Information processing device, factor estimation method, and program

Abstract

This information processing device comprises: a first processing unit that acquires, from a communication system, a plurality of time-varying feature quantities and generates, for each combination of two feature quantities, a relational feature quantity representing the relationship between the feature quantities; a second processing unit that estimates whether a predetermined event has occurred on the basis of the relational feature quantities; and a third processing unit that estimates a factor of the predetermined event on the basis of one or more relational feature quantities serving as a basis for an estimation result indicating the occurrence of the predetermined event.

Description

Information Processing Apparatus, Factor Estimation Method, and Program

[0001] The present invention relates to a technique for estimating a factor when the communication quality of an application deteriorates in a system that transmits application data via a communication network.

[0002] In recent years, the development of autonomous driving technology has advanced, and level 4 highway driving in which the system assumes the driving entity is being realized under specific conditions. On the other hand, due to legal and safety reasons, remote monitoring by video is always required during autonomous driving, and it is necessary to transmit the video clearly and without interruption via a mobile network with unstable quality.

[0003] In order to realize stable video transmission, application-specific controls such as line quality prediction, line control using a plurality of communication networks, and dynamic control of bit rate and frame rate in the case of video are simultaneously and comprehensively implemented.

[0004] When control is implemented in a plurality of layers, when the quality of the application layer deteriorates, it may be a complex factor caused by the control of the plurality of layers, and it is difficult to immediately identify the factor manually and change to appropriate control.

[0005] As a conventional technique for estimating a factor, Non-Patent Document 1 discloses a technique for calculating the importance of input features of machine learning and estimating a factor based on the importance.

[0006] In the conventional technique disclosed in Non-Patent Document 1, in a video streaming service, regarding the factor when video viewing is stopped based on feature amounts such as application quality and user behavior, it is determined whether it is due to the quality of the streaming video or due to the content (inconsistency with the user's preference).

[0007] Specifically, the causal relationship between feature amounts is analyzed manually, and by performing the analysis using Asymmetric Shapley Value that can calculate the importance of feature amounts in consideration of the causal relationship, the feature amounts (factors) that have affected the classification of machine learning are analyzed.

[0008] Sosa Akimoto,et al. Quantitative causality analysis of viewing abandonment reasons using Shapley value

[0009] As described above, conventional techniques manually analyze causal relationships between features, but they assume that these relationships are fixed across all features and do not change over time. Furthermore, conventional techniques cannot consider the time-series nature of multiple logs, making it impossible to analyze the factors involved when classifying KPIs (Key Performance Indicators, such as quality-related or content-related video interruptions) by capturing the temporal changes in input features that occurred in the past.

[0010] Therefore, when conventional technologies are applied to use cases such as the degradation of application quality in remote control of autonomous driving, it is expected that the contributing factors will not be able to be analyzed effectively.

[0011] This invention has been made in view of the above points, and aims to provide a technology that enables factor analysis of a predetermined event, taking into account changes in the relationships between feature quantities over time.

[0012] According to the disclosed technology, an information processing device is provided, comprising: a first processing unit that acquires multiple feature quantities that change over time from a communication system and generates relational feature quantities that express the relationship between the feature quantities for each combination of two feature quantities; a second processing unit that estimates whether or not a predetermined event has occurred based on the relational feature quantities; and a third processing unit that estimates the factors of the predetermined event based on one or more relational feature quantities that formed the basis of the estimation result of whether or not the predetermined event has occurred.

[0013] The disclosed technology provides a method that enables factor analysis of a given event, taking into account changes in the relationships between features over time.

[0014] This is a diagram illustrating the definition of a factor. This is a diagram illustrating the problems in the prior art. This is a diagram illustrating the image of factor analysis in the prior art. This is a diagram illustrating the image of factor analysis in an embodiment of the present invention. This is a diagram illustrating an example of the functional configuration of the information processing device 100. This is a diagram illustrating the image of the processing of the pre-processing unit 110. This is a flowchart illustrating the operation of the pre-processing unit 110. This is a diagram illustrating the image of the processing performed by the pre-processing unit 110. This is a diagram illustrating the image of the processing of the importance calculation unit 130. This is a flowchart illustrating the operation of the post-processing unit 140. This is a diagram illustrating a specific example of the processing of the post-processing unit 140. This is a diagram illustrating an example of the hardware configuration of the device.

[0015] Hereinafter, embodiments of the present invention (this embodiment) will be described with reference to the drawings. The embodiments described below are merely examples, and the embodiments to which the present invention is applied are not limited to the embodiments described below.

[0016] In the following embodiments, the technology for analyzing the causes of video interruptions in remote monitoring will be described, but video interruptions are merely one example. The technology according to the present invention is applicable to a variety of phenomena, not limited to video interruptions.

[0017] In the following, we will first explain the definition of the factors in this embodiment, then describe the problems in more detail, and then describe the technology related to this embodiment in detail.

[0018] (Definition of Factors) First, referring to Figure 1, we will explain the definition of factors for the interruption (stoppage) of remote monitoring video. As shown in Figure 1, a factor is the earliest event in a causal relationship. Video interruption occurs through a chain of factors and influencing events. Note that "events" also include "settings".

[0019] In other words, a contributing factor is the initial event that caused the video to freeze, and resolving or avoiding it will reduce the likelihood of the video freezing to the greatest extent possible.

[0020] The influencing events shown in Figure 1 are events that have a causal relationship with a factor (events caused by the factor). Examples of influencing events include increased latency, increased packet loss rate, and decreased bitrate.

[0021] (Regarding the problem) As mentioned above, in the prior art disclosed in Non-Patent Document 1, the causal relationships between features are manually analyzed and defined in the stage before constructing the machine learning model. The importance of the features (factors in classification and prediction results) is calculated based on the assumption that the relationships in these causal relationships are fixed across all features, regardless of the passage of time.

[0022] In addition, the prior art disclosed in Non-Patent Document 1 does not take into account the time-series nature of multiple logs, so when classifying KPIs (for example, quality-related / content-related determination tasks or video interruptions), it is not possible to capture changes in input features that have occurred in the past and use them as factors.

[0023] On the other hand, in the case of application quality (video quality) degradation in autonomous driving remote control systems, the relationships between input features may change over time. For example, at certain times, increased latency may be caused by deterioration of wireless quality, leading to video interruptions, while at other times, increased latency may be caused by inappropriate settings of the video bitrate, leading to video interruptions.

[0024] Furthermore, when implementing control using multiple layers, fluctuations in values ​​occur in a chain reaction between multiple logs (=input features), meaning that changes in input features may have occurred in the past before the KPI classification is performed. For example, if latency increases and video is interrupted after a decrease in wireless quality, the decrease in wireless quality → video interruption occurs within a few milliseconds to a few seconds. If the system cannot tolerate a delay of a few seconds in events, it may not be able to identify factors such as "decreased wireless quality." However, as mentioned above, the conventional technology disclosed in Non-Patent Document 1 does not take into account the time-series nature of multiple logs, so when classifying KPIs, it cannot capture changes in input features that occurred in the past and use them as factors. Therefore, when this conventional technology is applied to use cases such as the deterioration of application quality in remote control of autonomous driving, it is expected that the factors cannot be analyzed effectively.

[0025] Refer to Figure 2 to explain the challenges in conventional technology in more detail. Figure 2 shows examples of the relationship between features (events) and KPIs (relationships focusing on time), including "simultaneous," "time-direction shift," "chained," "composite," and "chained + composite."

[0026] "Simultaneous" indicates that feature 3 and KPI occurred at the same time. "Time-shifted" indicates that KPI occurred after a certain amount of time had passed since feature 3 occurred. "Chain" indicates that there is a chain reaction: feature 1 -> feature 2 -> feature 3 -> KPI. "Combined" indicates that both feature 1 and feature 3 are factors in KPI. "Chain + Combined" indicates that both chaining and combined reactions are occurring.

[0027] Conventional technology cannot account for relationships that change over time. Therefore, while conventional technology can handle "simultaneous" events, it struggles to handle other types of relationships. However, for "time-direction shifts," conventional technology may be able to handle them if the shift magnitude is small. For this reason, it is indicated with a "△" in Figure 2.

[0028] (Outline of the Embodiment) In order to solve the above problems, in this embodiment, the information processing device 100, which will be described later, performs preprocessing, prediction of video interruption, calculation of the degree of contribution (importance) to the video interruption, and postprocessing. Of these processes, for the prediction of video interruption and the calculation of the degree of contribution to the video interruption, existing technologies disclosed in Non-Patent Document 1, for example, can be used.

[0029] In preprocessing, the information processing device 100 analyzes the relationships between input features (logs obtained from the system) for the machine learning model used to predict video interruptions in a time series, and expresses the results as vectors. In postprocessing, the information processing device 100 estimates the factors based on the output of the machine learning model and the importance of the vectors generated in preprocessing.

[0030] For comparison, Figure 3 shows an image of the factor analysis of the conventional technology. Figure 4 shows an image of the factor analysis in this embodiment.

[0031] As illustrated in Figure 4, in the preprocessing stage, the information processing device 100 obtains subvectors from the feature quantities in time units, determines whether there is a relationship between each pair of feature quantities, and determines the value of the generated vector according to the determination result. This process is repeated while sliding the acquisition position of the subvectors in the time direction.

[0032] In post-processing, the information processing device 100 performs a threshold-based determination of the importance of the vectors generated in pre-processing, and analyzes the factors from the content of the vectors that remain after the threshold determination.

[0033] The configuration and operation of the information processing device 100 will be described in detail below.

[0034] (Example of Functional Configuration of Information Processing Device 100) Figure 5 shows an example of the functional configuration of the information processing device 100 in this embodiment. As shown in Figure 5, the information processing device 100 has a pre-processing unit 110, an estimation unit 120, an importance calculation unit 130, and a post-processing unit 140.

[0035] The pre-processing unit 110 may also be called the first processing unit. The estimation unit 120 may also be called the second processing unit. The "importance calculation unit 130 + post-processing unit 140" may also be called the third processing unit. The general operation of each unit is as follows.

[0036] The preprocessing unit 110 performs causal relationship inference in a time series as a preprocessing step. Specifically, the preprocessing unit 110 performs the processing described later on the logs (features) acquired from the autonomous driving remote control system and generates a vector that represents the relationships between the features. The autonomous driving remote control system is an example of a communication system.

[0037] The prediction unit 120 performs prediction of video interruptions. Specifically, the prediction unit 120 is a time-series machine learning model that learns a vector formed by combining the vector generated in preprocessing with KPI (video interruption information), using vectors other than KPI as explanatory variables and the KPI vector as the target variable. The prediction unit 120 classifies or predicts video interruptions as binary (interruption present / interruption absent) and outputs the result. In this embodiment, a neural network such as RNN or LSTM is used as the machine learning model that handles time series.

[0038] The importance calculation unit 130 calculates the importance of the features related to the video interruption. Specifically, when the estimation unit 120 outputs a binary value, the importance calculation unit 130 calculates which features were given more weight (which features contributed to the judgment) from among the input features used as the basis for that value. As a method for calculating feature importance, for example, Shapley Value or LIME can be used.

[0039] The post-processing unit 140 performs a causal relationship analysis as a post-processing step. Specifically, the post-processing unit 140 uses the classification and prediction results of video interruptions and the output of the feature importance calculation at the time of video interruption to determine the features that are thought to have influenced the video interruption through the processing described later, and estimates the factors.

[0040] The operation of each part will be explained in detail below.

[0041] (Preprocessing Unit 110) First, the preprocessing unit 110 will be described. The preprocessing unit 110 performs the following processing on the log (referred to as feature quantities) obtained from the automatic driving remote control system, and generates a vector expressing the relationship between the feature quantities. An image of the processing of the preprocessing unit 110 is shown in FIG. 6.

[0042] First, the preprocessing unit 110 obtains partial vectors (the shaded frame part in FIG. 6) within a specific time width for two feature quantities, and determines the presence or absence of a causal relationship for the two obtained partial vectors.

[0043] The method for determining the presence or absence of a causal relationship (also referred to as causality) is not limited to a specific method. For example, vector autoregression (VAR) and Granger test, which are existing technologies, can be used.

[0044] When the preprocessing unit 110 determines that there is a causal relationship between two feature quantities, it calculates the harmonic mean of the values of each vector and sets it as the value of the generated vector. Note that using the harmonic mean is just an example, and other means (e.g., arithmetic mean, weighted mean) may also be used.

[0045] When the preprocessing unit 110 determines that there is no causal relationship between two feature quantities, it sets a pre-set specific value (such as -1) as the value of the generated vector.

[0046] The name of the vector newly generated by the preprocessing unit .

[0047] The preprocessing unit 110 executes the above processing for all combinations of feature quantities.

[0048] (Processing Flow of Preprocessing Unit 110) The operation of the preprocessing unit 110 will be described more specifically with reference to the flowchart in FIG. 7. In the following description, it is assumed that there are feature quantity 1, feature quantity 2, and KPI as feature quantities obtained as logs.

[0049] <S101 (Step 101)> The preprocessing unit 110 acquires and inputs the time-series data for each feature. The preprocessing unit 110 also inputs the size (time width) of the subvectors.

[0050] For example, the preprocessing unit 110 obtains "a1, a2, ..., at, ..., aT" as time series data (time t=1 to T) for feature quantity 1 (e.g., wireless quality), "b1, b2, ..., bt, ..., bT" as time series data for feature quantity 2 (e.g., inflight), and "k1, k2, ..., kt, ..., bT" as time series data for KPI (e.g., video interruption). For example, kt=0 represents no video interruption, and kt=1 represents video interruption.

[0051] <S102> The preprocessing unit 110 obtains a subvector of the above size for each feature quantity.

[0052] For example, if the time width is assumed to be 3, the preprocessing unit 110 obtains (a1, a2, a3) as a subvector of feature quantity 1, (b1, b2, b3) as a subvector of feature quantity 2, and (k1, k2, k3) as a subvector of KPI.

[0053] <S103, S104> The preprocessing unit 110 determines whether or not there is a causal relationship for each combination of two subvectors, for example, by using vector autoregression (VAR) and Granger tests.

[0054] Specifically, the causal relationships between (a1, a2, a3) and (b1, b2, b3), between (b1, b2, b3) and (k1, k2, k3), and between (k1, k2, k3) and (a1, a2, a3) are determined.

[0055] For combinations where a causal relationship is determined, the process proceeds to S106; for combinations where a causal relationship is determined, the process proceeds to S105.

[0056] <S105, S106> In S105, when it is determined that there is no causal relationship, the preprocessing unit 110 sets, for example, (-1, -1, -1) as the value of the vector to be generated for that combination.

[0057] In S106, if a causal relationship is determined to exist, the preprocessing unit 110 sets the harmonic mean of the vectors of the combination as the value of the vector to be generated.

[0058] For example, if it is determined that there is a causal relationship between (a1, a2, a3) and (b1, b2, b3), the preprocessing unit 110 sets the vectors (harmonic mean of a1 and b1, harmonic mean of a2 and b2, harmonic mean of a3 and b3) as the values ​​of the vectors to be generated.

[0059] <S107> The preprocessing unit 110 performs the processes S102 to S106 with a time interval shift. That is, if the time interval is 3, the processes 102 to S106 are performed for each of the time intervals: time = 2, 3, 4; time = 3, 4, 5; ...; time = T-2, T-1, T.

[0060] The preprocessing unit 110 may also perform normalization on the vector obtained by the above process.

[0061] Figure 8 shows an image of the processing performed by the preprocessor 110 for the aforementioned "simultaneous," "time-direction shift," "chaining," and "combination" processes.

[0062] From now on, the vector that shows the relationship between two subvectors generated in the preprocessing stage will be called a "relational feature."

[0063] (About the prediction unit 120) The prediction unit 120 is a time-series machine learning model that learns using a vector formed by combining the vectors (relational features) generated in preprocessing with KPI (video interruption information), with the vectors other than KPI (relational features) as explanatory variables and the KPI vector as the target variable. The trained machine learning model takes the relational features as input, classifies or predicts video interruptions as binary (interruption present / interruption absent), and outputs the result.

[0064] In this embodiment, it is assumed that RNN or LSTM, etc., are used as the time-series machine learning model. Furthermore, it is assumed that this machine learning model is a supervised learning model (training data: KPI vectors).

[0065] The inference unit 120 may either hold a machine learning model or be a machine learning model itself.

[0066] The following describes the processing in the learning phase and the inference phase of a machine learning model. In the learning phase, the inference unit 120 is assumed to have a learning function and hold the machine learning model to be learned. The inference unit 120 in the learning phase may also be called the "learning unit". Here, too, the explanation assumes that there is feature 1, feature 2, and KPI.

[0067] <Learning Phase> The three relational features generated in preprocessing for each time interval are denoted as (1,2), (1,KPI), and (2,KPI). Since (1,2), (1,KPI), and (2,KPI) are generated for each time interval, the relational features for time interval k are denoted as (1,2)k, (1,KPI)k, and (2,KPI)k. For example, for the first time interval 1, the features are (1,2)1, (1,KPI)1, and (2,KPI)1.

[0068] In the learning phase, the inference unit 120 first inputs a relational feature with a time interval of 1 (for example, (1, KPI)1) into the machine learning model. The machine learning model outputs a prediction result (classification result) of whether or not there was a video interruption within a time interval of 1. The inference unit 120 updates the parameters of the machine learning model so that the error between this output and the correct answer (whether or not there was actually a video interruption within a time interval of 1) is reduced.

[0069] Next, a relational feature with a time interval of 2 (e.g., (1, KPI)2)) is input to the machine learning model, and the parameters of the machine learning model are updated to minimize the error between the output and the correct answer (whether or not there was actually a video interruption with a time interval of 2). This process is performed for data with a time interval of 2. For each time interval k, one relational feature is used from among the three relational features "(1, 2)k, (1, KPI)k, (2, KPI)k" that is appropriate for the characteristics of the data.

[0070] <Inference Phase> In the inference phase, for example, when predicting whether or not there was a video interruption based on (1, KPI)k, (1, KPI)k is input to the trained machine learning model, and the machine learning model outputs a prediction result indicating whether or not there was a video interruption within a time interval k.

[0071] (Regarding the importance calculation unit 130) When the inference unit 120 outputs a binary result which is the prediction result of whether or not there was a video interruption, the importance calculation unit 130 calculates which of the input relational features were given more weight (which relational features contributed to the judgment) from among the relational features that were used as the basis for that result.

[0072] Representative methods for calculating such importance include Shapley Value and LIME. These methods provide explainability to many deep learning models, including RNNs and LSTMs, and allow the relational features that were most relevant in deriving the prediction / classification results to be expressed by their numerical magnitude.

[0073] Shapley Value or LIME approximates a Black-Box model to the feature vector (relational feature) being analyzed using a White-Box model that can explain the neighborhood of the explanatory variable. It then compares the classification / prediction results for each combination of features (for example, (1,2), (2,3), and (1,3) in the case of feature 1, feature 2, and feature 3) and calculates importance based on which relational feature caused the most change in the classification / prediction results.

[0074] Figure 9 shows an image illustrating how the importance calculation unit 130 calculates the importance of each related feature based on the classification / prediction results for video interruption. Note that #1, #2, and #3 in the lower left of Figure 9 indicate the order of importance.

[0075] (Regarding the post-processing unit 140) The post-processing unit 140 determines the relative features that are thought to have influenced the video interruption based on the classification and prediction results of the video interruption and the importance of the relative features at the time of the video interruption, and estimates the factors by processing as described later. The processing of the post-processing unit 140 will be explained according to the procedure in the flowchart of Figure 10. In addition, the naming convention for relative features (e.g., (1,2) is generated from feature 1 and feature 2) follows the one explained above.

[0076] <S201> First, the post-processing unit 140 calculates the average importance value as a threshold for each related feature in the negative examples (in this embodiment, related features that did not cause video interruption are considered negative examples). The threshold is calculated for each related feature.

[0077] <S202> In this embodiment, relational features determined to be video interruptions by the machine learning model are treated as positive examples. For the positive examples, the post-processing unit 140 uses the importance calculation unit 130 to calculate the importance of the relational features. As mentioned above, it is assumed that the importance of the relational features will be calculated using Shapley or LIME.

[0078] <S203> The post-processing unit 130 extracts only the relational features whose importance is "positive and above the threshold" from among the multiple relational features whose importance was calculated in S202. Note that if the threshold is positive, "positive and above the threshold" may be written as "above the threshold". The threshold here is the threshold calculated in S201. Steps S204 and S205 below are performed on the relational features extracted in S203. In S204 and S205, factors are obtained by combining the relational features remaining after the threshold determination with features from the same generator. Specifically, it is as follows.

[0079] <S204> The post-processing unit 140 constructs a tree structure by matching the log names (feature 1, feature 2, etc.) that constitute the relational features.

[0080] <S205> The post-processing unit 140 determines the log name (leaf in the tree structure) furthest from the video interruption as the cause and outputs that cause.

[0081] (Specific examples of S204 and S205) Specific examples of S204 and S205 will be explained with reference to Figure 11. For example, suppose that in time interval 3 (the third time interval), the prediction result is "video interruption occurred," and that "(2, KPI) and (1, 2)" in time interval 3 are extracted as relational features whose importance is above a threshold. Figure 11 shows that (2, KPI) and (1, 2) have been extracted.

[0082] At this point, since there is a feature named "Feature 2" from the same source, it can be combined to construct a tree structure of "KPI -> Feature 2 -> Feature 1". Alternatively, as shown in Figure 11, this tree structure can be written as "Feature 1 -> Feature 2 -> KPI". The post-processing unit 140 determines that Feature 1, which is furthest from the video interruption, is the cause. This process is performed for each time interval in which the prediction result is "video interruption present".

[0083] Furthermore, if, for example, (1, KPI) and (2, KPI) are extracted in S203, then feature 1 and feature 2 are each considered as factors.

[0084] Furthermore, for example, if there are features 1, 2, and 3 other than KPI, and the relational features (1, KPI), (1, 2), and (1, 3) are extracted as having an importance above a threshold, then the following tree structure is obtained (intended to have two branches from feature 1 to features 2 and 3).

[0085] KPI -> Feature 1 -> Feature 2 -> Feature 3 In this case, the post-processing unit 140 determines that each of Feature 2 and Feature 3 is a factor.

[0086] (Example Hardware Configuration) The information processing device 100 described in this embodiment can be realized, for example, by having a computer execute a program. This computer may be a physical computer or a virtual machine on the cloud.

[0087] In other words, the device can be realized by using hardware resources such as the CPU and memory built into a computer to execute a program corresponding to the processing performed by the device. The program can be recorded on a computer-readable recording medium (such as portable memory), saved, and distributed. It can also be provided via a network, such as the Internet or email.

[0088] Figure 12 shows an example of the hardware configuration of the computer described above. The computer in Figure 12 has a drive device 1000, an auxiliary storage device 1002, a memory device 1003, a CPU 1004, an interface device 1005, a display device 1006, an input device 1007, an output device 1008, etc., all of which are interconnected by bus B. The computer may also be equipped with a GPU.

[0089] The program that enables processing on the computer is provided on a recording medium 1001, such as a CD-ROM or memory card. When the recording medium 1001 containing the program is set in the drive device 1000, the program is installed from the recording medium 1001 to the auxiliary storage device 1002 via the drive device 1000. However, the program does not necessarily have to be installed from the recording medium 1001; it may also be downloaded from another computer via a network. The auxiliary storage device 1002 stores the installed program as well as necessary files and data.

[0090] The memory device 1003 reads and stores a program from the auxiliary storage device 1002 when a program startup command is received. The CPU 1004 implements the functions related to the memory device 1003 according to the program stored in the memory device 1003. The interface device 1005 is used as an interface for connecting to a network, etc. The display device 1006 displays a GUI (Graphical User Interface) etc., based on a program. The input device 1007 consists of a keyboard and mouse, buttons, or a touch panel, etc., and is used to input various operation commands. The output device 1008 outputs the calculation results.

[0091] (Summary of Embodiments, Effects, etc.) As described above, the technology described in this embodiment makes it possible to generate a new vector that captures the changes in the relationships between features over time, and by constructing a machine learning model using this vector, it becomes possible to analyze the correct factors based on the relationships between features.

[0092] Furthermore, by using a time-series model as the machine learning model, it becomes possible to analyze logs while considering their time-series nature, thus addressing cases where conventional techniques could not effectively analyze the contributing factors.

[0093] The following additional information is disclosed regarding the embodiments described above.

[0094] <Notes> (Note 1) An information processing device comprising: a first processing unit that acquires multiple feature quantities that change over time from a communication system and generates relational feature quantities that express the relationship between feature quantities for each combination of two feature quantities; a second processing unit that estimates whether or not a predetermined event has occurred based on the relational feature quantities; and a third processing unit that estimates the factors of the predetermined event based on one or more relational feature quantities that formed the basis of the estimation result of whether or not the predetermined event has occurred. (Note 2) The information processing device according to Note 1, wherein the first processing unit acquires temporal subvectors as feature quantities and generates relational feature quantities as new vectors by determining whether or not there is a relationship between the subvectors. (Note 3) The information processing device according to Note 1, wherein the second processing unit estimates whether or not a predetermined event has occurred using a machine learning model suitable for processing time-series data. (Appendix 4) The information processing device according to Appendix 1, wherein the third processing unit calculates the importance of each related feature for the estimation result of whether the predetermined event has occurred, and estimates the factors of the predetermined event based on one or more related features whose importance satisfies a predetermined condition. (Appendix 5) The information processing device according to Appendix 4, wherein the third processing unit estimates the factors of the predetermined event by combining at least two related features that satisfy the predetermined condition and have the same source. (Appendix 6) A factor estimation method executed by an information processing device, comprising: acquiring a plurality of feature quantities that change over time from a communication system, and generating a related feature quantity that expresses the relationship between the feature quantities for each combination of two feature quantities; estimating whether the predetermined event has occurred based on the related feature quantity; and estimating the factors of the predetermined event based on one or more related features that formed the basis of the estimation result of whether the predetermined event has occurred. (Appendix 7) A non-temporary storage medium storing a program for causing a computer to function as a part of an information processing device described in any one of Appendix 1 to 5.

[0095] Although this embodiment has been described above, the present invention is not limited to this specific embodiment, and various modifications and changes are possible within the scope of the gist of the invention as described in the claims.

[0096] 100 Information processing device 110 Pre-processing unit 120 Estimation unit 130 Importance calculation unit 140 Post-processing unit 1000 Drive device 1001 Recording medium 1002 Auxiliary storage device 1003 Memory device 1004 CPU 1005 Interface device 1006 Display device 1007 Input device 1008 Output device

Claims

1. An information processing device comprising: a first processing unit that acquires multiple feature quantities that change over time from a communication system and generates relational feature quantities that express the relationship between the feature quantities for each combination of two feature quantities; a second processing unit that estimates whether or not a predetermined event has occurred based on the relational feature quantities; and a third processing unit that estimates the factors of the predetermined event based on one or more relational feature quantities that formed the basis of the estimation result of whether or not the predetermined event has occurred.

2. The information processing apparatus according to claim 1, wherein the first processing unit acquires a temporal subvector as the feature quantity, determines whether or not there is a relationship between the subvectors, and generates the relational feature quantity as a new vector.

3. The information processing apparatus according to claim 1, wherein the second processing unit estimates whether or not the predetermined event has occurred using a machine learning model suitable for processing time-series data.

4. The information processing apparatus according to claim 1, wherein the third processing unit calculates the importance of each related feature for the estimation result of whether the predetermined event has occurred, and estimates the factors of the predetermined event based on one or more related features whose importance satisfies predetermined conditions.

5. The information processing apparatus according to claim 4, wherein the predetermined condition is that the importance is greater than or equal to a threshold which is the average value of the importance in negative examples, and the third processing unit estimates the factors of the predetermined event by combining the same source feature quantities in at least two relational feature quantities that satisfy the predetermined condition.

6. A factor estimation method executed by an information processing device, comprising: a step of acquiring a number of time-series changing feature quantities from a communication system, and for each combination of two feature quantities, generating a relational feature quantity that expresses the relationship between the feature quantities; a step of estimating whether or not a predetermined event has occurred based on the relational feature quantity; and a step of estimating the factors of the predetermined event based on one or more relational feature quantities that formed the basis of the estimation result of whether or not the predetermined event has occurred.

7. A program for causing a computer to function as a component of the information processing apparatus described in any one of claims 1 to 5.

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