Log management system

Abstract

To objectively output a work order of superiority for a plurality of work contents.SOLUTION: To record and accumulate the use state of a personal computer(PC) used for business. To determine the aptitude of an employee to his / her job by analyzing his / her favorite job from recorded and accumulated data. To automatically collect and analyze work contents from the recording of the use state of a PC without direct recording by an operator. To objectively output a work order for a plurality of work contents without introducing a subjective factor of an operator. Then, by providing information on a person most suitable for performing an arbitrary task, it is possible to improve the task efficiency.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to a log management system that determines the suitability of a person for a given task. [Background technology]

[0002] One way to increase operational efficiency across a company is to assign the most suitable personnel to each task. In this case, only the individual himself / herself knows whether he / she is good or bad at the task to be assigned. A typical way to find out about an individual's strengths and weaknesses is to interview their superiors. However, finding the best person for each task from among many employees through interviews requires a great deal of time and effort. Furthermore, because the interviewing method involves the individual's subjective opinion, it is possible that a task that the individual thinks is their strength may actually be inefficient. The same is also true in reverse.

[0003] Patent Document 1 discloses a worker evaluation support system that collects the work records of the work performed by the workers and evaluates the workers. In the worker evaluation, not only is the actual work content analyzed, but also the characteristics of the workers are analyzed from a worker's attitude survey questionnaire and free description field. The results of the analysis are presented to the workers to improve their motivation. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2023-12591 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the technology in Patent Document 1 often relies on workers recording their own work performance, attitude surveys, etc. This has the disadvantage that data is input based on the subjective experience of the workers, making it difficult to make objective judgments. [Means for solving the problem]

[0006] Therefore, in the present invention, an operation history recording means for recording operations of the information processing terminal by a user in association with a task classification and operation content; score calculation means for calculating a score for each of the task categories and the operation details for each of the users using the operation history; a comparison means for calculating a comparison score by comparing the scores calculated by the score calculation means for each of the users, taking into consideration an average score of the scores calculated by the score calculation means for the plurality of users; The present invention is characterized by having the following. [Effects of the Invention]

[0007] According to the present invention, aptitude for a job can be determined objectively without relying on self-reporting. [Brief explanation of the drawings]

[0008] [Figure 1] System configuration diagram of a log management system according to a first embodiment [Figure 2] An example of an operation log [Figure 3] Example showing what is retrieved for each process [Figure 4] Examples of work hours for each task [Figure 5] Example showing first comparison score and second comparison score [Figure 6] Flowchart showing the flow of a process for calculating operation time [Figure 7] Flowchart showing the process of calculating task hours [Figure 8] An example of an operation history database [Figure 9] Flowchart showing the procedure for calculating the second comparison score [Figure 10] System configuration diagram of a log management system according to a second embodiment [Figure 11]Flowchart showing the process of suggesting suitable people [Figure 12] Example of rank information DETAILED DESCRIPTION OF THE INVENTION

[0009] First Embodiment <System configuration diagram> First, the configuration of the log management system according to this embodiment will be described with reference to the system configuration diagram of Fig. 1. As shown in Fig. 1, the log management system according to this embodiment has computers, i.e., information processing devices 10, each of which is connected to a network 30 such as a LAN or the Internet. Hereinafter, the information processing devices 10 will be referred to as client terminal devices. Note that there may be multiple client terminal devices 10. Furthermore, the client terminal device 10 may be any device, such as a PC or mobile terminal device, that is capable of executing the processes described below as being performed by an information processing device.

[0010] In addition, in this embodiment, the explanation will be given on the assumption that there are a plurality of client terminal devices 10, each of which is connected via a network 30.

[0011] <Client terminal device> In the client terminal device 10, the arithmetic unit 10C is a microprocessor (CPU). The arithmetic unit 10C starts an operating system (OS) stored in the storage device 10B in accordance with a boot program such as BIOS stored in the ROM of the memory 10E, and then starts various programs in accordance with the OS or user operations. This storage device 10B stores computer programs and data for causing the arithmetic unit 10C to execute the OS and various processes performed by this device, which will be described later. These computer programs include a client program 10F.

[0012] The computer programs and data stored in the storage device 10B are loaded into the ROM of the memory 10E as appropriate under the control of the arithmetic device 10C, and are then processed by the arithmetic device 10C. The OS of the arithmetic device 10C is, for example, Windows (registered trademark), MacOS (registered trademark), Linux (registered trademark), iOS (registered trademark), Android (registered trademark), etc.

[0013] The storage device 10B is a hard disk drive (HDD) or a solid state drive (SSD), and stores the OS as well as the client program 10F running on the client terminal device 10. The I / O device 10A is an input / output interface (I / F) for connecting to a pointing device (such as a mouse) or a keyboard, or a display incorporating a touch panel. The keyboard may be a software keyboard. The I / O device 10A may also be a voice-based input unit including a microphone, which recognizes the operator's voice input using a voice recognition function and transmits the recognized voice to the arithmetic device 10C. The I / O device 10A also functions as a user interface (UI) for displaying information.

[0014] The network I / F 10D is an interface with the network 30 and is a communication circuit for communicating with other information processing devices (for example, other client terminal devices 10, etc.). The computing device 10C may transmit a request packet to, for example, an external site on the Internet via the network I / F 10D.

[0015] The client program 10F is a program for monitoring data, programs, etc. of the client terminal device 10, and has a function for monitoring, for example, file operations (copy, move, rename, etc.) and program launches performed via the I / O device 10A by a user operating the client terminal device 10. The client program 10F also stores the file operations, program launches, etc., as history in the storage device 10B.

[0016] FIG. 2 shows an example of an operation history recorded by the client program 10F.

[0017] Fig. 2 shows an example of an operation log that shows information about a window displayed on the screen and mouse and keyboard operations performed on the window in the client terminal device 10. In Fig. 2, as an example, the operation log number, the PC name of the client terminal device 10, the user name that performed the operation, the time when the operation was performed, the title of the active window, the process name, the number of keyboard strokes, and the distance the mouse pointer moved are recorded, but this does not limit the information to be recorded, and information not shown in Fig. 2 may also be recorded.

[0018] The process flow is described below. While a user is using the client terminal device 10, the client program 10F records the operation history of the terminal. This operation history is recorded in the form of operation history 10K in the storage device 10B.

[0019] In addition, the client program 10F can calculate how long each process was operated for by arranging the operation logs shown in Figure 2 in chronological order and calculating the difference in the time difference between each operation.In the case of Figure 2, the 60 minutes from the operation time of program.py, 09:00:00, to the operation time of the next operation log, 10:00:00, is determined to be the usage time of Code.exe, and it is recorded in the operation history DB (see Figure 8) that "Code.exe was operated for 60 minutes."

[0020] Also, even if a process was being used, if there was no mouse or keyboard operation, it may not be possible to say that the process was actually being operated, so the presence or absence of operation can be determined by referring to this information. In this case, referring to Figure 2, although document creation.exe was used from 10:20:00 to 11:00:00, there were no mouse or keyboard operations, so the operation time for the process is determined to be 0. The calculated operation time is recorded in storage device 10B as operation time 10L.

[0021] Figure 8 shows an example of an operation history DB, including an example of operation time 10L. For example, the first line stores operation number 1, PC name: PCA, user name: userA, date: 2023 / 10 / 1 09:00:00, operation time (seconds): 3600 seconds, and process name: Code.exe. In this example, operation times are recorded by operation number, PC, user, date, time, and process name, but the unit of collection can be any combination of items.

[0022] FIG. 6 is a flowchart showing the flow of the process for calculating the operation time L.

[0023] First, the client program 10F refers to the operation history 10K in FIG. 2 and arranges it in chronological order (S-101).

[0024] Next, it is determined whether or not mouse and keyboard operations exist in the operation history 10K (S-102).

[0025] If S-102 determines that mouse or keyboard operation has occurred, it is determined that an operation has occurred, and the time difference from the previous log is recorded as the operation time (S-103).

[0026] At the same time, the client program 10F determines the task classification for each recorded operation history.

[0027] Task classification is determined by the content entered by typing on the keyboard. First, the input content is acquired by word or phrase. For example, if the text says, "Sales performance in April 2023 improved by 30% compared to last year," then it would be written as "Sales performance in April 2023 improved by 30% compared to last year." Words likely to be related to the task (in this example, "sales performance") are extracted from the phrase. The extraction method involves using text mining or other methods to extract multiple frequently used words. Machine learning is used to infer related tasks from the multiple extracted words and classify the tasks. This process is just one example, and existing text classification technologies can be used.

[0028] For tasks other than document creation, the task content is inferred from the operations performed. For example, in the case of programming languages, the task is classified as coding work based on errors and bugs output by the editor tool used. When using spreadsheet software, the task content is determined based on the frequency of use of function calculations and pivot tables.

[0029] When images are used in work, image caption generation is used to create a sentence that describes the situation in the image. Words from that sentence are then listed and used with machine learning to infer related tasks, along with words collected from other tasks.

[0030] Figure 3 shows an example of the content acquired for each process. In this way, the information acquired for each process can be changed, making it possible to classify tasks and operations. That is, tasks can be classified based on the acquired information, and the operation contents can be classified based on the process name at the same time. At this time, classification can also be performed using the user name as a condition. In other words, even if the same process and the same information are acquired, the tasks classified can differ depending on the user's role or position.

[0031] This allows the work time for each classified task to be calculated. The work time for each task is recorded in the form of task time 10H in storage device 10B, as shown in FIG.

[0032] Figure 4 shows an example of task time for each task. By referring to this diagram, it is possible to understand who performed what task, when, and for how long. Note that this is just an example, and task time can also be calculated for each execution process in the operation content, for example.

[0033] FIG. 7 is a flowchart showing the process of calculating task hours.

[0034] First, the client program 10F refers to the operation time 10L and acquires the operation time for each process (S-201).

[0035] Next, the client program 10F acquires the process name of the corresponding operation time (S-202).

[0036] It is checked whether the acquired process name exists in the acquired information 10G and whether there is a corresponding process name (S-203).

[0037] The information to be acquired from the corresponding process is acquired from Acquired Information 10G, and the business classification and operation content are determined based on the information (S-204).

[0038] The task time information for each operation is recorded according to the task classification and operation content (S-205).

[0039] These processes allow you to understand the time and content of the work performed.

[0040] By referring to this information on working hours, for example, when deciding who to assign to new accounting work, one possible method would be to assign someone who has been working on the work for a longer period of time.However, working hours alone cannot be used to determine suitability for the work.

[0041] Generally, one would consider referring to information such as the person's areas of expertise or how they promoted themselves during the interview, but because such information is subjectively entered, it often deviates from the actual situation.

[0042] Therefore, in the present invention, a "score determination" is performed as a method for objectively grasping aptitude.

[0043] Here, "score judgment" is performed by comparing "scores," which are calculated indicators that include productivity to determine the number of mistakes in work and whether advanced work is being performed. For example, in the case of programming, judgments are made for each operation based on whether there are few bugs or errors, whether indentation is aligned, whether redundant coding is used, etc.

[0044] When creating documents, the frequency of typos and omissions and the consistency of the context are judged.

[0045] In the case of spreadsheet software, determine whether functions, pivot tables, and macros are being used.

[0046] In the case of slides, the frequency of use of animations, the number of characters / character size per page (fewer and larger characters give a higher score as presentation materials, etc.), and the consistency of layout between pages are quantified and judged.

[0047] By normalizing the scores generated in this way per unit task time and referring to them, if a task with a high score is being performed, it can be determined that the person is suited to that task and has high productivity. Note that, although the example of calculating a score for each operation content has been described above, scores may also be calculated for each operation content for each task classification.

[0048] Additionally, the scoring process identifies tasks that the worker excels at compared to themselves and other workers. in particular, First comparison score: Ranks how efficiently a worker performs a task relative to other workers. Second comparison score: Ranks a worker's strengths in each task.

[0049] The first comparison score outputs the analysis result of "how much the score of the worker to be evaluated differs from the average score of a certain task calculated from the task times of multiple people." The second comparison score outputs the analysis result of "ranking the tasks that the worker is best at."

[0050] Record and tally the work classification and working hours, and prepare data on each individual's work as well as data tallying multiple people. In this case, the unit of multiple people can be the entire company, or it can be tallying by department or team.

[0051] When data is aggregated by multiple workers, the average time taken for each task is calculated from the aggregated data. In addition to the average time, the number of errors in the task and whether the task is performed at a high level are also quantified. For example, in the case of programming, the number of errors in the task can be determined by the bug rate, low error rate, whether indentation is consistent, etc., following the examples mentioned above. The scores for each task calculated based on these values ​​are calculated for each worker, and the ranking is recorded as the first comparison score. In addition to ranking, the first comparison score can also be recorded as a numerical value showing the difference between each worker's average score and the average score for all workers, as shown in Figure 5. In other words, the difference between the average score and the individual score is recorded as a numerical value relative to the average of others. As shown in parentheses in Figure 5, a percentage or other ratio can also be recorded as the average score relative to others.

[0052] When calculating the first comparison score, as described above, the accuracy of work, whether the work is performed at a high level, and the efficiency of work time are weighted and compared with others.

[0053] For work accuracy, specify an accuracy that matches the work content. For example, in the case of document creation, the number of characters entered and the number deleted are obtained. Here, the amount of words deleted refers to the number of times deleted with the backspace key or delete key, or the number of characters when deleting all at once. The result of "number of deletions / number of entries" for each work hour can be used as the accuracy of document creation. For coding, the bug rate can be obtained from all code content measured by the editor, and the result can be used as the accuracy of coding. In this way, the accuracy of work is output in line with the work content.

[0054] For advanced tasks, specify the advanced tasks that match the business content. For example, in the case of spreadsheets, the number of times functions included in the spreadsheet software were used without using arithmetic operations, the number of times pivot tables were used, and the number of times macros were used can be considered as advanced tasks for spreadsheets. In the case of slides, the number of times animations were used, the small number of characters / character size per page, and the consistency of layout between pages can be considered as advanced tasks. In this way, advanced task content that matches the business content can be output.

[0055] Let "Work time" x "Work accuracy" x "Advanced work skill" be the individual score for a given worker. Let's also consider "Population average work time" x "Population average work accuracy" x "Population average advanced work skill." For example, let's say Worker A performs Work B. As shown at the top of Figure 5, if the work time is 60 minutes, the work accuracy is 0.5, and the advanced work skill is 1.2, then Worker A's individual score is (6000 / 60) x 0.5 x 1.2 = 60. Note that for work time, a constant is divided by the work time so that shorter work tasks result in higher scores. Similarly, if the average work time of the comparison population is 50, the average work accuracy is 0.8, and the average advanced work skill is 0.5, then the population average score is (6000 / 50) x 0.8 x 0.5 = 48. In this case, since the individual score of worker A is higher than the average score of the population, it can be determined that worker A is good at this task among the population.

[0056] When calculating the second comparison score, the scores for each worker created in the calculation of the first comparison score are compared across multiple tasks. Figure 9 is a flowchart showing the procedure for calculating the second comparison score.

[0057] First, the client program 10F acquires the working hours 10H and the score information 10J (S-301).

[0058] Next, the user name and task classification for the acquired 10-hour working time are acquired (S-302).

[0059] Here, when comparing the proficiency levels for different tasks, normalization is performed so that they can be compared using the same criteria. Specifically, an "individual score" / "average score" is calculated for each task (S-303). The normalized values ​​are compared for each task (S-304). This makes it possible to output the individual skill ranking for a given worker. For example, suppose worker A performs tasks B, C, and D. If the normalized values ​​for tasks B, C, and D are 1.8, 2.4, and 0.6, respectively, worker A's individual skill ranking is task C, task B, and task D from the top. Note that normalization is used here as an example, but other methods may also be used. For example, a method may be used in which deviation values ​​are calculated and then compared.

[0060] In this way, by calculating the first comparison score, which is based on the individual score and the average score of the population, it is possible to determine whether the worker can perform advanced tasks on the population, and by calculating the second comparison score, it is possible to determine the worker's own level of expertise.

[0061] Figure 5 shows an example of the first comparison score and the second comparison score. From Figure 5, it is possible to determine how efficiently a certain worker can perform tasks relative to the population. It is also possible to confirm which tasks a certain worker is good at, in order of their strengths.

[0062] Specifically, for each user name, an individual score is calculated for each task classification and operation, as well as an average score for tasks and operations under the same conditions. In addition, a comparison of these scores is made to calculate the average score compared to others (first comparison score) and the individual's order of expertise (second comparison score).

[0063] In the embodiment described above, the suitability and order of expertise for a task are calculated by performing a "score determination." However, this is not limited to this. For example, a "ranking" process may be performed to correct the score for a task by referencing profile information (personnel information) such as the qualifications held by the user. FIG. 12 shows an example of this process, showing the task categories to which a certain qualification can be applied and rank information indicating the validity of the qualification. For example, an IT Passport can be applied to information processing as a task category, and its rank information is set to C and is taken into account in calculating the suitability and order of expertise for the task. Note that the profile information (personnel information) for each user may refer to data stored in another system, or may be stored in the log management system of this embodiment.

[0064] Second Embodiment In the first embodiment, the operation history in the client terminal device 10 is recorded only in the storage device 10B, and the information on business hours based on this history can be referenced only within the client terminal device 10.

[0065] Therefore, the information recorded in the storage device 10B is transmitted to the server device 20 via the network 30 and recorded in the storage device 20B. As a result, information from multiple client terminal devices 10 connected to the server device 20 is aggregated in the server device 20, making it possible to tally and compare the information from each client terminal.

[0066] The information sent to the server device 20 may be the operation history itself in the client terminal device 10, or the operation time calculated based on that, and the type and number of information are not limited.

[0067] <System configuration diagram> Fig. 10 is a diagram showing the configuration of a log management system according to the second embodiment. As shown in Fig. 10, the log management system according to this embodiment has computers, i.e., information processing devices 10 and 20, each of which is connected to a network 30 such as a LAN or the Internet. Hereinafter, the information processing device 10 will be referred to as a client terminal device, and the information processing device 20 will be referred to as a server device that manages the client terminal device 10. There may be multiple client terminal devices 10 and server devices 20. Furthermore, the client terminal devices 10 and the server devices 20 may be any devices that can execute the processes described below as being performed by an information processing device such as a PC or mobile terminal device.

[0068] In addition, the second embodiment has some common parts with the first embodiment, since the server device 20 is added to the first embodiment. Therefore, in the following, a description of the common parts will be omitted, and only the differences will be described.

[0069] <Server device> In the server device 20, the arithmetic device 20C is a microprocessor (CPU). The arithmetic device 20C starts an operating system (OS) stored in the storage device 20B in accordance with a boot program such as BIOS stored in the ROM of the memory 20E, and then starts various programs in accordance with the OS or user operations. This storage device 20B stores computer programs and data for causing the arithmetic device 20C to execute the OS and various processes performed by this device, which will be described later. These computer programs include a server program 20G.

[0070] The server program 20G is a program for managing data, programs, etc. of the server device 20, and has functions such as recording data received from the client terminal device 10 in the memory device 20B and calculating arbitrary data in the calculation device 20C.

[0071] The computer programs and data stored in the storage device 20B are loaded into the RAM of the memory 20E as appropriate under the control of the arithmetic device 20C, and are then processed by the arithmetic device 20C. The operating system of the arithmetic device 20C is, for example, Windows, MacOS, Linux, iOS, Android, or the like.

[0072] The storage device 20B is a hard disk drive (HDD) or a solid state drive (SSD), and stores the OS as well as the server program 20F running on the server device 20. The I / O device 10A is an input / output interface (I / F) for connecting to a pointing device (such as a mouse) or a keyboard, or a display incorporating a touch panel. The keyboard may be a software keyboard. The I / O device 10A may also be a voice-based input unit including a microphone, which recognizes the operator's voice input using a voice recognition function and transmits the recognized voice to the arithmetic device 10C. The I / O device 10A also functions as a user interface (UI) for displaying information.

[0073] The network I / F 20D is an interface with the network 30 and is a communication circuit for communicating with other information processing devices (for example, the client terminal device 10, etc.). The arithmetic device 20C has functions such as receiving request packets issued from the network I / F 10D of the client terminal device 10, etc., via the network I / F 20D.

[0074] <Processing flow> The processing content in this embodiment is the same as that in the first embodiment, but the difference from the first embodiment is that some or all of the processing (S-101 to S-304) that was performed by the client terminal device 10 in the first embodiment is performed by the server device 20.

[0075] Which of the processes from S-101 to S-304 should be performed by the server device 20 is not uniquely limited.

[0076] Furthermore, all information such as the working hours 10 hours that was stored in the client terminal device 10 in the first embodiment is now stored on the server device 20. As a result, all information such as operation times of the client terminal devices 10 connected to the server device 20 via the network 30 is collected on the server device 20, and it is possible to compare the ranks and working hours of each user.

[0077] <Third embodiment> Generally, when it comes to deciding who to assign to a task, managers need to understand the characteristics of each person, such as their areas of expertise, by observing them in their daily work. However, in an environment where managers are overwhelmed with other work, it is difficult to accurately understand such characteristics.

[0078] Therefore, by referring to information such as the individual score and average score created in the first embodiment, it is possible to suggest a suitable person to perform a given task.

[0079] <Processing flow> The first comparison score, which is a comparison between the agents, and the ranking of each agent's skill based on the second comparison score are as shown in Fig. 5. Fig. 11 is a flowchart showing the process of suggesting an appropriate person.

[0080] Using these figures, a process of suggesting the most suitable person for a task whose task category is "sales" and whose operation type is "spreadsheet" will be described as an example.

[0081] The subject that performs this process may be either the client terminal device 10 or the server device 20, as long as it can access the score information 10J.

[0082] First, a target business is specified by a user or the like who wishes to receive suggestions of people using this embodiment (S-401).

[0083] The user also specifies the selection criteria for the suggested items (S-402).

[0084] In the processing of S-403 to S-405, the client program 10F is the main processing entity when the processing is performed on the client terminal device 10, and the server program 20F is the main processing entity when the processing is performed on the server device 20, so the subject is omitted in the description.

[0085] Next, it is checked whether the business record of the specified type exists in the score information 10J. If the record does not exist, it is assumed that there is no suggestion target and the process ends. If the record exists, it proceeds to S-404 (S-403).

[0086] Next, from the records found in S-403, select one user who best matches the items specified as the suggestion selection criteria.

[0087] For example, if the criterion selected is "experience using spreadsheets in sales work," then "userA," "userB," and "userC" are selected, as shown in Figure 5. If the criterion selected is "high work efficiency" to further narrow down the search, then "userA," who has the highest score in the same work, becomes optimal (S-404). Note that even for a criterion such as "high work efficiency," multiple people may be suggested. For example, all workers with individual scores (first comparison scores) above the average score, or the top several workers, may be suggested.

[0088] Next, the users extracted as suggestion targets in S-404 are output as suggestion targets (S-405).

[0089] In this way, the most suitable person can be suggested for any task using any criteria.

[0090] <Fourth embodiment> In the first embodiment, when ranking tasks, the personal scores held by the user and the average score information of the population were referenced, but by changing the information used at this time to personnel information instead of scores or combining them, it is possible to rank tasks from various perspectives.

[0091] <Other embodiments> The present invention can also be realized by supplying a program that realizes one or more functions of the above-described embodiments to a system or device via a network or a storage medium, and having one or more processors in the computer of the system or device read and execute the program. It can also be realized by a circuit (e.g., ASIC) that realizes one or more functions. [Explanation of symbols]

[0092] 10 Client terminal device 10F Client Program 10G acquisition information 10H business hours 10I Rank Information 10J Score Information 10K operation history 10L operation time 20 Server equipment 20G Client Program 20H acquisition information 20I Business hours 20J Score Information 20K operation history 20L operation time 30 Network

Claims

1. an operation history recording means for recording operations of the information processing terminal by a user in association with a task classification and operation content; score calculation means for calculating a score for each of the task categories and the operation details for each of the users using the operation history; a comparison means for calculating a comparison score by comparing the scores calculated by the score calculation means for each of the users, taking into consideration an average score of the scores calculated by the score calculation means for the plurality of users; A log management system comprising:

2. 2. The log management system according to claim 1, wherein the comparison means calculates the comparison score based on a difference from the average score or a deviation from the average score calculated for each of the task categories and the operation contents.

3. 3. The log management system according to claim 2, wherein an order of preference for the task classification and the operation content for each user is calculated based on the comparison score calculated by the comparison means.

4. 4. The log management system according to claim 3, wherein the ranking is calculated based on a rank set based on personnel information of the user.

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