Information processing apparatus, information processing method, and program

Abstract

To appropriately evaluate training.SOLUTION: There is provided an information processing apparatus including collection means for collecting a business log related to a business activity of an employee, determination means for determining a degree of relevance between the business log and a training that the employee has taken, transmission means for transmitting a question for evaluation of the training to the employee in a case where the degree of relevance is equal to or greater than a threshold value, and reception means for receiving answer data of the employee to the question.SELECTED DRAWING: Figure 4

Description

[Technical Field]

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

[0002] Patent Document 1 discloses a method for automatically collecting information indicating the state of students and providing it to the lecturer. In Patent Document 1, facial information of students is extracted from multiple images, and students in a predetermined state of attendance, such as "nodding" or "dozing," are identified based on the facial information. Then, an evaluation of the lecture is output only for the time period when it is detected that the number of students in a predetermined state of attendance exceeds a predetermined number. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2007-102344 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the technology described in Patent Document 1 has a problem in that, for example, the training may not be evaluated appropriately.

[0005] In view of the above-mentioned problems, an object of the present disclosure is to provide a technology that allows appropriate evaluation of training. [Means for solving the problem]

[0006] In a first aspect of the present disclosure, an information processing device is provided that has a collection means for collecting work logs related to an employee's work activities, a determination means for determining the degree of relevance between the work logs and training that the employee has taken, a transmission means for sending the employee evaluation questions regarding the training if the degree of relevance is equal to or greater than a threshold, and a reception means for receiving the employee's response data to the questions.

[0007] In addition, a second aspect of the present disclosure provides an information processing method in which an information processing device collects work logs related to an employee's work activities, determines the degree of relevance between the work logs and training that the employee has taken, and if the degree of relevance is equal to or greater than a threshold, sends the employee evaluation questions about the training and receives the employee's response data to the questions.

[0008] In addition, a third aspect of the present disclosure provides a program that causes a computer to execute a process of collecting work logs related to an employee's work activities, determining the degree of relevance between the work logs and training that the employee has taken, and if the degree of relevance is equal to or greater than a threshold, sending the employee evaluation questions about the training, and receiving the employee's response data to the questions. [Effects of the Invention]

[0009] On the one hand, training can be properly evaluated. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of an information processing device according to an embodiment. [Figure 2] 1 is a diagram illustrating an example of the configuration of an information processing system according to an embodiment. [Figure 3] FIG. 1 is a diagram illustrating an example of a hardware configuration of an information processing apparatus according to an embodiment. [Figure 4] 10 is a flowchart illustrating an example of processing by the information processing apparatus according to the embodiment. [Figure 5] FIG. 2 is a diagram illustrating an example of information stored in an employee DB (database) according to the embodiment. [Figure 6] FIG. 4 is a diagram showing an example of information stored in a training evaluation DB according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] The principles of the present disclosure will be described with reference to some exemplary embodiments. It should be understood that these embodiments are set forth for illustrative purposes only, to aid those skilled in the art in understanding and practicing the present disclosure, without implying any limitation on the scope of the disclosure. The disclosure described herein may be implemented in various ways other than those described below.

[0012] In the following description and claims, unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0013] Hereinafter, embodiments of the present disclosure will be described with reference to the drawings. Note that each drawing is merely an example for describing one or more embodiments. Each drawing may not relate to only one particular embodiment, but may also relate to one or more other embodiments. As will be understood by those skilled in the art, various features or steps described with reference to any one drawing can be combined with features or steps shown in one or more other drawings to create, for example, an embodiment not explicitly shown or described. Not all features or steps shown in any one drawing are necessarily required to describe an exemplary embodiment, and some features or steps may be omitted. The order of steps described in any drawing may be changed as appropriate.

[0014] (Embodiment 1) <Configuration> The configuration of an information processing device 10 according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of the configuration of the information processing device 10 according to an embodiment. The information processing device 10 has a collection unit 11, a determination unit 12, a transmission unit 13, and a reception unit 14. Each of these units may be realized by cooperation between one or more programs installed in the information processing device 10 and hardware such as a processor and memory of the information processing device 10.

[0015] The collection unit 11 collects business logs related to the business activities of employees. The determination unit 12 determines the degree of association between the business logs collected by the collection unit 11 and the training that the employees have taken.

[0016] If the relevance determined by the determination unit 12 is equal to or greater than a threshold, the transmission unit 13 transmits a question for evaluation of the training to the employee. The reception unit 14 receives the employee's response data to the question transmitted by the transmission unit 13. This allows, for example, the training to be appropriately evaluated.

[0017] (Embodiment 2) <System configuration> Next, the configuration of the information processing system 1 according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of the information processing system 1 according to the embodiment. In the example of Fig. 2, the information processing system 1 includes an information processing device 10 and employee terminals 20A to 20C. Hereinafter, when there is no need to distinguish between the employee terminals 20A to 20C, they will be simply referred to as "employee terminal 20".

[0018] 2, the information processing devices 10 and employee terminals 20 are connected to each other so as to be able to communicate with each other via a network N. The number of information processing devices 10 and employee terminals 20 is not limited to that shown in the example of FIG.

[0019] Examples of the network N include, for example, the Internet, a mobile communication system, a wireless LAN (Local Area Network), a LAN, a bus, etc. Examples of the mobile communication system include, for example, a fifth generation mobile communication system (5G), a sixth generation mobile communication system (6G, Beyond 5G), a fourth generation mobile communication system (4G), a third generation mobile communication system (3G), etc.

[0020] The information processing device 10 is, for example, a device such as a server, a cloud server, a personal computer, a smartphone, etc. The information processing device 10 determines, for example, the degree of association between the actual work activity of an employee and training.

[0021] The employee terminal 20 is, for example, a terminal such as a personal computer (PC), a smartphone, a tablet, or a wearable device that is used by an employee in his or her work activities.

[0022] <Hardware configuration> Fig. 3 is a diagram showing an example of the hardware configuration of an information processing device 10 according to an embodiment. In the example of Fig. 3, the information processing device 10 (computer 100) includes a processor 101, a memory 102, and a communication interface 103. These components may be connected via a bus or the like. The memory 102 stores at least a part of a program 104. The communication interface 103 includes an interface required for communication with other network elements.

[0023] When the program 104 is executed by the processor 101, memory 102, and the like in cooperation with each other, the computer 100 performs at least some of the processing of the embodiments of the present disclosure. The memory 102 may be of any type. As a non-limiting example, the memory 102 may be a non-transitory computer-readable storage medium. The memory 102 may also be implemented using any suitable data storage technology, such as semiconductor-based memory devices, magnetic memory devices and systems, optical memory devices and systems, fixed memory, and removable memory. Although only one memory 102 is shown in the computer 100, several physically different memory modules may exist in the computer 100. The processor 101 may be of any type. The processor 101 may include one or more of a general-purpose computer, a special-purpose computer, a microprocessor, a digital signal processor (DSP), and a processor based on a multi-core processor architecture, as a non-limiting example. The computer 100 may have multiple processors, such as application-specific integrated circuit chips that are time-slaved to a clock that synchronizes the main processor.

[0024] Embodiments of the present disclosure may be implemented in hardware or special purpose circuits, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software that may be executed by a controller, microprocessor, or other computing device.

[0025] The present disclosure also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, that execute on a target real or virtual processor or device to perform the processes or methods of the present disclosure. Program modules include routines, programs, libraries, objects, classes, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The functionality of the program modules may be combined or divided among program modules as desired in various embodiments. The machine-executable instructions of the program modules may be executed in local or distributed devices. In a distributed device, the program modules may be located in both local and remote storage media.

[0026] The program code for executing the methods of the present disclosure may be written in any combination of one or more programming languages. The program code may be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus. When the program code is executed by the processor or controller, the functions / acts in the flowcharts and / or implementing block diagrams are performed. The program code may be executed entirely on the machine, partly on the machine, as a standalone software package, partly on the machine and partly on a remote machine, or entirely on a remote machine or server.

[0027] The program can be stored and provided to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible recording media. Examples of non-transitory computer-readable media include magnetic recording media, magneto-optical recording media, optical disk media, and semiconductor memory. Magnetic recording media include, for example, flexible disks, magnetic tapes, and hard disk drives. Magneto-optical recording media include, for example, magneto-optical disks. Optical disk media include, for example, Blu-ray discs, CD (Compact Disc)-ROMs (Read Only Memory), CD-Rs (Recordable), and CD-RWs (Rewritable). Semiconductor memory includes, for example, solid-state drives, mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory). The program may also be provided to a computer by various types of temporary computer-readable media. Examples of temporary computer-readable media include electrical signals, optical signals, and electromagnetic waves. The temporary computer-readable medium can supply the program to the computer via a wired communication path such as an electric wire or an optical fiber, or via a wireless communication path.

[0028] <Processing> Next, an example of processing of the information processing device 10 according to the embodiment will be described with reference to Fig. 4 to Fig. 6. Fig. 4 is a flowchart showing an example of processing of the information processing device 10 according to the embodiment. Fig. 5 is a diagram showing an example of information stored in an employee DB (database) 501 according to the embodiment. Fig. 6 is a diagram showing an example of information stored in a training evaluation DB 601 according to the embodiment. Note that the processing of Fig. 4 may be executed, for example, periodically.

[0029] In step S101, the collection unit 11 collects business logs related to the business activities of employees. The business logs may include, for example, at least one of operation logs of devices used by employees in their business activities, the usage status of conference rooms by employees, the progress of work by employees registered in the business management system, videos of employees working, the contents of documents created by employees, and data measured by sensors.

[0030] Next, the determination unit 12 determines the content and progress of the employee's work activities based on the work logs collected by the collection unit 11 (step S102). Here, the determination unit 12 may determine the content and progress of the work activities based on, for example, a video of the employee working using image recognition technology. The video of the work may be captured using a camera or a wearable device such as smart glasses.

[0031] The determination unit 12 may also determine the content and progress of an employee's work activities by analyzing keywords and context in a document created by the employee using natural language processing technology. The document created by the employee may be, for example, a daily work log, a report, or an email.

[0032] The determination unit 12 may also determine the content and progress of business activities based on data measured by a sensor, for example. The sensor may be, for example, an IoT (Internet of Things) sensor attached to a factory machine or a wearable sensor. The type of business log may be selected by an administrator depending on the employee's work content, occupation, and business type of the company.

[0033] The determination unit 12 may record the determination result in the employee DB 501. In the example of FIG. 5, the employee DB 501 records a training attendance history, the contents of work activities, the progress of work activities, and a work log in association with an employee ID. The employee ID is identification information for an employee. The training attendance history is a history of training attended by an employee. The training attendance history may include, for example, one or more combinations of a training ID and a completion date and time of the training. The training attendance history may be set (registered, recorded) in advance in the employee DB 501 by, for example, an administrator or the like.

[0034] Next, the determination unit 12 determines the degree of association between the employee's work activities and the training that the employee has taken, based on the work logs collected by the collection unit 11 (step S103). Here, the determination unit 12 may calculate the degree of association based on, for example, at least one of the commonality and similarity between keywords included in the work logs and keywords included in the training documents.

[0035] In this case, the determination unit 12 may extract, for example, keywords contained in a business log, which is a document, and keywords contained in the work content determined based on the business log. The determination unit 12 may also extract, for example, keywords contained in a curriculum or materials, which are training documents. The determination unit 12 may then estimate the degree of association between the keywords based on the business log and the keywords contained in the training documents, for example, using natural language processing technology.

[0036] Furthermore, the determination unit 12 may analyze each of the work log and the training document using a topic analysis method and calculate the similarity of the topic distribution between them as the relevance. This allows for quantitative evaluation of the overlap of the themes covered by the work and training. In this case, the determination unit 12 may use, for example, LSI (Latent Semantic Index), PLSI (Probabilistic Latent Semantic Indexing), or LDA (Latent Dirichlet Allocation) as the topic analysis method.

[0037] Furthermore, the determination unit 12 may use, for example, Doc2Vec technology to convert each of the work log and the training document into a document vector, and calculate the distance in the vector space as the relevance. Because the document vector captures the semantic features of the document, it is possible to measure the semantic similarity between the work and the training.

[0038] Furthermore, the determination unit 12 may determine (infer, estimate) the degree of relevance between the business log and the training document, for example, using supervised learning. In this case, for example, pairs of business log and training document may be manually labeled as to whether or not they are relevant. Then, the determination unit 12 may train a machine learning model using the labeled data as training data.

[0039] Furthermore, the determination unit 12 may determine the degree of association between the business log and the training document, for example, using network analysis. In this case, the determination unit 12 may construct (generate, calculate) a network in which the business log and the training content are nodes and the strength of association is the weight of the edge. Then, the determination unit 12 may estimate the overall relationship between the business and the training based on the network, for example.

[0040] Furthermore, the determination unit 12 may determine the degree of relevance between the work log and the training document, for example, using time series analysis. In this case, the determination unit 12 may analyze the chronological relationship between the work and the training, for example, based on the time information in the work log and the training completion date and time in the training attendance history. Then, the determination unit 12 may determine the degree of relevance to be a higher value, for example, the more the efficiency (e.g., the degree of progress) of the related work improves after attending a specific training course.

[0041] Next, the determination unit 12 determines whether the relevance is equal to or greater than a threshold (step S104). If the relevance is not equal to or greater than the threshold (NO in step S104), the process ends. On the other hand, if the relevance is equal to or greater than the threshold (YES in step S104), the transmission unit 13 transmits a message to employees (employee terminals 20) who have taken one or more training courses for which the determination unit 12 has determined that the relevance is equal to or greater than the threshold, asking them to evaluate the training courses (step S105). The message may include, for example, a training ID and a question, or a URL (Uniform Resource Locator) of a website where the answer is to be entered.

[0042] In the above example, the determination unit 12 determines the degree of relevance between a training document and a business log. However, the determination target of the determination unit 12 of the present disclosure is not limited to the training document, and any information related to the training can be used. For example, the determination unit 12 may determine the degree of relevance using video content or audio data of the training. In the case of video content, the determination unit 12 may use video analysis technology to identify the work procedures and equipment used in the video and determine the degree of relevance by analyzing the similarity between these and the information included in the business log. In the case of audio data, the determination unit 12 may convert the audio data into text using speech recognition technology and analyze the relevance between the text and the business log.

[0043] This allows, for example, after a business marketing meeting has ended, to send a notification to employees who participated in the meeting asking them to evaluate the marketing training. The notification may include, for example, a survey asking how useful the content learned in the training was in the discussions and decision-making at the meeting. The survey may ask employees to specifically answer how the knowledge and skills were used in the meeting for each topic covered in the training. For example, a response such as "I gained a deeper understanding of customer segmentation techniques and was able to use them to select target customers" is expected.

[0044] Next, the receiving unit 14 receives the employee's response data to the message sent by the sending unit 13 (step S106). This makes it possible to obtain, for example, an employee's evaluation of training that is highly relevant to their actual work. Therefore, compared to, for example, asking employees to answer a questionnaire about all training, it is possible to reduce the amount of work required for the employee to respond. For example, the receiving unit 14 may receive information indicating one training selected by the employee from multiple trainings whose relevance is equal to or greater than a threshold. In this case, the employee may select (respond to) the training that is thought to be most relevant to their work from the multiple trainings whose relevance is equal to or greater than a threshold and presented by the sending unit 13.

[0045] The response data may include, for example, the degree of application of the training content to work, the degree of understanding of the training content, and the level of satisfaction with the training content. The degree of application of the training content to work may be, for example, an index that indicates the degree to which the knowledge and skills learned in the training were applied to work, as subjectively evaluated by the employee.

[0046] The level of understanding of the training content may be an index showing the degree to which the training content was understood, as subjectively evaluated by the employee, and specifically, the answer may be obtained from options such as "I understood it," "I understood it somewhat," "I didn't understand it very well," and "I didn't understand it at all." The level of satisfaction with the training content may be an index showing the degree of satisfaction with the training content, as subjectively evaluated by the employee, and specifically, the answer may be obtained from options such as "satisfied," "somewhat satisfied," "somewhat dissatisfied," and "dissatisfied."

[0047] The receiving unit 14 records data based on the response data in the training evaluation DB 601. In the example of FIG. 6, the training evaluation DB 601 stores the degree of application of the training content to work, the degree of understanding of the training content, and the level of satisfaction with the training content, in association with a combination of a training ID and an employee ID. It is also possible to simplify the responses in order to reduce the burden on employees (users) of inputting feedback. For example, the system may be configured to present multiple trainings that may have been useful in the employee's work and allow the employee to simply select one from among them. In this case, a detailed evaluation of the level of understanding, satisfaction, etc. is not performed, but rather a simple evaluation of whether the training was useful in the employee's work is performed.

[0048] Next, the determination unit 12 calculates the contribution of the training to work (usefulness, effectiveness, contribution) based on the relevance determined in the process of step S103 and the response data received by the receiving unit 14 (step S107). The contribution may be, for example, an index showing the extent to which the training contributed to work. For example, the determination unit 12 may determine a higher value of the contribution as the relevance is higher and as the employee's evaluation (at least one of the degree of use of the training content in work, the level of understanding of the training content, and the satisfaction with the training content) is higher.

[0049] Furthermore, the determination unit 12 may calculate the degree of contribution of the training to work based on the progress of the employee's work activities determined in the processing of step S102, the relevance determined in the processing of step S103, and the response data received by the receiving unit 14. In this case, the determination unit 12 may determine a higher value for the degree of contribution, for example, the faster the degree of progress of the work activities, the higher the relevance, and the higher the employee's evaluation (at least one of the degree of application of the training content to work, the degree of understanding of the training content, and the degree of satisfaction with the training content). Note that the degree of progress of the work activities may be, for example, the progress of the work activities per unit time. Note that the processing of step S107 is not essential.

[0050] The determination unit 12 may send a message recommending taking a training course whose contribution level is equal to or greater than a threshold to one or more employees who have not taken the training course and whose relevance level between the employee's work activities and the training course is equal to or greater than a threshold. This makes it possible to recommend employees related to a specific job to take a training course whose contribution level is high for the specific job.

[0051] In this case, the determination unit 12 may determine whether the degree of contribution calculated in step S107 is equal to or greater than a threshold. If the degree of contribution is equal to or greater than the threshold, the determination unit 12 may refer to the employee DB 501 to extract employees who have not yet taken the training. The determination unit 12 may then determine the degree of association between the employee's work activities and the training that the employee has taken, based on the work log, by processing similar to that of step S103. The determination unit 12 may then cause the transmission unit 13 to send a message recommending that the employee take the training to the employee terminal 20 of the employee whose degree of association is equal to or greater than the threshold.

[0052] (Example of a use case) An example of a use case in which an employee acquires marketing skills using the information processing device 10 of this embodiment will be described below. For example, an employee assigned to the marketing department needs to acquire knowledge and skills related to marketing. Therefore, the employee accesses the company's internal training management system and takes training related to marketing. In the training, the employee learns basic knowledge of marketing, market research techniques, how to develop promotion strategies, etc. The training attendance record is automatically registered in the training management system (for example, the training attendance history in the employee DB 501).

[0053] Employees utilize the knowledge they have learned in training when they perform their actual work. For example, they plan sales strategies for new products and analyze customer data. The collection unit 11 collects data such as employees' PC operation logs, schedules from the schedule management system, and progress reports from the marketing department's business management system. From this data, the content and progress of the marketing work being performed by employees can be ascertained.

[0054] The determination unit 12 then analyzes the collected work logs and the content of the marketing training that the employee has attended. Specifically, it compares keywords (e.g., market research, sales strategy, promotion) contained in the work logs with keywords contained in the training curriculum and text, and calculates the degree of association between them.

[0055] If the employee's work is highly related to the marketing training he or she has taken (for example, the relatedness is equal to or greater than a threshold), the transmission unit 13 transmits a message to the employee's PC or smartphone prompting him or her to measure the effectiveness of the training.

[0056] Employees who receive the messages are asked to fill out a questionnaire to measure the effectiveness of the training, asking questions such as the extent to which the knowledge and skills they learned in the training are being applied to their work, their level of understanding of the training content, and their satisfaction.

[0057] The receiving unit 14 collects the results of the questionnaires answered by employees and stores them in a storage unit (for example, the training evaluation DB 601). The determining unit 12 then integrates the results of the relevance analysis and the effectiveness measurement to calculate the contribution of the marketing training to business operations. If the relevance is high and the satisfaction and understanding levels of the effectiveness measurement are high, the contribution of the training is evaluated as high.

[0058] The calculated contribution level is reflected in the training management system and personnel evaluation system. This makes it possible to visualize the progress of employees' marketing skills acquisition and the effectiveness of training. Employee managers can analyze training with high contribution levels and use this information in their subordinates' development plans. The human resources department can also use this information to optimize training programs, such as by prioritizing budget allocations to training with high contribution levels.

[0059] (Example of calculating the training contribution from the work product of an employee) In the above example, an example was described in which the contribution of training to work was calculated based on the responses of employees to a questionnaire. Below, an example is described in which the contribution of training to work is calculated instead of, or in addition to, the contribution based on work deliverables registered by employees. In the example described below, even if an employee simply inputs the deliverables of their work, the training that they have taken in the past that influenced their results is automatically identified. Furthermore, because the contribution of training is calculated quantitatively, the effect of training on individual employees and the organization as a whole can be visualized.

[0060] The collection unit 11 may accept work deliverables input by employees as work logs. Examples of the deliverables may include reports, sales materials, programming code, design drawings, product specifications, research papers, patent specifications, etc. The input form (data format) of the deliverables may be in various file formats, such as text files, PDFs, presentation files, image files, and video files.

[0061] The determination unit 12 can also understand the business content from deliverables in different file formats. Specifically, the determination unit 12 may extract information about the business content contained in the deliverables by using techniques such as keyword extraction from text, layout analysis and optical character recognition (OCR) from PDF, image analysis and document structure analysis of slides from presentation files, object recognition from images, and motion recognition from videos.

[0062] The determination unit 12 may calculate the relevance each time a business log is collected by the collection unit 11. Alternatively, the determination unit 12 may collectively compare multiple business logs input within a predetermined time period with the training documents to calculate the relevance. In this case, the determination unit 12 may, for example, calculate the average or maximum value of the relevance between the training and each business log as the relevance for that training. Alternatively, the determination unit 12 may treat multiple business logs as a single business log and calculate the relevance. Alternatively, the determination unit 12 may receive information from an employee indicating that the input of the business log has been completed, and calculate the relevance based on the business logs input before receiving the information.

[0063] For training whose relevance exceeds a predetermined threshold, the determination unit 12 may compare the work log before the employee took the training with the work log after the employee took the training, and estimate the contribution level based on the difference between the two and the training content. The work log before the training may be a work log before the date of taking the training, and may be, for example, a work log closest to the date of taking the training, or multiple work logs recorded a predetermined period of time before the date of taking the training.

[0064] The determination unit 12 may analyze the difference in keywords contained in the work log before and after the training, and use the rate at which new keywords that were supposed to have been learned in the training appear in the work log as an index of the degree of contribution. The determination unit 12 may also use the degree of progress of work, changes in productivity, and qualitative changes in deliverables in calculating the degree of contribution. Furthermore, the determination unit 12 may also reflect the employee's subjective answers (such as degree of utilization, level of understanding, and satisfaction) in calculating the degree of contribution.

[0065] <Modification> The information processing device 10 may be a device contained in a single housing, but the information processing device 10 of the present disclosure is not limited to this. Each unit of the information processing device 10 may be realized, for example, by cloud computing configured with one or more computers. Furthermore, the information processing device 10 and the employee terminal 20 may be housed in the same housing and configured as an integrated information processing device. Furthermore, at least a portion of the processing of each functional unit of the information processing device 10 may be executed by the employee terminal 20. Such information processing devices 10 are also included as examples of the "information processing device" of the present disclosure.

[0066] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, each embodiment can be combined with other embodiments as appropriate.

[0067] Some or all of the above embodiments may also be described as, but are not limited to, the following appendices. Note that some or all of the elements (e.g., configurations and functions) described in each appendix dependent on appendix 1 may also be dependent on independent appendices in other categories in a similar dependency relationship. Some or all of the elements described in any appendix may be applied to various hardware, software, recording means for recording software, systems, and methods. (Appendix 1) a collection means for collecting work logs related to employee work activities; a determination means for determining the degree of association between the business log and the training attended by the employee; a transmission means for transmitting an evaluation question for the training to the employee when the degree of association is equal to or greater than a threshold; receiving means for receiving the employee's response data to the question; An information processing device having the above. (Appendix 2) the determining means calculates a degree of contribution of the training to the work based on the degree of relevance and the response data received by the receiving means. 2. The information processing device according to claim 1. (Appendix 3) the determining means determines the progress of the employee's work activities based on the work log, and calculates the contribution of the training to the work based on the progress, the relevance, and the response data received by the receiving means; 3. The information processing device according to claim 2. (Appendix 4) When the degree of contribution is equal to or greater than a threshold, the determination means transmits a message recommending that the employee take the training to one or more employees who have not taken the training and whose relevance between the employee's work activity and the training is equal to or greater than a threshold. 4. The information processing device according to claim 2 or 3. (Appendix 5) The business log includes at least one of an operation log of a device used by the employee in business activities, a usage status of a conference room by the employee, a progress status of work by the employee, a video of the employee working, the contents of a document created by the employee, and data measured by a sensor. 3. The information processing device according to claim 1 or 2. (Appendix 6) the determination means calculates the relevance based on at least one of a commonality and a similarity between the keywords included in the business log and the keywords included in the training document; 3. The information processing device according to claim 1 or 2. (Appendix 7) The transmission means presents to the employee a plurality of trainings whose relevance is equal to or greater than a threshold value; the receiving means receives a selection result from the plurality of trainings by the employee; 3. The information processing device according to claim 1 or 2. (Appendix 8) The determining means determines the relevance using at least one of topic analysis, document vector analysis, supervised learning, network analysis, and time series analysis. 3. The information processing device according to claim 1 or 2. (Appendix 9) The response data includes at least one of the degree of application of the training content to work, the degree of understanding of the training content, and the degree of satisfaction with the training content. 3. The information processing device according to claim 1 or 2. (Appendix 10) The information processing device Collecting work logs related to employee work activities, determining the degree of relevance between the business log and the training attended by the employee; If the relevance is equal to or greater than a threshold, sending evaluation questions for the training to the employee; receiving response data of the employee to the question; Information processing methods. (Appendix 11) Collecting work logs related to employee work activities, determining the degree of relevance between the business log and the training attended by the employee; If the relevance is equal to or greater than a threshold, sending evaluation questions for the training to the employee; receiving response data of the employee to the question; A program that causes a computer to perform a process. [Explanation of symbols]

[0068] 1. Information Processing Systems 10. Information processing equipment 11 Collection Department 12 Judgment section 13 Transmitter 14 Receiving unit 20 Employee terminals

Claims

1. a collection means for collecting work logs related to employee work activities; a determination means for determining the degree of association between the business log and the training attended by the employee; a transmission means for transmitting an evaluation question for the training to the employee when the degree of association is equal to or greater than a threshold; receiving means for receiving the employee's response data to the question; An information processing device having the above.

2. the determining means calculates a degree of contribution of the training to the work based on the degree of relevance and the response data received by the receiving means. The information processing device according to claim 1 .

3. the determining means determines the progress of the employee's work activities based on the work log, and calculates the contribution of the training to the work based on the progress, the relevance, and the response data received by the receiving means; The information processing device according to claim 2 .

4. When the degree of contribution is equal to or greater than a threshold, the determination means transmits a message recommending that the employee take the training to one or more employees who have not taken the training and whose degree of relevance between the employee's work activity and the training is equal to or greater than a threshold.

4. The information processing device according to claim 2 or 3.

5. The business log includes at least one of an operation log of a device used by the employee in business activities, a usage status of a conference room by the employee, a progress status of work by the employee, a video of the employee working, the contents of a document created by the employee, and data measured by a sensor.

3. The information processing device according to claim 1.

6. the determination means calculates the relevance based on at least one of a commonality and a similarity between the keywords included in the business log and the keywords included in the training document; 3. The information processing device according to claim 1.

7. The transmission means presents to the employee a plurality of trainings whose relevance is equal to or greater than a threshold value; the receiving means receives a selection result from the plurality of trainings by the employee; 3. The information processing device according to claim 1.

8. The response data includes at least one of the degree of application of the training content to work, the degree of understanding of the training content, and the degree of satisfaction with the training content.

3. The information processing device according to claim 1.

9. The information processing device Collecting work logs related to employee work activities, determining the degree of relevance between the business log and the training attended by the employee; If the relevance is equal to or greater than a threshold, sending evaluation questions for the training to the employee; receiving response data of the employee to the question; Information processing methods.

10. Collecting work logs related to employee work activities, determining the degree of relevance between the business log and the training attended by the employee; If the relevance is equal to or greater than a threshold, sending evaluation questions for the training to the employee; receiving response data of the employee to the question; A program that causes a computer to perform a process.

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