). The data acquisition unit 50 outputs the acquired elements of the sequence data to the first calculation unit 110.

[0069] The first calculation unit 110 then calculates an individual likelihood ratio based on the two consecutive elements obtained (step S22). Thereafter, the second calculation unit 120 calculates an integrated likelihood ratio based on the multiple individual likelihood ratios calculated by the first calculation unit 110 (step S23).

[0070] Next, the score calculation unit 150 calculates a score by adding the urgency signal to the integrated likelihood ratio (step S24).The classification unit 200 then performs classification based on the calculated integrated likelihood ratio (step S25).The classification may determine a single class to which the sequence data belongs, or may determine multiple classes to which the sequence data is likely to belong.

[0071] (Technical Effects) Next, technical effects obtained by the information processing device 10 according to the fourth embodiment will be described.

[0072] 10 and 11 , the information processing device 10 according to the fourth embodiment first calculates individual likelihood ratios based on two elements, and then calculates an integrated likelihood ratio based on multiple individual likelihood ratios. Using the integrated likelihood ratio calculated in this manner, it becomes possible to appropriately select the class to which the sequence data belongs. Furthermore, the information processing device 10 according to the fourth embodiment calculates a score by adding an urgency signal, thereby enabling early class classification.

[0073] Fifth Embodiment An information processing device 10 according to a fifth embodiment will be described with reference to Figures 12 and 13. The fifth embodiment differs from the fourth embodiment described above only in part of its configuration and operation, and other parts may be the same as those of the fourth embodiment. Therefore, the following will describe in detail only the parts that differ from the embodiments already described, and will omit a description of other overlapping parts as appropriate.

[0074] (Functional Configuration) First, the functional configuration of the information processing device 10 according to the fifth embodiment will be described with reference to Fig. 12. Fig. 12 is a block diagram showing the functional configuration of the information processing device according to the fifth embodiment. Note that in Fig. 12, the same elements as those shown in Fig. 10 are denoted by the same reference numerals.

[0075] As shown in FIG. 12 , the information processing device 10 according to the fifth embodiment includes, as components for realizing its functions, a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, and a classification unit 200. The likelihood ratio calculation unit 100 includes a first calculation unit 110 and a second calculation unit 120, similar to the fourth embodiment. In the fifth embodiment, the first calculation unit 110 includes an individual likelihood ratio calculation unit 111 and a first storage unit. The second calculation unit 120 includes an integrated likelihood ratio calculation unit 121 and a second storage unit 122. Each of the individual likelihood ratio calculation unit 111 and the integrated likelihood ratio calculation unit 121 may be a processing block implemented by, for example, the processor 11 (see FIG. 1 ). Each of the first storage unit 112 and the second storage unit 122 may be implemented by, for example, the storage device 14 (see FIG. 1 ).

[0076] The individual likelihood ratio calculation unit 111 is configured to calculate an individual likelihood ratio based on two consecutive elements among the elements sequentially acquired by the data acquisition unit 50. More specifically, the individual likelihood ratio calculation unit 111 calculates an individual likelihood ratio based on a newly acquired element and past data stored in the first storage unit 112. The information stored in the first storage unit 112 is configured to be readable by the individual likelihood ratio calculation unit 111. When the first storage unit 112 stores past individual likelihood ratios, the individual likelihood ratio calculation unit 111 may read the stored past individual likelihood ratios and calculate new individual likelihood ratios that take the acquired elements into consideration. On the other hand, when the first storage unit 112 stores the elements acquired in the past, the individual likelihood ratio calculation unit 111 may calculate past individual likelihood ratios from the stored past elements and calculate a likelihood ratio for the newly acquired element.

[0077] The integrated likelihood ratio calculation unit 121 is configured to be able to calculate an integrated likelihood ratio based on a plurality of individual likelihood ratios. The integrated likelihood ratio calculation unit 121 calculates a new integrated likelihood ratio using the individual likelihood ratios calculated by the individual likelihood ratio calculation unit 111 and past integrated likelihood ratios stored in the second storage unit 122. The information stored in the second storage unit 122 (i.e., past integrated likelihood ratios) is configured to be readable by the integrated likelihood ratio calculation unit 121.

[0078] (Likelihood Ratio Calculation Operation) Next, the flow of the likelihood ratio calculation operation (i.e., the operation when the likelihood ratio calculation unit 100 calculates the likelihood ratio) in the information processing device 10 according to the fifth embodiment will be described with reference to Fig. 13. Fig. 13 is a flowchart showing the flow of the likelihood ratio calculation operation in the information processing device according to the fifth embodiment.

[0079] 13 , when the likelihood ratio calculation operation according to the fifth embodiment is started, the individual likelihood ratio calculation unit 111 in the first calculation unit 110 first reads past data from the first storage unit 112 (step S31). The past data may be, for example, the processing result by the individual likelihood ratio calculation unit 111 of the element acquired immediately before the element currently acquired by the data acquisition unit 50 (in other words, the individual likelihood ratio calculated for the immediately previous element). Alternatively, the past data may be the element itself acquired immediately before the element currently acquired.

[0080] Next, the individual likelihood ratio calculation unit 111 calculates a new individual likelihood ratio (i.e., an individual likelihood ratio for the element currently acquired by the data acquisition unit 50) based on the element acquired by the data acquisition unit 50 and the past data read from the first storage unit 112 (step S32). The individual likelihood ratio calculation unit 111 outputs the calculated individual likelihood ratio to the second calculation unit 120. The individual likelihood ratio calculation unit 111 may store the calculated individual likelihood ratio in the first storage unit 112.

[0081] Next, the integrated likelihood ratio calculation unit 121 in the second calculation unit 120 reads out a past integrated likelihood ratio from the second storage unit 122 (step S33). The past integrated likelihood ratio may be, for example, the processing result by the integrated likelihood ratio calculation unit 121 for the element acquired immediately before the element currently acquired by the data acquisition unit 50 (in other words, the integrated likelihood ratio calculated for the immediately previous element).

[0082] Next, the integrated likelihood ratio calculation unit 121 calculates a new integrated likelihood ratio (i.e., an integrated likelihood ratio for the element currently acquired by the data acquisition unit 50) based on the likelihood ratio calculated by the individual likelihood ratio calculation unit 111 and the past integrated likelihood ratio read from the second storage unit 122 (step S34). The integrated likelihood ratio calculation unit 121 outputs the calculated integrated likelihood ratio to the classifying unit 200. The integrated likelihood ratio calculation unit 121 may store the calculated integrated likelihood ratio in the second storage unit 122.

[0083] (Technical Effects) Next, technical effects obtained by the information processing device 10 according to the fifth embodiment will be described.

[0084] 12 and 13 , in the information processing device 10 according to the fifth embodiment, individual likelihood ratios are calculated using past individual likelihood ratios, and then an integrated likelihood ratio is calculated using past integrated likelihood ratios. Using the integrated likelihood ratio calculated in this manner makes it possible to appropriately select the class to which the sequence data belongs. Furthermore, in the information processing device 10 according to the fifth embodiment, the score is calculated by adding an urgency signal as described above, thereby enabling early class classification.

[0085] The scope of each embodiment also includes a processing method in which a program that operates the configuration of each embodiment to realize the functions of the above-described embodiments is recorded on a recording medium, the program recorded on the recording medium is read as code, and the program is executed on a computer. In other words, a computer-readable recording medium is also included in the scope of each embodiment. Furthermore, each embodiment includes not only a recording medium on which the above-described program is recorded, but also the program itself.

[0086] Examples of recording media that can be used include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, magnetic tapes, non-volatile memory cards, and ROMs. Furthermore, the scope of each embodiment is not limited to programs that execute processes by themselves, but also includes programs that execute processes by operating on an OS in conjunction with other software or expansion board functions. Furthermore, the program itself may be stored on a server, and part or all of the program may be downloadable from the server to a user terminal. The program may be provided to the user in, for example, a SaaS (Software as a Service) format.

[0087] <Supplementary Notes> The above-described embodiment may be further described as in the following supplementary notes, but is not limited to the following.

[0088] (Supplementary Note 1) The information processing device described in Supplementary Note 1 is an information processing device including: an acquisition means for acquiring multiple elements included in sequence data; a likelihood ratio calculation means for inputting the multiple elements and calculating the relationship between each element to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs; a score calculation means for adding a value corresponding to an urgency signal to the likelihood ratio to calculate a score; and a classification means for classifying the sequence data into at least one class of multiple candidate classes based on the score.

[0089] (Supplementary Note 2) The information processing device according to Supplementary Note 2 is the information processing device according to Supplementary Note 1, in which a maximum time allowed from when acquisition of the plurality of elements starts until when the sequence data is classified is determined in advance.

[0090] (Supplementary Note 3) The information processing device described in Supplementary Note 3 is the information processing device described in Supplementary Note 2, further comprising a determination means for determining whether or not the state is an emergency state in which it is possible that the sequence data cannot be classified before the maximum time has elapsed, and the score calculation means adds a value corresponding to the urgency signal to the likelihood ratio if the state is an emergency state, and does not add a value corresponding to the urgency signal to the likelihood ratio if the state is not an emergency state.

[0091] (Supplementary Note 4) The information processing device according to Supplementary Note 4 is the information processing device according to any one of Supplementary Notes 1 to 3, further comprising: a generation unit configured to generate a value according to the urgency signal using a trained model.

[0092] (Supplementary Note 5) The information processing device according to Supplementary Note 5 is the information processing device according to Supplementary Note 4, in which the trained model is trained using a loss function plus a value according to a time elapsed since acquisition of the plurality of elements began.

[0093] (Supplementary Note 6) The information processing method described in Supplementary Note 6 is an information processing method that, by at least one computer, acquires multiple elements included in sequence data, inputs the multiple elements and calculates the relationships between the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs, calculates a score by adding a value corresponding to an urgency signal to the likelihood ratio, and classifies the sequence data into at least one class of multiple candidate classes based on the score.

[0094] (Supplementary Note 7) The recording medium described in Supplementary Note 7 is a recording medium having recorded thereon a computer program for causing at least one computer to execute an information processing method, which includes acquiring multiple elements included in sequence data, inputting the multiple elements and calculating the relationships among the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs, calculating a score by adding a value corresponding to an urgency signal to the likelihood ratio, and classifying the sequence data into at least one class out of multiple candidate classes based on the score.

[0095] (Supplementary Note 8) The computer program described in Supplementary Note 8 is a computer program that causes at least one computer to execute an information processing method that acquires multiple elements included in sequence data, inputs the multiple elements and calculates relationships among the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs, calculates a score by adding a value corresponding to an urgency signal to the likelihood ratio, and classifies the sequence data into at least one class of multiple candidate classes based on the score.

[0096] (Supplementary Note 9) The information processing system described in Supplementary Note 9 is an information processing system including: an acquisition means for acquiring a plurality of elements included in sequence data; a likelihood ratio calculation means for inputting the plurality of elements and calculating the relationship between the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs; a score calculation means for adding a value corresponding to an urgency signal to the likelihood ratio to calculate a score; and a classification means for classifying the sequence data into at least one class of a plurality of candidate classes based on the score.

[0097] This disclosure may be modified as appropriate within the scope that does not contradict the gist or idea of ​​the invention that can be read from the claims and the entire specification, and information processing devices, information processing methods, and recording media that involve such modifications are also included in the technical idea of ​​this disclosure.

[0098] REFERENCE SIGNS LIST 10 Information processing device 50 Data acquisition unit 100 Likelihood ratio calculation unit 110 First calculation unit 111 Individual likelihood ratio calculation unit 112 First storage unit 120 Second calculation unit 121 Integrated likelihood ratio calculation unit 122 Second storage unit 150 Score calculation unit 200 Classification unit 310 Urgency determination unit 320 Urgency signal generation unit

Claims

1. an acquisition means for acquiring a plurality of elements included in the sequence data; a likelihood ratio calculation means for inputting the plurality of elements and calculating a relationship between the elements to calculate a likelihood ratio indicating the likelihood of a class to which the sequence data belongs; a score calculation means for calculating a score by adding a value corresponding to an urgency signal to the likelihood ratio; a classification means for classifying the sequence data into at least one class out of a plurality of candidate classes based on the score; An information processing device comprising:

2. a maximum time allowed from the start of acquisition of the plurality of elements to the classification of the sequence data is determined in advance; The information processing device according to claim 1 .

3. a determination unit that determines whether or not an emergency situation exists in which the sequence data may not be classified before the maximum time has elapsed, the score calculation means, when the emergency state exists, adds a value corresponding to the urgency signal to the likelihood ratio, and when the emergency state does not exist, does not add a value corresponding to the urgency signal to the likelihood ratio; The information processing device according to claim 2 .

4. Further, a generating means is provided for generating a value according to the urgency signal using a trained model. The information processing device according to claim 1 .

5. The trained model is trained using a loss function plus a value corresponding to the time elapsed since acquisition of the plurality of elements began. The information processing device according to claim 4 .

6. by at least one computer, Obtain multiple elements contained in the sequence data, calculating a likelihood ratio indicating the likelihood of a class to which the sequence data belongs by inputting the plurality of elements and calculating the relationship between the elements; calculating a score by adding a value corresponding to an urgency signal to the likelihood ratio; classifying the sequence data into at least one class from a plurality of candidate classes based on the score; Information processing methods.

7. At least one computer Obtain multiple elements contained in the sequence data, calculating a likelihood ratio indicating the likelihood of a class to which the sequence data belongs by inputting the plurality of elements and calculating the relationship between the elements; calculating a score by adding a value corresponding to an urgency signal to the likelihood ratio; classifying the sequence data into at least one class from a plurality of candidate classes based on the score; A computer program that executes an information processing method.