Memory medium support system

Abstract

To provide highly convenient failure prediction and related services for users of a storage medium.SOLUTION: A support system comprises a user terminal 2 on which a storage device SD is mounted, the storage device sold from a seller B to a user U and used by the user U, and a failure prediction diagnostic program transferred from the seller B to the user U, the program, when being installed in the user terminal 2 with the storage device SD mounted thereon, causing the user terminal 2 to exert a function of executing control processing of predicting a failure of the storage device SD and when a failure prediction diagnostic result represents that support is required, outputting an alert. The failure prediction diagnostic program causes the user terminal 2 with the storage device SD mounted thereon, in an alert output state to function as a medium of representing to the seller B that the user U has a right to receive the support.SELECTED DRAWING: Figure 12

Description

[Technical field]

[0001] The present invention relates to storage media support systems. [Background technology]

[0002] Conventionally, storage media such as hard disk drives have been provided with a function for performing self-diagnosis of the storage media itself and acquiring the resulting SMART information (SMART function: Self-Monitoring, Analysis and Reporting Technology function). There is also technology for performing fault diagnosis of hard disks using the SMART information (for example, see Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2018-173809 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the prior art including the above-mentioned Patent Document 1, the occurrence of a failure was merely identified by the fact that a predetermined parameter included in the smart information exceeded a predetermined value. Furthermore, even if the failure was discovered when it was still minor, that is, at an early stage, once a storage medium failed, the failure progressed rapidly, and even if an attempt was made to copy the data stored in the storage medium, the data could not be read out in many cases. Thus, there has been a demand for fault prediction and related services that are highly convenient for users of storage media.

[0005] The present invention has been made in view of the above circumstances, and has an object to provide highly convenient failure prediction and related services for users of storage media. [Means for solving the problem]

[0006] In order to achieve the above object, a storage medium support system according to one aspect of the present invention comprises: an information processing device equipped with a predetermined storage medium that is sold to a user by a seller and used by the user; a program which, when installed in the information processing device having the specified storage medium mounted therein, causes the information processing device to perform a function of executing a control process for predicting a failure of the specified storage medium and outputting an alert when a failure prediction diagnosis result indicates that support is required, the program being transferred from the seller to the user; Equipped with The information processing device in which the specified storage medium is mounted, in the state in which the alert is output, is made to function as a medium for indicating to the seller that the user has the right to receive the support. Effect of the Invention

[0007] According to the present invention, it is possible to provide a highly convenient failure prediction and related services for users of storage media. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram showing an example of a configuration of an information processing system including a server according to a first embodiment of an information processing device of the present invention. [Diagram 2] 2 is a block diagram showing an example of a hardware configuration of a server in the information processing system of FIG. 1. [Diagram 3] 2 is a block diagram showing an example of a hardware configuration of a user terminal in the information processing system of FIG. 1. [Figure 4] 4 is a diagram illustrating an example of a functional configuration of an information processing system including the server and the user terminal illustrated in FIGS. 2 and 3. FIG. [Diagram 5] 5 is a diagram showing an example of conditions for failure prediction executed by a server having the functional configuration of FIG. 4. [Figure 6] 5 is a diagram for explaining an overview of a method for calculating a total state value by the server in FIG. 4. [Figure 7] 5 is a diagram showing an example of a failure prediction result generated by the server in FIG. 4 and presented to a user via a user terminal. FIG. [Figure 8] 8 is a diagram showing an example of a failure prediction result generated by the server in FIG. 4 and presented to a user via a user terminal, the example being different from that in FIG. 7. FIG. [Figure 9] 5 is a diagram for explaining an example of a proactive response of this service realized by a server having the functional configuration of FIG. 4. [Figure 10] FIG. 5 is a diagram illustrating features of a failure prediction technique using a server having the functional configuration of FIG. 4. [Figure 11] 5 is a diagram showing an example of a screen including a failure prediction diagnosis result provided to a user terminal by a server having the functional configuration of FIG. 4. [Figure 12] FIG. 11 is a diagram showing an example of an overview of the present service of the second embodiment using a user terminal according to an embodiment of the information processing device of the present invention. [Figure 13] FIG. 11 is a diagram for explaining the overall flow of the present service according to the second embodiment. [Figure 14] FIG. 11 is a diagram illustrating a basic commercial flow of the present service according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] First, a brief description will be given of a service (hereinafter, referred to as "this service") to which an information processing system including a server according to an embodiment of the information processing device of the present invention is applied. This service predicts failures in storage devices used in information processing devices, such as hard disk drives and solid state drives. In other words, this service acquires smart information from a storage device provided in an information processing device (hereinafter referred to as a "user terminal") managed by the user, performs a failure prediction diagnosis of the storage device based on the smart information, and notifies the user of the results of the failure prediction diagnosis, etc.

[0010] Here, SMART information refers to information about the status of a storage device that can be obtained by the SMART function, such as product specifications of the storage device, self-diagnosis results, and information on the degree of use that can be obtained by the SMART function. Specifically, for example, the SMART information includes information on items such as the frequency of occurrence of various errors, temperature, cumulative usage time, number of startups, and usage information on bad sectors, pending sectors, and uncorrected sectors for the storage device. For example, the SMART information in this example also includes information on increased torque to compensate for deterioration of the spindle motor and bearings. For example, the SMART information may also include parameters such as total written LBA (sector number) / total read LBA. Here, these items are merely examples adopted in this example, and the SMART information includes information on various items related to the status of the storage device. In addition, when a virtual server is configured in the user terminal, information necessary for configuring the virtual server may also be included in the smart information. By combining these pieces of information contained in smart information, this service is able to perform highly accurate fault prediction diagnosis.

[0011] Furthermore, in the failure prediction diagnosis of this service, multiple levels are adopted, which are set from the viewpoint of the probability of the storage device failing in a specified time period after the prediction (for example, from the viewpoint of failure probability). In other words, among these multiple levels, the specified level that corresponds to the storage device to be diagnosed is determined as the failure prediction diagnosis result. This allows the user to take measures for the storage device according to the specified level. In the following example, the storage device is a hard disk drive, and a predetermined level out of five levels is determined as the failure prediction diagnosis result.

[0012] An information processing system including a server according to an embodiment of an information processing device of the present invention, and a first embodiment and a second embodiment to which the information processing system is applied will be described below in order. [First embodiment]

[0013] FIG. 1 is a diagram showing an example of the configuration of an information processing system including a server according to a first embodiment of an information processing device of the present invention. The information processing system shown in FIG. 1 is configured to include a server 1 managed by a service provider of this service, and user terminals 2-1 to 2-n used by n users (n is an arbitrary integer value equal to or greater than 1), respectively. The server 1 and each of the user terminals 2-1 to 2-n are connected to one another via a predetermined network NW such as the Internet. Here, it is assumed that each of the user terminals 2-1 to 2-n includes a storage device SD-1 to SD-n, as shown in FIG. In the user terminal 2, dedicated application software (for example, a failure prediction program described below) is installed so that the user U can receive this service.

[0014] In the following, when there is no need to distinguish between the user terminals 2-1 to 2-n, they will be collectively referred to as the "user terminal 2." Furthermore, when referring to the user terminal 2, the storage devices SD-1 to SD-n will be collectively referred to as the "storage device SD."

[0015] First, as a premise, the service provider of this service has been providing a data recovery service for recovering data from a broken storage device SD in response to a request from a user before providing this service. Therefore, the service provider has been provided by the user with a storage device SD that has already broken down or is immediately before the breakdown (e.g., malfunctioning), and has been able to copy data from that storage device SD. Therefore, the service provider possesses data such as the cause of the breakdown of the storage device SD and the smart information at that time. Furthermore, as a result of repairing the storage device SD in order to copy data from it, the service provider also possesses the smart information of the storage device SD after the breakdown, which cannot be read when the storage device SD is normally installed in the user terminal 2. Such information as the model number of the failed storage device SD, the cause of the failure, smart information, etc., is stored and managed in a learning data DB 81 provided in the server 1 as learning data used in the learning process described later.

[0016] In a learning process described later, the server 1 uses the learning data stored in the learning data DB 81 to generate or update a model for performing a failure prediction diagnosis of the storage device SD, and stores and manages the model in the learning data DB 81.

[0017] In the example of the first embodiment, the smart information of the storage device SD acquired by each of the user terminals 2 is transmitted to the server 1. The server 1 executes an inference process using the transmitted smart information and the model stored in the model DB 82, thereby performing a failure prediction diagnosis of the storage device SD.

[0018] Here, the smart information may include values ​​for more than 100 parameters (items), including the temperature of the storage device SD, vibration during writing, number of times the power was turned on / off, number of communication errors, occurrence rate of writing errors, average number of erasures and maximum number of erasures, depending on the manufacturer and model of the storage device SD. More details will be provided later, but the failure prediction diagnosis in this service obtains parameter values ​​for approximately 300 items contained in the smart information of the storage device SD being diagnosed. Then, the acquired values ​​of each item are compared with the first index set for each item, and a diagnosis result (hereinafter referred to as "item diagnosis result") from the viewpoint of each item (micro viewpoint) is obtained according to a predetermined first judgment method using the result of the comparison. That is, the first index and the predetermined first judgment method using the result of the comparison with the first index are one of the models used in this service. That is, the first index and the first judgment method are updated daily by a learning process. Furthermore, a value indicating the overall condition of the storage device SD to be diagnosed (hereinafter referred to as the "overall condition value") is obtained from the values ​​of each of these acquired items, and this overall condition value is compared with the second index, and a diagnosis result (hereinafter referred to as the "overall diagnosis result") from the perspective of the overall condition (macro perspective) of the storage device SD to be diagnosed is obtained according to a predetermined second judgment method that uses the results of the comparison. That is, the second index and the predetermined second judgment method that uses the results of the comparison with the second index are one of the models used in this service. That is, the second index and the second judgment method are updated daily by a learning process. Then, based on the micro diagnosis result and the macro diagnosis result, a failure prediction diagnosis result for the storage device SD is determined as a final result. The failure prediction diagnosis result determined in this manner makes it possible to diagnose failures in the storage device SD in advance with a high degree of accuracy.

[0019] At this time, the smart information and the like of the storage device SD transmitted to the server 1 are stored in the learning data DB 81 and managed. The server 1 appropriately executes the learning process to update the model (the first index and the first judgment method, and at least a part of the second index and the second judgment method) for performing failure diagnosis prediction of the storage device SD stored in the model DB 82. As a result, the accuracy of failure prediction diagnosis is improved day by day.

[0020] FIG. 2 is a block diagram showing an example of a hardware configuration of a server in the information processing system of FIG.

[0021] The server 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0022] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 . The RAM 13 also stores information necessary for the CPU 11 to execute various processes as appropriate.

[0023] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0024] The input unit 16 is made up of various hardware such as a keyboard and a mouse, and is used to input various types of information. The output unit 17 is composed of various hardware such as a liquid crystal display and a speaker, and outputs various information. The storage unit 18 is composed of a hard disk, a DRAM (Dynamic Random Access Memory), etc., and stores various information. The communication unit 19 controls communication with other devices (for example, the user terminal 2 in the example of FIG. 1) via a network NW including the Internet.

[0025] The drive 20 is provided as necessary. Removable media 31, which may be a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is appropriately mounted in the drive 20. The program read from the removable media 31 by the drive 20 is installed in the storage unit 18 as necessary. The removable media 31 can also store various pieces of information stored in the storage unit 18 in the same manner as the storage unit 18.

[0026] FIG. 3 is a block diagram showing an example of a hardware configuration of a user terminal in the information processing system of FIG.

[0027] The user terminal 2 includes a CPU 41 , a ROM 42 , a RAM 43 , a bus 44 , an input / output interface 45 , an input unit 46 , an output unit 47 , a storage unit 48 , a communication unit 49 , and a drive 50 .

[0028] The CPU 41 executes various processes according to a program recorded in the ROM 42 or a program loaded from the storage unit 48 into the RAM 43 . The RAM 43 also stores information necessary for the CPU 41 to execute various processes as appropriate.

[0029] The CPU 41, the ROM 42, and the RAM 43 are connected to one another via a bus 44. An input / output interface 45 is also connected to the bus 44. An input unit 46, an output unit 47, a storage unit 48, a communication unit 49, and a drive 50 are connected to the input / output interface 45.

[0030] The input unit 46 is composed of various hardware such as a keyboard and a mouse, and is used to input various information. The output unit 47 is composed of a display unit D including a liquid crystal display and various hardware such as a speaker, and outputs various information. The storage unit 48 is composed of a storage device SD, a DRAM, etc., and stores various information. Note that the storage device SD is assumed to have the above-mentioned SMART function.

[0031] The communication unit 49 controls communication with other devices (for example, the server 1 in the example of FIG. 1) via a network NW including the Internet.

[0032] The drive 50 is provided as necessary. Removable media 61, which may be a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is appropriately mounted in the drive 50. The program read from the removable media 61 by the drive 50 is installed in the storage unit 48 as necessary. The removable media 61 can also store various pieces of information stored in the storage unit 48 in the same manner as the storage unit 48.

[0033] The server 1 and the user terminal 2 configured in this manner are capable of executing various processes through cooperation between the various hardware and software components.

[0034] For example, the server 1 and the user terminal 2 have a functional configuration as shown in FIG. 4 when executing various processes including the failure prediction diagnosis process and the learning process. The failure prediction diagnosis process refers to a series of processes in which the server 1 acquires smart information from the user terminal 2, executes a failure prediction diagnosis based on the smart information, and presents the failure prediction diagnosis result to the user terminal 2. The learning process refers to a process in which the server 1 executes machine learning to generate or update a model (a first index and a first judgment method, and a second index and a second judgment method) used in the failure prediction process.

[0035] When the failure prediction diagnosis process is executed in the server 1, a learning data management unit 72, a failure prediction diagnosis unit 73, and a failure prediction diagnosis result presentation control unit 74 function in the CPU 11.

[0036] When a failure prediction diagnosis process is executed, the learning data management unit 72 obtains a model (first index and first judgment method, and second index and second judgment method) from the learning data DB 81 and provides it to the failure prediction diagnosis unit 73.

[0037] In the failure prediction diagnosis unit 73, when failure prediction diagnosis processing is executed, a smart information acquisition unit 731, a comprehensive state calculation unit 732, an item diagnosis unit 733, a comprehensive diagnosis unit 734, and a final diagnosis result determination unit 735 function.

[0038] Here, a smart monitoring unit 91 functions in the CPU 41 of the user terminal 2 that is the target of the failure prediction diagnosis process. The smart monitoring unit 91 acquires smart information from the storage device SD mounted in the above-mentioned user terminal 2 and transmits it to the server 1. Then, the smart information acquisition unit 731 of the server 1 acquires the values ​​of N items (N is an integer value of 2 or more) from among the multiple items of the smart information transmitted from the user terminal 2.

[0039] The overall status calculation unit 732 calculates an overall status value indicating the overall status of the storage device SD of the user terminal 2 from the values ​​of the N items acquired by the smart information acquisition unit 731 according to a predetermined calculation method that uses the N items of smart information as parameters.

[0040] The item diagnosis unit 733 compares the values ​​of at least some of the N items acquired by the smart information acquisition unit 731 with a first index set for each item, and determines the item diagnosis result regarding failure prediction according to a predetermined first judgment method using the result of the comparison.

[0041] The final diagnosis result determination unit 735 determines a failure prediction diagnosis result as the final diagnosis result for the storage device SD of the user terminal 2 based on the item diagnosis results determined by the item diagnosis unit 733 and the overall diagnosis result determined by the overall diagnosis unit 734.

[0042] The failure prediction diagnosis result presentation control unit 74 executes control for presenting the failure prediction diagnosis result, which is the final diagnosis result of the failure prediction diagnosis unit 73, to the user via the user terminal 2. That is, the failure prediction diagnosis result presentation control unit 74 transmits the failure prediction diagnosis result, which is the final diagnosis result of the failure prediction diagnosis unit 73, to the user terminal 2 via the communication unit 19. Then, in the CPU 41 of the user terminal 2, the failure prediction diagnosis result presentation unit 92 functions. That is, the failure prediction diagnosis result presentation unit 92 receives the failure prediction diagnosis result via the communication unit 49 and displays it on the display unit D, thereby presenting it to the user.

[0043] Here, as a specific example of the failure prediction diagnosis result, as described later with reference to Figures 7 and 8, either a first type that recommends backing up data from the storage device SD of the user terminal 2 or a second type level that does not require backing up data from the storage device SD is determined. Further, the first level is divided into M levels (M is an integer value of 2 or more, and M=2 in the examples of FIGS. 7 and 8). The second level is divided into L levels (L is an integer value of 2 or more independent of M, and L=3 in the examples of FIGS. 7 and 8). In this case, the failure prediction diagnosis unit 73 (final diagnosis result determination unit 735) determines a predetermined level from among (M+L) levels (levels 1 to 5 in the examples of Figures 7 and 8) as the failure prediction diagnosis result. The failure prediction diagnosis result presentation control unit 74 executes control to present the predetermined level to the user via the user terminal 2 as a failure prediction diagnosis result, which is the final diagnosis result of the failure prediction diagnosis unit 73 . Furthermore, when the predetermined level is the first level, the failure prediction diagnosis result presentation control unit 74 executes control to output an alert from the user terminal 2 in which the storage device SD is mounted. A specific example of the alert will be described later with reference to Figs. 8 and 11.

[0044] When the learning process is executed in the server 1, a learning unit 71 and a learning data management unit 72 function in the CPU 11. The learning section 71 includes an item diagnostic learning section 711 and a comprehensive diagnostic learning section 712 .

[0045] As described above, the smart information and the like obtained from a plurality of storage devices whose fault or normal states are known is stored as learning data in the learning data DB 81. Here, the learning data also includes failure prediction diagnosis results previously output from the failure prediction diagnosis unit 73 (failure prediction diagnosis results for various user terminals 2). Therefore, the learning data management unit 72 extracts learning data from the learning data DB 81 and provides it to the learning unit 71 .

[0046] The item diagnosis learning unit 711 executes a predetermined machine learning process using the learning data, and uses the results to generate or update at least a part of the first index and the first judgment move (a part of the model). The comprehensive diagnostic learning unit 712 executes a predetermined machine learning process using the learning data, and uses the results to generate or update at least a portion of the second index and the second judgment move (a portion of the model).

[0047] The learning data management unit 72 stores the models (the first index and the first judgment method, and the second index and the second judgment method) generated or updated by the learning unit 71 in the learning data DB 81.

[0048] The outline and configuration of the information processing system including the server 1 of the first embodiment have been described above with reference to Fig. 1 to Fig. 4. Below, Fig. 5 and Fig. 6 are diagrams showing an example of the first index and the first determination method, and the second index and the second determination method in the information processing system of the above-mentioned first embodiment. FIG. 5 is a diagram showing an example of conditions for failure prediction executed by a server having the functional configuration of FIG.

[0049] The table in Figure 5 shows an example of a conditional expression for evaluating each of the five levels. The conditional expressions in the table of Figure 5 use threshold values ​​generated or updated by the learning process of the server 1 (e.g., learning process using a machine learning algorithm) to determine the conditions of smart information that correspond to each level. Then, for the storage device SD to be judged, the condition expressions are applied to the smart value acquired from that storage device SD, in order starting from level 5. If the result of applying the condition expression is a match, the level of that condition expression is output as the result of the failure prediction diagnosis.

[0050] Specifically, for example, the conditional expression is applied when the SMART information of the storage device SD shows that the worst value of the number of uncorrectable sectors (ID198) is 200, the worst value of the temperature (ID194) is 70, the worst value of the read error rate (ID1) is 50, the number of reallocated sectors (ID5) is 30, the number of times the spindle motor has rotated / stopped (ID4) is 38, and the number of sectors waiting for replacement processing (ID197) is 0. In addition, the overall evaluation value of the storage device SD (for example, the area S in FIG. 7) is also included in the condition formula for each level, as will be described in detail later with reference to FIG. 7. It is assumed that the overall evaluation value of the storage device SD is 70 (for example, the area Sa in FIG. 7 is 70). Such a condition for the overall evaluation value is also included in the condition formula.

[0051] As a result, the condition in the second line (confirmation) of conditional expression 2 is met, namely, the worst value of the number of uncorrectable sectors (ID198) is less than 227, the worst value of the temperature (ID194) is 65 or more, the worst value of the read error rate (ID1) is 57 or less, the number of reallocated sectors (ID5) is 23 or more, the number of times the spindle motor has rotated / stopped (ID4) is 36 or more, and the number of sectors awaiting replacement processing (ID197) is less than 1. In addition, the condition that the overall evaluation value (area SA in FIG. 7) is 50 or more in the second line of the conditional expression is met. As a result, the overall evaluation of the storage device SD is determined to be at the second level.

[0052] Here, Sa in Fig. 5 corresponds to a comprehensive evaluation value. The numerical value to be compared with Sa (for example, 60 in the first line of the fifth level) is an example of a second index. Also, the symbol between Sa and the numerical value to be compared with it (for example, "=>" (the left side is equal to or greater than the right side) in the first line of the fifth level) is an example of a second judgment method.

[0053] Moreover, sa in FIG. 5 corresponds to the numerical value of a certain item included in the smart information. The numerical value to be compared with sa (for example, 50 in the first line of the fifth level) is an example of a first index. Moreover, the symbol between sa and the numerical value to be compared with it (for example, ">" (the left side is greater than the right side) in the first line of the fifth level) is an example of a first judgment method. As shown in FIG. 5, the first index and the first judgment method are defined for each of a plurality of items. In the model DB 82 of the first embodiment, a set of judgment formulas for judging whether or not each of these levels applies is stored as a model.

[0054] Here, the smart information that can be acquired from the storage device SD differs depending on the manufacturer, model number, manufacturing date, etc. Therefore, different conditional expressions are used appropriately depending on the manufacturer, model number, manufacturing date, etc. of the storage device SD. The example in FIG. 5 is a conditional expression relating to a storage device SD with a certain manufacturer, model number, and manufacturing date.

[0055] In the explanation of Figure 5, the overall evaluation is performed by determining the level of the storage device SD when the smart information of the target storage device SD matches the judgment formula for each level. However, each formula in the example of Figure 5 is generated or updated based on the following concept.

[0056] FIG. 6 is a diagram for explaining an outline of a method for calculating the overall state value by the server in FIG. For convenience of explanation, FIG. 6A limits the number of smart information items to 6 (N=6), and explains an overview of a method for calculating a comprehensive state value from values ​​of six smart information items. In the example of Figure 6 (A), six items of smart information are assigned at equal intervals around the circumference of a specified circle, and an axis from the center of the specified circle to the circumference is assigned to each of the six items, and the coordinates of each axis (numbers from 0 to 5) are a radar chart showing the value of the assigned item. That is, in the radar chart of the example of FIG. 6(A), six items are assigned clockwise from the top: temperature, read / write amount, number of bad sectors, usage time, number of startups, and startup time. The overall status calculation unit 732 in Fig. 4 plots each of the values ​​of the six items acquired from the user terminal 2 at the corresponding coordinates of the assigned axis. In the radar chart of the example in Fig. 6(A), clockwise from the top, "3" is plotted as the temperature value, "4" is plotted as the read / write amount value, "5" is plotted as the number of bad sectors value, "2" is plotted as the usage time value, "1" is plotted as the number of startups value, and "5" is plotted as the time required for startup. Thus, in the example of Figure 6, the values ​​of N items are not the raw data of each item (e.g., temperature is 65 degrees), but are normalized to be one of the values ​​between 0 and 5 based on the first index and first judgment method used in item diagnosis. In this case, the overall state calculation unit 732 in FIG. 4 calculates, as the overall state value, the area S of a closed curve formed by connecting each of the plotted points of the six items as shown in FIG. 6(A). That is, in the example of FIG. 6(A), for example, the area SA is used as the second index, and the second judgment method is a method of comparing the area S of the closed curve formed by connecting each of the points of the six items plotted on the radar chart of FIG. 6(A) with the area SA as the second index, and determining the overall judgment result based on the comparison result.

[0057] In the example of FIG. 6(A), for convenience of explanation, only N=6 items out of many items of smart information are reflected in the radar chart. However, as described above, the smart information may contain more than 1000 items including the manufacturer and model of the storage device SD. Therefore, in reality, as shown in Fig. 6(B), it becomes a polygon with N=number of vertices (the more N, the closer the polygon becomes to a circle).

[0058] FIG. 7 is a diagram showing an example of a failure prediction result generated by the server of FIG. 4 and presented to a user via a user terminal. In the example of Figure 7, as an example of a failure prediction result, information identifying the storage device SD (here, the string "Drive 01" indicating that this is the first drive) is shown, along with the string "The drive is operating normally," indicating that it is at the third level. That is, in the example of Figure 7, the failure prediction diagnosis unit 73 (final diagnosis result determination unit 735) of Figure 4 determines as a failure prediction diagnosis result either a first type level that recommends backing up data from the storage device SD of the user terminal 2, or a second type level that does not require backing up data from the storage device SD. The first level is divided into two levels: the fifth level (danger level) and the fourth level (caution level). The second level is the normal level, but is further divided into three levels, namely, the first level, the second level, and the third level. Here, the first level is the same level as a new product. The second level is the same level as a product that has been used for 1 to 3 years. The third level is the same level as a product that has been used for 3 years or more. That is, the failure prediction diagnostic means, the failure prediction diagnostic unit 73 (final diagnosis result determination unit 735) in FIG. 4, determines a predetermined level out of five levels (the third normal level in the same figure) as the failure prediction diagnostic result. As shown in Figure 7, among the failure prediction results for the user, the results indicating soundness are presented in three levels as the level of aging deterioration that does not correspond to a failure and has no signs of failure under normal conditions. This allows the user to understand the deterioration over time and the possibility that a failure prediction will one day present a warning of danger.

[0059] FIG. 8 is a diagram showing an example of a failure prediction result generated by the server in FIG. 4 and presented to a user via a user terminal, and is a diagram showing an example different from that in FIG.

[0060] FIG. 8 is a diagram showing an example of a screen on which a result indicating that the device is unsound is output, out of the results of failure prediction for the user by the server having the functional configuration of FIG. In the screen shown in Figure 8 (A), as an example of a failure prediction, information identifying the storage device SD (here, the string "Drive 01" indicating that this is the first drive) is displayed, along with the string "There is a possibility of a failure, so please make a backup just in case," indicating that it is at the fourth level. That is, in the example of Figure 8 (A), the failure prediction diagnosis unit 73 (final diagnosis result determination unit 735) of Figure 4 diagnoses the failure prediction diagnosis result as being the fourth level (caution level) of the first type level that recommends backing up data from the storage device SD of the user terminal 2. That is, the failure prediction diagnostic means, the failure prediction diagnostic section 73 (final diagnostic result determining section 735) in FIG. 4, determines the fourth level as a predetermined level among the five levels as the failure prediction diagnostic result. As a result, as shown in the example of FIG. 8(A), a message is presented to the user indicating that the storage device SD has been determined to be at the fourth level.

[0061] FIG. 8 is a diagram showing an example of a screen on which a result indicating that the device is unsound is output, out of the results of failure prediction for the user by the server having the functional configuration of FIG. In the screen shown in Figure 8 (B), as an example of a failure prediction, information identifying the storage device SD (here, the string "Drive 01" indicating that it is the first drive) is displayed, along with the string "There is a possibility that it may fail at any moment, so please make a backup immediately," indicating that it is at the fifth level. That is, in the example of Figure 8 (B), the failure prediction diagnosis unit 73 (final diagnosis result determination unit 735) of Figure 4 diagnoses the failure prediction diagnosis result as being the fifth level (risk level) of the first type level that recommends backing up data from the storage device SD of the user terminal 2. That is, the failure prediction diagnostic means, the failure prediction diagnostic section 73 (final diagnostic result determining section 735) in FIG. 4, determines the fifth level as a predetermined level among the five levels as a failure prediction diagnostic result. As a result, as shown in the example of FIG. 8(B), a message is presented to the user indicating that the storage device SD has been determined to be at the fifth level.

[0062] As shown in Figure 8(A) and Figure 8(B), among the failure prediction results for the user, the results that indicate an unhealthy state are presented in two levels as a level of backup recommendation that corresponds to a failure in the future (or already corresponds to a failure). As a result, the user is recommended to make a backup based on the failure prediction, so the user can take appropriate action for backup.

[0063] FIG. 9 is a diagram for explaining an example of a proactive response of this service, which is realized by a server having the functional configuration of FIG. As shown in Fig. 9, in the conventional approach, a failure occurs after normal operation. In other words, it is normal for a user to recognize a failure when the device no longer operates normally. Specifically, when a user attempts to read data stored in a storage device SD provided in the user terminal 2, the operating system of the user terminal 2 will attempt to read the data multiple times, and if the reading still fails, a warning or the like will usually be displayed. That is, the user will become aware of the malfunction at the point when the data reading has already failed.

[0064] In such a situation, it is difficult for the user to fix the problem by himself. That is, among storage devices SD, especially hard disk drives, cannot be disassembled and repaired unless in a clean room. Also, repairs must be performed by using sound parts from the same type of storage device SD. Also, for example, if data stored in a storage device SD cannot be read because it is logically (bit-wise) corrupted, it is necessary to read the data as is, check the bit sequence of the corrupted data, and repair the broken bit sequence. This is something that the user cannot cope with.

[0065] Therefore, users who are unable to repair their own devices will request data recovery from a data recovery company at the time a malfunction occurs. As a result, the data recovery company performs data recovery. This operation may take more than one week if the user terminal 2 is a home personal computer, or more than three weeks if the user terminal 2 is a business server and the data is distributed and stored in multiple storage devices SD (for example, when a RAID system is used).

[0066] As mentioned above, even before the provision of this service, the service provider of this service has been providing a data recovery service to recover data from broken storage devices SD in response to requests from users. The service provider of this service is currently the largest in the industry and has achieved one of the best recovery rates, but even if a data recovery request is made immediately after the occurrence of a failure, the recovery rate is only 95.2%, not 100%. Furthermore, the reality is that data recovery requires a considerable amount of money. This shows how difficult it is to recover data from a storage device SD after a failure.

[0067] In contrast, when the failure prediction of the first embodiment is performed, the failure prediction is performed during the period of normal operation before the occurrence of the failure in the above-mentioned conventional system. Therefore, data copying can be performed within the period of normal operation. As a result, data recovery work is not required, and the data migration rate (data backup completion rate) is close to 100%. In other words, data problems such as being unable to actually read data can be prevented. In other words, by using this service to predict failures, data problems in the world can be reduced to zero. The reason why it is said to be close to 100% is because strictly speaking, the data transfer rate is not 100%. In other words, there is a possibility that a very small number of bad sectors or minor logical errors have occurred in the areas of the storage device SD that are not accessed. Such bad sectors in the areas that are not accessed are not counted in the SMART information. In other words, for example, even if the condition for normality is that the number of bad sectors is zero, there is a possibility that there are bad sectors that have not yet been detected and data transfer will fail. For example, there is also the possibility that a completely new failure may occur during data migration. For this reason, a perfect 100% data migration rate cannot be achieved.

[0068] FIG. 10 is a diagram showing features of a failure prediction technique using a server having the functional configuration of FIG. The failure prediction method shown in FIG. 10(A) makes a judgment by individually using each item included in the smart information, without using the above-mentioned overall evaluation value. 10A, the failure degree is predicted to be low (second level) when the worst value of the number of unrepairable sectors is less than 10 and the worst value of the temperature is equal to or greater than 30 and less than 60. Therefore, for example, when the worst value of the number of unrepairable sectors is 5 and the worst value of the temperature is 50, this condition is met and the failure degree (degree of failure) is determined to be low (second level). Here, in the example shown in Fig. 10(A), only the second level and the fourth level exist. This indicates that in the example shown in Fig. 10(A), as a result of failure prediction, no sign of failure is observed, and only the second level exists as a level at which backup of backup is unnecessary, and the fourth level 4 exists as a level at which backup is recommended. In this way, the user is notified of a level that is determined based solely on the presence or absence of a symptom of a malfunction.

[0069] In contrast, the failure prediction method shown in FIG. 10(B) makes a judgment using the above-mentioned overall evaluation value and each item included in the smart information individually. That is, in the example shown in FIG. 10(B), if the overall evaluation value meets the predetermined condition, the worst value of the number of unrecoverable sectors is less than 10, and the worst value of the temperature is 0 or more and less than 30, the failure degree is predicted to be the first level of low. If the overall evaluation value meets the predetermined condition, the worst value of the number of unrecoverable sectors is less than 10, and the worst value of the temperature is 30 or more and less than 45, the failure degree is predicted to be the second level of low. If the overall evaluation value meets the predetermined condition, the worst value of the number of unrecoverable sectors is less than 10, and the worst value of the temperature is 45 or more and less than 60, the failure degree is predicted to be the third level of low. If the overall evaluation value meets the predetermined condition, the worst value of the number of unrecoverable sectors is less than 10, and the worst value of the temperature is 60 or more, the failure degree is predicted to be the fourth level of medium. In this way, the failure prediction method shown in FIG. 10(B) makes a judgment by individually using the above-mentioned overall evaluation value and each item included in the smart information, so that a failure level that would have been simply judged to be low (second level) in the failure prediction method shown in FIG. 10(A) can be judged to be the first to third levels. As a result, the user is notified in stages, from the first level to the third level. Therefore, when the third level is notified to the user, the user is notified that the fourth level is approaching, that is, the failure prediction result indicates that the storage device SD is likely to fail and backup is recommended. This enables the user to prepare in advance for backup, such as by preparing software and hardware for backing up the storage device SD.

[0070] FIG. 11 is a diagram showing an example of a screen including a failure prediction diagnosis result performed on a user terminal by a server having the functional configuration of FIG. As shown in FIG. 11, the failure diagnosis prediction result is output by being displayed on a display D which is an example of the output unit 147 included in the user terminal 2, for example. The failure prediction diagnosis results will display messages such as "SMART information warning," "The storage device you are using is likely to fail. Please back it up immediately," and "You can also request support from the mass retailer XX Company YY store where you purchased the device." In this way, the user terminal 2 outputs to the user the result of the failure prediction diagnosis by the server 1. This allows the user to complete the backup before the storage device SD fails, which is why a 100% data migration rate is achieved as shown in Figure 9.

[0071] As described above, the user can request support from the mass retailer where the user purchased the software, rather than performing the backup themselves. This service can also manage and present information for support at such mass retailers. This point will be described in detail in the explanation of the second embodiment below. [Second embodiment]

[0072] In the first embodiment, the failure prediction diagnosis process is executed in the server 1. That is, in the first embodiment, communication for transmitting the smart information acquired from the storage device SD to be diagnosed from the user terminal 2 to the server 1, and communication for transmitting the failure diagnosis prediction result from the server 1 to the user terminal 2 were required. In contrast to this, in the second embodiment, the failure prediction diagnosis process is executed in the user terminal 2. That is, in the second embodiment, communication between the user terminal 2 and the server 1 involved in the failure prediction diagnosis process is not required.

[0073] FIG. 12 is a diagram showing an example of an overview of the present service of the second embodiment using a user terminal according to an embodiment of the information processing device of the present invention.

[0074] As shown in FIG. 12, a USB memory storing a program (hereinafter referred to as a “failure prediction program”) for causing a computer (here, the CPU 41 of the user terminal 2) to execute a failure prediction diagnosis process is provided by a provider T to a seller B. Here, the provider T is a provider of this service (this service of the second embodiment) that causes the user terminal 2 to execute the failure prediction diagnosis process. The USB memory is provided by provider T to seller B via a third party as necessary.

[0075] Seller B is, for example, a mass retailer that sells various types of information processing devices BP, and sells the information processing devices BP equipped with a predetermined storage device SD together with a USB memory that stores a failure prediction program.

[0076] A user U purchases from a seller B an information processing device BP equipped with a predetermined storage device SD, together with a USB memory storing a failure prediction program. The user U inserts the USB into the information processing device BP and installs the failure prediction program into the information processing device BP, which then becomes the user terminal 2 of the second embodiment. That is, the hardware configuration of the user terminal 2 of the second embodiment is the same as that of Fig. 3. As a functional configuration of the user terminal 2 of the second embodiment, although not shown, in addition to the smart monitoring unit 91 and the failure prediction diagnosis result presentation unit 92 of Fig. 4, the CPU 41 further functions with a failure prediction diagnosis unit 73 and a failure prediction diagnosis result presentation control unit 74, and a model (the first index and the first judgment method, and the second index and the second judgment method) is stored in the storage unit 48. This allows the user terminal 2 to constantly execute the failure prediction diagnosis process.

[0077] Here, if the failure prediction diagnosis result indicates that support is required (for example, the first level described in Figures 6 and 7 of the first embodiment, i.e., the fourth level or fifth level), a specified alert (for example, see Figure 11) is displayed on the display unit D of the user terminal 2. In the second embodiment, a user terminal 2 equipped with a storage device SD in a state in which such an alert has been output can function as a medium for indicating to seller B that user U has the right to receive support from seller B.

[0078] In other words, when user U shows the user terminal 2 equipped with the storage device SD with the alert output, seller B can provide various types of support to user U, assuming that user U has the right to receive support. Here, "the user U presents the user terminal 2 equipped with a storage device SD with the alert output" means that the user terminal 2 is directly brought to the seller B, or a photograph of the user terminal 2 with the alert displayed is brought, or the seller B's terminal is notified by communication in a specified manner, or any other method is used to show that the user terminal 2 is supported by the seller B. In addition, support is a broad concept that includes not only inspection and repair of the user terminal 2 itself, but also things that can be decided freely by the seller B, such as selling a new information processing device BP to the user U in exchange for trading in the user terminal 2. In this way, by providing the service of the second embodiment, the user U will go to the seller B to receive support, which has the effect of encouraging seller B to have users U visit their store.

[0079] Furthermore, the failure prediction program can cause the user terminal 2 to exhibit a function of executing a control process that outputs an alert even when an error exists in the program itself. In the second embodiment, a user terminal 2 equipped with a storage device SD in a state in which an alert has been output indicating that there is an error in such a failure prediction program itself can function as a medium indicating to provider T that user U has the right to receive support from provider T.

[0080] In other words, when user U shows the user terminal 2 equipped with a storage device SD with an alert output indicating that there is an error in the failure prediction program itself, provider T can provide various types of support to user U, assuming that user U has the right to receive support. Here, "the user terminal 2 equipped with a storage device SD with an alert output is shown by the user U" means that the actual user terminal 2 is brought directly to the provider T, or the actual user terminal 2 is brought to the seller B after a contract has been made between the provider T and the seller B, or a photo of the user terminal 2 with an alert displayed is brought to the provider T or the seller B, or the terminal of the provider T or the seller B is notified by communication in a specified manner, or the user terminal 2 is shown in any manner so that it is clear that it is supported by the provider T. Moreover, the support referred to here is for alerts related to the failure prediction program, and therefore includes repair, replacement, or resale of the failure prediction program. Depending on the circumstances of the malfunction in the failure prediction program, it may be possible to deal with only the failure prediction program where the alert occurred, or it may affect all other failure prediction programs. In cases where it affects all other failure prediction programs, support may include seller B collecting inventory from the store, or provider T reporting the malfunction on seller B's website.

[0081] FIG. 13 is a diagram illustrating the overall flow of the present service according to the second embodiment. FIG. 14 is a diagram illustrating a basic commercial flow of the present service according to the second embodiment. 13, the USB is manufactured by a USB manufacturer, and third parties P1 and P2 are involved in the process from provider T to seller B. However, these are merely examples, and for example, the provider T may execute the processes (tasks) of third parties P1 and P2 to exclude third parties P1 and P2, or the processes (tasks) of third parties P1 and P2 may be shared among one or three or more third parties.

[0082] In step S1 in FIG. 13 and FIG.

[0083] In step S2 in FIGS. 13 and 14, provider T issues a license key. In step S21 of FIG. 14, the USB memory and the license key are shared and sent to a third party P1.

[0084] 13 and 14, the third party P1 performs kitting on the USB memory. For example, the third party P1 transfers an installer of a failure prediction program to the USB memory. In step S31 of FIG. 14, the third party P1 sends the USB memory (license key) to the third party P2.

[0085] 13 and 14, the third party P2 performs packaging for the USB memory (license key). For example, the third party P2 packages the USB memory (license key) into a product state. Here, packaging refers to work such as printing and packing the package. In step S41 of FIG. 14, the packaged USB memory (license key) is sent to distributor B as a product.

[0086] In step S5 in FIGS. 13 and 14, a seller B sells a product, a USB memory (license key), to a user U. In step S6 of FIG. 13, the user U purchases a USB memory (license key) as a product from the seller B. In step S7 in FIG. 13, seller B and user U make a payment at the register for the product, that is, the USB memory (license key). In step S8 of Fig. 13, seller B performs POS linkage. That is, in step S9 of Fig. 13, seller B manages sales information and stock confirmation of USB memory (license key), and in step S19 of Fig. 13, provider T manages sales information and stock confirmation of USB memory (license key). In step S10 in Fig. 13, seller B places an additional order for the manufacture of a USB memory to a USB manufacturer in response to the need for an additional order. For the person who has placed this additional order, the processes of steps S1 in Fig. 13 and 14 to step S41 in Fig. 14 are repeatedly executed, and the USB memory (license key) as the additionally ordered product is sent to seller B.

[0087] On the other hand, on the user U side, the processes from step S11 onward in FIG. 13 are executed. That is, in step S11 in FIG. 13 and FIG. 14, the user U inserts the USB memory into the user terminal 2 and installs the failure prediction program. In step S12 of FIG. 13, the user U inputs the license key into the user terminal 2. The user terminal 2 checks the consistency in step S13 in FIG. 13, and performs an activation process on the failure prediction program in step S14 in FIG. As a result, in the CPU 41 of the user terminal 2, in addition to the smart monitoring unit 91 and the fault prediction diagnosis result presentation unit 92 of Figure 4, the fault prediction diagnosis unit 73 and the fault prediction diagnosis result presentation control unit 74 also function, and the model (the first index and the first judgment method, and the second index and the second judgment method) is stored in the memory unit 48. This allows the user terminal 2 to constantly execute the failure prediction diagnosis process. Therefore, in step S15 of FIG. 13, the user U starts to use the user terminal 2 in which the storage device SD is installed.

[0088] 14, the user terminal 2 runs a failure prediction program as a resident program. That is, the user terminal 2 appropriately executes a failure prediction diagnosis process. In step S23 in FIG. 14, the user terminal 2 determines whether or not a malfunction has occurred. If no malfunction has occurred, that is, if the failure prediction diagnosis result is any of the first to third levels and the failure prediction program itself is normal, the answer is determined to be NO in step S23 of FIG. 14, and the process returns to step S22 of FIG. 14, and the subsequent processes are repeatedly executed. On the other hand, if a malfunction occurs, i.e., if the malfunction prediction diagnosis result is either the fourth or fifth level, or if a malfunction occurs in the malfunction prediction program itself, a specified alert is displayed on the user terminal 2, and the process proceeds to step S16 in FIG. 14.

[0089] Here, for example, it is assumed that the failure prediction diagnosis result is the fourth or fifth level, so that the result is determined as YES (a malfunction) in step S23 of Fig. 13 and an alert is generated. In this case, as described above, the user terminal 2 in which the storage device SD is mounted and in which the alert regarding the failure prediction of the storage device SD has been outputted functions as a medium for indicating to the seller B that the user U has the right to receive support from the seller B. That is, when a user terminal 2 equipped with a storage device SD in a state in which an alert regarding a predicted failure of the storage device SD has been output is presented to seller B, seller B provides support (alert response) as step S16 in Figures 13 and 14. At this time, in step S17 of FIG. 13, the third party P1 can cooperate as the seller B to handle the alert (provide support). In step S24 of Fig. 14, it is determined whether the malfunction has been resolved. If the malfunction has not been resolved, the determination is NO in step S24 of Fig. 14, and the process returns to step S16 of Fig. 14, where further support is performed. On the other hand, if the defect is resolved, the answer is determined to be YES in step S24 of Fig. 14, the process returns to step S22, and the subsequent processes are repeatedly executed. Here, if the method of resolving the defect is to repair the user terminal 2 itself in which the defect occurred and use it again by the user, then in step S22 of Fig. 14, the user terminal 2 itself runs a resident failure prediction program. On the other hand, if the method of reselling a new user terminal 2 is adopted as the method of resolving the defect, then in step S22 of Fig. 14, the resold user terminal 2 runs a resident failure prediction program.

[0090] Also, for example, assume that the failure prediction program is defective, so that the result is YES (defective) in step S23 of Fig. 13 and an alert is generated. In this case, as described above, the user terminal 2 equipped with the storage device SD in a state in which an alert indicating that the failure prediction program is defective is outputted functions as a medium indicating to the provider T that the user U has the right to receive support from the provider T. That is, when provider T is shown a user terminal 2 equipped with a storage device SD in which an alert has been output indicating that the failure prediction program is defective, provider T takes support action (responds to the alert) as step S18 in FIG. 13 (corresponding to step S16 in the example of FIG. 14). Then, in step S24 of Fig. 14, it is determined whether or not the malfunction has been resolved. If the malfunction has not been resolved, the determination is NO in step S24 of Fig. 14, and the process returns to step S16 of Fig. 14, where further support is performed. On the other hand, if the problem has been solved, the determination in step S24 of FIG. 14 is YES, the process returns to step S22, and the subsequent steps are repeatedly executed.

[0091] Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, etc., within the scope in which the object of the present invention can be achieved, are included in the present invention. In the above embodiment, failure prediction is performed using SMART information as information related to the storage device SD, but the present invention is not limited to this. Specifically, for example, as described above, configuration information of a file system or RAID system constructed using the storage device SD may also be used for learning and failure prediction diagnosis. Also, for example, firmware information regarding bad sectors may also be used for learning and failure prediction diagnosis. That is, information that the handling conditions for bad sectors differ depending on the firmware of the storage device SD and its version may be used for learning and failure prediction diagnosis.

[0092] Furthermore, apart from the above-mentioned smart information, terminal information and data exchange information may be used as information related to the user terminal 2. The terminal information refers to information on the state of the user terminal 2, other than the smart information. Specifically, for example, the terminal information may include information such as the usage rate of the CPU 41 of the user terminal 2, the usage rate of the RAM 43 of the user terminal 2, and the number of input / output operations per second of the communication unit 49 of the user terminal 2. Here, these are merely examples, and the terminal information may include all kinds of information on the state of the user terminal 2 other than the smart information. The data transfer information refers to information related to data transfer between a storage device SD provided in the user terminal 2 and other hardware devices (such as the CPU 41) provided in the user terminal 2. Specifically, for example, the data transfer information may include information such as the number of input / output operations per second, throughput, and latency information related to data input / output to and from the storage device SD. Here, these are merely examples, and the data transfer information may include all kinds of information related to data transfer between the user terminal 2 and the storage device SD provided in the user terminal 2. The server 1 can also calculate a highly accurate failure probability using the smart information, terminal information, and data transfer information. In this way, the server 1 can perform failure prediction diagnosis using "smart information, etc." that may include not only smart information but also terminal information and data exchange information.

[0093] In the above-described second embodiment, support provided by the seller B and the provider T can be freely decided by the seller B, such as inspecting and repairing the user terminal 2 itself, or selling a new information processing device BP to the user U by trading in the user terminal 2, but can also include the following: That is, for example, seller B can provide warranty support for user terminal 2 if the user terminal 2 is within the warranty period. Furthermore, for example, if the user terminal 2 is outside the warranty period and data can be copied from the storage device SD, seller B can introduce and sell another storage device (for example, an external hard disk drive) to the user.

[0094] Furthermore, for example, seller B can purchase the user terminal 2 as a used information processing terminal. At this time, if the user terminal 2 is not in a state where a failure is predicted (for example, a state where the above-mentioned fourth or fifth level alert has been issued), the alert of this service can be used as information for appraisal, such as purchasing the user terminal 2 at a lower price compared to when the user terminal 2 is in a healthy state (backup is not recommended, and backup is unnecessary (first to third levels)). Furthermore, at this time, the user U can expect to receive a higher purchase price by selling the user terminal 2 to seller B or the like as a used information processing terminal while the user terminal 2 is in a healthy state (a level where backup is not recommended and backup is not necessary (first to third levels)).

[0095] Also, for example, provider T or seller B may grant the failure prediction program the right to support data recovery of the storage device SD. This allows provider T to recover the data if user U requests support in the fourth level state and the storage device SD breaks down during data backup. This allows provider T to recover data from the storage device SD when the probability of failure is lower, thereby increasing the data recovery rate.

[0096] Also, for example, seller B can provide provider T with a storage device SD that has been removed as a result of support. As a result, provider T can use the storage device SD of levels 1 to 5 as a repair part for other storage devices SD, use it for research into rare cases of failure, collect data on the relationship between the state of failure and smart information, etc., and use it as learning data by receiving the storage device SD of levels 1 to 5. This is expected to improve the accuracy of failure prediction diagnosis. As described above, the premise is that the service provider of this service (Provider T) has been providing a data recovery service to recover data from broken storage devices SD in response to requests from users before providing this service. Therefore, the service provider (Provider T) has data recovery technology for storage devices SD and related technology for analyzing firmware for storage devices SD. This allows for efficient collection of learning data, etc.

[0097] In the above description of the second embodiment, the USB memory storing the failure prediction program is provided from the provider T to the seller B, but this is not particularly limited. That is, for example, the failure prediction program may be provided in any removable medium. Furthermore, the failure prediction program may be downloaded via the Internet.

[0098] Furthermore, for example, the above-described series of processes can be executed by hardware or software. In other words, the functional configuration in FIG. 4 is merely an example and is not particularly limited. That is, it is sufficient that the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and the type of functional block or database used to realize this function is not limited to the example of FIG. 4. The location of the functional block is also not particularly limited to that of FIG. 4 and may be arbitrary. For example, the functional block of the server 1 may be transferred to the user terminal 2, etc. The functional block of the user terminal 2 may be transferred to the server 1, etc. Furthermore, the server 1 and the user terminal 2 may be the same hardware.

[0099] Furthermore, for example, when a series of processes is executed by software, a program constituting the software is installed into a computer or the like from a network or a recording medium. The computer may be a computer implemented with dedicated hardware. Furthermore, the computer may be a computer capable of executing various functions by installing various programs thereon, such as a server, a general-purpose smartphone, or a personal computer.

[0100] Furthermore, for example, a recording medium containing such a program may not only be constituted by a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to the user, but may also be constituted by a recording medium that is provided to the user in a state in which it is pre-installed in the device main body.

[0101] In this specification, the steps of describing a program to be recorded on a recording medium include not only processes that are performed chronologically according to the order, but also processes that are not necessarily performed chronologically but are executed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is composed of a plurality of devices, a plurality of means, etc.

[0102] In other words, the information processing device to which the present invention is applied can take various embodiments having the following configurations.

[0103] That is, an information processing device to which the present invention is applied (the server 1 in the first embodiment, and the user terminal 2 in the second embodiment) An information processing device that performs a failure prediction diagnosis before a failure occurs on a storage medium (e.g., a storage device SD in FIG. 1), A smart information acquisition means (e.g., the smart information acquisition unit 731 in FIG. 4) for acquiring values ​​of N items (N is an integer value of 2 or more) from a target storage medium for failure prediction out of multiple items of smart information indicating the state of the storage medium (e.g., items such as temperature, read / write amount, number of bad sectors, usage time, number of startups, and startup time in FIG. 6); a comprehensive status calculation means (e.g., the comprehensive status calculation unit 732 in FIG. 4) that calculates a comprehensive status value (e.g., the area S in FIG. 6) indicating a comprehensive status of the target storage medium from the values ​​of the N items acquired from the target storage medium according to a predetermined calculation method using the N items as parameters; an item diagnosis means (e.g., the item diagnosis unit 733 in FIG. 4) for comparing at least some of the values ​​of the N items acquired from the target storage medium with a first index set for each item (e.g., in the description of FIG. 5, an index in which the worst value of the number of unrepairable sectors is 227) and determining an item diagnosis result related to failure prediction according to a predetermined first judgment method using the result of the comparison (e.g., in the description of FIG. 5, a judgment method in which the worst value of the number of unrepairable sectors is "less than" the first index); a comprehensive diagnosis means (e.g., the comprehensive diagnosis unit 734 in FIG. 4 ) for comparing the comprehensive state value with a second index (e.g., the area S is 50) and determining a comprehensive diagnosis result related to failure prediction according to a predetermined second judgment method using the result of the comparison (e.g., a judgment index that the area S is “less than” the second index); a failure prediction diagnostic means (for example, the final diagnostic result determination unit 735 in FIG. 4) for determining a failure prediction diagnostic result for the target storage medium based on the item diagnostic results and the overall diagnostic result; and, It will be enough to have this. This makes it possible to provide highly convenient failure prediction and related services for users of storage media.

[0104] The failure prediction diagnosis means can determine, as the failure prediction diagnosis result, either a first level that recommends backing up data from the target storage medium, or a second level that does not require backing up data from the target storage medium.

[0105] The device may further include an alert output control means that, when the failure prediction diagnosis result for the target storage medium is determined to be at the first level, executes control to output an alert from an installation device in which the target storage medium is installed.

[0106] The first level is divided into M levels (M is an integer value of 2 or more), The second level is divided into L levels (L is an integer value of 2 or more independent of M), The failure prediction and diagnosis means can determine a predetermined level out of (M+L) levels as the failure prediction and diagnosis result.

[0107] The N items are assigned at equal intervals on the circumference of a predetermined circle, an axis from the center of the predetermined circle to the circumference is assigned to each of the N items, and the coordinates of the axis indicate the values ​​of the assigned items; The overall state calculation means can calculate, when each of the values ​​of the N items obtained from the target storage medium is plotted on the corresponding coordinates of the assigned axis, the area of ​​a closed curve formed by connecting each of the plotted points of the N items as the overall state value.

[0108] At least one of the first index of a predetermined item and the first judgment method for the predetermined item can be set in consideration of a correlation with other items.

[0109] A learning means may generate or update at least a portion of the first index, the first judgment method, the second index, and the second judgment method based on the results of a predetermined machine learning process using smart information obtained from multiple storage media whose faulty or normal states are known.

[0110] Furthermore, the information processing device to which the present invention is applied can take various forms having the following configurations.

[0111] That is, a system to which the present invention is applied (for example, a support system for the service shown in FIG. 12) is An information processing device (e.g., the user terminal 2 in FIG. 12) equipped with a predetermined storage medium that is sold to a user by a seller and used by the user; a program (e.g., the failure prediction and diagnosis program of FIG. 12) which, when installed in the information processing device having the specified storage medium mounted therein, causes the information processing device to perform a function of executing a control process for predicting a failure of the specified storage medium and outputting an alert when a failure prediction and diagnosis result indicates that support is required, the program being transferred from the seller to the user; Equipped with It is sufficient that the information processing device equipped with the specified storage medium in the state in which the alert is output functions as a medium that indicates to the seller that the user has the right to receive the support. This makes it possible to provide highly convenient failure prediction and related services for users of storage media.

[0112] The program is provided by the provider to the distributor, the program causes the information processing device to perform a function of executing a control process to output a first alert when a failure prediction diagnosis result indicates that support is required, and to output a second alert when an error exists in the program itself; causing the information processing device in which the predetermined storage medium is mounted, in a state in which the first alert has been output, to function as a medium for indicating to the seller that the user has a right to receive the support from the seller; The information processing device equipped with the specified storage medium in a state in which the second alert is output can function as a medium that indicates to the provider that the user has the right to receive support from the provider.

[0113] The program is stored in a removable storage medium (e.g., a USB memory in FIG. 12), and is provided to the distributor together with a license key for the program issued by the provider, and is transferred from the distributor to the user; the user installs the removable storage medium into the information processing device having the predetermined storage medium mounted therein, thereby installing the program into the information processing device; By inputting the license key into the information processing device, it is possible to cause the information processing device to generate the function of executing the control process.

[0114] The program, as the control process, a smart information acquisition step of acquiring, from the predetermined storage medium, values ​​of N items (N is an integer value of 2 or more) among a plurality of items of smart information indicating a state of the predetermined storage medium; a comprehensive status calculation step of calculating a comprehensive status value indicating a comprehensive status of the predetermined storage medium from the values ​​of the N items acquired from the predetermined storage medium according to a predetermined calculation method using the N items as parameters; an item diagnosis step of comparing at least some of the values ​​of the N items acquired from the predetermined storage medium with a first index set for each item, and determining an item diagnosis result related to failure prediction according to a predetermined first judgment method using the result of the comparison; a comprehensive diagnosis step of comparing the comprehensive state value with a second index and determining a comprehensive diagnosis result related to failure prediction according to a second predetermined judgment method using a result of the comparison; a failure prediction diagnosis step of determining a failure prediction diagnosis result for the predetermined storage medium based on the item diagnosis results and the overall diagnosis result; an alert output control step of executing the failure prediction control for the predetermined storage medium; The information processing device may be configured to execute a control process including the steps of: [Explanation of symbols]

[0115] 1 server, 2 user terminal, 11 CPU, 18 storage unit, 20 drive, 31 removable media, 41 CPU, 42 ROM, 48 storage unit, 50 drive, 61 removable media, 71 learning unit, 72 learning data management unit, 73 failure prediction and diagnosis unit, 74 failure prediction and diagnosis result presentation unit display control unit, 81... learning data DB, 82... model DB, 91... smart monitoring unit, 92... fault prediction diagnosis result presentation unit, 147... output unit, 711... item diagnosis learning unit, 712... overall diagnosis learning unit, 731... smart information acquisition unit, 732... overall state calculation unit, 733... item diagnosis unit, 734... overall diagnosis unit, 735... final diagnosis result determination unit

Claims

1. an information processing device equipped with a predetermined storage medium that is sold to a user by a seller and used by the user; a program that, when installed in an information processing device equipped with the predetermined storage medium, causes the information processing device to perform a function of executing a control process that detects a problem related to the predetermined storage medium or the program itself, outputs a first alert when a first problem that should be notified to the seller is detected, and outputs a second alert when a second problem that should be notified to the provider is detected, the program being provided by the provider to the seller and transferred from the seller to the user; Equipped with causing the information processing device in which the predetermined storage medium is installed, in a state in which the first alert has been output, to function as a medium for indicating to the seller that the first trouble should be dealt with; causing the information processing device in which the predetermined storage medium is installed, in a state in which the second alert has been output, to function as a medium for indicating to the provider that the second trouble should be dealt with; Storage media support system.

2. The program is stored on a removable storage medium, and is provided to the distributor together with a license key for the program issued by the distributor, and is transferred from the distributor to the user; the user installs the removable storage medium into the information processing device in which the predetermined storage medium is installed, thereby installing the program into the information processing device; inputting the license key into the information processing device to cause the information processing device to perform the function of executing the control process; 10. The storage medium support system of claim 1.

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