Information processing apparatus, information processing method, and information processing program

Abstract

To more appropriately monitor incidents and provide incident information in advertisement distribution.SOLUTION: An information processing apparatus according to the present application is provided with, in advertisement distribution in which a plurality of server devices cooperate to distribute advertisements, an acquisition unit for acquiring log data related to advertisement distribution from each server device, a learning unit for feeding the log data acquired from each server device as learning data to an AI for learning, a determination unit for feeding the log data acquired from each server apparatuses to the AI to determine whether or not the log data when an accident occurred in the past and the current log data are similar, a generation unit for automatically generating incident information based on an example when an accident occurred in the past when it is determined that the log data when an accident occurred in the past and the current log data are similar, and a provision unit for providing the incident information.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

[0002] Conventionally, a technique for automatically monitoring and verifying Internet-based advertisements has been disclosed (see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Special Publication No. 2011-525674 Summary of the Invention [Problem to be solved by the invention]

[0004] However, there is room for improvement in the above-mentioned conventional technology. For example, in the above-mentioned conventional technology, a mapping crawler searches for pages with a high probability of an incident and generates a list. A crawler manager assigns pages among multiple visual crawlers that are allowed to access the list of pages and loads them onto the visual crawlers so as to obtain sufficient incident coverage as required. An automated computer program (incident identifier) ​​compares the ad insertion order with the delivery data and generates an incident report whenever the ad insertion order and the delivery data corresponding to the ad insertion order do not match. However, there is room for improvement in monitoring incidents in ad delivery and providing incident information. Therefore, there is a need for a means for more appropriately monitoring incidents in ad delivery and providing incident information.

[0005] The present application has been made in consideration of the above, and aims to more appropriately monitor incidents in advertisement delivery and provide incident information. [Means for solving the problem]

[0006] The information processing device of the present application is characterized in that it comprises an acquisition unit that acquires log data related to advertisement delivery from each server device in advertisement delivery in which multiple server devices work together to deliver advertisements; a learning unit that inputs the log data acquired from each server device as learning data into an AI for learning; a determination unit that inputs the log data acquired from each server device into the AI ​​and determines whether or not the log data from a past accident is similar to current log data; a generation unit that automatically generates incident information based on cases of past accidents when it is determined that the log data from a past accident is similar to the current log data; and a provision unit that provides the incident information. Effect of the Invention

[0007] According to one aspect of the embodiment, it is possible to more appropriately monitor incidents in advertisement distribution and provide incident information. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of an information processing system according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing an image of a series of logs of a group of linked servers being referenced. [Diagram 3] FIG. 3 is a diagram showing an example of a record of responses to past accidents. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of the configuration of a server device according to the embodiment. [Figure 6] FIG. 6 is a flowchart showing a processing procedure according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to the embodiments. In addition, the same components in the following embodiments are denoted by the same reference numerals, and duplicated descriptions are omitted.

[0010] [1. Overview of the information processing system] First, an overview of an information processing system according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing system according to an embodiment. As shown in Fig. 1, an information processing system 1 according to an embodiment includes a terminal device 10 and a server device 100. These various devices are connected to each other via a network N (not shown) in a wired or wireless manner so as to be able to communicate with each other. This allows the terminal device 10 to cooperate with the server device 100. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0011] The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a mobile phone such as a feature phone, a personal computer (PC), a personal digital assistant (PDA), a game machine or AV device equipped with a communication function, an information appliance or digital appliance, a car navigation system, a smart watch or a head-mounted display, a wearable device such as smart glasses, etc. The terminal device 10 may also be a house or building, a car, a home appliance, an electronic device, etc. that supports the Internet of Things (IOT).

[0012] In this embodiment, the terminal device 10 is a smart device such as a smartphone or tablet terminal used by a user U, and is a mobile terminal device that can communicate with any server device via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system). The terminal device 10 has a screen such as a liquid crystal display having a touch panel function, and accepts various operations on display data such as content, such as tapping, sliding, and scrolling, performed by a finger or a stylus from the user U. An operation performed on an area of ​​the screen where the content is displayed may be regarded as an operation on the content. The terminal device 10 may be not only a smart device, but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

[0013] In addition, the terminal device 10 can connect to the network N via a wireless communication network such as LTE, 4G, or 5G, or a short-range wireless communication such as Bluetooth (registered trademark) or a wireless LAN (Local Area Network), and communicate with the server device 100.

[0014] The server device 100 is, for example, a computer such as a PC or a blade server, or a mainframe or a workstation, etc. The server device 100 may be realized by cloud computing.

[0015] In this embodiment, the server device 100 is an information processing device that cooperates with the terminal device 10 of each user U and provides API (Application Programming Interface) services for various applications (hereinafter, apps), etc., as well as various data, to the terminal device 10 of each user U, and is realized by a computer, a cloud system, etc.

[0016] The server device 100 may also be an information processing device that provides some kind of online Web service to the terminal device 10 of each user U. For example, the server device 100 may provide services such as Internet connection, search service, SNS (Social Networking Service), electronic commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecasts as Web services. In practice, the server device 100 may cooperate with various servers that provide the above-mentioned Web services, and may mediate the Web services or be responsible for processing the Web services.

[0017] The server device 100 can acquire user information about the user U. For example, the server device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of ​​the user U. The server device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) indicating the user U.

[0018] The server device 100 also acquires various types of history information (log data) indicating the actions of the user U from the terminal device 10 of the user U or from various servers based on the user ID or the like. For example, the server device 100 acquires a location history, which is a history of the location and date and time of the user U, from the terminal device 10. The server device 100 also acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). The server device 100 also acquires a browsing history, which is a history of content browsed by the user U, from a content server. The server device 100 also acquires a purchase history (payment history), which is a history of product purchases and payment processing by the user U, from an electronic commerce server or a payment processing server. The server device 100 may also acquire a listing history, which is a history of listings on the marketplace by the user U, and a sales history, from the electronic commerce server or the payment processing server. The server device 100 also acquires a posting history, which is a history of posts by the user U, from a posting server that provides a word-of-mouth posting service or an SNS server. The above-mentioned various servers and the like may be the server device 100 itself. That is, the server device 100 may function as the above-mentioned various servers and the like.

[0019] 1, an SSP server device 100SSP, a DSP server device 100DSP, an ASO server device 100ASO, a BS server device 100BS, and an MS server device 100MS are shown as examples of the server device 100. Each server device 100 can communicate with each other via a network N. Details of each server device 100 will be described later.

[0020] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 1 is not limited to that shown in the figure. For example, in Fig. 1, for the sake of simplicity, only one terminal device 10 is shown, but this is merely an example and is not limiting, and two or more devices may be included.

[0021] [2. Automatic generation of incident information by analyzing distribution logs] The MS server device 100MS according to this embodiment automatically generates incident information by analyzing a delivery log when an accident (trouble) occurs in advertisement delivery (when a malfunction or failure occurs).

[0022] (1) Conventional mechanism for internet advertising delivery Ad delivery has become multifunctional, so the reality is that many servers work together to deliver just one ad. The servers through which ad delivery communication passes store various information as logs, which are then used for analysis and optimization.

[0023] For example, a typical Internet advertisement distribution system includes, as the server device 100, an SSP server device 100SSP, a DSP server device 100DSP, an ASO server device 100ASO, and a BS server device 100BS.

[0024] The SSP server device 100 SSP is a server device 100 that functions as an SSP (Supply-Side Platform) and supports the sale of advertising space in media and maximization of advertising revenue.

[0025] For example, the SSP server device 100SSP receives an advertisement request from a website displayed on the browser of the terminal device 10 of the user U, and creates and accumulates SSP request logs. The SSP request logs include request parameters, the time the request was received, and information about the website that made the request. The SSP server device 100SSP then transmits the advertisement request to the DSP server device 100DSP.

[0026] The DSP server device 100 DSP is a server device 100 that functions as a DSP (Demand-Side Platform), and centrally manages the purchase of advertisement inventory used by advertisers and advertising agencies, advertisement delivery, placement space, and audience targeting.

[0027] For example, the DSP server device 100DSP receives an advertisement request (Ad Request) from the SSP server device 100SSP, and creates and accumulates DSP request logs (DSP Request logs). The DSP request logs include request parameters, the time of the request from the SSP, ID information (identification information) of the request, and the like. Then, the DSP server device 100DSP transmits an advertisement search request (Ad Search Request) to the ASO server device 100ASO.

[0028] The ASO server device 100ASO is a server device 100 that functions as an optimizer in ad serving, and performs "optimization" of ad serving operations.

[0029] For example, the ASO server device 100ASO receives an ad search request from the DSP server device 100DSP, and creates and accumulates ASO request logs. The ASO request logs include request parameters, the time of the request from the DSP, and ID information (identification information) of the request.

[0030] In addition, the ASO server device 100ASO transmits an ad response (Ad Response) to the DSP server device 100DSP, and creates and accumulates ASO response logs (ASO response logs). The ASO response logs include information on the response, the time the response was sent to the DSP, ID information related to the response, and the like.

[0031] In addition, the DSP server device 100DSP receives an ad response from the ASO server device 100ASO and transmits the ad response to the SSP server device 100SSP. At this time, the DSP server device 100DSP creates and accumulates a DSP response log. The DSP response log includes response information from the ASO, the time the response was sent to the SSP, information about the matched advertisement, and the like.

[0032] In addition, the SSP server device 100SSP receives an advertisement response (Ad Response) from the DSP server device 100DSP, and transmits the advertisement response (Ad Response) to the website (Website) displayed in the browser of the terminal device 10 of the user U. At this time, the SSP server device 100SSP creates and accumulates SSP response logs. The SSP response logs include response information from the DSP, the time when the website sent the response to the displayed browser, information about the matched advertisement, and the like.

[0033] The BS server device 100BS is a server device 100 that functions as a beacon server, and receives an ad tracking request related to an advertisement on a website displayed on the browser of the terminal device 10 of a user U, and creates and accumulates BS request logs. The BS request logs include an advertisement measurement request such as an ad impression (ad impression: number of times an advertisement is displayed), the time when the request was received, information on the placement surface, and the like.

[0034] (2) Information indicated by abnormalities in log data When a problem occurs in an optimizer such as the ASO server device 100ASO, the optimizer cannot return an ad response, causing an accident such as "advertisement not being displayed." When viewed at the log level, the response log from the DSP following the optimizer onwards may not show anything.

[0035] In addition, if a problem occurs in an optimizer such as the ASO server device 100ASO, some kind of problem will occur in the subsequent log data. In the worst case, an accident occurs in which advertisements are not displayed on the website displayed in the browser of the terminal device 10 of the user U.

[0036] Therefore, in this embodiment, an MS server device 100MS is further installed as the server device 100. The MS server device 100MS is a server device 100 that functions as a monitoring server that monitors the logs of the SSP server device 100SSP, the DSP server device 100DSP, the ASO server device 100ASO, and the BS server device 100BS described above, and when a problem occurs in advertisement distribution, it analyzes the logs of each server, automatically generates incident information, and posts it to a business messaging app (business chat tool) such as Slack (registered trademark). Note that the destination to which the incident information is posted and the method of providing the incident information are arbitrary.

[0037] 1, in an advertisement distribution in which a plurality of server devices 100 cooperate with each other to distribute advertisements, the MS server device 100MS acquires log data (request log, response log, etc.) related to advertisement distribution from each server device 100 via a network N (step S1). Note that the MS server device 100MS may constantly collect log data from each server device 100, or may acquire only log data when an accident has occurred in the past.

[0038] Next, the MS server device 100MS inputs the log data acquired from each server device 100 as learning data into AI (Artificial Intelligence) to make it learn (step S2). At this time, the AI ​​learns not only the time-series logs (vertical report logs) on the server (for each server), but also the logs of a series of flows between servers (horizontal report logs). The AI ​​also learns about logs of past accidents.

[0039] Next, the MS server device 100MS acquires the current log data from each server device 100 via the network N, inputs it into the trained AI, and judges whether the log data from past accidents is similar to the current log data (step S3). At this time, the AI ​​judges whether not only the chronological logs on the server (vertical report logs) but also the logs of a series of flows between servers (horizontal report logs) are similar.

[0040] Next, if the MS server device 100MS determines that the log data from a past accident is similar to the current log data, it automatically generates incident information based on the past accident (step S4).

[0041] Next, the MS server device 100MS provides the incident information to a person (responder) who handles advertisement distribution operations or distribution troubles (step S5). Note that the person (responder) who handles advertisement distribution operations or distribution troubles may be the advertiser.

[0042] [2-1. Trouble detection] For example, as shown in Fig. 2, the MS server device 100MS allows logs of a group of servers that cooperate with it to be viewed in a series of flows, and judges the possibility of an accident (trouble) occurring by comparing the logs with log data from past incidents. Fig. 2 is a diagram showing an image of referring to logs of a group of servers that cooperate with it in a series of flows. In the example shown in Fig. 2, the servers included in the group of servers that cooperate with it are represented as Server A, Server B, and Server C.

[0043] In the example shown in Figure 2, if the log of Server A is "Error," the log of Server B is "Warning," and the log of Server C is "Warning," the presence or absence of similar past cases is determined to be "past cases match," and the similarity rates with past cases are estimated to be "80%" with Accident A, "60%" with Accident B, and "10%" with Accident C.

[0044] In addition, if the log of Server A is "Warning", the log of Server B is "Warning", and the log of Server C is "Warning", the presence or absence of similar past cases will be determined to be "No past cases match", and the similarity rate with past cases will also be estimated to be "No match".

[0045] In addition, if the log of Server A is "Error," the log of Server B is "Error," and the log of Server C is "Error," the presence or absence of similar past cases will be determined to be "past cases match," and the similarity rate with past cases will be estimated to be "65%" with Accident A and "40%" with Accident D.

[0046] At this time, the MS server device 100MS provides the AI ​​(Artificial Intelligence) with log data from past accidents as learning data. The AI ​​may be a generation AI such as a Generative Pre-trained Transformer (GPT).

[0047] In addition, the MS server device 100MS analyzes a log (horizontal report log) of a series of current flows between servers, and checks whether any behavior is similar to that of past accidents.

[0048] If the MS server device 100MS detects behavior similar to a past accident, it estimates whether an accident has actually occurred and, if so, which past cases it resembles to and to what extent (whether there are similar past cases, the rate of similarity, etc.), and creates and reports incident information.

[0049] [2-2. Create examples of how to respond to current accidents based on records of responses to past accidents] For example, as shown in Fig. 3, when an accident occurs, the MS server device 100MS keeps a record of the accident up to the settlement (a record of responses to past accidents), and when an incident similar to the past accident occurs next time, it creates a response example (incident response information) based on the past data (record of responses to past accidents). Fig. 3 is a diagram showing an example of a record of responses to past accidents.

[0050] In the example shown in Figure 3, for Accident A that occurred in the past, a "loss amount calculation" was performed as a history of impact investigation, and notifications were made to "advertisers" and "general users" as a history of external Comm (external communication).

[0051] Also, in the case of Accident B, which occurred in the past, the history of the impact investigation shows that "calculation of the amount of loss" and "calculation of lost ad impressions (number of ad impressions)" were carried out, and the history of external Comm shows that notification was given to "advertisers."

[0052] In addition, in the case of Accident C, which occurred in the past, the history of the impact investigation shows that "calculation of lost ad impressions" and "refund processing" were carried out, and the history of external Comm shows that notification was given to the "advertiser."

[0053] For example, the MS server device 100MS inputs the log data of past accidents and the records of responses to past accidents as learning data into the AI ​​to make it learn.Then, the MS server device 100MS inputs the current log data into the trained AI, and if it determines that the log data of past accidents and the current log data are similar, it generates incident information based on the records of responses to past accidents together with examples of past accidents.

[0054] At this time, the MS server device 100MS creates examples of responses, such as a procedure for calculating an estimated amount of damage caused by an accident based on a record of responses to past accidents, and a procedure for creating and sending an email for external notification using an external communication function.

[0055] In addition, the MS server device 100MS utilizes the incident response work log as learning data for the AI, and utilizes it for the convergence response in the event of a subsequent accident. That is, the MS server device 100MS inputs the incident response work log as learning data into the AI ​​to make it learn.

[0056] In addition, the MS server device 100MS automatically generates countermeasures for the next time a similar accident occurs by recording a log of what was done for each accident. In other words, the MS server device 100MS has an AI learn the incident response work log for each accident, and automatically generates countermeasures for the accident based on the past incident response work log when an accident similar to a past accident occurs.

[0057] [2-3. Notes] The MS server device 100MS compares (collates) the logs of each server device when an accident occurred in the past during advertising distribution in which multiple server devices work together to deliver advertisements with the logs of each server device acquired at a specified timing, estimates the occurrence of an accident based on the similarity between the logs of the past accidents and the logs acquired at the specified timing, and generates and provides incident information based on the estimation result.

[0058] When a problem occurs in ad delivery, it takes time to identify the cause. Accuracy and speed are required for analyzing the cause of the problem, but the time it takes to analyze the cause varies depending on the level of knowledge of the person in charge.

[0059] Components that affect ad delivery include the media side (player), the media side delivery settings, the ad delivery server, and errors in ad applications or bidding on the DSP side (inability to apply or bid).

[0060] In this embodiment, the AI ​​leads to an optimal cause analysis flow and narrows down the components that are likely to be bottlenecks and that should be investigated in more detail.

[0061] The AI ​​also reads files and information that it thinks might be useful and determines whether there are any abnormalities. For example, the AI ​​reads the URL of the page, the ad request sent from the player to the ad server, HAR (HTTP Archive) files, etc., and determines whether there are any abnormalities.

[0062] In addition, the AI ​​may narrow down the components to be explored based on the responses of the responder to about five questions about distribution settings.

[0063] In addition, it is difficult to reproduce and isolate ad display problems. Ad display problems can be caused not only by your own ad network, but also by external advertising factors. For example, there are problems such as ads being displayed in a size other than the expected delivery size, the ad frame being shifted to the right edge, and ads being displayed with inappropriate content.

[0064] Possible causes include problems on the part of the publishing surface, problems on the part of the ad delivery company, or problems with the ads being delivered (such as which SSP they are delivered through).

[0065] Therefore, the MS server device 100MS mechanically reads the page and reproduces it when an advertisement display failure occurs, etc. Also, the MS server device 100MS learns source information and the like in advance and then isolates the cause.

[0066] In this embodiment, in order to address the problem of determining the cause of an unsuccessful advertisement delivery, the MS server device 100MS estimates from the logs of each server where the error is occurring and what type of error is occurring in the flow from upstream to downstream (advertisement display) of advertisement delivery.

[0067] At this time, the MS server device 100MS estimates it from past logs acquired from each server device 100. For example, the MS server device 100MS calculates and provides a similarity percentage for similar past cases. If there are multiple similar past cases, the MS server device 100MS may calculate a similarity percentage for each of the multiple past cases. The MS server device 100MS may also propose a response example (incident response information) based on the response action taken in the past incident together with the incident information (or as the incident information). The MS server device 100MS may also provide information on losses due to accidents that have occurred based on examples of past incidents.

[0068] The MS server device 100MS also estimates the component in which the accident occurred, and provides the estimated component and the probability (degree of estimation) that the accident occurred in the component as incident information. At the same time, the MS server device 100MS may also propose an example of how to deal with the occurring accident.

[0069] Furthermore, when an accident occurs, the MS server device 100MS refers to the log data of each server device 100 during that time period. At this time, it is possible to trace the logs of the relationships between the servers using a common request ID, etc. The chronological logs on the server (vertical report logs) make it possible to check the number of warnings, the number of errors, and the increase or decrease in the number of requests. The logs of the series of flows between servers (horizontal report logs) make it possible to detect which server device or which component the accident occurred in, and which server device or which component is the root cause.

[0070] In addition, the MS server device 100MS detects accidents by comparing them with past accidents as a detection logic. Also, if the MS server device 100MS learns the precursors of past accidents, it can predict the occurrence of a new accident when it detects the precursors from the current log.

[0071] In addition, when the MS server device 100MS makes the AI ​​learn log data, the MS server device 100MS makes the AI ​​learn logs with associated IDs.

[0072] Furthermore, the MS server device 100MS determines whether the similarity rate between past log data and current log data is equal to or higher than a threshold (eg, 80% or higher), and if the similarity rate is equal to or higher than the threshold, automatically generates and notifies incident information.

[0073] In addition, the MS server device 100MS may automatically generate and notify incident information when the similarity rate between past log data and current log data fluctuates depending on the sampling timing, and the difference between the similarity rates exceeds a threshold (or the similarity rate suddenly rises). For example, when an advertisement goes viral, the load on the server may suddenly rise in a short period of time, increasing the probability of trouble occurring. In addition, when a system bug gradually spreads the damage, the probability of trouble occurring may also increase.

[0074] In addition, the MS server device 100MS suggests a priority for the investigation. For example, the MS server device 100MS suggests which component to investigate in depth.

[0075] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 4, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

[0076] (Communications Department 11) The communication unit 11 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, or the like.

[0077] (Display section 12) The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). The display unit 12 is a touch panel display, but is not limited to this.

[0078] (Input section 13) The input unit 13 is an input device that accepts various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, and the like. The input unit 13 may be an input / output port (I / O port), a USB (Universal Serial Bus) port, or the like. In addition, when the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. In addition, the input unit 13 may be a microphone that accepts voice input from the user U, or the like. The microphone may be wireless.

[0079] (Positioning unit 14) The positioning unit 14 receives a signal (radio wave) transmitted from a satellite of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10, which is the device itself, based on the received signal. That is, the positioning unit 14 measures the position of the terminal device 10. Note that the GPS is merely an example of a GNSS (Global Navigation Satellite System).

[0080] The positioning unit 14 can measure the position by various methods other than GPS. For example, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as described below as an auxiliary positioning means for position correction or the like.

[0081] (Wi-Fi positioning) For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point to measure the position of the terminal device 10.

[0082] (Beacon positioning) The positioning unit 14 may measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0083] (geomagnetic positioning) The positioning unit 14 also measures the position of the terminal device 10 based on a geomagnetic pattern of a structure measured in advance and a geomagnetic sensor included in the terminal device 10.

[0084] (RFID positioning) Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, stores, etc., or has a function of reading an RFID tag, the location of use is recorded together with information on a payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to measure the location of the terminal device 10. Furthermore, the location may be measured by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

[0085] The positioning unit 14 may, if necessary, measure the position of the terminal device 10 using one or a combination of the above-mentioned positioning means.

[0086] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices and wireless devices. In the example shown in FIG. 4, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, an air pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0087] The above-mentioned sensors 21 to 28 are merely examples and are not intended to be limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0088] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10, such as the moving direction, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10, such as tilts in three axial directions, based on the angular velocity, etc. of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

[0089] Since the terminal device 10 includes the above-mentioned acceleration sensor 21, gyro sensor 22, and air pressure sensor 23, it is possible to measure the position of the terminal device 10 using a technique such as Pedestrian Dead-Reckoning (PDR) that uses these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using a positioning system such as GPS.

[0090] For example, the number of steps, walking speed, and distance walked can be calculated by a pedometer using the acceleration sensor 21. In addition, the moving direction, line of sight direction, and body inclination of the user U can be known by using the gyro sensor 22. In addition, the altitude and floor number on which the terminal device 10 of the user U is located can be known from the air pressure detected by the air pressure sensor 23.

[0091] The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

[0092] The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the air pressure, temperature, sound, and illuminance, and capture images of the surroundings, thereby detecting the environment and situation around the terminal device 10. In addition, the accuracy of the location information of the terminal device 10 can be improved based on the environment and situation around the terminal device 10.

[0093] (Control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, an input / output port, and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmitting unit 31, a receiving unit 32, and a processing unit 33.

[0094] (Transmitter 31) The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21-28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the server device 100 via the communication unit 11.

[0095] (Receiving unit 32) The receiving unit 32 can receive various types of information provided by the server device 100 and requests for various types of information from the server device 100 via the communication unit 11.

[0096] (Processing section 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12, etc. For example, the processing unit 33 can output various information transmitted by the transmission unit 31 and various information received from the server device 100 by the reception unit 32 to the display unit 12 for display.

[0097] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk drive (HDD), a solid state drive (SSD), an optical disk, etc. Various programs and various data are stored in the storage unit 40.

[0098] [4. Example of Server Device Configuration] Next, a configuration of the server device 100 according to the embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Fig. 5, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0099] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N in a wired or wireless manner.

[0100] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a HDD, an SSD, an optical disk, etc. The storage unit 120 may store attribute information and history information (log data) of the user U together with identification information (such as a user ID) indicating the user U.

[0101] (Control unit 130) The control unit 130 is a controller, and is realized, for example, by a CPU (Central Processing Unit), MPU (Micro Processing Unit), ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), etc., by executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the server device 100 using a storage area such as a RAM as a working area.

[0102] 5, the control unit 130 of the MS server device 100MS will be described. The control unit 130 includes an acquisition unit 131, a learning unit 132, a determination unit 133, a generation unit 134, and a provision unit 135.

[0103] (Acquisition part 131) The acquisition unit 131 acquires a search query input by a user U (user). For example, when the user U inputs a search query into a search engine or the like to perform a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. That is, the acquisition unit 131 acquires, via the communication unit 110, a keyword input by the user U into a search box of a search engine, a site, or an app.

[0104] Furthermore, the acquisition unit 131 acquires user information regarding the user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as a user ID) indicating the user U, location information of the user U, attribute information of the user U, and the like from the terminal device 10 of the user U. Furthermore, the acquisition unit 131 may acquire the identification information indicating the user U, attribute information of the user U, and the like when the user U is registered. Then, the acquisition unit 131 stores the user information in the storage unit 120.

[0105] Furthermore, the acquisition unit 131 acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the acquisition unit 131 acquires various types of history information indicating the behavior of the user U from the terminal device 10 of the user U, or from various servers based on a user ID or the like. Then, the acquisition unit 131 stores the various types of history information in the storage unit 120.

[0106] Furthermore, in the advertisement distribution in which a plurality of server devices 100 cooperate with each other to distribute advertisements, the acquisition unit 131 acquires log data related to the advertisement distribution from each server device 100 via the communication unit 110.

[0107] (Learning Section 132) The learning unit 132 inputs the log data acquired from each server device 100 as learning data into an AI (Artificial Intelligence) for learning. Note that the AI ​​may be a generation AI such as a Generative Pre-trained Transformer (GPT).

[0108] At this time, the learning unit 132 inputs the log data acquired from each server device 100 into the AI ​​as learning data, and causes the AI ​​to learn the log of a series of flows between servers in advertisement delivery.

[0109] Furthermore, the learning unit 132 uses the log data acquired from each server device 100 as learning data to have the AI ​​learn the predictive parts of past accidents.

[0110] Furthermore, the learning unit 132 may input the log data of past accidents as well as records of responses to past accidents as learning data into the AI ​​to make it learn.

[0111] (Judgment unit 133) The determination unit 133 inputs the log data acquired from each server device 100 into the AI ​​and determines whether or not the log data from a past accident is similar to the current log data. Note that the determination unit 133 may be an estimation unit that determines the presence or absence of similar past cases based on the log data from a past accident and the current log data, and estimates the similarity rate with each past case.

[0112] At this time, the determination unit 133 analyzes the log of the current series of events between the servers, and determines whether or not the events are similar to those of past accidents.

[0113] Furthermore, when the determination unit 133 determines that the movement is similar to that of a past accident, it estimates whether an accident has actually occurred and, if so, to which past case the accident is similar and to what extent.

[0114] In addition, if the judgment unit 133 determines that the behavior is similar to that of a past accident, it estimates which of the multiple server devices 100 or which component the accident occurred in, based on the past cases.

[0115] Furthermore, the determination unit 133 inputs the log data acquired from each server device 100 into the AI, and determines whether the current log data contains a portion similar to the predictive portion.

[0116] Furthermore, the judgment unit 133 may input the current log data into a trained AI, and if it determines that the log data from past accidents is similar to the current log data, it may output a record of how the past accidents were handled along with examples of past accidents.

[0117] Furthermore, the determination unit 133 analyzes the time-series logs on the server for each server device 100, and checks the increase or decrease in the number of warnings, errors, and requests.

[0118] (Generation unit 134) The generation unit 134 generates incident information based on the estimation result by the determination unit 133. For example, when it is determined that log data when an accident occurred in the past and current log data are similar, the generation unit 134 automatically generates incident information based on cases when accidents occurred in the past.

[0119] Furthermore, when the current log data contains a portion similar to the precursor portion, the generating unit 134 generates incident information related to a precursor of the occurrence of a new accident.

[0120] Furthermore, the generation unit 134 generates response examples based on cases of accidents that have occurred in the past and records of responses to the past accidents, and generates incident information including the response examples.

[0121] (Provider 135) The providing unit 135 provides the incident information. For example, the providing unit 135 posts the incident information to a business messaging app (business chat tool) such as Slack (registered trademark) via the communication unit 110, thereby providing the incident information to a person (responder) engaged in the advertisement delivery operation business or the delivery trouble response business.

[0122] 5. Processing Procedure Next, a processing procedure by the server device 100 according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the MS server device 100MS.

[0123] For example, as shown in FIG. 6, in an advertisement distribution in which multiple server devices 100 cooperate to distribute advertisements, the acquisition unit 131 of the MS server device 100MS acquires log data related to advertisement distribution from each server device 100 via the communication unit 110 (step S101).

[0124] Next, the learning unit 132 of the MS server device 100MS inputs the log data acquired from each server device 100 as learning data into AI (Artificial Intelligence), and causes AI to learn the log of a series of flows between servers in advertisement delivery (step S102).

[0125] Further, the learning unit 132 of the MS server device 100MS uses the log data acquired from each server device 100 as learning data and causes the AI ​​to learn predictive parts of accidents that have occurred in the past (step S103).

[0126] Furthermore, the learning unit 132 of the MS server device 100MS inputs the log data of past accidents and the records of responses to past accidents as learning data into the AI ​​to make it learn (step S104).

[0127] Next, the judgment unit 133 of the MS server device 100MS inputs the log data acquired from each server device 100 into the AI ​​and judges whether the log data from a past accident is similar to the current log data (step S105).

[0128] At this time, if the judgment unit 133 of the MS server device 100MS judges that the log data from a past accident is not similar to the current log data (step S105: No), it determines that advertisement delivery is being performed normally and continues the current processing (return to step S101).

[0129] Next, if the generation unit 134 of the MS server device 100MS determines that the log data from a past accident is similar to the current log data (step S105: Yes), it automatically generates incident information based on cases of past accidents (step S106).

[0130] Next, the provision unit 135 of the MS server device 100MS provides the incident information to a person engaged in advertising delivery operations or delivery trouble response work (responder) by posting the incident information to a business messaging app (business chat tool) such as Slack (registered trademark) via the communication unit 110 (step S107).

[0131] 6. Modifications The above-described terminal device 10 and server device 100 may be implemented in various different forms other than the above-described embodiment. Therefore, the following describes modifications of the embodiment.

[0132] In the above embodiment, some or all of the processes executed by the MS server device 100MS may actually be executed by the terminal device 10 (or an application running on the terminal). For example, the terminal device 10 may execute the operations as the MS server device 100MS. In this case, it is assumed that the terminal device 10 has the functions of the MS server device 100MS in the above embodiment. Alternatively, the terminal device 10 may cooperate with the MS server device 100MS and perform some of the functions of the MS server device 100MS.

[0133] In the above embodiment, the advertisement may be a listing advertisement or a display advertisement. In reality, the advertisement may be an online advertisement that can be distributed via a network. The type of the online advertisement does not matter. The advertisement may be a retargeting advertisement.

[0134] In the above embodiment, the website displayed on the browser of the terminal device 10 of the user U may be an application started on the terminal device 10 of the user U. In this case, the advertisement may be an application advertisement.

[0135] Furthermore, in the above embodiment, the MS server device 100MS may provide incident information to the advertiser or the user U. Furthermore, the MS server device 100MS may provide the advertiser or the user U with examples of responses based on past cases.

[0136] In the above embodiment, the MS server device 100MS may generate a prompt (instruction sentence) based on the log data acquired from each server device 100, and input the prompt to the AI. In this case, the AI ​​may generate and output incident information related to similarities with past cases and examples of responses as a response to the prompt (instruction sentence).

[0137] In the above embodiment, the MS server device 100MS may acquire encrypted data obtained by encrypting log data from each server device 100. Alternatively, the MS server device 100MS may acquire vectorized data obtained by converting the log data into text and vectorizing it by natural language processing, instead of the log data itself. The vectorized data is irreversible information that cannot be restored to the original log data.

[0138] 7. Effects As described above, the information processing device (MS server device 100MS) of the present application is characterized by having an acquisition unit that acquires log data related to advertisement delivery from each server device 100 in advertisement delivery in which multiple server devices 100 work together to deliver advertisements, a learning unit 132 that inputs the log data acquired from each server device 100 as learning data into an AI for learning, a determination unit 133 that inputs the log data acquired from each server device 100 into the AI ​​and determines whether the log data from a past accident is similar to the current log data, a generation unit 134 that automatically generates incident information based on cases of past accidents when it is determined that the log data from a past accident is similar to the current log data, and a provision unit 135 that provides the incident information.

[0139] The learning unit 132 inputs the log data acquired from each server device 100 into the AI ​​as learning data, and causes the AI ​​to learn the log of a series of flows between servers in advertisement delivery.

[0140] The determination unit 133 analyzes the log of the current series of events between the servers, and determines whether the events are similar to those of past accidents.

[0141] When the determination unit 133 determines that the movement is similar to a past accident, it estimates whether an accident has actually occurred and, if so, to which past case the accident is similar to and to what extent. The generation unit 134 generates incident information based on the estimation result by the determination unit 133.

[0142] When the determination unit 133 determines that the behavior is similar to that of a past accident, the determination unit 133 estimates, based on the past cases, which of the multiple server devices 100 or which component the accident occurred in. The generation unit 134 generates incident information based on the estimation result by the determination unit 133.

[0143] The learning unit 132 uses the log data acquired from each server device 100 as learning data and causes the AI ​​to learn predictive parts of past accidents. The determination unit 133 inputs the log data acquired from each server device 100 into the AI ​​and determines whether the current log data contains a part similar to the predictive part. The generation unit 134 generates incident information related to a predictive part of the occurrence of a new accident when the current log data contains a part similar to the predictive part.

[0144] The learning unit 132 inputs the log data of past accidents and the response records of past accidents as learning data into the AI ​​to make it learn. The determination unit 133 inputs the current log data into the trained AI, and when it is determined that the log data of past accidents and the current log data are similar, outputs the response records of past accidents together with examples of past accidents. The generation unit 134 generates response examples based on the examples of past accidents and the response records of past accidents, and generates incident information including the response examples.

[0145] The determination unit 133 analyzes the time-series logs on the server for each server device 100, and checks the increase or decrease in the number of warnings, errors, and requests.

[0146] By performing any one or a combination of the above-described processes, the information processing device according to the present application can more appropriately monitor incidents in advertisement distribution and provide incident information.

[0147] [8. Hardware Configuration] Moreover, the terminal device 10 and the server device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration as shown in Fig. 7, for example. The server device 100 will be described below as an example. Fig. 7 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected by a bus 1090.

[0148] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0149] The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that primarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may be a removable storage medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) memory card. The secondary storage device 1050 may be a cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

[0150] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, a printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input I / F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, a keyboard, a keypad, a button, a scanner, etc., and is realized by a USB, etc.

[0151] Furthermore, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

[0152] The output device 1010 and the input device 1020 may be integrated into one device, such as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated into one device, such as an input / output I / F.

[0153] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0154] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0155] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0156] For example, when the computer 1000 functions as the server device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with the other device via the network I / F 1080 and use functions and data of a program by calling them from another program of the other device.

[0157] [9. Others] Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. The above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be appropriately combined. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

[0158] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0159] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads, usage conditions, etc.

[0160] For example, the above-mentioned server device 100 may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0161] Furthermore, the above-described embodiments and modifications can be appropriately combined as long as the processing contents are not contradictory.

[0162] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit", etc. For example, an acquisition section can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0163] 1. Information Processing Systems 10 Terminal Equipment 100 Server device 100SSP SSP server device 100DSP DSP server device 100ASO ASO server device 100BS BS server device 100MS MS server device 110 Communications Department 120 Storage section 130 Control section 131 Acquisition Department 132 Learning Department 133 Judgment section 134 Generation part 135 Provision Department

Claims

1. An acquisition unit that acquires log data related to advertisement delivery from each server device in an advertisement delivery in which a plurality of server devices cooperate to deliver advertisements; A learning unit that inputs the log data acquired from each of the server devices as learning data into an AI to cause the AI ​​to learn the log data; a determination unit that inputs the log data acquired from each of the server devices into the AI ​​and determines whether or not the log data from a past accident is similar to the current log data; a generation unit that automatically generates incident information based on cases of past accidents when it is determined that the log data of past accidents is similar to the current log data; A providing unit that provides the incident information; An information processing device comprising:

2. The learning unit inputs the log data acquired from each of the server devices as learning data into an AI, and causes the AI ​​to learn a log of a series of flows between servers in the advertisement delivery.

2. The information processing apparatus according to claim 1,

3. The determination unit analyzes the log of the current series of events between the servers and determines whether the events are similar to those of past accidents.

3. The information processing apparatus according to claim 2.

4. When the determination unit determines that the movement is similar to a past accident, the determination unit estimates whether an accident has actually occurred and, if an accident has occurred, to which past case the accident is similar and to what extent; The generating unit generates the incident information based on a result of the estimation by the determining unit.

4. The information processing apparatus according to claim 3.

5. When the determination unit determines that the behavior is similar to a past accident, the determination unit estimates, based on past cases, which server device or which component among the plurality of server devices the accident occurred in; The generating unit generates the incident information based on a result of the estimation by the determining unit.

4. The information processing apparatus according to claim 3.

6. The learning unit uses the log data acquired from each of the server devices as learning data to cause the AI ​​to learn predictive parts of accidents that have occurred in the past, The determination unit inputs the log data acquired from each of the server devices into the AI ​​and determines whether the current log data includes a portion similar to the predictive portion; The generating unit generates incident information related to a predictor of a new accident when the current log data includes a portion similar to the predictor portion.

2. The information processing apparatus according to claim 1,

7. The learning unit inputs log data of past accidents and records of responses to past accidents as learning data into the AI ​​to make it learn, The determination unit inputs the current log data into the trained AI, and when it is determined that the log data from a past accident is similar to the current log data, outputs a record of how the past accident was handled together with examples of past accidents, The generating unit generates response examples based on cases of accidents that have occurred in the past and records of responses to the past accidents, and generates incident information including the response examples.

2. The information processing apparatus according to claim 1,

8. The determination unit analyzes the time-series logs on the server for each of the server devices, and checks the increase or decrease in the number of warnings, errors, and requests.

2. The information processing apparatus according to claim 1,

9. An information processing method executed by an information processing device, An acquisition step of acquiring log data related to advertisement delivery from each server device in advertisement delivery in which a plurality of server devices cooperate to deliver advertisements; A learning process in which the log data acquired from each of the server devices is input as learning data into an AI to allow the AI ​​to learn the log data; a determination step of inputting the log data acquired from each of the server devices into the AI ​​and determining whether or not the log data from a past accident is similar to the current log data; a generating step of automatically generating incident information based on cases of past accidents when it is determined that the log data of past accidents is similar to the current log data; providing the incident information; 13. An information processing method comprising:

10. An acquisition step of acquiring log data related to advertisement delivery from each server device in advertisement delivery in which a plurality of server devices cooperate with each other to deliver advertisements; A learning procedure in which the log data acquired from each of the server devices is input as learning data into an AI to allow the AI ​​to learn the log data; a determination step of inputting the log data acquired from each of the server devices into the AI ​​and determining whether or not the log data from a past accident is similar to the current log data; a generation step of automatically generating incident information based on cases of past accidents when it is determined that the log data of past accidents is similar to the current log data; a provision step for providing the incident information; An information processing program characterized by causing a computer to execute the above.

Images