Information processing device, method for predicting the lifespan of an information processing device, and program for predicting the lifespan of an information processing device.

Abstract

This enables more accurate and personalized lifespan predictions for each user of the information processing device. [Solution] An image forming apparatus 100, as an example of an information processing device equipped with storage 124, includes a lifespan-related SMART information acquisition unit 11 that acquires SMART information relating to the lifespan of the image forming apparatus 100, a user information acquisition unit 12 that acquires user information stored in storage 124, a trained model 13 that predicts the lifespan from the SMART information and user information, and a lifespan prediction unit 14 that outputs lifespan prediction data for the image forming apparatus 100 by periodically inputting the acquired SMART information and user information to the trained model 13.

Description

TECHNICAL FIELD

[0001] The present invention relates to an information processing apparatus, a life prediction method for an information processing apparatus, and a life prediction program for an information processing apparatus.

BACKGROUND ART

[0002] In an information processing apparatus such as an image forming apparatus equipped with a storage such as an SSD, when the number of write operations to the storage reaches the end of its life, user data can no longer be stored, and the information processing apparatus may become unusable. For this reason, a life prediction means for statistically determining the life of a storage from S.M.A.R.T. information is known (for example, Patent Document 1).

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0003] However, with conventional life prediction means, since information processing apparatuses are used in a wide variety of applications, depending on how a user uses the apparatus, there are cases where, for example, the number of write operations for image forming processing increases sharply and the end of life is reached, which may lead to unexpected failures. Therefore, prediction that relies solely on S.M.A.R.T. information has risks, and there is a problem that accurate life prediction is difficult.

[0004] An object of the present invention is to enable more accurate life prediction tailored to each user of an information processing apparatus.

MEANS FOR SOLVING THE PROBLEMS

[0005] To solve the above-mentioned problems, an information processing device according to one aspect of the present invention is an information processing device equipped with storage, comprising: a first acquisition unit that acquires the number of writes to the storage from management information; a second acquisition unit means that acquires user information stored in the storage; a trained model that predicts the lifespan from the management information and the user information; and a lifespan prediction unit that outputs lifespan prediction data of the information processing device by periodically inputting the number of writes acquired from the management information acquired by the first acquisition unit and the user information acquired by the second acquisition unit to the trained model. [Effects of the Invention]

[0006] This enables more accurate and personalized lifespan predictions for each user of the information processing device. [Brief explanation of the drawing]

[0007] [Figure 1] Hardware configuration diagram of an image forming apparatus as an example of an information processing apparatus according to the embodiment. [Figure 2] Diagram illustrating the life prediction function according to this embodiment. [Figure 3] Functional block diagram of an image forming apparatus [Figure 4] Flowchart for predicting and controlling the lifespan of an SSD storage device. [Figure 5] Flowchart for predicting the lifespan of an HDD when the storage is an HDD. [Figure 6] Flowchart of the first example of fault notification control for an image forming apparatus [Figure 7] Flowchart of a second example of fault notification control for an image forming apparatus [Modes for carrying out the invention]

[0008] The embodiments will be described below with reference to the attached drawings. To facilitate understanding of the explanation, the same reference numerals are used for identical components in each drawing whenever possible, and redundant explanations are omitted.

[0009] Figure 1 is a hardware configuration diagram of an image forming apparatus 100 as an example of an information processing apparatus according to the embodiment.

[0010] As shown in Figure 1, the hardware of the information processing device according to this embodiment consists of a CPU 101, RAM 102, ROM 103, network I / F 104, panel I / F 111, scanner I / F 112, engine I / F 113, external I / F 114, operation panel 121, scanner engine 122, plotter engine 123, and storage 124 located within the image forming apparatus 100, and each of these components is interconnected.

[0011] The image forming apparatus 100 is equipped with a CPU 101, RAM (Random Access Memory) 102, and ROM (Read Only Memory) 103 to control it. The ROM 103 stores programs (information processing programs) and various data that are executed when the image forming apparatus 100 is started up. The RAM 102 temporarily holds various programs and print data read from the ROM 103.

[0012] The panel I / F 111 is an interface for operating the operation panel 121. The operation panel 121 has a display function for displaying messages to the user and keys for receiving operations from the user.

[0013] The scanner interface 112 is an interface for communication with the scanner engine 122. Image data read from the scanner engine 122 is written to the RAM 102 via the scanner interface 112.

[0014] The engine interface 113 is an interface for communication with the plotter engine 123. By sending print data from the engine interface 112 to the plotter engine 123, the plotter engine 123 prints the image onto the paper.

[0015] The external I / F 114 is an interface for exchanging data with the storage 124. The storage 124 temporarily stores image data when using the sorting function, and accumulates image data with the document accumulation function.

[0016] Particularly in the present embodiment, the storage 124 includes an SSD or an HDD.

[0017] The image forming apparatus 100 as an example of the information processing apparatus according to the present embodiment can predict the service life of the apparatus, and can display the prediction on an operation unit of the apparatus such as the operation panel 121 when it is predicted that the apparatus is approaching the end of its service life. Further, when the image forming apparatus 100 acquires information related to a failure of the apparatus, the image forming apparatus 100 can notify the corresponding information.

[0018] The service life prediction function among the functions of the image forming apparatus 100 described above will be further described. Figure 2 is a diagram for explaining the service life prediction function according to the embodiment. In the service life prediction function, "S.M.A.R.T. information" and "user information" of the devices (image forming apparatuses 100) in the market are acquired, a learning model is created in advance, and then the information of the device in use is input to the trained model 13 (see Figure 3) to output service life prediction data. As a result, the relationship between the user's device usage tendency and the actual progress of the service life is learned, and a highly accurate remaining service life can be obtained.

[0019] Note that as the service life prediction learning model, for example, a multilayer neural network or the like can be applied. As the machine learning method, for example, deep learning, an error backpropagation method, or the like can be applied.

[0020] Here, the S.M.A.R.T. information used in the present embodiment is an example of management information, which is information for predicting service life, failures, and the like, and refers to, for example, various types of information issued by the storage 124 itself regarding the health status of the storage. The S.M.A.R.T. information includes, for example, the following types of information. · Number of rewrites (SSD) · Power-on time (HDD) · Operating time (HDD) ·Head ON / OFF count (HDD) ·Time until head retraction (HDD) ·Read error occurrence rate ·Bad sector rate ·Seek error rate

[0021] In the above example, information used when the storage 124 includes an SSD and information used when the storage 124 includes an HDD are marked with "(SSD)" and "(HDD)" respectively.

[0022] S.M.A.R.T. information refers to numerical values representing the state of the apparatus stored in the storage 124 itself such as HDDs and SSDs, and was originally installed for the purpose of failure diagnosis. Therefore, while a certain degree of lifespan prediction can be performed based on S.M.A.R.T. information, there is always a possibility that a sudden failure may occur, and it is known that diagnosis relying solely on S.M.A.R.T. information has risks.

[0023] Therefore, in order to perform more flexible lifespan prediction adapted to users, user information of an MFP (an example of an information processing apparatus including the image forming apparatus 100) is used in the learning dataset in addition to S.M.A.R.T. information.

[0024] The user information used in the present embodiment includes, for example, the following types of information. ·Job execution count ·Number of job pages ·Color / monochrome setting ·Presence or absence of printing and stamping ·Accumulated job count ·Address book update

[0025] User information refers to all general device usage logs and setting values stored in the MFP. From these, numerical values that indicate the user's usage status are extracted and used in the dataset. Examples of usable information include the following.

[0026] ·Job execution count The number of times each job has been executed is recorded for each basic function, such as copying, printing, scanning, and faxing. Even with normal printing, there is a mechanism to save images to a temporary area of ​​storage. Since the amount of data written to storage varies depending on the function, information on how many times each function's job was executed is useful for estimating the number of times storage has been written.

[0027] • Number of pages in the job The job information includes page count details. Some jobs allow you to write an entire page to storage at once, while others allow you to write it in multiple parts. When data is written to storage 10 times, it's impossible to tell from SMART information whether this is because the job was executed 10 times or because the function split the writing process into 10 parts. Therefore, this method allows for a more detailed understanding of user usage.

[0028] • Color / Monochrome setting In the case of color, there are four CMYK colors, and each requires an access to storage. In the case of monochrome, only one color is accessed. A simple calculation shows that there is a four-fold difference in the number of storage accesses between monochrome and color.

[0029] • Presence or absence of printing or stamping This value records whether the executed job included image synthesis such as printing or stamping. In MFPs, the data used for image synthesis, such as printing and stamping, is often stored within the storage. When printing on a document, storage access occurs to obtain the printed image.

[0030] • Number of accumulated jobs The number of times a document storage job is executed. Many MFPs (Multifunction Printers) have a built-in document storage function. When storing documents, data is stored in storage, resulting in more access to storage than normal printing.

[0031] • Updating the address book MFPs have an address book function that stores information such as the recipients of documents. Updates to the address book are recorded in the device's log. Since address book data is stored in storage, new entries and edits to the address book write data to the storage, so the frequency of updates affects the device's lifespan.

[0032] The above is a typical example of user information. There are other functions that utilize storage 124, and the settings related to those functions are likely to affect the number of writes to storage 124. This extracted user information will be used as training data.

[0033] Furthermore, if the image forming apparatus 100 is an MFP, it is preferable to define weights for each job's application type (e.g., copy, printer, scanner, fax, etc.) within the user information, and to use functions that access the storage 124 more frequently as high-importance training datasets. This allows for a greater impact on the model's machine learning of information that is strongly related to the lifespan of the image forming apparatus 100, thereby enabling the acquisition of a highly accurate lifespan prediction model.

[0034] Figure 3 is a functional block diagram of the image forming apparatus 100. As shown in Figure 2, the image forming apparatus 100 includes, with respect to the functions related to life prediction and fault notification described above, a life-related SMART information acquisition unit 11 (first acquisition unit), a user information acquisition unit 12 (second acquisition unit), a learned model 13, a life prediction unit 14, a display control unit 15, a fault-related SMART information acquisition unit 16, and a notification unit 17.

[0035] The lifespan-related SMART information acquisition unit 11 acquires SMART information related to the lifespan of the image forming apparatus 100. If the storage 124 is an SSD, the lifespan-related SMART information acquisition unit 11 acquires the number of writes to the storage (the "number of rewrites" mentioned above) from the SMART information as lifespan-related SMART information. If the storage 124 is an HDD, the lifespan-related SMART information acquisition unit 11 acquires at least one of the following from the SMART information as lifespan-related SMART information: the power-on time of the storage 124, the operating time, the number of head ON / OFF cycles, and the time until the head is retracted. The lifespan-related SMART information acquisition unit 11 outputs the acquired lifespan-related SMART information to the lifespan prediction unit 14.

[0036] The user information acquisition unit 12 acquires user information stored in the storage 124. The types of user information to be acquired include at least one of the following: the number of job executions, the number of pages in the job, color / monochrome settings, printing, presence or absence of stamps, the number of accumulated jobs, and address book updates. The user information acquisition unit 12 outputs the acquired user information to the life prediction unit 14.

[0037] As explained with reference to Figure 2, the trained model 13 is a model that predicts the lifespan of the image forming apparatus 100 from SMART information and user information. The SMART information and user information are used as input information to the trained model 13. The trained model 13 outputs the number of days until the end of its lifespan, according to the input information.

[0038] The lifespan prediction unit 14 periodically receives lifespan-related SMART information and user information acquired by the device (lifespan-related SMART information acquisition unit 11 and user information acquisition unit 12) as input to the trained model 13. Then, it acquires lifespan prediction data (for example, the number of days until the end of life) of the image forming apparatus 100 output by the trained model 13 according to the input information. The lifespan prediction unit 14 outputs the acquired lifespan prediction data to the display control unit 15.

[0039] If the storage 124 is an SSD, the life prediction unit 14 includes information on the number of writes obtained from SMART information as input information to the learned model 13. If the storage 124 is an HDD, the unit includes at least one of the following (i.e., information related to HDDs) obtained from SMART information: the power-on time of the storage 124, the operating time, the number of head ON / OFF cycles, and the time until the head is retracted.

[0040] The display control unit 15 calculates the replacement time for the storage 124 from the life prediction data input from the life prediction unit 14, and if the calculated replacement time is approaching, it displays a message to that effect on the operation unit of the image forming apparatus 100 (for example, the operation panel 121 in Figure 1).

[0041] The fault-related SMART information acquisition unit 16 acquires SMART information related to faults in the image forming apparatus 100. The fault-related SMART information includes at least one of the above-mentioned SMART information, such as the read error rate, bad sector rate, and seek error rate. The fault-related SMART information acquisition unit 16 outputs the acquired fault-related SMART information to the notification unit 17.

[0042] The notification unit 17 issues a warning message to the operation panel of the image forming apparatus 100 (for example, the operation panel 121 in Figure 1) if the fault-related SMART information input from the fault-related SMART information acquisition unit 16 changes at a frequency exceeding a predetermined value. Alternatively, in the same case, the notification unit 17 contacts the image forming apparatus 100 customer support.

[0043] Furthermore, it is preferable to limit the frequency of data acquisition and learning to once a day, and to further reduce the frequency of information acquisition when the remaining lifespan is short. "Frequency of data acquisition and learning" includes, for example, the frequency of acquisition of lifespan-related SMART information by the lifespan-related SMART information acquisition unit 11, the frequency of acquisition of user information by the user information acquisition unit 12, the frequency of additional learning of the trained model 13, and the frequency of acquisition of failure-related SMART information by the failure-related SMART information acquisition unit 16. Frequent data acquisition and learning can shorten the lifespan of the image forming apparatus 100 and affect its lifespan. For this reason, setting the frequency to, for example, once a day can reduce the impact on lifespan that depends on data acquisition and learning. Furthermore, if the remaining lifespan is short, further reducing the frequency of information acquisition can further reduce the impact on lifespan.

[0044] Next, the life prediction function of the image forming apparatus 100 will be explained with reference to Figures 4 and 5. Figure 4 is a flowchart of the life prediction control when the storage 124 is an SSD.

[0045] In step S11, the subsequent processing is controlled to be executed periodically. "Periodicly" preferably means, for example, once a day, as mentioned above, and it is preferable to reduce the frequency further as the remaining lifespan decreases.

[0046] In step S12, the lifespan-related SMART information acquisition unit 11 acquires the write count value from the SMART information of the SSD, which is the storage device 124.

[0047] In step S13, the user information acquisition unit 12 acquires user information from the SSD, which is the storage 124.

[0048] In step S14, the life prediction unit 14 inputs the write count value acquired in step S11 and the user information acquired in step S12 into the trained model 13, and life prediction data for the image forming apparatus 100 is obtained based on the output information of the trained model 13 corresponding to these input information.

[0049] In step S15, the display control unit 15 determines whether the SSD as storage 124 is nearing the end of its lifespan based on the lifespan prediction data acquired in step S14. The display control unit 15 maintains a lifespan threshold, for example, which is set to an arbitrary predetermined number of days until the image forming apparatus 100 reaches the end of its lifespan. When the lifespan prediction data falls below the lifespan threshold, it can determine that the storage 124 is nearing the end of its lifespan.

[0050] If it is determined in step S15 that the storage 124 is nearing the end of its lifespan (Yes in S15), the process proceeds to step S16, where the display control unit 15 displays the replacement time for the storage 124 on the operation panel of the image forming apparatus 100 (for example, the operation panel 121 in Figure 1). Once the processing in step S16 is complete, this control flow is terminated.

[0051] On the other hand, if it is determined in step S15 that the storage 124 is not nearing the end of its lifespan (No in S15), the control flow is terminated without performing the process in step S16.

[0052] Figure 5 is a flowchart of the lifespan prediction control when storage 124 is an HDD.

[0053] In step S21, the subsequent processing is controlled to be executed periodically. "Periodicly" preferably means, for example, once a day, as described above, and it is preferable to reduce the frequency further as the remaining lifespan decreases.

[0054] In step S22, the lifespan-related SMART information acquisition unit 11 acquires values ​​related to the HDD (such as power-on time, operating time, and head ON / OFF count) from the storage 124, which is the HDD, using SMART information.

[0055] In step S23, the user information acquisition unit 12 acquires user information from the storage 124, which is an HDD.

[0056] In step S24, the life prediction unit 14 inputs the HDD information values ​​acquired in step S21 and the user information acquired in step S22 into the trained model 13, and life prediction data for the image forming apparatus 100 is obtained based on the output information of the trained model 13 corresponding to these input information.

[0057] In step S25, the display control unit 15 determines whether the HDD as storage 124 is nearing the end of its lifespan based on the lifespan prediction data acquired in step S24. The display control unit 15 maintains a lifespan threshold, for example, which is set to an arbitrary predetermined number of days until the image forming apparatus 100 reaches the end of its lifespan. When the lifespan prediction data falls below the lifespan threshold, it can determine that the storage 124 is nearing the end of its lifespan.

[0058] If it is determined in step S25 that the storage 124 is nearing the end of its lifespan (Yes in S25), the process proceeds to step S26, where the display control unit 15 displays the replacement time for the storage 124 on the operation panel of the image forming apparatus 100 (for example, the operation panel 121 in Figure 1). Once the processing in step S26 is complete, this control flow is terminated.

[0059] On the other hand, if it is determined in step S25 that the storage 124 is not nearing the end of its lifespan (No in S25), the control flow is terminated without performing the process in step S26.

[0060] Furthermore, if the image forming apparatus 100 is configured to include both an SSD and an HDD as storage 124, then both flowcharts shown in Figures 4 and 5 should be followed.

[0061] Thus, the image forming apparatus 100, as an example of an information processing device according to this embodiment equipped with storage 124, includes a lifespan-related SMART information acquisition unit 11 that acquires SMART information relating to the lifespan of the image forming apparatus 100, a user information acquisition unit 12 that acquires user information stored in storage 124, a trained model 13 that predicts the lifespan from the SMART information and user information, and a lifespan prediction unit 14 that outputs lifespan prediction data for the image forming apparatus 100 by periodically inputting the acquired SMART information and user information to the trained model 13. Furthermore, it is preferable that the image forming apparatus 100 has a display control unit 15 that displays on the operation panel 121 of the image forming apparatus 100 when the replacement time for storage 124, calculated from the lifespan prediction data acquired by the lifespan prediction unit 14, is approaching.

[0062] In particular, in the case of an image forming apparatus 100 equipped with an SSD as storage 124, the lifespan-related SMART information acquisition unit 11 acquires the number of writes to storage 124 from the SMART information as SMART information related to the lifespan of the image forming apparatus 100. The lifespan prediction unit 14 outputs lifespan prediction data for the image forming apparatus 100 by periodically inputting the number of writes acquired from the SMART information acquired by the lifespan-related SMART information acquisition unit 11 and the user information acquired by the user information acquisition unit 12 to the trained model 13.

[0063] Similarly, in the case of an image forming apparatus 100 equipped with an HDD as storage 124, the lifespan-related SMART information acquisition unit 11 acquires information about the HDD, including at least one of the following from the SMART information: power-on time, operating time, number of head ON / OFF cycles, and time until head retraction, as SMART information related to the lifespan of the image forming apparatus 100. The lifespan prediction unit 14 outputs lifespan prediction data for the image forming apparatus 100 by periodically inputting information about the HDD acquired from the SMART information acquired by the lifespan-related SMART information acquisition unit 11 and user information acquired by the user information acquisition unit 12 to the trained model 13.

[0064] These configurations allow for the prediction of the lifespan of the storage 124 of the image forming apparatus 100 by considering user information, which provides insight into how the user uses the image forming apparatus 100, in addition to SMART information, and by using different types of information depending on the type of storage 124. This enables more accurate lifespan predictions tailored to each user of the image forming apparatus 100 (information processing device).

[0065] Next, the fault notification function of the image forming apparatus 100 will be described with reference to Figures 6 and 7. Figure 6 is a flowchart of the first example of fault notification control of the image forming apparatus 100.

[0066] In step S31, the subsequent processing is controlled to be executed periodically. "Periodicly" preferably means, for example, once a day, as described above, and it is preferable to reduce the frequency further as the remaining lifespan decreases.

[0067] In step S32, the failure-related SMART information acquisition unit 16 acquires numerical values ​​related to failures of the image forming apparatus 100 (read error rate, bad sector rate, seek error rate, etc.) from the storage 124 as SMART information.

[0068] In step S33, the notification unit 17 determines whether the fault-related SMART information acquired in step S32 has changed significantly from past SMART information (for example, fault-related SMART information from when this control flow was performed a predetermined number of times previously). The notification unit 17 maintains threshold information for each piece of fault-related SMART information, each of which has a predetermined deviation set. When the deviation between the fault-related SMART information and the past SMART information exceeds the threshold, it can determine that the fault-related SMART information has changed significantly.

[0069] If it is determined in step S33 that the fault-related SMART information has changed significantly (Yes in S33), the process proceeds to step S34, and a warning message indicating a fault has occurred is displayed on the operation panel of the image forming apparatus 100 (for example, the operation panel 121 in Figure 1). Once the processing in step S34 is complete, this control flow is terminated.

[0070] On the other hand, if it is determined in step S33 that the fault-related SMART information has not changed significantly (No. in S33), the control flow will be terminated without performing the processing in step S34.

[0071] As explained with reference to Figure 6, in this embodiment, the notification unit 17 can control the operation panel 121 of the image forming apparatus 100 to display a warning message when the fault-related information recorded in the SMART information changes at a frequency exceeding a predetermined value. This allows for rapid notification of a fault to the user or administrator of the image forming apparatus 100, enabling a quick recovery.

[0072] Figure 7 is a flowchart of a second example of fault notification control for the image forming apparatus 100.

[0073] In step S41, the subsequent processing is controlled to be executed periodically. "Periodicly" preferably means, for example, once a day, as described above, and it is preferable to reduce the frequency further as the remaining lifespan decreases.

[0074] In step S42, the failure-related SMART information acquisition unit 16 acquires numerical values ​​related to failures of the image forming apparatus 100 (read error rate, bad sector rate, seek error rate, etc.) from the storage 124 as SMART information.

[0075] In step S43, the notification unit 17 determines whether the fault-related SMART information acquired in step S42 has changed significantly from past SMART information (for example, fault-related SMART information from when this control flow was performed a predetermined number of times previously). The notification unit 17 maintains threshold information for each piece of fault-related SMART information, each of which has a predetermined deviation set. When the deviation between the fault-related SMART information and the past SMART information exceeds the threshold, it can determine that the fault-related SMART information has changed significantly.

[0076] If step S43 determines that the fault-related SMART information has changed significantly (Yes in S33), the process proceeds to step S44, where the customer support for the image forming apparatus 100 is notified of the malfunction. Once the processing in step S44 is complete, this control flow is terminated.

[0077] On the other hand, if it is determined in step S43 that the fault-related SMART information has not changed significantly (No. in S43), the control flow will be terminated without performing the processing in step S44.

[0078] As explained with reference to Figure 7, in this embodiment, the notification unit 17 can control the image forming apparatus 100 to contact customer support when the fault-related information recorded in the SMART information changes at a frequency exceeding a predetermined value. This allows the image forming apparatus 100's customer support to be quickly notified of the fault and to facilitate a rapid recovery.

[0079] The embodiments have been described above with reference to specific examples. However, this disclosure is not limited to these specific examples. Modifications made to these specific examples by those skilled in the art are also included within the scope of this disclosure, as long as they retain the features of this disclosure. The elements, their arrangement, conditions, shapes, etc., of each of the aforementioned specific examples are not limited to those illustrated and can be modified as appropriate. The elements of each of the aforementioned specific examples can be combined in different ways as appropriate, as long as no technical inconsistencies arise. [Explanation of Symbols]

[0080] 100 Image forming apparatus (information processing apparatus) 11. Life-related SMART information acquisition unit (1st acquisition unit) 12. User Information Acquisition Unit (Second Acquisition Unit) 13. Pre-trained models 14 Life Prediction Unit 15 Display Control Unit 17 Hochi Department [Prior art documents] [Patent Documents]

[0081] [Patent Document 1] Japanese Patent Publication No. 2019-008372

Claims

1. An information processing device equipped with storage, A first acquisition unit that obtains the number of writes to the aforementioned storage from management information, A second acquisition unit that acquires user information stored in the aforementioned storage, A trained model that predicts lifespan from the aforementioned management information and user information, A life prediction unit outputs life prediction data for the information processing device by periodically inputting the number of writes obtained from the management information acquired by the first acquisition unit and the user information acquired by the second acquisition unit to the trained model. An information processing device having

2. The aforementioned storage is an SSD. The information processing apparatus according to claim 1.

3. An information processing device equipped with storage, A first acquisition unit acquires information about the storage, including at least one of the following as management information regarding lifespan: power-on time, operating time, number of times the head is turned ON / OFF, and time until the head is retracted. A second acquisition unit that acquires user information stored in the aforementioned storage, A trained model that predicts lifespan from the aforementioned management information and user information, A life prediction unit outputs life prediction data for the information processing device by periodically inputting information about the storage obtained from the management information acquired by the first acquisition unit and user information acquired by the second acquisition unit to the trained model. An information processing device having

4. The aforementioned storage is an HDD. The information processing apparatus according to claim 3.

5. Weights are assigned to each job based on the type of application, and features that access the aforementioned storage frequently are used as the training dataset, giving them higher importance. The information processing apparatus according to any one of claims 1 to 4.

6. The information processing apparatus according to any one of claims 1 to 4, wherein the frequency of data acquisition and learning is limited to once a day, and the frequency of information acquisition is further reduced when the remaining lifespan is short.

7. When the fault-related information recorded in the aforementioned management information changes at a frequency exceeding a predetermined value, the notification unit issues a warning message to the operation unit of the information processing device. An information processing apparatus according to any one of claims 1 to 4, having the following features.

8. If the fault-related information recorded in the aforementioned management information changes at a frequency exceeding a predetermined value, the notification unit will contact the customer support of the information processing device. An information processing apparatus according to any one of claims 1 to 4, having the following features.

9. A method for predicting the lifespan of an information processing device equipped with storage, A first acquisition step of acquiring management information regarding the lifespan of the information processing device stored in the storage, A second acquisition step involves obtaining user information stored in the aforementioned storage, A lifespan prediction step outputs lifespan prediction data for the information processing device by periodically inputting the management information acquired in the first acquisition step and the user information acquired in the second acquisition step into a trained model that predicts the lifespan from the management information and the user information. A method for predicting the lifespan of an information processing device, including the following.

10. A life prediction program for an information processing device equipped with storage, A first acquisition function for acquiring management information regarding the lifespan of the information processing device stored in the storage, A second acquisition function for acquiring user information stored in the aforementioned storage, A lifespan prediction function outputs lifespan prediction data for the information processing device by periodically inputting the management information acquired by the first acquisition function and the user information acquired by the second acquisition function into a trained model that predicts lifespan from the management information and user information. A program for predicting the lifespan of an information processing device, which is used to have a computer execute a command.

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