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

Technical Field

[0001] This disclosure relates to the technical field of information processing apparatuses, information processing methods, and recording media.

Background Art

[0002] As this type of apparatus, there is known one that classifies sequence data using a likelihood ratio. For example, Patent Document 1 discloses classifying sequence data into any one of a plurality of predetermined classes by sequentially acquiring and analyzing a plurality of elements included in the sequence data. Further, Patent Document 2 discloses determining a threshold value for a score every time a plurality of elements are acquired.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0004] This disclosure aims to improve the related technologies described above.

Means for Solving the Problems

[0005] One aspect of the information processing apparatus of this disclosure includes an acquisition unit that acquires a plurality of elements included in sequence data, a likelihood ratio calculation unit that calculates a likelihood ratio indicating the likelihood of the class to which the sequence data belongs by inputting the plurality of elements and calculating the relationship between the elements, a score calculation unit that calculates a score by adding a value corresponding to an urgency signal to the likelihood ratio, and a classification unit that classifies the sequence data into at least one class among a plurality of classification candidate classes based on the score.

[0006] One aspect of the information processing method of this disclosure involves using at least one computer to acquire a plurality of elements contained in a sequence of data, inputting the plurality of elements and calculating the relationship between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequence of data belongs, adding a value corresponding to the urgency signal to the likelihood ratio to calculate a score, and classifying the sequence of data into at least one of a plurality of candidate classes based on the score.

[0007] One aspect of the recording medium of this disclosure includes recording a computer program that causes at least one computer to execute an information processing method, which involves acquiring a plurality of elements contained in sequential data, inputting the plurality of elements and calculating the relationships between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequential data belongs, adding a value corresponding to an urgency signal to the likelihood ratio to calculate a score, and classifying the sequential data into at least one of a plurality of classes that are classification candidates based on the score. [Brief explanation of the drawing]

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

[0009] The following describes embodiments of the information processing device, information processing method, and recording medium with reference to the drawings.

[0010] <First Embodiment> The information processing device according to the first embodiment will be described with reference to Figures 1 to 5.

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

[0012] As shown in FIG. 1, the information processing apparatus 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 apparatus 10 may further include an input device 15 and an output device 16. The above-described processor 11, RAM 12, ROM 13, storage device 14, input device 15, and output device 16 are each connected via a data bus 17.

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

[0014] Processor 11 may be configured as, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), an FPGA (field-programmable gate array), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), or a quantum processor. Processor 11 may be composed of one of these, or may be configured to use a plurality of them in parallel.

[0015] RAM 12 temporarily stores the computer programs executed by processor 11. RAM 12 temporarily stores the data that processor 11 temporarily uses when executing a computer program. RAM 12 may be, for example, a D-RAM (Dynamic Random Access Memory) or an SRAM (Static Random Access Memory). Also, instead of RAM 12, other types of volatile memory may be used.

[0016] ROM 13 stores the computer programs executed by processor 11. ROM 13 may also store other fixed data. ROM 13 may be, for example, a P-ROM (Programmable Read Only Memory) or an EPROM (Erasable Read Only Memory). Also, instead of ROM 13, other types of non-volatile memory may be used.

[0017] Storage device 14 stores the data that information processing device 10 stores long-term. Storage device 14 may operate as a temporary storage device for processor 11. 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 the 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 tablet. The input device 15 may also be a device capable of voice input, for example, including a microphone.

[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) capable of displaying information related to the information processing device 10. Alternatively, the output device 16 may be a speaker or the like capable of outputting information related to the information processing device 10 as sound. The output device 16 may also be configured as a mobile terminal such as a smartphone or tablet.

[0020] Although Figure 1 shows an example of an information processing device 10 comprising multiple devices, all or some of these functions may be implemented in a single device. Such an information processing device may consist only of the processor 11, RAM 12, and ROM 13 described above, with other components (i.e., storage device 14, input device 15, output device 16, etc.) provided by external devices connected to the information processing device 10. Furthermore, the information processing device 10 may implement some of its computational functions through external devices (e.g., external servers or cloud services).

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

[0022] As shown in Figure 2, the information processing device 10 according to the first embodiment is a device that classifies input sequence data, and is configured to realize this function by comprising 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 processor 11 (see Figure 1) described above.

[0023] The data acquisition unit 50 is configured to acquire multiple elements included in sequential data. The data acquisition unit 50 may acquire data directly from any data acquisition device (e.g., a camera or microphone), or it may read data that has been acquired in advance by a data acquisition device and stored in storage. 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 sequential data acquired by the data acquisition unit 50 are output to the likelihood ratio calculation unit 100. Sequential data is data that includes multiple elements arranged in a predetermined order, and time series data is one example. More specific examples of sequential 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 calculate the likelihood ratio based on multiple elements acquired by the data acquisition unit 50. The likelihood ratio calculation unit 100 may be configured to calculate the likelihood ratio based on the relationship between at least two consecutive elements among the acquired multiple elements. Here, "likelihood ratio" is an index that indicates the likelihood of the class to which the sequence data belongs. Specific examples of the likelihood ratio and specific calculation methods will be explained in detail in other embodiments described later. The likelihood ratio calculated by the likelihood ratio calculation unit 100 is then output to the score calculation unit 150.

[0025] The score calculation unit 150 is configured 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 calculate a score by adding a value corresponding to the urgency signal to the likelihood ratio. Here, the "urgency signal" is a signal indicating the urgency of the response, and is used, for example, when making an early decision (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] The score calculation unit 150 calculates a higher score than when simply calculating the score from the likelihood ratio by adding the aforementioned urgency signal to the likelihood ratio. In other words, the urgency signal according to this embodiment is used to increase the calculated score. The score calculation unit 150 may calculate the score by, for example, 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. In other words, the process of "adding" the urgency signal according to this embodiment can be any calculation process that uses the urgency signal to increase the score calculated from the likelihood ratio.

[0028] The value corresponding to the emergency signal may be calculated using, for example, a fixed functional form. However, the functional form is not particularly limited and may be a constant, linear, or nonlinear. The value corresponding to the emergency signal may be input from outside the information processing device 10, or it may be stored inside the information processing device 10. Alternatively, the value corresponding to the emergency signal may be generated appropriately within the information processing device 10. The configuration for generating the value corresponding to the emergency signal will be described in detail in other embodiments described later.

[0029] The class 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 class classification unit 200 selects at least one class to which the sequential data belongs from among a plurality of classification candidate classes. The plurality of classification candidate classes may be pre-set. Alternatively, the plurality of classification candidate classes may be set as appropriate by the user, or as appropriate based on the type of sequential data being handled. For example, the plurality of classes may be set as a class indicating that the face included in the video data is a real face (i.e., a living face) and a class indicating that it is a fake face (e.g., a face from a photograph or 3D mask). In this way, the information processing device 10 can be used as a spoofing detection device. Alternatively, the plurality of classes may be set as a class indicating that the object included in the video taken from a moving object such as a car is an obstacle to be avoided and a class indicating that it is an object other than an obstacle. In this case, the information processing device 10 can be used as an obstacle detection device. Furthermore, the number of classes that are candidates for classification is not limited to two; there may be three or more classes.

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

[0031] As shown in Figure 3, when the information processing device 10 starts operating, the data acquisition unit 50 first acquires the elements included in the sequence data (step S11). The data acquisition unit 50 outputs the acquired elements of the sequence data to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates the likelihood ratio from the relationships between the multiple acquired elements (step S12).

[0032] Next, the score calculation unit 150 adds the 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). Then, the classification unit 200 performs classification based on the calculated score (step S14). Classification may determine one class to which the series data belongs, or it may determine multiple classes to which the series data is likely to belong.

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

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

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

[0036] As shown in Figure 4, the class classification unit 200 may determine the class to classify when the calculated score reaches a predetermined judgment threshold. In this embodiment, the information processing device 10 calculates the score by adding the urgency signal to the likelihood ratio, so the score increases relatively rapidly. Therefore, the score calculated in this embodiment reaches the judgment threshold at a relatively early time (time T1 in the figure). On the other hand, in the comparative example, the information processing device 10 calculates the score without adding the urgency signal to the likelihood ratio. Therefore, the score increases relatively slowly. Therefore, the score calculated in the comparative example reaches the judgment threshold at a later time (time T2 in the figure) than in this embodiment. Thus, according to the information processing device 10 of this embodiment, early class classification can be achieved using a score to which the urgency signal has been added.

[0037] As shown in Figure 5, the information processing device 10 may have a maximum allowable time Tmax before class classification is performed. For example, when performing class classification using video of a person passing in front of a camera, it is required that the classification be completed before the person leaves the camera's field of view. In such a case, in the comparative example where the urgency signal is not added to the likelihood ratio, the score may not reach the judgment threshold by the maximum time Tmax. That is, the information processing device 10 in the comparative example may not be able to perform class classification properly. However, in this embodiment, since the score is calculated by adding the urgency signal to the likelihood ratio, the score reaches the judgment threshold at time T3, which is before the maximum time Tmax. Thus, according to the information processing device 10 of this embodiment, sequential data can be appropriately classified even when there is a limit to the time allowed for class classification.

[0038] Another possible method to obtain similar results is to vary the judgment threshold according to the elapsed time (i.e., to use a dynamic threshold). However, determining the optimal value for the dynamic threshold requires precise calculations (for example, calculating the likelihood ratio up to the maximum time Tmax and then working backward). In this case, the calculation to set the optimal dynamic threshold takes time, resulting in a longer time to perform classification. However, in this embodiment, since the urgency signal is simply added to the likelihood ratio, classification can be achieved earlier than when using a dynamic threshold.

[0039] <Second Embodiment> The information processing device 10 according to the second embodiment will be described with reference to Figures 6 and 7. Note that the second embodiment differs from the first embodiment described above only in some configurations and operations; other parts may be the same as those of the first embodiment. Therefore, the parts that differ from the first embodiment will be described in detail below, while other overlapping parts will be omitted 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 Figure 6. Figure 6 is a block diagram showing the functional configuration of the information processing device according to the second embodiment. Note that in Figure 6, the same reference numerals are used for elements similar to those shown in Figure 2.

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

[0042] The urgency determination unit 310 is configured to determine whether the current state is an emergency state. Here, an "emergency state" is a state in which it is possible that the sequence data cannot be classified before the maximum time Tmax has elapsed (i.e., the score will not reach the determination threshold). Whether or not it is an emergency state 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 it is an emergency state using the value of the likelihood ratio or the rate of change. The determination result by the urgency determination unit 310 is 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 in this case will be explained in detail below.

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

[0044] As shown in Figure 7, when the information processing device 10 starts operating, the data acquisition unit 50 first acquires the elements included in the sequential data (step S11). The data acquisition unit 50 outputs the acquired elements of the sequential data to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates the likelihood ratio from the relationships between the multiple acquired elements (step S12).

[0045] Next, the urgency determination unit 310 determines whether or not there is an emergency situation based on the calculated likelihood ratio, etc. (step S15). If it is determined that there is an emergency situation (step S15: YES), the score calculation unit 150 adds the 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 there is no emergency situation (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] Subsequently, the classification unit 200 performs classification based on the calculated score (step S14). That is, if it is an emergency situation, the classification unit 200 performs classification using the score with the urgency signal added. On the other hand, if it is not an emergency situation, the classification unit 200 performs classification using the score without the urgency signal added. If it is not determined to be an emergency situation at the initial stage but is determined to be an emergency situation later, the score without the urgency signal added up to that point should be used, and then the score with the urgency signal added after it is determined to be an emergency situation should be used.

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

[0048] As explained in Figures 6 and 7, in the information processing device 10 according to the second embodiment, if an emergency state is determined, a value corresponding to the emergency signal is added to calculate the score, and if it is determined that the state is not an emergency, the value corresponding to the emergency is not added to calculate the score. In this way, it is possible to suppress the unnecessary addition of the emergency signal and to achieve more appropriate class classification.

[0049] <Third Embodiment> The information processing device 10 according to the third embodiment will be described with reference to Figures 8 and 9. Note that the third embodiment differs from the first and second embodiments only in some configurations and operations; other parts may be the same as those of the first embodiment. Therefore, the following will provide detailed explanations of the parts that differ from the embodiments already described, while explanations of other overlapping parts will be omitted 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 Figure 8. Figure 8 is a block diagram showing the functional configuration of the information processing device according to the third embodiment. Note that in Figure 8, the same reference numerals are used for elements similar to those shown in Figure 2.

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

[0052] The urgency signal generation unit 320 is configured to generate values ​​corresponding to urgency signals using a trained model. That is, the urgency signal generation unit 320 is configured to generate values ​​corresponding to urgency that the score calculation unit 150 uses when calculating a score. The trained model may include, for example, a neural network. The trained model may be one that takes values ​​such as likelihood ratios as input and outputs values ​​corresponding to urgency signals. The trained model may have been machine-learned using pre-prepared training data.

[0053] For example, a trained model may be one that has been trained using a loss function that includes a value corresponding to the time elapsed since the start of acquiring multiple elements. More specifically, a trained model may be one that has been trained using the loss function LOSS shown in equation (1) below.

[0054]

number

[0055] Here, "t" represents the current time (i.e., the time elapsed since the start of element acquisition). "const." is a predetermined constant, which can be set by the user or others as appropriate, for example, depending on how quickly the classification needs to be performed. By using the loss function LOSS described above, a model can be trained that can appropriately generate values ​​corresponding to the urgency signal.

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

[0057] As shown in Figure 9, when the information processing device 10 starts operating, the data acquisition unit 50 first acquires the elements included in the sequential data (step S11). The data acquisition unit 50 outputs the acquired elements of the sequential data to the likelihood ratio calculation unit 100. The likelihood ratio calculation unit 100 then calculates the likelihood ratio from the relationships between the multiple acquired elements (step S12).

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

[0059] Subsequently, the classification unit 200 performs classification based on the calculated score (step S14). Classification may determine one class to which the series data belongs, or it may determine multiple classes to which the series data is likely to belong.

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

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

[0062] <Fourth Embodiment> The information processing device 10 according to the fourth embodiment will be described with reference to Figures 10 and 11. The fourth embodiment differs from the first to third embodiments described above only in some configurations and operations; other parts may be the same as those of the first to third embodiments. Therefore, the following will explain in detail the parts that differ from the embodiments already described, while omitting explanations 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 Figure 10. Figure 10 is a block diagram showing the functional configuration of the information processing device according to the fourth embodiment. Note that in Figure 10, the same reference numerals are used for elements as in Figure 2.

[0064] As shown in Figure 10, the information processing device 10 according to the fourth embodiment is configured to include a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, and a class classification unit 200 as components for realizing its functions. 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 implemented, for example, by the processor 11 (see Figure 1) described above.

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

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

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

[0068] As shown in Figure 11, when the operation of the information processing device 10 according to the fourth embodiment is started, the data acquisition unit 50 first acquires the 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] Then, the first calculation unit 110 calculates individual likelihood ratios based on two consecutive elements obtained (step S22). Subsequently, the second calculation unit 120 calculates a combined 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 combined likelihood ratio (step S24). Then, the classification unit 200 performs classification based on the calculated combined likelihood ratio (step S25). The classification may determine one class to which the series data belongs, or it may determine multiple classes to which the series data is likely to belong.

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

[0072] As explained in Figures 10 and 11, in the information processing device 10 according to the fourth embodiment, individual likelihood ratios are first calculated based on two elements, and then a combined likelihood ratio is calculated based on multiple individual likelihood ratios. Using the combined likelihood ratio calculated in this way, it is possible to appropriately select the class to which the sequence data belongs. Furthermore, in the information processing device 10 according to the fourth embodiment, a score is calculated by adding an urgency signal, which enables early class classification.

[0073] <Fifth Embodiment> The information processing device 10 according to the fifth embodiment will be described with reference to Figures 12 and 13. Note that the fifth embodiment differs from the fourth embodiment described above only in some configurations and operations; other parts may be the same as those of the fourth embodiment. Therefore, the following will explain in detail the parts that differ from each embodiment already described, and will omit explanations 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 Figure 12. Figure 12 is a block diagram showing the functional configuration of the information processing device according to the fifth embodiment. In Figure 12, the same reference numerals are used for elements similar to those shown in Figure 10.

[0075] As shown in Figure 12, the information processing device 10 according to the fifth embodiment is configured to include a data acquisition unit 50, a likelihood ratio calculation unit 100, a score calculation unit 150, and a class classification unit 200 as components for realizing its functions. The likelihood ratio calculation unit 100 includes a first calculation unit 110 and a second calculation unit 120, similar to the fourth embodiment described above. In the fifth embodiment, the first calculation unit 110 is configured to include an individual likelihood ratio calculation unit 111 and a first storage unit. The second calculation unit 120 is configured to include an integrated likelihood ratio calculation unit 121 and a second storage unit 122. Note that the individual likelihood ratio calculation unit 111 and the integrated likelihood ratio calculation unit 121 may each be processing blocks realized by, for example, the processor 11 (see Figure 1) described above. Also, the first storage unit 112 and the second storage unit 122 may each be realized by, for example, the storage device 14 (see Figure 1) described above.

[0076] The individual likelihood ratio calculation unit 111 is configured to calculate an individual likelihood ratio based on two consecutive elements from 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 the newly acquired elements 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. If the first storage unit 112 stores past individual likelihood ratios, the individual likelihood ratio calculation unit 111 can read the stored past individual likelihood ratios and calculate a new individual likelihood ratio that takes into account the acquired elements. On the other hand, if the first storage unit 112 stores the elements themselves that were acquired in the past, the individual likelihood ratio calculation unit 111 can calculate the past individual likelihood ratios from the stored past elements and calculate the likelihood ratio for the newly acquired elements.

[0077] The integrated likelihood ratio calculation unit 121 is configured to calculate an integrated likelihood ratio based on multiple 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, with reference to Figure 13, the flow of the likelihood ratio calculation operation in the information processing device 10 according to the fifth embodiment (i.e., the operation when the likelihood ratio calculation unit 100 calculates the likelihood ratio) will be described. Figure 13 is a flowchart showing the flow of the likelihood ratio calculation operation in the information processing device according to the fifth embodiment.

[0079] As shown in Figure 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 of the individual likelihood ratio calculation unit 111 of the element acquired by the data acquisition unit 50 immediately before the element acquired this time (in other words, the individual likelihood ratio calculated for the previous element). Alternatively, the past data may be the element acquired immediately before the element acquired this time.

[0080] Next, the individual likelihood ratio calculation unit 111 calculates a new individual likelihood ratio (i.e., the individual likelihood ratio for the element acquired this time by the data acquisition unit 50) based on the elements acquired by the data acquisition unit 50 and 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 also 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 past integrated likelihood ratios from the second storage unit 122 (step S33). Past integrated likelihood ratios may be, for example, the processing result of the integrated likelihood ratio calculation unit 121 for the element acquired by the data acquisition unit 50 immediately before the element acquired this time (in other words, the integrated likelihood ratio calculated for the previous element).

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

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

[0084] As explained in Figures 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 a combined likelihood ratio is calculated using past combined likelihood ratios. By using the combined likelihood ratio calculated in this way, it becomes possible to appropriately select the class to which the sequence data belongs. Furthermore, in the information processing device 10 according to the fifth embodiment, as described above, an urgency signal is added to calculate the score, so early class classification can be achieved.

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

[0086] As recording media, for example, floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, magnetic tapes, non-volatile memory cards, and ROMs can be used. Furthermore, the scope of each embodiment is not limited to programs that perform processing on the recording media alone, but also includes programs that operate on the OS and perform processing in cooperation with other software and the functions of expansion boards. In addition, the program itself may be stored on a server, and part or all of the program may be made available for download from the server to the user terminal. The program may be provided to the user in, for example, SaaS (Software as a Service) format.

[0087] <Note> The embodiments described above may also be described in the following appendix, but are not limited to these.

[0088] (Note 1) The information processing device described in Appendix 1 is an information processing device comprising: an acquisition means for acquiring multiple elements included in sequential 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 the class to which the sequential 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 sequential data into at least one of a plurality of classification candidate classes based on the score.

[0089] (Note 2) The information processing device described in Appendix 2 is the information processing device described in Appendix 1, wherein the maximum allowable time from the start of acquiring the multiple elements to classifying the sequence data is predetermined.

[0090] (Note 3) The information processing device described in Appendix 3 further comprises a determination means for determining whether or not there is an emergency state in which it may not be possible to classify the sequence data before the maximum time elapses, and the score calculation means adds a value corresponding to the urgency signal to the likelihood ratio when there is an emergency state, and does not add a value corresponding to the urgency signal to the likelihood ratio when there is no emergency state, as described in Appendix 2.

[0091] (Note 4) The information processing device described in Appendix 4 is the information processing device described in any one of Appendix 1 to 3, further comprising a generation means for generating a value corresponding to the emergency signal using a trained model.

[0092] (Note 5) The information processing device described in Appendix 5 is the information processing device described in Appendix 4, wherein the trained model is trained using a loss function that adds a value corresponding to the time elapsed since the start of acquiring the multiple elements.

[0093] (Note 6) The information processing method described in Appendix 6 is an information processing method which involves using at least one computer to acquire multiple elements contained in sequential data, inputting the multiple elements and calculating the relationship between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequential data belongs, adding a value corresponding to the urgency signal to the likelihood ratio to calculate a score, and classifying the sequential data into at least one of multiple candidate classes based on the score.

[0094] (Note 7) The recording medium described in Appendix 7 is a recording medium on which a computer program is recorded that causes at least one computer to execute an information processing method that involves acquiring multiple elements included in sequential data, inputting the multiple elements and calculating the relationships between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequential data belongs, adding a value corresponding to the urgency signal to the likelihood ratio to calculate a score, and classifying the sequential data into at least one of multiple candidate classes based on the score.

[0095] (Note 8) The computer program described in Appendix 8 is a computer program that causes at least one computer to execute an information processing method which involves acquiring multiple elements contained in sequential data, inputting the multiple elements and calculating the relationships between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequential data belongs, adding a value corresponding to the urgency signal to the likelihood ratio to calculate a score, and classifying the sequential data into at least one of a plurality of candidate classes based on the score.

[0096] (Note 9) The information processing system described in Appendix 9 is an information processing system comprising: an acquisition means for acquiring multiple elements included in sequential data; a likelihood ratio calculation means for inputting the multiple elements and calculating the relationships between each element to calculate a likelihood ratio indicating the likelihood of the class to which the sequential 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 sequential data into at least one of a plurality of classification candidate classes based on the score.

[0097] This disclosure may be modified as appropriate, insofar as it does not contradict the gist or idea of ​​the invention as can be inferred from the claims and the specification as a whole, and information processing devices, information processing methods, and recording media with such modifications are also included in the technical idea of ​​this disclosure. [Explanation of symbols]

[0098] 10 Information Processing Devices 50 Data Acquisition Unit 100 Likelihood Ratio Calculation Unit 110 First Calculation Unit 111 Individual Likelihood Ratio Calculation Unit 112 1st memory section 120 Second Calculation Unit 121 Integrated Likelihood Ratio Calculation Unit 122 2nd memory section 150 Score Calculation Unit 200 Classification Section 310 Urgency Judgment Department 320 Urgency signal generation section

Claims

1. A means for obtaining multiple elements included in sequential data, A likelihood ratio calculation means calculates a likelihood ratio indicating the likelihood of the class to which the series data belongs by inputting the aforementioned multiple elements and calculating the relationship between each element, A score calculation means that calculates a score by adding a value corresponding to the urgency signal to the likelihood ratio, A classification means that classifies the sequence data into at least one of several classes that are classification candidates based on the score, An information processing device equipped with the following features.

2. The maximum allowable time from the start of acquiring the aforementioned multiple elements to the classification of the series data is predetermined. The information processing apparatus according to claim 1.

3. The system further includes a determination means for determining whether or not it is an emergency situation in which it may not be possible to classify the series data before the aforementioned maximum time has elapsed. The score calculation means adds a value corresponding to the emergency signal to the likelihood ratio when the emergency situation is present, and does not add a value corresponding to the emergency signal to the likelihood ratio when the emergency situation is not present. The information processing apparatus according to claim 2.

4. The system further comprises a generation means for generating a value corresponding to the emergency signal using a trained model. The information processing apparatus according to any one of claims 1 to 3.

5. The aforementioned trained model is trained using a loss function that includes a value corresponding to the time elapsed since the start of acquiring the multiple elements. The information processing apparatus according to claim 4.

6. By at least one computer, Retrieve multiple elements contained in the series data, By inputting the aforementioned multiple elements and calculating the relationships between each element, a likelihood ratio indicating the likelihood of the class to which the series data belongs is calculated. The score is calculated by adding a value corresponding to the urgency signal to the aforementioned likelihood ratio. Based on the aforementioned score, the sequence data is classified into at least one of several candidate classes. Information processing methods.

7. On at least one computer, Retrieve multiple elements contained in the series data, By inputting the aforementioned multiple elements and calculating the relationships between each element, a likelihood ratio indicating the likelihood of the class to which the series data belongs is calculated. The score is calculated by adding a value corresponding to the urgency signal to the aforementioned likelihood ratio. Based on the aforementioned score, the sequence data is classified into at least one of several candidate classes. A computer program that executes information processing methods.

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