Personnel data interpretation support system, method thereof, and operating program for personnel data interpretation support system

Abstract

We will organize the indicators that are required to be disclosed by law and the optional disclosure indicators that are disclosed in the guidelines, grasp the overall picture, and clarify what needs to be done.We will then provide a system that supports strategy formulation, such as HR analytics functions, the type of administrative support functions required for information disclosure in securities reports and integrated reports, and the quantitative understanding and analysis of human resources information. [Solution] The personnel data interpretation support system has an item-specific personnel-related data acquisition unit that acquires personnel-related data accumulated by dividing it into multiple items related to personnel management, a distribution type-specific template storage unit that stores template messages to be presented to users in advance according to the distribution type of the personnel-related data acquired by item, a template acquisition unit that acquires template messages stored based on distribution type identification information, and a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data.

Description

[Technical Field]

[0001] The present invention relates to a personnel data interpretation support system that supports the visualization of human capital. [Background technology]

[0002] Starting with the release of the International Standard for Human Capital Management (ISO30414) in 2018, the disclosure of human capital information has been included as a requirement under US securities law, and in Japan, disclosure of the "gender wage gap" will become mandatory from 2023 as one of the measures of "new capitalism." The Financial Instruments and Exchange Act also requires disclosure in securities reports for investors, and companies are working to make their human resource investments more visible. Additionally, securities reports have been expanded to include corporate governance provisions, such as the Corporate Governance Code 3-1(3), 4-2(2), and 5-1(3). Against this background, there has been an increase in cases where large companies, in particular, are disclosing human capital information in integrated reports and sustainability reports. However, the true purpose of visualizing human capital is not to end with visualization alone, but to continuously quantify and analyze the gap between "as is" (current state) and "to be" (ideal state), leading to increased corporate value.

[0003] For example, Patent Document 1 describes a program that aims to provide technology for obtaining appropriate data analysis results at low cost and in a short time, regardless of the user's level of data analysis skill, and causes a computer to function as a means for managing the progress of a dialogue regarding data analysis with a user, a means for identifying the user's purpose of analysis through the progress of the dialogue, a means for analyzing a target dataset in accordance with the purpose of the analysis, and a means for outputting the results of the analysis of the target dataset. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7369320 Summary of the Invention [Problem to be solved by the invention]

[0005] As an example, Patent Document 1 displays a line graph showing the relationship between the objective variable "sales volume" and the explanatory variable "date," along with information indicating outliers as important points in the graph, and a suggestion that reads, "Sales volume generally fluctuates between approximately 100 and 400 units per day. During the New Year's holiday and Golden Week, sales volume increased to approximately 600 units per day, which is an outlier." This suggestion includes information about a summary of the objective variable and information explaining the outliers.

[0006] Another example shows a box plot showing the relationship between the target variable "sales volume" and the explanatory variable "neighborhood event," along with information showing the difference between the baseline (median sales volume with "no" nearby event) and the target value (median sales volume with "nationwide" nearby event) as important points in the graph, and a suggestion that "When there is a "nationwide" nearby event, sales volume tends to be higher compared to when there is no nearby event. When comparing the medians, the increase in sales volume is 30 units per day." This suggestion also includes information explaining the relationship between the target variable and the explanatory variables.

[0007] However, although visualization using objective variables and explanatory variables and quantitative understanding of the As is-To be gap are supported, they do not yet go so far as to support analysis of that gap, consideration of countermeasures, or implementation of measures.

[0008] When it comes to visualizing human capital, while the content and calculation methods for legally mandated information disclosure are set, voluntary information disclosure, although the "Human Capital Visualization Guidelines" were published in August 2022, is based on a company's management strategy and human resources strategy, making it difficult to find common ground among companies. In other words, companies need to organize the indicators required by law and the voluntary disclosure indicators disclosed under the guidelines to understand the overall picture and clarify what needs to be done. Regarding HR (Human Resources) analytics functions, companies need to go beyond simple dashboard displays to provide administrative support functions for disclosing information in securities reports and integrated reports, and provide systems to support strategy formulation, such as quantitatively understanding and analyzing human resources information. [Means for solving the problem]

[0009] In order to solve the above problem, as a first invention, we provide a personnel data interpretation support system having a personnel-related data storage unit that stores personnel-related data including n-dimensional (n≧2) data that can be statistically processed, separated into multiple items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information determination rule storage unit that stores distribution type identification information determination rules that are rules for acquiring distribution type identification information from the distribution of the acquired item-specific personnel-related data; a distribution type identification information acquisition unit that acquires distribution type identification information of the acquired item-specific personnel-related data based on the acquired item-specific personnel-related data and the stored distribution type identification information determination rules; a template acquisition unit that acquires the stored template messages based on the acquired distribution type identification information; and a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data.

[0010] As a second invention, we provide a personnel data interpretation support system as described in the first invention, which has a target value storage unit that stores target values ​​that are target values ​​for at least some of the items, and a distribution type target-specific template storage unit that stores template messages to be presented to users in advance depending on distribution type identification information and the target values, and the template acquisition unit has a goal-dependent template acquisition means that acquires the stored template messages based on the distribution type identification information and, if there is a target value assigned to the item for which the distribution type identification information was obtained, the target value.

[0011] As a third invention, there is provided a personnel data interpretation support system as described in the first or second invention, further comprising a cluster analysis rule storage unit that stores cluster analysis rules for cluster analyzing personnel-related data acquired by item, a cluster analysis unit that performs cluster analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored cluster analysis rules, and a cluster analysis result output unit that outputs the cluster analysis results.

[0012] As a fourth invention, we provide a personnel data interpretation support system as described in the first or second invention, further comprising a correlation analysis rule storage unit that stores correlation analysis rules for performing correlation analysis on personnel-related data acquired by item, a correlation analysis unit that performs correlation analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored correlation analysis rules, and a correlation analysis result output unit that outputs the correlation analysis results.

[0013] As a fifth invention, we provide a personnel data interpretation support system described in the first or second invention, further comprising: a feature word analysis rule storage unit that stores feature word analysis rules for analyzing natural language data for feature words when the personnel-related data acquired by item contains natural language data; a feature word analysis unit that performs feature word analysis on the natural language data based on the natural language data and the stored feature word analysis rules; and a feature word analysis result output unit that outputs the feature word analysis results.

[0014] As a sixth invention, there is provided a human resources data interpretation support system as described in the first invention, wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers and turnover, information on skills and abilities, information on succession plans, and information on the workforce.

[0015] As a seventh invention, there is provided a personnel data interpretation support system as described in the first invention, wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding the rate of paid leave taken, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

[0016] An eighth invention provides a personnel data interpretation support system as described in the first invention, wherein the distribution type identification information is distribution type identification information for at least one of a bar graph, a histogram, a pie chart, a line graph, a scatter plot, a radar chart, a cross tabulation, and a scalar chart.

[0017] The ninth invention provides a method executed by a CPU in a personnel data interpretation support system that is a computer, the method comprising: a personnel-related data accumulation step of accumulating personnel-related data including n-dimensional (n≧2) data that can be statistically processed, sorting the data into a plurality of items related to personnel management; an item-specific personnel-related data acquisition step of acquiring personnel-related data by item; a distribution type-specific template storage step of storing template messages to be presented to users in advance in accordance with distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information determination rule storage step of storing distribution type identification information determination rules that are rules for acquiring distribution type identification information from the distribution of the acquired item-specific personnel-related data; a distribution type identification information acquisition step of acquiring distribution type identification information for the acquired item-specific personnel-related data based on the acquired item-specific personnel-related data and the stored distribution type identification information determination rules; a template acquisition step of acquiring template messages that have been stored based on the acquired distribution type identification information; and a template output step of outputting the acquired template messages in association with the acquired item-specific personnel-related data.

[0018] The tenth invention provides a method described in the ninth invention, which is executed by a CPU in a personnel data interpretation support system that is a computer, and includes a target value retention step for retaining target values ​​that are target values ​​for at least some of the items, and a distribution type target-specific template retention step for retaining template messages to be presented to users in advance based on distribution type identification information and the target values, and the template acquisition step further includes a target-dependent template acquisition substep for acquiring a stored template message based on the distribution type identification information and, if there is a target value assigned to the item for which the distribution type identification information was obtained, the target value.

[0019] An eleventh invention provides the method described in the ninth or tenth invention, which is executed by a CPU in a personnel data interpretation support system that is a computer, and further includes a cluster analysis rule storage step for storing cluster analysis rules for cluster analyzing personnel-related data acquired by item, a cluster analysis step for cluster analyzing the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored cluster analysis rules, and a cluster analysis result output step for outputting the cluster analysis results.

[0020] As a twelfth invention, there is provided a method described in the ninth or tenth invention, which is executed by a CPU in a personnel data interpretation support system that is a computer, and further includes a correlation analysis rule storage step for storing correlation analysis rules for performing correlation analysis on personnel-related data acquired by item, a correlation analysis step for performing correlation analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored correlation analysis rules, and a correlation analysis result output step for outputting the correlation analysis results.

[0021] As a thirteenth invention, we provide a method described in the ninth or tenth invention, which is executed by a CPU in a human resources data interpretation support system that is a computer, and which further includes a feature word analysis rule storage step for storing feature word analysis rules for analyzing natural language data when the human resources-related data acquired by item contains natural language data, a feature word analysis step for performing feature word analysis on the natural language data based on the natural language data and the stored feature word analysis rules, and a feature word analysis result output step for outputting the feature word analysis results.

[0022] As a fourteenth invention, we provide the method described in the ninth invention, which is executed by a CPU in a human resources data interpretation support system that is a computer, and wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers, and turnover, information on skills and abilities, information on succession plans, and information on the workforce.

[0023] As a fifteenth invention, there is provided a method described in the fourteenth invention, which is executed by a CPU in a human resources data interpretation support system that is a computer, and wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding the rate of paid leave taken, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

[0024] As a sixteenth invention, there is provided the method described in the ninth invention, which is executed by a CPU in a personnel data interpretation support system that is a computer, and wherein the distribution type identification information is distribution type identification information for at least one of a bar graph, a histogram, a pie chart, a line graph, a scatter plot, a radar chart, a cross tabulation, and a scalar chart.

[0025] The seventeenth invention is an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, and includes a personnel-related data accumulation step for accumulating personnel-related data including statistically processable n-dimensional (n≧2) data, classified into a plurality of items related to personnel management; an item-specific personnel-related data acquisition step for acquiring personnel-related data by item; a distribution-type-specific standard phrase storage step for storing standard phrases to be presented to a user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; and an acquisition step for acquiring distribution type identification information from the distribution of the acquired item-specific personnel-related data. and a distribution type identification information acquisition step for acquiring the distribution type identification information of the acquired item-specific personnel related data based on the acquired item-specific personnel related data and the stored distribution type identification information judgment rule. The present invention provides an operating program for a personnel data interpretation support system that is executed by the computer having the following features: a distribution type identification information determination rule storage step for storing distribution type identification information determination rules, which are rules for determining the distribution type identification information of the acquired item-specific personnel related data; a distribution type identification information acquisition step for acquiring the stored standard phrases based on the acquired distribution type identification information; and a standard phrase output step for outputting the acquired standard phrases in association with the acquired item-specific personnel related data.

[0026] The eighteenth invention provides an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, the operating program being to be executed by the personnel data interpretation support system that is a computer as described in the seventeenth invention, and that includes a target value retention step for retaining target values ​​that are target values ​​for at least some of the items, and a distribution type target-specific standard phrase retention step for retaining standard phrases to be presented to users in advance depending on distribution type identification information and the target values, wherein the standard phrase acquisition step further includes a target-dependent standard phrase acquisition substep for acquiring a stored standard phrase based on the distribution type identification information and, if there is a target value assigned to the item for which the distribution type identification information was obtained, the target value.

[0027] As a nineteenth invention, we provide an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, and that is to be executed by the personnel data interpretation support system, which is a computer described in the seventeenth or eighteenth invention, and further has: a cluster analysis rule storage step that stores cluster analysis rules for cluster analyzing personnel-related data acquired by item; a cluster analysis step that performs cluster analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored cluster analysis rules; and a cluster analysis result output step that outputs the cluster analysis results.

[0028] As a twentieth invention, we provide an operating program for a personnel data interpretation support system that is written in a manner that can be read and executed by the personnel data interpretation support system, and that is to be executed by the personnel data interpretation support system, which is a computer described in the seventeenth or eighteenth invention, and further has the following steps: a correlation analysis rule storage step for storing correlation analysis rules for performing correlation analysis on personnel-related data acquired by item; a correlation analysis step for performing correlation analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored correlation analysis rules; and a correlation analysis result output step for outputting the correlation analysis results.

[0029] As a twenty-first invention, we provide an operating program for a personnel data interpretation support system that is written in a manner that can be read and executed by a personnel data interpretation support system, and that further has the following: a characteristic word analysis rule storage step that stores characteristic word analysis rules for analyzing natural language data when the personnel-related data acquired by item contains natural language data; a characteristic word analysis step that performs characteristic word analysis on the natural language data based on the natural language data and the stored characteristic word analysis rules; and a characteristic word analysis result output step that outputs the characteristic word analysis results.

[0030] The twenty-second invention provides an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, the operating program being executed by the personnel data interpretation support system, which is a computer as described in the seventeenth invention, and wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers and turnover, information on skills and abilities, information on succession plans, and information on the workforce.

[0031] The twenty-third invention provides an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, and is to be executed by the personnel data interpretation support system, which is a computer as described in the twenty-second invention, and wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding the rate of paid leave taken, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

[0032] As a twenty-fourth invention, we provide an operating program for a personnel data interpretation support system that is written so as to be readable and executable by the personnel data interpretation support system, and that is to be executed by the personnel data interpretation support system, which is a computer as described in the seventeenth invention, and in which the distribution type identification information is distribution type identification information for at least one of bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts. [Effects of the Invention]

[0033] According to the present invention, it is possible to provide a personnel data interpretation support system, a method thereof, and an operating program for the personnel data interpretation support system that guides the user (teaches the key points of data analysis) from, for example, OODA loop observation (situation confirmation, detailed confirmation) to situation judgment (automated dynamic analysis, analysis result confirmation) by having the user answer questions. [Brief explanation of the drawings]

[0034] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a hardware configuration according to an embodiment of the present invention. [Figure 2] FIG. 1 is a conceptual diagram illustrating an example of the functional configuration of a personnel data interpretation support system according to a first embodiment. [Figure 3] FIG. 1 is a model diagram showing an example of the overall flow of work related to visualization of human capital information in the first embodiment of the present invention. [Figure 4] FIG. 10 is a diagram for explaining an example of instructions for a bar graph according to the first embodiment. [Figure 5] FIG. 10 is a diagram for explaining an example of instructions for a histogram according to the first embodiment. [Figure 6] FIG. 10 is a diagram for explaining an example of instructions for a pie chart according to the first embodiment. [Figure 7] FIG. 10 is a diagram for explaining an example of instructions for a line graph according to the first embodiment. [Figure 8] FIG. 1 is a diagram for explaining an example of instructions for a scatter diagram according to the first embodiment. [Figure 9] FIG. 10 is a diagram for explaining an example of instructions for a radar chart according to the first embodiment. [Figure 10] FIG. 1 is a diagram for explaining an example of a cross-tabulation instruction in the first embodiment. [Figure 11] FIG. 1 is a diagram for explaining an example of an instruction of a scalar chart according to the first embodiment. [Figure 12] FIG. 1 is a conceptual diagram illustrating an example of a hardware configuration of a system according to a first embodiment. [Figure 13] FIG. 1 is a flowchart showing an example of the processing flow of the system according to the first embodiment. [Figure 14] OODA Conceptual Diagram [Figure 15] Conceptual diagram of a system incorporating the OODA loop to support strategic execution [Figure 16] A diagram showing an example of a problem area in observing the OODA loop [Figure 17] FIG. 10 is a diagram for explaining the operation of the question phase of the instruction in the first embodiment. [Figure 18] FIG. 10 is a diagram for explaining the operation of the comment phase of an instruction in the first embodiment. [Figure 19] FIG. 10 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system according to a second embodiment. [Figure 20] FIG. 10 is a conceptual diagram illustrating an example of the hardware configuration of a system according to a second embodiment. [Figure 21] FIG. 10 is a flow chart showing an example of the processing flow of the system according to the second embodiment. [Figure 22] A diagram showing an example of a problem area in OODA loop situational judgment. [Figure 23] FIG. 10 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system according to a third embodiment. [Figure 24] FIG. 10 is a diagram illustrating an example of a cluster analysis method according to the third embodiment. [Figure 25] FIG. 10 is a conceptual diagram illustrating an example of the hardware configuration of a system according to a third embodiment. [Figure 26] FIG. 10 is a flow chart showing an example of the processing flow of the system according to the third embodiment. [Figure 27]FIG. 10 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system according to a fourth embodiment. [Figure 28] FIG. 10 is a diagram for explaining an example of a correlation analysis method according to the fourth embodiment. [Figure 29] FIG. 10 is a conceptual diagram showing an example of the hardware configuration of a system according to a fourth embodiment. [Figure 30] FIG. 10 is a flow chart showing an example of the processing flow of the system according to the fourth embodiment. [Figure 31] FIG. 10 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system according to a fifth embodiment. [Figure 32] FIG. 10 is a diagram illustrating an example of a feature word analysis method according to the fifth embodiment. [Figure 33] FIG. 10 is a conceptual diagram showing an example of the hardware configuration of a system according to a fifth embodiment. [Figure 34] FIG. 10 is a flow chart showing an example of the processing flow of the system according to the fifth embodiment. [Figure 35] FIG. 10 is a diagram illustrating an example of cluster analysis in the second embodiment. [Figure 36] FIG. 10 is a diagram for explaining an example of correlation analysis in the second embodiment. [Figure 37] FIG. 10 is a diagram illustrating a detailed display example of correlation analysis in the second embodiment. [Figure 38] FIG. 10 is a diagram illustrating an example of execution of feature word analysis in the third embodiment. [Figure 39] FIG. 1 is a flowchart showing an example of instructions for a line graph according to the first embodiment. [Figure 40] A diagram showing some of the disclosure indicators in ISO30414 [Figure 41] FIG. 1 shows examples of personnel management-related items additionally handled in the present invention.

[0035] <Hardware that can constitute the present invention> FIG. 1 is a diagram showing a hardware configuration applied to the present invention. While the present invention is primarily a computer-based invention, it can also be realized by software, hardware, or a combination of software and hardware. Hardware that realizes all or part of the components of the present invention includes the basic components of a computer, such as a CPU, memory, bus, input / output devices, various peripherals, and a user interface. Peripheral devices include storage devices, internet interfaces, internet devices, displays, keyboards, mice, speakers, cameras, videos, televisions, various sensors for monitoring production status in laboratories or factories (e.g., flow sensors, temperature sensors, weight sensors, liquid volume sensors, infrared sensors, shipment counters, package counters, foreign body inspection devices, defective product counters, radiation inspection devices, surface condition inspection devices, circuit inspection devices, motion sensors, worker status monitoring devices (e.g., video, ID, PC workload), etc.), CD drives, DVD drives, Blu-ray drives, USB memory sticks, USB memory stick interfaces, removable hard disks, standard hard disks, projectors, SSDs, telephones, fax machines, copiers, printers, movie editing devices, and various sensor devices. Furthermore, the present system does not necessarily have to be composed of a single device, but may be composed of multiple devices connected via communication. Communication may be via LAN, WAN, Wi-Fi, Bluetooth (registered trademark), infrared communication, or ultrasonic communication. Furthermore, some of the devices may be installed across borders. Furthermore, the multiple devices may be operated by different entities, or may be operated by a single entity. The system of the present invention may be operated by a single or multiple entities. The invention may also be configured as a system that includes, in addition to the present system, a terminal used by a third party and a terminal used by yet another third party. These terminals may also be installed across borders. Furthermore, in addition to the present system and the aforementioned terminals, devices used to register related information about third parties, related persons, and databases for recording the registration details may also be provided. These may be included in the present system, or the present system may be configured to utilize such information by being installed outside the system.

[0036] As shown in Fig. 1, the computer is configured on a motherboard and includes a chipset, CPU, non-volatile memory, main memory, various buses, BIOS, various interfaces such as USB, HDMI (registered trademark), and LAN, a real-time clock, etc. These operate in cooperation with an operating system, device drivers (for various interfaces such as USB and HDMI (registered trademark), and various built-in devices such as cameras, microphones, speakers or headphones, and displays), various programs, etc. The various programs and data constituting the present invention are configured to efficiently utilize these hardware resources to execute various processes.

[0037] Chipset A "chipset" is a set of large-scale integrated circuits (LSI) mounted on a computer's motherboard that integrates a communication function, or bridge function, between the CPU's external bus and the standard bus that connects memory and peripheral devices. Two chipset configurations are used, or one chipset configuration. The northbridge is located on the side closest to the CPU and main memory, and the southbridge is located on the side farther away, which interfaces with relatively slow external I / O.

[0038] (Northbridge) The northbridge includes a CPU interface, memory controller, and graphics interface. Most of the functions of a conventional northbridge can be performed by the CPU. The northbridge connects to the main memory slot via a memory bus, and to the graphics card slot via a high-speed graphics bus (AGP, PCI Express).

[0039] (Southbridge) The southbridge connects to the PCI interface (PCI slot) via the PCI bus and handles I / O functions such as ATA (SATA), USB, and Ethernet interfaces, as well as sound functions. Incorporating circuits to support features such as PS / 2 ports, floppy disk drives, serial ports, parallel ports, and ISA buses, which do not require or are not capable of high-speed operation, would hinder the speed of the chipset itself, so these can be separated from the southbridge chip and placed in a separate LSI called a super I / O chip. Buses are used to connect the CPU (MPU) to peripheral devices and various control units. Buses are connected by the chipset. The memory bus used to connect to main memory may instead use a channel structure for increased speed. A serial bus or a parallel bus can be used as the bus. While a serial bus transfers data one bit at a time, a parallel bus transmits the original data or multiple bits extracted from the original data as a single block over multiple communication paths simultaneously. A dedicated line for the clock signal runs parallel to the data line, synchronizing data demodulation on the receiving side. It is also used as a bus to connect the CPU (chipset) to external devices, and includes GPIB, IDE / (Parallel) ATA, SCSI, PCI, etc. Because there is a limit to how fast it can be made, in PCI Express, an improved version of PCI, and Serial ATA, an improved version of Parallel ATA, the data line can be a serial bus.

[0040] CPU A CPU sequentially reads, interprets, and executes a sequence of instructions called a program stored in main memory, outputting signal-based information to the main memory. The CPU functions as the center of computation within a computer. A CPU consists of a CPU core, which is the center of computation, and its peripheral components, including registers, cache memory, an internal bus connecting the cache memory to the CPU core, a DMA controller, a timer, and an interface with the bus connecting to the north bridge. A single CPU (chip) may have multiple CPU cores. Processing may also be performed by a graphics interface (GPU) or FPU in addition to the CPU. While the embodiments are described as being of a two-core type, this is not limiting. Programs may also be embedded within the CPU.

[0041] <Non-volatile memory> (HDD) The basic structure of a hard disk drive consists of a magnetic disk, a magnetic head, and an arm on which the magnetic head is mounted. The external interface can be SATA (formerly ATA). A high-performance controller, such as SCSI, is used to support communication between hard disk drives. For example, when copying a file to another hard disk drive, the controller can read the sectors, transfer them to the other hard disk drive, and write them. This does not access the host CPU's memory, so there is no increase in the CPU load.

[0042] <Main memory> The CPU directly accesses and executes various programs in main memory. Main memory is volatile memory and uses DRAM. Programs in main memory are expanded from non-volatile memory to main memory upon receiving a program startup command. The CPU then executes the program according to various execution commands and execution procedures within the program.

[0043] Operating System (OS) An operating system is used to manage computer resources for use by applications, to manage various device drivers, and to manage the computer itself (the hardware). In small computers, firmware is sometimes used as the operating system.

[0044] ≪BIOS≫ The BIOS causes the CPU to execute procedures for starting up the computer hardware and running the operating system. It is most typically the hardware that the CPU reads first when it receives a computer startup command. The BIOS contains the address of the operating system stored on the disk (non-volatile memory), and the BIOS loaded into the CPU sequentially loads the operating system into main memory, putting it into operation. The BIOS also has a check function that checks the presence or absence of various devices connected to the bus. The check results are stored in main memory and made available to the operating system as appropriate. The BIOS may also be configured to check for external devices, etc. The above applies to all embodiments.

[0045] As shown in Figure 1, the present invention can basically be configured with a general-purpose computer program and various devices. The computer basically operates by loading a program recorded in non-volatile memory into main memory, and then executing processing using the main memory, CPU, and various devices. Communication with devices is performed via an interface connected to a bus line. Possible interfaces include a display interface, keyboard, and communication buffer. Below, an embodiment of the present invention will be described with reference to illustrative examples.

[0046] <Fulfillment of the Laws of Nature of the Invention> The present invention functions through the cooperation of a computer, communications equipment, and software. Specifically, it relates to a personnel data interpretation support system that stores pre-defined phrases to be presented to users in accordance with distribution type identification information for identifying the n-dimensional distribution type of personnel-related data acquired by category, associates the stored phrases with the acquired itemized personnel-related data, and outputs the stored phrases based on the distribution type identification information. Various information and data are exchanged between multiple user terminals, multiple administrator terminals, and multiple information sources via a network using hardware resources. Therefore, from this perspective, if we consider the resources, such as computers, based on the matters described in the claims and specification and the common general knowledge related to those matters, the present invention as a whole utilizes a law of nature and falls under the category of a computer-software-related invention.

[0047] <The significance of utilizing the laws of nature required by patent law> The Patent Act requires that an invention be industrially applicable and contribute to the development of industry. This requirement ensures that the invention is industrially applicable. In other words, the invention must be industrially useful; that is, the effects of the invention declared in the application must be reproducible with a certain degree of certainty through the practice of the invention. From this perspective, the application of the laws of nature is interpreted as the use of the laws of nature to achieve the functions of each of the invention's defining features (invention elements), which constitute the invention's effects. Furthermore, the effect of an invention is sufficient if it has the potential to provide a specific utility to users who use the invention, and should not be viewed in terms of how users feel or think about that utility. Therefore, even if the effect users gain from the system is a psychological effect, that effect itself does not fall within the scope of the required application of the laws of nature. DETAILED DESCRIPTION OF THE INVENTION

[0048] Hereinafter, embodiments of the present invention will be described with reference to the drawings. However, the present invention should not be limited to these embodiments and can be embodied in various forms without departing from the spirit and scope of the present invention.

[0049] The functional configuration of each embodiment described below can be realized as a combination of hardware and software, as will be described later. Furthermore, each embodiment described herein can be realized not only as an apparatus or system, but also as a method of operation, in part or in whole. Furthermore, a portion of such an apparatus can be configured as software. Furthermore, software products used to cause a computer to execute such software, and recording media on which such products are fixed, are naturally included within the technical scope of each embodiment described herein (the same applies throughout this specification).

[0050] In embodiment 1, claims 1, 6 to 8, 9, 14 to 16, 17, and 22 to 24 will be mainly described. In embodiment 2, claims 2, 10, and 18 will be mainly described. In embodiment 3, claims 3, 11, and 19 will be mainly described. In embodiment 4, claims 4, 12, and 20 will be mainly described. In embodiment 5, claims 5, 13, and 21 will be mainly described.

[0051] <Embodiment 1 (corresponding mainly to claims 1, 6-8, 9, 14-16, 17, and 22-24)> <Outline of Embodiment 1> The goal of human capital visualization is to visualize the status of human capital and to invest in human capital while continuously working to quantitatively understand and analyze the goals and indicators that companies have set according to their industry, business model, and strategy, in order to aim for improved corporate value and sustainable growth in the medium to long term. Generally, OODA is known as an exemplary behavioral process suitable for data-driven operations. OODA is a theory of decision-making and behavior that consists of Observe, Orient, Decide, and Act. However, even if a user who is not familiar with data analysis checks the current situation using a dashboard graph, simply displaying the graph or analysis results will not allow them to execute the subsequent OODA loop, and the results will end up being merely visualized with a "pretty graph." Embodiment 1 provides a personnel data interpretation support system that guides the user (teaches the key points of data analysis) from observing the OODA loop (checking the situation and details) to making a judgment on the situation (analyzing actions and checking the analysis results) by having the user answer questions from the personnel data interpretation support system.

[0052] <Functional Configuration of Embodiment 1> 2 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system 0200 according to embodiment 1. As shown in the figure, the "personnel data interpretation support system" 0200 has at least a "personnel related data accumulation unit" 0201, an "item-specific personnel related data acquisition unit" 0202, a "distribution type fixed phrase storage unit" 0203, a "distribution type identification information reception unit" 0204, a "fixed phrase acquisition unit" 0205, and a "fixed phrase output unit" 0206.

[0053] <Description of Each Configuration in Embodiment 1> (Embodiment 1: Personnel-related data storage unit) The personnel-related data storage unit 0201 is configured to store personnel-related data including statistically processable n-dimensional (n≧2) data, classified into a plurality of items related to personnel management.

[0054] The multiple items related to human resource management include, for example, information on compliance and ethics defined in ISO30414, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers, and turnover, information on skills and abilities, information on succession planning, and information on the workforce (see Figure 40 (excerpt)).

[0055] Compliance and ethics information includes the number and type of complaints (workplace complaints, harassment, etc.), the number and type of disciplinary actions, the percentage of employees who have completed compliance and ethics training, disputes referred to external parties (labor disputes), and the number, type and source of external audit results and resolutions arising from these.

[0056] Cost information includes all labor costs (total labor costs or total labor costs), external labor costs, average compensation and compensation ratios, total employment costs (salaries, social insurance, retirement benefit obligations, human resource development expenses, etc.), (recruitment) costs per employee, recruitment costs, and turnover costs.

[0057] Diversity information includes (workforce) diversity (a) age, b) gender, c) disabilities, d) other diversity measures (nationality, length of employment, etc.), and management diversity (age, gender, disability, nationality, etc.).

[0058] The information on leadership includes trust in leadership, span of control (number of subordinates per person), and leadership development (percentage of employees who have participated in training). Information about organizational culture includes engagement / employee satisfaction / commitment and retention rates.

[0059] Information on health management includes lost time (time off) due to work-related accidents, the number of work-related accidents, the number of deaths at work (work-related accidents), and the percentage of employees who participated in training.

[0060] Productivity information includes EBIT / revenue / sales / profit per employee and human capital ROI (return on invested capital).

[0061] Information on recruitment, transfers and turnover includes recruitment (IN): number of candidates per position, quality per employee (expectations before recruitment and evaluation after recruitment), average time (a) time required to fill a position, b) time required to fill a key business position), employee ability assessment and future potential (talent pool), transfers (THROUGH): percentage of positions filled with internal talent, percentage of key business positions filled internally, percentage of key business positions available, percentage of key business positions available to all positions available, internal transfer rate, ability to replace employees (internal talent supply capacity), turnover (OUT): resignations, voluntary resignation rate (excluding retirement at mandatory retirement age), percentage of key voluntary resignations (voluntary resignations by employees who are reluctant to leave), reasons for resignation / resignation / employees by reason.

[0062] Information on skills and abilities includes: 1. All costs related to human resource development and training, learning and growth (a) the percentage of employees who participated in training out of the total number of employees per year, b) the average number of hours of training per employee set out in the training program (average number of hours of training), c) the percentage of employees who participated in various categories of training set out in the training program), and employee competency rates (competency assessments, ability assessments, etc.).

[0063] The information on succession planning is succession effectiveness rate, succession coverage rate, and succession readiness rate (a) succession depth: ready, b) succession depth: ready within 1-3 years, c) succession depth: ready within 4-5 years.

[0064] Labour force information includes the number of employees, the number of full-time equivalents (FET), the temporary labour force (a) freelancers (number of contractors, consultants, gig workers, etc.), b) casual labour force (number of employees with fixed-term employment contracts), and absenteeism.

[0065] In addition to these, it is also possible to include information on the number of employees and their composition, overtime hours, paid leave utilization rates, training, skills, competencies, and goal setting and evaluation, which are topics that are highly requested by users (see Figure 41).

[0066] (Embodiment 1: Itemized Personnel-Related Data Acquisition Unit) The itemized personnel-related data acquisition unit 0202 is configured to acquire personnel-related data by item from the personnel-related data storage unit 0202. The personnel-related data may be distributed among a plurality of personnel-related data storage units. The itemized personnel-related data acquisition unit acquires personnel-related data corresponding to the items for which the human capital information has been visualized. Below, we will provide a supplementary explanation of an exemplary workflow for visualizing human capital information, with reference to Figure 3. In Figure 3, the "aggregated data" used in "(E) Information visualization work" corresponds to the "personnel-related data corresponding to the items for which human capital information visualization was performed."

[0067] <(A) Basic data creation work> Based on the personnel-related data collected by category, "point-in-time data" is created and managed historically. This data becomes the data to be aggregated. The functional requirements for the system are: -Users can specify the information they want to aggregate. - Ability to create point-in-time information with various base dates according to the user's aggregation characteristics (Example) Number of employees: as of the end of the fiscal year, Paid leave used: 4 / 1~3 / 31, Employment of people with disabilities: as of 6 / 1 Includes:

[0068] <(A) Data correction work> If necessary, the data to be aggregated is corrected by manually entering it or by importing data managed by past transfers. The functional requirements for the system are: · Ability to process and save data as of the base date - Ability to inherit past processing data · Ability to import (store) information managed externally · Ability to add, modify, and delete basic data by manually entering or importing data from spreadsheet tools Includes:

[0069] <(c) Tabulation and calculation work> Calculate only the most recent fiscal year, and use the saved figures for past years. This is the aggregated data. The functional requirements for the system are: - The calculation formula and aggregation unit can be specified according to the characteristics of the user's aggregation. -Ability to perform calculations based on a set logic for the most recent fiscal year only - Ability to register and modify the latest fiscal year's aggregate calculation results - Ability to correct aggregate calculation results from past years -Ability to display aggregated data on the screen in a layout defined by the user - The aggregated data can be output in a spreadsheet of any spreadsheet tool defined by the customer. Includes:

[0070] <(D) Offline operations using input and output from spreadsheet tools> The HR department can download the data to be compiled to their work PC and manually correct the figures as needed. The corrected data can then be uploaded and saved as compiled data. The functional requirements for the system are: - Ability to output summary calculation results in a spreadsheet tool with a user-defined layout - The ability to import data entered directly into the output spreadsheet and manage it on the system Includes:

[0071] <(E) Information visualization business> The aggregated data is graphed and displayed on a dashboard to visualize human capital information. The functional requirements for the system are: - Ability to display aggregate calculation results in graphs and dashboards with layouts defined by the customer · Ability to break down information · It can be opened up to not only the HR department but also to the executive and management levels. - Ability to present the key points and analytical methods implied by the displayed graphs Includes:

[0072] (Embodiment 1: Distribution Type Fixed Sentence Storage Unit) The distribution type-specific fixed message storage unit 0203 is configured to store fixed messages for users for each distribution type.

[0073] As one aspect, the template can be stored in a template storage unit for each distribution type in a form that can be referenced for each distribution type from instructions according to the type of graph. In another aspect, the template can be stored in the template storage unit for each distribution type in a form in which the template is embedded for each distribution type in a comment field of instructions corresponding to the type of graph.

[0074] Instructions are information describing the process flow to guide the user through the OODA loop from observation (checking the situation and checking the details) to situation judgment (analyzing actions and checking the analysis results). Figure 4 is an example of instructions for a bar graph. Figure 5 is an example of instructions for a histogram. Figure 6 is an example of instructions for a pie chart. Figure 7 is an example of instructions for a line graph. Figure 8 is an example of instructions for a scatter plot. Figure 9 is an example of instructions for a radar chart. Figure 10 is an example of instructions for a cross tabulation. Figure 11 is an example of instructions for a scalar chart.

[0075] "Distribution type" refers to the type of distribution when human capital information is visualized on the dashboard, such as "achievement type" or "time series type" in a bar graph, "isolated island type" or "double peak type" in a histogram, "repeated type" or "rising type" in a line graph, or "concentrated type" in a scatter plot.

[0076] For example, if the distribution type is "isolated island type" in the histogram, the template is: "For data that is significantly different, there are often special circumstances that require individual handling. Check the target data and identify the cause. -Perform correlation analysis to investigate items that have a strong relationship with the [X-axis item]. For data that is significantly different, there are often special circumstances that require individual handling. Check the target data and identify the cause. -Perform a correlation analysis to find out which items have a strong relationship with the [X-axis item]. The sentence is as follows. Also, if the distribution type is a "double peak" in the histogram, There may be multiple underlying causes. Let's perform a cluster analysis and investigate the trends for each "peak" of the [X-axis item]. -Perform a correlation analysis to find out which items have a strong relationship with the [X-axis item]. The sentence is as follows.

[0077] For example, if the distribution type is "repeated" in a line graph, the template is: "The same trend is repeated every [cycle]. If you have any idea what the cause is and it is possible to improve it, consider taking measures to address this periodicity. -Perform correlation analysis of time series data and investigate items that are strongly related to the [Y-axis item]. The sentence is as follows. Also, if the distribution type is "rising" in a line graph, "The situation is improving. If you need to make improvements quickly, perform a correlation analysis of the time series data and investigate items that are closely related to the [Y-axis item]." The sentence is as follows.

[0078] For example, if the distribution type is "concentrated" in a scatter plot, the template is: "There may be multiple underlying causes. Conduct a cluster analysis to explore population trends. -Perform correlation analysis to investigate items that are strongly related to the [Y-axis item]. The sentence is as follows.

[0079] (Distribution type identification information receiving unit in embodiment 1) The distribution type identification information receiving unit 0204 is configured to receive distribution type identification information from the distribution of the acquired itemized personnel-related data. For example, the instructions for the histogram in Figure 5 contain template text (comments) corresponding to four distribution types. Each of the four distribution types is assigned a distribution type identification. For example, a distribution that combines "isolated island" and "two-peak" types is assigned the distribution type identification "11," a distribution that is only "isolated island" is assigned the distribution type identification "10," a distribution that is only "two-peak" is assigned the distribution type identification "01," and a distribution that is neither "isolated island" nor "two-peak" is assigned the distribution type identification "00."

[0080] The distribution type identification information receiving unit receives distribution type identification information that indicates to which distribution type the graph displayed on the dashboard corresponds. For example, in the histogram instructions of Figure 5, the distribution type is identified based on the user's answer received to question 2 presented to the user and the user's answer received to question 3 presented to the user, and distribution type identification information is accepted based on this. As another example, the instructions for the line graph in Figure 7 hold template text (comments) corresponding to three distribution types. Each of the three distribution types is assigned distribution type identification information. For example, a "repeating" distribution is assigned the distribution type identification information "1X," an "increasing" distribution is assigned the distribution type identification information "01," and a distribution that is neither "repeating" nor "increasing" is assigned the distribution type identification information "00."

[0081] For example, in the instructions for the line graph in Figure 7, question 2 "Is there any periodicity?" is answered by detecting periodicity using the EPA method (for example, calculating the standard deviations of seasonal fluctuations (S) and irregular fluctuations (I), and determining that there is periodicity if the standard deviation of S is greater than the standard deviation of I). Also, question 3 "Is there an improving trend?" is answered by the result of automatically analyzing the trend (TC) using the EPA method (for example, calculating the derivative and second derivative of the most recent period (for example, the most recent 10 data points), and determining that there is an improving trend if all the derivatives are positive. If the derivative of the first half of the data is negative, and the corresponding data is less than half of the total data, and the second derivative is positive, determining that there is an improving trend. In all other cases, determining that there is no improving trend). The distribution type may be identified based on these automatic determinations, and distribution type identification information may be accepted based on this. If we express the instructions for the line graph in Figure 7 in a flowchart, it would look like Figure 39.

[0082] (Embodiment 1: Fixed phrase acquisition unit) The fixed phrase acquisition unit 0205 is configured to acquire a fixed phrase stored based on the acquired distribution type identification information. For example, based on the acquired distribution type identification information, it is possible to acquire a standard phrase stored in the distribution type standard phrase storage unit by referencing (or embedding in) the comment field of the instruction corresponding to the graph type.

[0083] (Embodiment 1: Fixed phrase output unit) The fixed phrase output unit 0207 is configured to output the acquired fixed phrase in association with the acquired itemized personnel-related data.

[0084] The template can include parameter specifications for replacing character strings. Parameter specifications are made by enclosing the parameter name in "[" and "]". In the template shown as an example in "Template storage section for each distribution type," [X-axis item], [Y-axis item], and [Period] correspond to the parameter specifications. If the acquired template contains a parameter specification, such as the template for a distribution type that combines the "isolated island type" and the "two-peak type" in Figure 5, "There may be multiple underlying causes. Let's conduct a cluster analysis to investigate the trends for each "peak" in the [X-axis item]," the template output unit 0207 will associate it with the acquired item-specific personnel-related data, replace "[X-axis item]" with "[average overtime hours]," and output the following: "There may be multiple underlying causes. Let's conduct a cluster analysis to investigate the trends for each "peak" in the [average overtime hours]." Also, in the "repeated type" template in Figure 7, "The same trend is repeated every [cycle]. If you have any idea what the cause might be and if it is possible to improve it, we should consider measures to address this periodicity," the acquired item-specific personnel-related data is replaced with the periodic value detected using the EPA method, for example, "[cycle]" is replaced with "[6 months]," and the output is "The same trend is repeated every [6 months]. If you have any idea what the cause might be and if it is possible to improve it, we should consider measures to address this periodicity."

[0085] <Embodiment 1: Personnel Data Interpretation Support System: Hardware Configuration> The hardware configuration of the personnel data interpretation support system according to the first embodiment will be described with reference to the drawings.

[0086] Fig. 12 is a diagram showing the hardware configuration of the personnel data interpretation support system in embodiment 1. As shown in this diagram, the information provision system in embodiment 1 includes a "CPU (Central Processing Unit)" 1201 that performs various types of arithmetic processing, and a "main memory" 1202. It also includes a "non-volatile memory" 1203 that stores predetermined information, and a "network I / F (interface)" 1204 that transmits and receives information to and from multiple user terminals 1206, multiple administrator terminals 1207, personnel system internal data 1208, personnel system external data 1209, and other personnel-related data 1210. These are then interconnected by a data communication path such as a "bus" 1205, and perform information transmission, reception, and processing.

[0087] "Main memory" reads out programs that perform various processes so that the "CPU" can execute them, and also provides a work area for those programs. In addition, this "main memory" and "non-volatile memory" are each assigned multiple addresses, and programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses. In embodiment 1, the programs stored in the "main memory" include a personnel-related data accumulation program, an item-specific personnel-related data acquisition program, a distribution type-specific template retention program, a distribution type identification information reception program, a template acquisition program, and a template output program. In addition, the "main memory" and "non-volatile memory" store personnel-related data, personnel-related data by category, standard phrases, distribution type identification information, and the like.

[0088] The "CPU" executes the personnel-related data accumulation program stored in the "main memory" to store the personnel-related data in the "main memory" or the "non-volatile memory." It also executes the item-specific personnel-related data acquisition program stored in the "main memory" to store the item-specific personnel-related data in the "main memory" or the "non-volatile memory." It also executes the distribution type-specific template retention program stored in the "main memory" to store the template in the "main memory" or the "non-volatile memory." It also executes the template acquisition program stored in the "main memory" to store the template in the "main memory" or the "non-volatile memory" based on the distribution type identification information. It also executes the template output program stored in the "main memory" to output the template in association with the item-specific personnel-related data.

[0089] <Embodiment 1: Personnel Data Interpretation Support System: Processing Flow> 13 is a diagram showing the processing flow when using the personnel data interpretation support system in embodiment 1. As shown in the figure, the processing method comprises personnel-related data accumulation step S1301, item-specific personnel data acquisition step S1302, distribution type-specific template message storage step S1303, distribution type identification information reception step S1304, template message acquisition step S1305, and template message output step S1306.

[0090] These processing methods are executed by a personnel data interpretation support system having a personnel-related data storage unit that stores personnel-related data including n-dimensional (n≧2) data that can be statistically processed, categorized into multiple items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information reception unit that receives distribution type identification information from the distribution of the acquired item-specific personnel-related data; a template acquisition unit that acquires the stored template messages based on the acquired distribution type identification information; and a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data.

[0091] The "personnel-related data accumulation step" S1301 is a stage in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated.

[0092] The "step of acquiring personnel-related data by item" S1302 is a step of acquiring personnel-related data by item.

[0093] The "step of storing template text for each distribution type" S1303 is a step of storing template text to be presented to the user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item.

[0094] The "distribution type identification information receiving step" S1304 is a stage in which distribution type identification information is received from the distribution of the acquired item-specific personnel-related data.

[0095] The "fixed phrase acquisition step" S1305 is a stage in which a fixed phrase stored based on the acquired distribution type identification information is acquired.

[0096] The "fixed phrase output step" S1306 is a stage in which the acquired fixed phrase is output in association with the acquired itemized personnel-related data.

[0097] <Embodiment 2 (corresponding mainly to claims 2, 10, and 18)> <Outline of Embodiment 2> In the second embodiment, which is based on the first embodiment, a target value storage unit stores target values ​​that are target values ​​for at least some of the items, a distribution type target-specific template storage unit stores template messages to be presented to users in advance according to the distribution type identification information and the target values, and a goal-dependent template acquisition unit acquires the stored template messages based on the distribution type identification information and, if there is a target value assigned to the item for which the distribution type identification information is obtained, the target value. This makes it possible to quantitatively grasp the gap between "As is" (the current state) and "To be" (the desired state). To achieve this, a method executed by a CPU in the information provision system, which is a computer, and an operating program for the information provision system, which is written so as to be readable and executable by the information provision system, are provided. Hereinafter, descriptions of the functional configuration, hardware configuration, and processing flow that are the same as those in the first embodiment will be omitted as appropriate.

[0098] <Functional Configuration of Second Embodiment> 19 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system 1900 according to embodiment 2. As shown in the figure, the "personnel data interpretation support system" 1900 has at least a "personnel related data accumulation unit" 1901, an "item-specific personnel related data acquisition unit" 1902, a "distribution type-specific fixed phrase storage unit" 1903, a "distribution type identification information reception unit" 1904, a "fixed phrase acquisition unit" 1905, a "goal-dependent fixed phrase acquisition means" 1905A, a "fixed phrase output unit" 1906, a "target value storage unit" 1907, and a "distribution type-specific target-specific fixed phrase storage unit" 1908.

[0099] <Description of Each Configuration in Embodiment 2> (Target value holding unit in embodiment 2) The target value holding unit 1907 is configured to hold target values ​​that are target values ​​for at least some of the items. The "item" is the same as that explained in the personnel-related data accumulation unit in the first embodiment, and therefore a repeated explanation will be omitted. For example, if the "item" is "average overtime hours," the value "24 hours" corresponds to the target value.

[0100] (Embodiment 2: Distribution Type Target-Specific Template Sentence Storage Unit) The distribution type target fixed phrase holding unit 1908 is configured to hold fixed phrases to be presented to the user in advance according to the distribution type identification information and the target value. The "distribution type" is the same as that explained in the distribution type-specific template message storage unit of the first embodiment, and therefore a repeated explanation will be omitted. The "distribution type identification information" is the same as that explained in the distribution type identification information receiving unit of the first embodiment, and therefore a repeated explanation will be omitted.

[0101] In one aspect, the template can be stored in a template storage unit for each distribution type and target in a form that is referenced according to distribution type identification information and target value from instructions according to the type of graph. In another embodiment, the template can be stored in the template storage unit for each distribution type in a form in which the template is embedded in a comment field of instructions corresponding to the type of graph according to the distribution type and target value.

[0102] For example, in the case of a bar graph with an "achievement level" type, the target value is set for the [Y-axis item], "--Target value provided--Perform feature word analysis and compare with a group that has achieved the target value (or a group that is being used as a benchmark) to investigate the cause. --No target value--Let's conduct a feature analysis and compare the categories with high and low [Y-axis items] to investigate the cause. The sentence is as follows. Here, "---with target value---" and "---without target value---" correspond to tags in a structured document, and depending on "---with target value---" and "---without target value---", the following template is stored in the distribution type target template storage section: "Perform feature word analysis and compare with a group that has achieved the target value (or a group that is being used as a benchmark) to investigate the cause.", and "Perform feature word analysis and compare categories where the [Y-axis item] is high with categories where it is low to investigate the cause."

[0103] For example, if the distribution type is "upward" in a line graph, the target value is set for the [Y-axis item], "--Target Value Included-- There is a trend toward improvement. If this trend continues, the target is expected to be reached around [target achievement date]. If urgent improvement is required, perform a correlation analysis of the time series data and investigate items that are closely related to [Y-axis item]. --No target value---The trend is improving. If you need to make improvements quickly, perform a correlation analysis of the time series data and investigate items that are closely related to the [Y-axis item]. The sentence is as follows. Then, according to "--Target value included--" and "--Target value not included--", ​​the following template message is stored in the distribution type target template storage section: "· There is a trend toward improvement. If this trend continues, the target is expected to be reached around [target achievement date]. If urgent improvement is required, perform a correlation analysis of the time series data and investigate items that are strongly related to the [Y-axis item]."; and the following template message: "· There is a trend toward improvement. If urgent improvement is required, perform a correlation analysis of the time series data and investigate items that are strongly related to the [Y-axis item]."

[0104] (Embodiment 2: Goal-dependent template acquisition means) The target-dependent template acquisition means 1905A is configured to acquire a stored template based on the distribution type identification information and, if there is a target value assigned to the item for which this distribution type identification information is obtained, the target value. For example, based on the acquired distribution type identification information and, if there is a target value assigned to the item from which this distribution type identification information was obtained, the target value, it is possible to acquire a template stored in the template storage unit for each distribution type target, referenced from (or embedded in) the comment field of the instruction corresponding to the graph type.

[0105] <Embodiment 2: Personnel Data Interpretation Support System: Hardware Configuration> The hardware configuration of the personnel data interpretation support system according to the second embodiment will be described with reference to the drawings.

[0106] 20 is a diagram showing the hardware configuration of a personnel data interpretation support system in embodiment 2. As shown in this diagram, the information provision system in this embodiment includes a "CPU (Central Processing Unit)" 2001 that performs various types of calculation processing, and a "main memory" 2002. It also includes a "non-volatile memory" 2003 that stores predetermined information, and a "network I / F (interface)" 2004 that transmits and receives information to and from multiple user terminals 2006, multiple administrator terminals 2007, personnel system internal data 2008, personnel system external data 2009, and other personnel-related data 2010.

[0107] "Main memory" reads out programs that perform various processes so that the "CPU" can execute them, and also provides a work area for those programs. In addition, this "main memory" and "non-volatile memory" are each assigned multiple addresses, and programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses. In embodiment 2, the programs stored in the "main memory" include a personnel-related data accumulation program, an item-specific personnel-related data acquisition program, a distribution type-specific template retention program, a distribution type identification information reception program, a template acquisition program, a template output program, a target value retention program, a distribution type-specific target-specific template retention program, and a target-dependent template acquisition subprogram. The "main memory" and "non-volatile memory" also store personnel-related data, personnel-related data by item, standard phrases, distribution type identification information, target values, standard phrases for each distribution type target, and the like.

[0108] The "CPU" executes a personnel-related data accumulation program stored in the "main memory" to store personnel-related data in the "main memory" or the "non-volatile memory." It also executes an item-specific personnel-related data acquisition program stored in the "main memory" to store item-specific personnel-related data in the "main memory" or the "non-volatile memory." It also executes a distribution type-specific fixed phrase storage program stored in the "main memory" to store fixed phrases in the "main memory" or the "non-volatile memory." It also executes a fixed phrase acquisition program stored in the "main memory" to store fixed phrases in the "main memory" or the "non-volatile memory" based on distribution type identification information. It also executes a fixed phrase output program stored in the "main memory" to output fixed phrases in association with item-specific personnel-related data. It also executes a target value storage program stored in the "main memory" to store target values ​​in the "main memory" or the "non-volatile memory." It also executes a distribution type-specific target-specific fixed phrase storage program stored in the "main memory" to store distribution type-specific target-specific fixed phrases in the "main memory" or the "non-volatile memory." Also, a target-dependent fixed phrase acquisition subprogram stored in the "main memory" is executed to acquire a fixed phrase.

[0109] <Embodiment 2: Personnel Data Interpretation Support System: Processing Flow> 21 is a diagram showing the processing flow when using the personnel data interpretation support system in embodiment 2. As shown in the diagram, the processing method includes a personnel-related data accumulation step S2101, an item-specific personnel data acquisition step S2102, a distribution type-specific fixed phrase storage unit step S2103, a distribution type identification information reception step S2104, a fixed phrase acquisition step S2105, a fixed phrase output step S2106, a target value storage step S2107, a distribution type-specific target-specific fixed phrase storage step S2108, and a goal-dependent fixed phrase acquisition substep S2105A.

[0110] These processing methods include a personnel-related data storage unit that stores personnel-related data including statistically processable n-dimensional (n≧2) data, classified into a plurality of items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information reception unit that receives distribution type identification information from the acquired distribution of item-specific personnel-related data; and a template storage unit that acquires the stored template messages based on the acquired distribution type identification information. The system is implemented by a personnel data interpretation support system having a sentence acquisition unit, a template output unit that outputs the acquired template in association with the acquired item-specific personnel-related data, a target value storage unit that stores target values ​​that are target values ​​for at least some of the items, a distribution type target template storage unit that stores template messages to be presented to users in advance based on distribution type identification information and the target values, and the template acquisition unit has a goal-dependent template acquisition means that acquires the stored template messages based on the distribution type identification information and, if there is a target value assigned to the item for which the distribution type identification information was obtained, the target value.

[0111] The "personnel-related data accumulation step" S2101 is a stage in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated.

[0112] The "step of acquiring personnel-related data by item" S2102 is a step of acquiring personnel-related data by item.

[0113] The "step of storing template text for each distribution type" S2103 is a step of storing template text to be presented to the user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item.

[0114] The "distribution type identification information receiving step" S2104 is a stage for receiving distribution type identification information from the distribution of the acquired item-specific personnel-related data.

[0115] The "fixed phrase acquisition step" S2105 is a stage in which a fixed phrase stored based on the acquired distribution type identification information is acquired.

[0116] The "target-dependent template acquisition substep" S2105A is a subordinate step that acquires a stored template based on distribution type identification information and, if there is a target value assigned to the item for which this distribution type identification information was obtained, that target value.

[0117] The "fixed phrase output step" S2106 is a stage in which the acquired fixed phrase is output in association with the acquired itemized personnel-related data.

[0118] The "target value holding step" S2107 is a stage in which target values ​​that are values ​​to be targeted for at least some of the items are held.

[0119] The "step of storing fixed phrases for each distribution type and target" S2108 is a step of storing fixed phrases to be presented to the user in advance according to the distribution type identification information and the target value.

[0120] Example 1 An example (Example 1) based on the first and second embodiments will be described below. Figure 14 is a conceptual diagram of OODA, an exemplary behavioral process suitable for data-driven operations. As shown in the figure, OODA is a theory of decision-making and behavior that consists of Observe, Orient, Decide, and Act. OODA was originally a theory proposed for military operations, where the situation changes from moment to moment, but it is now being proposed as being applicable to politics and business as well. Compared to the well-known PDCA behavioral process, OODA's distinctive feature is that action begins with "observation" to accurately understand the current situation, rather than "planning." OODA prevents plans from being formulated with a predetermined conclusion or preconceived notions about the current situation. The OODA loop involves grasping the current situation as it is, deductively deriving a strategy from it, and then pinpointing and implementing it to improve the situation.

[0121] Next, we will explain a system that incorporates this OODA loop to support strategic execution, with reference to Figure 15. Figure 15 is a conceptual diagram of a system that incorporates the OODA loop to support strategic execution.

[0122] <Observe - Observe the market, competitors, etc. and gather information> As described with reference to FIG. 2, the itemized personnel-related data acquisition unit 0202 is configured to acquire personnel-related data by item from the personnel-related data accumulation unit. <(A) Basic data creation work> Based on the personnel-related data collected by category, "point-in-time data" is created and managed historically. This data becomes the data to be aggregated. <(A) Data correction work> If necessary, the data to be aggregated is corrected by manually entering it or by importing data managed by past transfers. <(c) Tabulation and calculation work> Calculate only the most recent fiscal year, and use the saved figures for past years. This is the aggregated data. <(D) Offline operations using input and output from spreadsheet tools> The HR department can download the data to be compiled to their work PC and manually correct the figures as needed. The corrected data can then be uploaded and saved as compiled data. <(E) Information visualization business> The aggregated data is graphed and displayed on a dashboard to visualize human capital information. Through the above process, the visualization of personnel-related data is carried out.

[0123] However, even if users who are not familiar with data analysis check the current situation using a dashboard or the like, simply displaying graphs and analysis results will not allow them to execute the OODA loop shown in Figures 3 and 15, and they often end up just ``visualizing human capital information.'' This is thought to be because they do not understand what to look for when checking the current situation, as shown in Figure 16. To solve this problem, the present invention provides an environment in which HR personnel can execute (or continue) the OODA loop by following instructions from a HR data interpretation support system in an interactive format, rather than ending up with a "pretty graph." Below, we will explain in detail the function that is characteristic of the present invention, which is ``able to present the points and analytical methods that the displayed graphs mean.''

[0124] The personnel data interpretation support system according to the first embodiment is configured to support "points to look at when checking the current situation" based on "instructions" (see Figures 4 to 11) provided for each type of graph displayed on the dashboard.

[0125] Typical graphs that can be displayed on a dashboard include bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts, but here we will use histograms as an example for detailed explanation. Although the standard phrases for each distribution type differ for other graphs, the dialogue flow is similar, so a detailed explanation will be omitted. It goes without saying that standard phrases for each distribution type can also be used for graphs other than bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts by preparing them as appropriate.

[0126] The standard phrases for each classification type are stored in the comment section for each distribution type in tabular format, as shown in Figures 4 to 11, for example, bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts. For example, referring to the histogram in Figure 5, in the case where the "isolated island type" and the "two peaks type" are combined, the following standard text is maintained: "· There are many cases where data that is significantly separated has special circumstances and requires individual handling. Check the target data and identify the cause. · There may be multiple underlying causes. Conduct a cluster analysis to investigate the trends for each "peak" of the [X-axis item]. · Conduct a correlation analysis to investigate items that are strongly related to the [X-axis item]."

[0127] Figure 17 shows the distribution of average overtime hours as human capital information, visualized as a histogram and displayed on the left side of the dashboard. For example, when a "histogram" instruction is obtained from the distribution type template storage unit 0203, and when a target value (for example, 24 hours) is set for the average overtime hours, the personnel data interpretation support system detects, as question 1, that there are employees whose average overtime hours exceed 24 hours, and provides the following information: "Average overtime hours exceed the target value. Let's analyze this and come up with an improvement plan." If the personnel department staff selects "Analyze this problem," this is considered to be a Yes answer to question 1, and a dialogue with the personnel data interpretation support system begins on the right side of the screen. If no target value has been set, the system will ask, "Is the distribution of this graph (the shape of the graph) different from what you expected, or is there anything that concerns you?" If the HR department staff member selects "Analyze this issue," this will be treated as an answer to question 1, and a dialogue with the system will begin on the right side of the screen.

[0128] First, the distribution type identification information receiving unit 0204 receives distribution type identification information based on the answers received to questions stored in the instructions corresponding to the type of graph displayed on the left side of the screen ("histogram" in Figure 17). The dialogue proceeds according to the template texts (guidance texts, questions, and comments for the user) stored in the distribution type template text storage unit 0203 . For question 2, we ask, "Is there any data that is far apart in this graph?" and provide guidance by illustrating a typical example. If the HR department staff member follows the instructions and selects "Yes," the system will then ask a third question, "Are there multiple peaks in this graph?", as shown in Figure 18, illustrating a typical example. During this process, HR personnel can learn that when looking at a histogram, the key points to observe (to understand the current situation) are whether there are any data points that are far apart or whether there are multiple peaks.

[0129] If the answer to both questions 2 and 3 is "yes," the template output unit 0206 outputs the template stored in the histogram instructions based on the distribution type identification information "11" received by the distribution type identification information receiving unit 0205. For example, the dialogue screen may include: "The content you answered There is data that is far away There are several mountains There could be multiple underlying causes for this graph, please analyze the data to understand the cause. correspondence (1) For data that is significantly different, there are often special circumstances that require individual handling. Check the target data and identify the cause. (2) There may be multiple underlying causes. Let's conduct a cluster analysis to investigate the trends for each "peak" in [Average Overtime Hours]. (3) Let's conduct a correlation analysis to investigate the items that are most closely related to [average overtime hours]. By displaying such guidance and comments, HR personnel can learn to make situational judgments (automatic analysis and prediction of factors related to areas for improvement) based on observation (recognition of the current situation), in other words, to execute the subsequent OODA loop.

[0130] <Summary> As described above, the present invention provides a personnel data interpretation support system that can acquire standard phrases stored based on distribution type identification information in accordance with distribution type identification information for identifying the n-dimensional distribution type of personnel-related data acquired by item, and output the acquired standard phrases in association with the acquired item-specific personnel-related data. In addition, the present invention can provide a personnel data interpretation support system that can obtain a standard phrase to be presented to a user in advance based on distribution type identification information and a target value that is a target value for an item, and output the obtained standard phrase by associating it with the obtained item-specific personnel-related data.

[0131] <Embodiment 3 (corresponding mainly to claims 3, 11, and 19)> <Outline of Embodiment 3> The third embodiment, based on the first embodiment, includes a function that guides situational assessment based on OODA loop observation. As shown in Figure 22, even if you understand what to look for in the current situation and proceed to situation assessment, it often happens that you are unable to execute the subsequent OODA loop because you "don't know how to analyze" or "don't know what to look for in the analysis results." The third embodiment can resolve this situation and provides a function that can analyze the gap between "As is" (current situation) and "To be" (desired state). To achieve this, a method executed by a CPU in the information provision system, which is a computer, and an operating program for the information provision system, which is written so as to be readable and executable by the information provision system, are provided. Hereinafter, descriptions of the functional configuration, hardware configuration, and processing flow that are the same as those in the first embodiment will be omitted as appropriate.

[0132] <Functional Configuration of Third Embodiment> 23 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system 2300 according to embodiment 3. As shown in the figure, the "personnel data interpretation support system" 2300 has at least a "personnel related data accumulation unit" 2301, an "item-specific personnel related data acquisition unit" 2302, a "distribution type fixed phrase storage unit" 2303, a "distribution type identification information reception unit" 2304, a "fixed phrase acquisition unit" 2305, a "fixed phrase output unit" 2306, a "cluster analysis rule storage unit" 2307, a "cluster analysis unit" 2308, and a "cluster analysis result output unit" 2309.

[0133] <Description of Each Configuration in Embodiment 3> (Embodiment 3: Cluster Analysis Rule Storage Unit) In the third embodiment, cluster analysis is provided as a data analysis method for analyzing personnel-related data and presenting points of interest to the user. Cluster analysis groups similar data together and classifies them into multiple clusters. Grouping employees with similar attributes into one cluster makes it easier to analyze employees by type. The cluster analysis rule holding unit 2307 is configured to hold cluster analysis rules for analyzing the personnel-related data acquired by item. A cluster analysis rule, for example, is to "cluster similar data." Personnel information contains data that is difficult to interpret from a single item, such as the results of personality assessments and aptitude tests. For such data, there is a demand for a function that performs clustering and secondary analysis.

[0134] (Embodiment 3 Cluster Analysis Unit) The cluster analysis unit 2308 is configured to analyze the personnel-related data acquired for each item, based on the personnel-related data acquired for each item and the stored cluster analysis rules. For example, in cluster analysis, as shown in FIG. 24, the following three clustering algorithms are used selectively to perform data clustering. (1) Gaussian Mixture Model Fit multiple normal distributions to the graph and perform clustering for each normal distribution. Clustering can be performed without specifying the number of classes. (2) Hierarchical clustering This method clusters the most similar combinations first, and the process can be expressed as a hierarchy, with the final result being a tree diagram. It is easy to dynamically increase or decrease the number of clusters. (3) K-means method This algorithm divides the data into appropriate clusters by dividing it into a specified number (k) of clusters, calculating the average value for each cluster, and then repeating the process of clustering again for data points that are close to that average value.

[0135] (Embodiment 3: Cluster analysis result output unit) The cluster analysis result output unit 2309 is configured to output the cluster analysis result.

[0136] Third Embodiment: Personnel Data Interpretation Support System: Hardware Configuration The hardware configuration of the personnel data interpretation support system according to the third embodiment will be described with reference to the drawings.

[0137] 25 is a diagram showing the hardware configuration of a personnel data interpretation support system according to embodiment 3. As shown in this diagram, the information provision system according to this embodiment includes a "CPU (Central Processing Unit)" 2501 that performs various types of calculation processing, and a "main memory" 2502. It also includes a "non-volatile memory" 2503 that stores predetermined information, and a "network I / F (interface)" 2504 that transmits and receives information to and from multiple user terminals 2506, multiple administrator terminals 2507, personnel system internal data 2508, personnel system external data 2509, and other personnel-related data 2510.

[0138] "Main memory" reads out programs that perform various processes so that the "CPU" can execute them, and also provides a work area for those programs. In addition, this "main memory" and "non-volatile memory" are each assigned multiple addresses, and programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses. In embodiment 3, the programs stored in the "main memory" include a personnel-related data accumulation program, an item-specific personnel-related data acquisition program, a distribution type-specific template storage program, a distribution type identification information reception program, a template acquisition program, a template output program, a cluster analysis rule storage program, a cluster analysis program, and a cluster analysis result output program. The "main memory" and "non-volatile memory" also store personnel-related data, personnel-related data by category, standard phrases, distribution type identification information, cluster analysis rules, and the like.

[0139] The "CPU" executes a personnel-related data accumulation program stored in the "main memory" to store personnel-related data in the "main memory" or the "non-volatile memory." It also executes an item-specific personnel-related data acquisition program stored in the "main memory" to store item-specific personnel-related data in the "main memory" or the "non-volatile memory." It also executes a distribution type-specific template storage program stored in the "main memory" to store template messages in the "main memory" or the "non-volatile memory." It also executes a template acquisition program stored in the "main memory" to store template messages in the "main memory" or the "non-volatile memory" based on distribution type identification information. It also executes a template output program stored in the "main memory" to output template messages in association with item-specific personnel-related data. It also executes a cluster analysis rule storage program stored in the "main memory" to store cluster analysis rules in the "main memory" or the "non-volatile memory." It also executes a cluster analysis program stored in the "main memory" to perform cluster analysis. It also executes a cluster analysis result output program stored in the "main memory" to output cluster analysis results.

[0140] <Embodiment 3: Personnel Data Interpretation Support System: Processing Flow> 26 is a diagram showing the processing flow when using the personnel data interpretation support system in embodiment 3. As shown in the diagram, the processing method includes personnel-related data accumulation step S2601, item-specific personnel data acquisition step S2602, distribution type-specific template storage step S2603, distribution type identification information reception step S2604, template acquisition step S2605, template output step S2606, cluster analysis rule storage step S2607, cluster analysis step S2608, and cluster analysis result output step S2609.

[0141] These processing methods are executed by a personnel data interpretation support system having a personnel-related data storage unit that stores personnel-related data including n-dimensional (n≧2) data that can be statistically processed, separated into multiple items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information receiving unit that receives distribution type identification information from the distribution of the acquired item-specific personnel-related data; a template acquisition unit that acquires the stored template messages based on the acquired distribution type identification information; a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data; a cluster analysis rule storage unit that stores cluster analysis rules for performing cluster analysis on the personnel-related data acquired by item; a cluster analysis unit that performs cluster analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored cluster analysis rules; and a cluster analysis result output unit that outputs the cluster analysis results.

[0142] The "personnel-related data accumulation step" S2601 is a stage in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated.

[0143] The "step of acquiring personnel-related data by item" S2602 is a step of acquiring personnel-related data by item.

[0144] The "step of storing template text for each distribution type" S2603 is a step of storing template text to be presented to the user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item.

[0145] The "distribution type identification information receiving step" S2604 is a stage for receiving distribution type identification information from the distribution of the acquired item-specific personnel-related data.

[0146] The "fixed phrase acquisition step" S2605 is a stage in which a fixed phrase stored based on the acquired distribution type identification information is acquired.

[0147] The "fixed phrase output step" S2606 is a step of outputting the acquired fixed phrase in association with the acquired itemized personnel-related data.

[0148] The "step of storing cluster analysis rules" S2607 is a step of storing cluster analysis rules for performing cluster analysis on the personnel-related data acquired by item.

[0149] The "cluster analysis step" S2608 is a stage in which the personnel-related data acquired by item is subjected to cluster analysis based on the personnel-related data acquired by item and the stored cluster analysis rules.

[0150] The "cluster analysis result output step" S2609 is a step for outputting the cluster analysis results.

[0151] <Embodiment 4 (corresponding mainly to claims 4, 12, and 20)> <Outline of Embodiment 4> The fourth embodiment is based on the first embodiment and includes a function that guides situational assessment based on OODA loop observation. As shown in Figure 22, even if you understand what to look for in the current situation and proceed to situation assessment, it often happens that you are unable to execute the subsequent OODA loop because you "don't know how to analyze" or "don't know what to look for in the analysis results." The fourth embodiment can resolve this situation and provides a function that can analyze the gap between "As is" (current situation) and "To be" (desired state). To achieve this, a method executed by a CPU in the information provision system, which is a computer, and an operating program for the information provision system, which is written so as to be readable and executable by the information provision system, are provided. Hereinafter, descriptions of the functional configuration, hardware configuration, and processing flow that are the same as those in the first embodiment will be omitted as appropriate.

[0152] <Functional Configuration of Fourth Embodiment> 27 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system 2700 according to embodiment 4. As shown in the figure, the "personnel data interpretation support system" 2700 has at least a "personnel related data accumulation unit" 2701, an "item-based personnel related data acquisition unit" 2702, a "distribution type fixed phrase storage unit" 2703, a "distribution type identification information reception unit" 2704, a "fixed phrase acquisition unit" 2705, a "fixed phrase output unit" 2706, a "correlation analysis rule storage unit" 2707, a "correlation analysis unit" 2708, and a "correlation analysis result output unit" 2709.

[0153] <Description of Each Configuration in Embodiment 4> (Embodiment 4: Correlation Analysis Rule Storage Unit) In the fourth embodiment, correlation analysis, which describes the relationships between data, is provided as a data analysis method for analyzing personnel-related data and presenting points of interest to the user. Correlation analysis numerically expresses the relationships between data. This makes it possible to extract data that are closely related to specific data, making it easier to analyze the causes of events. The correlation analysis rule storage unit 2707 is configured to store correlation analysis rules for analyzing the personnel-related data acquired by item. A correlation analysis rule is, for example, "measure the correlation between data using MIC." Personnel information contains many categorical data items, such as employment classification and business establishment, and nonlinear correlations often exist between data items.

[0154] (Embodiment 4: Correlation Analysis Unit) The correlation analysis unit 2708 is configured to perform correlation analysis on the personnel-related data acquired for each item, based on the personnel-related data acquired for each item and the correlation analysis rule stored. For example, as shown in Figure 28, we use MIC (Maximum Information Coefficient), an algorithm that can measure the correlation between categorical data and arbitrary data. MIC captures the strength of the correlation better than Pearson's correlation coefficient.

[0155] (Embodiment 4 Correlation Analysis Result Output Unit) The correlation analysis result output unit 2709 is configured to output the correlation analysis result.

[0156] <Embodiment 4: Personnel Data Interpretation Support System: Hardware Configuration> The hardware configuration of the personnel data interpretation support system according to the fourth embodiment will be described with reference to the drawings.

[0157] 29 is a diagram showing the hardware configuration of a personnel data interpretation support system according to embodiment 4. As shown in this diagram, the information provision system according to this embodiment includes a "CPU (Central Processing Unit)" 2901 that performs various types of calculation processing, and a "main memory" 2902. It also includes a "non-volatile memory" 2903 that stores predetermined information, and a "network I / F (interface)" 2904 that transmits and receives information to and from multiple user terminals 2906, multiple administrator terminals 2907, personnel system internal data 2908, personnel system external data 2909, and other personnel-related data 2910.

[0158] "Main memory" reads out programs that perform various processes so that the "CPU" can execute them, and also provides a work area for those programs. In addition, this "main memory" and "non-volatile memory" are each assigned multiple addresses, and programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses. In embodiment 4, the programs stored in the "main memory" include a personnel-related data accumulation program, an item-specific personnel-related data acquisition program, a distribution type-specific template retention program, a distribution type identification information reception program, a template acquisition program, a template output program, a correlation analysis rule retention program, a correlation analysis program, and a correlation analysis result output program. In addition, the "main memory" and "non-volatile memory" store personnel-related data, personnel-related data by category, standard phrases, distribution type identification information, correlation analysis rules, and the like.

[0159] The "CPU" executes a personnel-related data accumulation program stored in the "main memory" to store personnel-related data in the "main memory" or the "non-volatile memory." It also executes an item-specific personnel-related data acquisition program stored in the "main memory" to store item-specific personnel-related data in the "main memory" or the "non-volatile memory." It also executes a distribution type-specific fixed phrase storage program stored in the "main memory" to store fixed phrases in the "main memory" or the "non-volatile memory." It also executes a fixed phrase acquisition program stored in the "main memory" to store fixed phrases in the "main memory" or the "non-volatile memory" based on distribution type identification information. It also executes a fixed phrase output program stored in the "main memory" to associate fixed phrases with item-specific personnel-related data and output them. It also executes a correlation analysis rule storage program stored in the "main memory" to store correlation analysis rules in the "main memory" or the "non-volatile memory." It also executes a correlation analysis program stored in the "main memory" to perform correlation analysis. It also executes a correlation analysis result output program stored in the "main memory" to output correlation analysis results.

[0160] <Embodiment 4: Personnel Data Interpretation Support System: Processing Flow> 30 is a diagram showing the processing flow when using the personnel data interpretation support system in embodiment 4. As shown in the figure, the processing method includes personnel-related data accumulation step S3001, item-specific personnel data acquisition step S3002, distribution type-specific template storage step S3003, distribution type identification information reception step S3004, template acquisition step S3005, template output step S3006, correlation analysis rule storage step S3007, correlation analysis step S3008, and correlation analysis result output step S3009.

[0161] These processing methods are executed by a personnel data interpretation support system having a personnel-related data storage unit that stores personnel-related data including n-dimensional (n≧2) data that can be statistically processed, separated into multiple items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information receiving unit that receives distribution type identification information from the distribution of the acquired item-specific personnel-related data; a template acquisition unit that acquires the stored template messages based on the acquired distribution type identification information; a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data; a correlation analysis rule storage unit that stores correlation analysis rules for performing correlation analysis on the personnel-related data acquired by item; a correlation analysis unit that performs correlation analysis on the personnel-related data acquired by item based on the personnel-related data acquired by item and the stored correlation analysis rules; and a correlation analysis result output unit that outputs the correlation analysis results.

[0162] The "personnel-related data accumulation step" S3001 is a stage in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated.

[0163] The "step of acquiring personnel-related data by item" S3002 is a step of acquiring personnel-related data by item.

[0164] The "step of storing template text for each distribution type" S3003 is a step of storing template text to be presented to the user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item.

[0165] The "distribution type identification information receiving step" S3004 is a stage for receiving distribution type identification information from the distribution of the acquired item-specific personnel-related data.

[0166] The "fixed phrase acquisition step" S3005 is a stage in which a fixed phrase stored based on the acquired distribution type identification information is acquired.

[0167] The "fixed phrase output step" S3006 is a stage in which the acquired fixed phrase is output in association with the acquired itemized personnel-related data.

[0168] The "step of storing correlation analysis rules" S3007 is a step of storing correlation analysis rules for performing correlation analysis on the personnel-related data acquired by item.

[0169] The "correlation analysis step" S3008 is a stage in which correlation analysis is performed on the personnel-related data acquired by item based on the personnel-related data acquired by item and the correlation analysis rules that have been stored.

[0170] The "correlation analysis result output step" S3009 is a step of outputting the correlation analysis result.

[0171] <Embodiment 5 (corresponding mainly to claims 5, 13, and 21)> <Outline of Embodiment 5> The fifth embodiment, based on the first embodiment, includes a function that guides situational assessment based on OODA loop observation. As shown in Figure 22, even if you understand what to look for in the current situation and proceed to situation assessment, it often happens that you are unable to execute the subsequent OODA loop because you "don't know how to analyze" or "don't know what to look for in the analysis results." The fifth embodiment can resolve this situation and provides a function that can analyze the gap between "As is" (current situation) and "To be" (desired state). To achieve this, a method executed by a CPU in the information provision system, which is a computer, and an operating program for the information provision system, which is written so as to be readable and executable by the information provision system, are provided. Hereinafter, descriptions of the functional configuration, hardware configuration, and processing flow that are the same as those in the first embodiment will be omitted as appropriate.

[0172] <Functional Configuration of Fifth Embodiment> 31 is a conceptual diagram showing an example of the functional configuration of a personnel data interpretation support system 3100 according to embodiment 5. As shown in the figure, the "personnel data interpretation support system" 3100 has at least a "personnel related data accumulation unit" 3101, an "item-specific personnel related data acquisition unit" 3102, a "distribution type fixed phrase storage unit" 3103, a "distribution type identification information reception unit" 3104, a "fixed phrase acquisition unit" 3105, a "fixed phrase output unit" 3106, a "characteristic word analysis rule storage unit" 3107, a "characteristic word analysis unit" 3108, and a "characteristic word analysis result output unit" 3109.

[0173] <Description of Each Configuration in Embodiment 5> (Fifth embodiment: characteristic word analysis rule storage unit) In the fifth embodiment, a feature word analysis that summarizes data is provided as a data analysis method for analyzing personnel-related data and presenting points of interest to the user. Feature word analysis expresses a large amount of data with a small amount of data. By being able to see the whole picture of the data in a short time, the time it takes from checking the data to planning measures can be significantly reduced. The characteristic word analysis rule holding unit 3107 is configured to hold characteristic word analysis rules for analyzing the natural language data for characteristic words when the personnel-related data acquired by item contains natural language data. Feature word analysis rules involve aggregating natural language data using, for example, a co-occurrence network or word cloud. Human resource information contains a certain amount of natural language data collected from goal management, evaluations, and surveys. A method is needed to aggregate natural language data, focus on the areas of interest, and visualize the data.

[0174] (Feature word analysis unit in embodiment 5) The characteristic word analysis unit 3108 is configured to perform characteristic word analysis on the natural language data based on the natural language data and the characteristic word analysis rules stored therein. For example, as shown in Figure 32, by creating a co-occurrence network to show the relationships between the words that make up a sentence and a word cloud to show the overall picture of the sentence, it is possible to see at a glance the distinctive parts of the natural language data.

[0175] (Fifth embodiment: characteristic word analysis result output unit) The characteristic word analysis result output unit 3109 is configured to output the characteristic word analysis result.

[0176] <Embodiment 5: Personnel Data Interpretation Support System: Hardware Configuration> The hardware configuration of the personnel data interpretation support system according to the fifth embodiment will be described with reference to the drawings.

[0177] 33 is a diagram showing the hardware configuration of a personnel data interpretation support system according to embodiment 5. As shown in this diagram, the information provision system according to this embodiment comprises a "CPU (Central Processing Unit)" 3301 that performs various types of calculation processing, and a "main memory" 3302. It also comprises a "non-volatile memory" 3303 that stores predetermined information, and a "network I / F (interface)" 3304 that transmits and receives information to and from multiple user terminals 3306, multiple administrator terminals 3307, personnel system internal data 3308, personnel system external data 3309, and other personnel-related data 3310.

[0178] "Main memory" reads out programs that perform various processes so that the "CPU" can execute them, and also provides a work area for those programs. In addition, this "main memory" and "non-volatile memory" are each assigned multiple addresses, and programs executed by the "CPU" can exchange data with each other and perform processing by identifying and accessing those addresses. In embodiment 5, the programs stored in the "main memory" include a personnel-related data accumulation program, an item-specific personnel-related data acquisition program, a distribution type-specific template retention program, a distribution type identification information reception program, a template acquisition program, a template output program, a characteristic word analysis rule retention program, a characteristic word analysis program, and a characteristic word analysis result output program. In addition, the "main memory" and "non-volatile memory" store personnel-related data, personnel-related data by category, standard phrases, distribution type identification information, characteristic word analysis rules, and the like.

[0179] The "CPU" executes the personnel-related data accumulation program stored in the "main memory" to store the personnel-related data in the "main memory" or the "non-volatile memory." It also executes the item-specific personnel-related data acquisition program stored in the "main memory" to store the item-specific personnel-related data in the "main memory" or the "non-volatile memory." It also executes the distribution type-specific template sentence storage program stored in the "main memory" to store the template sentences in the "main memory" or the "non-volatile memory." It also executes the template sentence acquisition program stored in the "main memory" to store the template sentences in the "main memory" or the "non-volatile memory" based on the distribution type identification information. It also executes the template sentence output program stored in the "main memory" to output the template sentences in association with the item-specific personnel-related data. It also executes the characteristic word analysis rule storage program stored in the "main memory" to store the characteristic word analysis rules in the "main memory" or the "non-volatile memory." It also executes the characteristic word analysis program stored in the "main memory" to perform characteristic word analysis. It also executes the characteristic word analysis result output program stored in the "main memory" to output the characteristic word analysis results.

[0180] <Embodiment 5: Personnel Data Interpretation Support System: Processing Flow> 34 is a diagram showing the processing flow when using the personnel data interpretation support system in embodiment 5. As shown in the diagram, the processing method includes personnel-related data accumulation step S3401, item-specific personnel data acquisition step S3402, distribution type-specific template storage step S3403, distribution type identification information reception step S3404, template acquisition step S3405, template output step S3406, correlation analysis rule storage step S3407, correlation analysis step S3408, and correlation analysis result output step S3409.

[0181] These processing methods are executed by a personnel data interpretation support system having a personnel-related data storage unit that stores personnel-related data including n-dimensional (n≧2) data that can be statistically processed, divided into multiple items related to personnel management; an item-specific personnel-related data acquisition unit that acquires personnel-related data by item; a distribution-type-specific template storage unit that stores template messages to be presented to users in advance depending on distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information reception unit that receives distribution type identification information from the distribution of the acquired item-specific personnel-related data; a template acquisition unit that acquires the stored template messages based on the acquired distribution type identification information; a template output unit that outputs the acquired template messages in association with the acquired item-specific personnel-related data; a feature word analysis rule storage unit that stores feature word analysis rules for feature word analysis of the natural language data when the personnel-related data acquired by item includes natural language data; a feature word analysis unit that performs feature word analysis on the natural language data based on the natural language data and the stored feature word analysis rules; and a feature word analysis result output unit that outputs the feature word analysis results.

[0182] The "personnel related data accumulation step" S3401 is a stage in which personnel related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated.

[0183] The "step of acquiring personnel-related data by item" S3402 is a step of acquiring personnel-related data by item.

[0184] The "step of storing template text for each distribution type" S3403 is a step of storing template text to be presented to the user in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item.

[0185] The "distribution type identification information receiving step" S3404 is a stage for receiving distribution type identification information from the distribution of the acquired item-specific personnel-related data.

[0186] The "fixed phrase acquisition step" S3405 is a stage for acquiring a fixed phrase stored based on the acquired distribution type identification information.

[0187] The "fixed phrase output step" S3406 is a step of outputting the acquired fixed phrase in association with the acquired itemized personnel-related data.

[0188] The "step of storing characteristic word analysis rules" S3407 is a step of storing characteristic word analysis rules for analyzing the natural language data for characteristic words when the personnel-related data acquired by item contains natural language data.

[0189] The "characteristic word analysis step" S3408 is a step of analyzing the natural language data for characteristic words based on the natural language data and the characteristic word analysis rules that have been stored.

[0190] The "characteristic word analysis result output step" S3409 is a step of outputting the characteristic word analysis result.

[0191] <Example 2> An example (Example 2) based on the third to fifth embodiments will be described below. First, we will explain a system that incorporates the OODA loop to support strategic execution with reference to Figure 22.

[0192] Below, we will explain in detail the function that is characteristic of the present invention, which is ``able to present the points and analytical methods that the displayed graphs mean.''

[0193] The personnel data interpretation support system according to the second embodiment is configured to provide support on "how to perform the analysis" and "what points to look at in the analysis results" based on "instructions" (see Figures 4 to 11) provided for each type of graph displayed on the dashboard.

[0194] Typical graphs that can be displayed on a dashboard include bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts, but here we will use histograms as an example for detailed explanation. Although the standard phrases for each distribution type differ for other graphs, the dialogue flow is similar, so a detailed explanation will be omitted. It goes without saying that standard phrases for each distribution type can also be used for graphs other than bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts by preparing them as appropriate.

[0195] The standard phrases for each classification type are stored in the comment section for each distribution type in tabular format, as shown in Figures 4 to 11, for example, bar graphs, histograms, pie charts, line graphs, scatter plots, radar charts, cross tabulations, and scalar charts. For example, referring to the histogram in Figure 5, in the case where the "isolated island type" and the "two peaks type" are combined, the following standard text is maintained: "· There are many cases where data that is significantly separated has special circumstances and requires individual handling. Check the target data and identify the cause. · There may be multiple underlying causes. Conduct a cluster analysis to investigate the trends for each "peak" of the [X-axis item]. · Conduct a correlation analysis to investigate items that are strongly related to the [X-axis item]."

[0196] Figure 17 shows the distribution of average overtime hours as human capital information, visualized as a histogram and displayed on the left side of the dashboard. For example, when a "histogram" instruction is obtained from the distribution type template storage unit 0203, and when a target value (for example, 24 hours) is set for the average overtime hours, the personnel data interpretation support system detects, as question 1, that there are employees whose average overtime hours exceed 24 hours, and provides the following information: "Average overtime hours exceed the target value. Let's analyze this and come up with an improvement plan." If the personnel department staff selects "Analyze this problem," this is considered to be a Yes answer to question 1, and a dialogue with the personnel data interpretation support system begins on the right side of the screen. If no target value has been set, the system will ask, "Is the distribution of this graph (the shape of the graph) different from what you expected, or is there anything that concerns you?" If the HR department staff member selects "Analyze this issue," this will be treated as an answer to question 1, and a dialogue with the system will begin on the right side of the screen.

[0197] First, the distribution type identification information receiving unit 0204 receives distribution type identification information based on the answers received to questions stored in the instructions corresponding to the type of graph displayed on the left side of the screen ("histogram" in Figure 17). The dialogue proceeds according to the template texts (guidance texts, questions, and comments for the user) stored in the distribution type template text storage unit 0203 . For question 2, we ask, "Is there any data that is far apart in this graph?" and provide guidance by illustrating a typical example. If the HR department staff member follows the instructions and selects "Yes," the system will then ask a third question, "Are there multiple peaks in this graph?", as shown in Figure 18, illustrating a typical example. During this process, HR personnel can learn that when looking at a histogram, the key points to observe (to understand the current situation) are whether there are any data points that are far apart or whether there are multiple peaks.

[0198] If the answer to both questions 2 and 3 is "yes," the template output unit 0306 outputs the template stored in the histogram instructions based on the distribution type identification information "11" received by the distribution type identification information receiving unit 0305.

[0199] For example, as shown in FIG. 18, the dialogue screen may include: "The content you answered There is data that is far away There are several mountains There could be multiple underlying causes for this graph, please analyze the data to understand the cause. correspondence (1) For data that is significantly different, there are often special circumstances that require individual handling. Check the target data and identify the cause. (2) There may be multiple underlying causes. Let's conduct a cluster analysis to investigate the trends for each "peak" in [Average Overtime Hours]. (3) Let's conduct a correlation analysis to investigate the items that are most closely related to [average overtime hours]. An instruction message and comment will be displayed.

[0200] This allows HR personnel to learn that "individual analysis is appropriate for data that is widely separated," "cluster analysis is appropriate for investigating trends at each "peak" of [average overtime hours]," and "correlation analysis is appropriate for investigating items that are closely related to [average overtime hours]."

[0201] Similarly, in the instructions for the "bar graph" in Figure 4, if the distribution type is "achievement level type," the standard text "Perform a correlation analysis and investigate items that are strongly related to [Y-axis item]" is displayed. Furthermore, if the personnel-related data obtained by category contains natural language data, the following standard phrase will be displayed: "Conduct feature word analysis and compare the high and low categories of [Y-axis items] to investigate the cause." This allows human resources personnel to learn that "correlation analysis is suitable for investigating items that are strongly related to [Y-axis item]" and "characteristic word analysis is suitable for comparing high and low categories of [Y-axis item]."

[0202] Similarly, in the instructions for the "Scatter Plot" in Figure 8, if the distribution type is "Centralized," standard phrases such as "There may be multiple underlying causes. Conduct a cluster analysis to investigate group trends" and "Conduct a correlation analysis to investigate items that are strongly related to the [Y-axis item]" are displayed. This allows human resources personnel to learn that "cluster analysis is suitable for investigating group trends," and "correlation analysis is suitable for investigating items that are strongly related to the [Y-axis item]."

[0203] Returning to Figure 18, if you select "Perform analysis," "cluster analysis" and "correlation analysis" will be automatically performed according to the template. Figure 35 is an example of the results of an automatically executed cluster analysis displayed on an interactive screen. Not only does the system report the results of the cluster analysis as "Cluster analysis was performed. Four groups were found [for this overtime work period]," but it also displays the characteristics of each group and recommendations from the personnel data interpretation support system, providing a function that tells you what you should look for in the analysis results (key points). Next, Figure 36 shows an example of the results of an automatically executed correlation analysis displayed on an interactive screen. "We performed a correlation analysis. The items that are most strongly related to [annual income] are as follows," the automatically created graph with the strongest correlation is initially displayed, and multiple higher-ranking graphs are also expanded and displayed.

[0204] In addition, if you select "View details," the screen will change to one similar to the image shown in Figure 37. This screen also displays a comment, such as "--If there is a correlation--Overtime hours, personnel evaluations, and training participation hours have a particularly strong correlation with annual income. By improving this relationship, you can create a compensation system that rewards outstanding employees.--If there is no correlation--There was no correlation in personnel information. If something seems strange, try analyzing it together with other information (accounting information, for example)," and is equipped with a function that tells you what to look for (key points) in the analysis results.

[0205] Example 3 Furthermore, an example (Example 3) based on the second and fifth embodiments will be described. The third embodiment differs from the second embodiment in that a "target value" is set. Following the flow explained in Figure 3, let's say you have progressed to (e) information visualization and a bar graph is displayed on the dashboard. As already explained, the personnel data interpretation support system supports the OODA loop from observation (Observe) to situational judgment (Orient) (Figure 22) by following the bar graph instructions (Figure 4). Here, the distribution type target fixed phrase holding unit 1908 is configured to hold fixed phrases to be presented to the user in advance according to the distribution type identification information and the target value.

[0206] For example, in the case of a bar graph with an "achievement level" type, the target value is set for the [Y-axis item], "--Target value provided--Perform feature word analysis and compare with a group that has achieved the target value (or a group that is being used as a benchmark) to investigate the cause. --No target value--Let's conduct a feature analysis and compare the categories with high and low [Y-axis items] to investigate the cause. The sentence is as follows. Here, "---with target value---" and "---without target value---" correspond to tags in a structured document, and depending on "---with target value---" and "---without target value---", the following template is stored in the distribution type target template storage section: "Perform feature word analysis and compare with a group that has achieved the target value (or a group that is being used as a benchmark) to investigate the cause.", and "Perform feature word analysis and compare categories where the [Y-axis item] is high with categories where it is low to investigate the cause." In other words, in the bar graph instructions (Figure 4), if the distribution type is set to "achievement type" and "target value" is set, and natural language data is included in the personnel-related data acquired by item, the goal-dependent template acquisition means 1905A acquires a template saying, "Perform characteristic word analysis and investigate the cause by comparing with a group that has achieved the target value (or a group that is being used as a benchmark)." The template output unit 1906 outputs this template and displays it on the interactive screen. The interactive screen displays the "Analyze" and "Don't Analyze" buttons, just like the average overtime hours distribution graph in Figure 18, and if "Analyze" is selected, automatic analysis will be performed. FIG. 38 is an example of a display in which the results of automatically executed characteristic word analysis are displayed on an interactive screen. In this example, not only is the result of the comparative analysis of characteristic words reported, "A characteristic word analysis was performed. Frequently occurring words were linked to [goal]," but a "comment from the HR data interpretation support system" is also displayed, letting you know the key points (key points) to look at in the analysis results.

[0207] In the above-mentioned embodiments 1 to 5 and examples 1 to 3, since OODA is generally known as an exemplary behavioral process suitable for data-driven, a personnel data interpretation support system applied to OODA loop observation (situation confirmation, detailed confirmation) and situation judgment (behavior analysis, analysis result confirmation) has been described in detail, but the present invention is not limited to this.

[0208] As is well known, OODA focuses on decision-making in competitive environments and is effective in making quick and accurate judgments and taking rapid action. For example, Observe: Recognize which occupations have the most overtime hours Orient (situation assessment): Sales staff in the XX division work a lot of overtime. Decide: Increase the number of sales staff in the XX division. Act: Add two sales staff to the XX division through internal transfers is. In contrast, PDCA is a method used in quality control and project management, and is effective in achieving continuous improvement. For example, Plan: Limit overtime hours to 20 hours per month: Do: Implement the work for one month and three months and determine overtime hours Check: Investigate organizations and occupations where overtime hours exceed 20 hours per month and analyze the causes Action (Improvement): Increase the number of sales staff in the XX division by two through internal transfers. is. In other words, OODA allows for quick identification of problems related to overwork, and allows for quick and accurate judgment and action in real time, even in the rapidly changing business world. PDCA involves planning, execution, evaluation, and improvement, which takes time to improve, and can result in reactive measures, such as overwork leading to employee turnover. Although there are advantages and disadvantages depending on the field of application, it should be noted that there is nothing preventing the application of the technical ideas of this invention to the Do (execution) and Check (evaluation) stages of the PDCA loop as a data interpretation support system. [Explanation of symbols]

[0209] 0200, 1900, 2300, 2700, 3100 Personnel data interpretation support system 0201, 1901, 2301, 2701, 3101 Personnel-related Data Storage Department 0202, 1902, 2302, 2702, 3102 Personnel Data Acquisition Department 0203, 1903, 2303, 2703, 3103 Distribution type fixed sentence storage section 0204, 1904, 2304, 2704, 3104 Distribution type identification information reception section 0205, 1905, 2305, 2705, 3105 Fixed phrase acquisition department 0206, 1906, 2306, 2706, 3106 Fixed phrase output section 1905A Target-dependent fixed phrase acquisition part 1907 Target value holding unit 1908 Distribution type target template storage section 2307 Cluster Analysis Rule Storage Unit 2308 Cluster Analysis Department 2309 Cluster analysis result output section 2707 Correlation analysis rule storage unit 2708 Correlation Analysis Department 2709 Correlation analysis result output unit 3107 Characteristic Word Analysis Rule Storage Unit 3108 Feature Word Analysis Unit 3109 Feature word analysis result output unit

Claims

1. a personnel-related data storage unit that stores personnel-related data including statistically processable n-dimensional (n≧2) data, classified into a plurality of items related to personnel management; an itemized personnel-related data acquisition unit that acquires personnel-related data by item; a distribution type-specific template storage unit that stores template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by each item; a distribution type identification information receiving unit that receives, in accordance with an instruction, distribution type identification information that indicates to which distribution type the distribution of the acquired item-specific personnel-related data corresponds; a fixed phrase acquisition unit that acquires a fixed phrase stored based on the acquired distribution type identification information; a fixed phrase output unit that outputs the acquired fixed phrase in association with the acquired itemized personnel-related data; A personnel data interpretation support system.

2. a target value storage unit that stores target values ​​that are target values ​​for at least some of the items; a distribution type target fixed phrase storage unit that stores fixed phrases to be presented to users in advance according to distribution type identification information and the target value; and The personnel data interpretation support system of claim 1, wherein the template acquisition unit has a target-dependent template acquisition means for acquiring a stored template based on distribution type identification information and, if there is a target value assigned to the item from which the distribution type identification information was obtained, the target value.

3. a cluster analysis rule storage unit that stores cluster analysis rules for performing cluster analysis on the personnel-related data acquired by each item; a cluster analysis unit that performs cluster analysis on the personnel-related data acquired by category based on the personnel-related data acquired by category and the stored cluster analysis rules; a cluster analysis result output unit that outputs the cluster analysis result; 3. The personnel data interpretation support system according to claim 1, further comprising:

4. a correlation analysis rule storage unit that stores correlation analysis rules for performing correlation analysis on the personnel-related data acquired by each item; a correlation analysis unit that performs correlation analysis on the personnel-related data acquired by each item based on the personnel-related data acquired by each item and the correlation analysis rule that is stored; a correlation analysis result output unit that outputs the correlation analysis result; 3. The personnel data interpretation support system according to claim 1, further comprising:

5. a feature word analysis rule storage unit that stores feature word analysis rules for analyzing feature words in natural language data when the personnel-related data acquired by item includes the natural language data; a feature word analysis unit that performs feature word analysis on the natural language data based on the natural language data and a stored feature word analysis rule; a feature word analysis result output unit that outputs the feature word analysis result; 3. The personnel data interpretation support system according to claim 1, further comprising:

6. The human resources data interpretation support system of claim 1, wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers and turnover, information on skills and abilities, information on succession planning, and information on workforce.

7. The personnel data interpretation support system of claim 6, wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding paid leave utilization rate, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

8. 2. The personnel data interpretation support system according to claim 1, wherein the distribution type identification information is distribution type identification information for at least one of a bar graph, a histogram, a pie chart, a line graph, a scatter plot, a radar chart, a cross tabulation, and a scalar chart.

9. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: a personnel-related data accumulation step in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated; an itemized personnel-related data acquisition step for acquiring personnel-related data by item; a distribution type-specific template storage step for storing template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information receiving step of receiving, in accordance with instructions, distribution type identification information indicating to which distribution type the distribution of the acquired item-specific personnel-related data corresponds; a fixed phrase acquisition step of acquiring a fixed phrase stored based on the acquired distribution type identification information; a template output step for outputting the acquired template in association with the acquired itemized personnel-related data; A method having the following.

10. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: a target value holding step of holding target values ​​that are target values ​​for at least some of the items; a distribution type target fixed sentence storage step for storing fixed sentences to be presented to a user in advance according to distribution type identification information and the target value; and The method according to claim 9, wherein the template acquisition step further includes a target-dependent template acquisition substep of acquiring a stored template based on distribution type identification information and, if there is a target value assigned to the item from which the distribution type identification information was obtained, the target value.

11. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: a cluster analysis rule holding step for holding a cluster analysis rule for performing cluster analysis on the personnel-related data acquired by each item; a cluster analysis step of performing cluster analysis on the personnel-related data acquired by each item based on the personnel-related data acquired by each item and the stored cluster analysis rules; a cluster analysis result output step for outputting the cluster analysis result; 11. The method of claim 9 or claim 10, further comprising:

12. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: a correlation analysis rule holding step for holding correlation analysis rules for performing correlation analysis on the personnel-related data acquired by item; a correlation analysis step of performing correlation analysis on the personnel-related data acquired by each item based on the personnel-related data acquired by each item and the correlation analysis rule stored; a correlation analysis result output step for outputting the correlation analysis result; 11. The method of claim 9 or claim 10, further comprising:

13. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: a feature word analysis rule holding step for holding feature word analysis rules for analyzing feature words in natural language data when the personnel-related data acquired by item includes the natural language data; a feature word analysis step of analyzing the natural language data for feature words based on the natural language data and a stored feature word analysis rule; a feature word analysis result output step for outputting the feature word analysis result; 11. The method of claim 9 or claim 10, further comprising:

14. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising:

10. The method of claim 9, wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers, and turnover, information on skills and abilities, information on succession planning, and information on the workforce.

15. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: The method according to claim 14, wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding paid leave utilization rate, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

16. A method executed by a CPU in a personnel data interpretation support system that is a computer, comprising: The method according to claim 9 , wherein the distribution type identification information is distribution type identification information for at least one of a bar graph, a histogram, a pie chart, a line graph, a scatter plot, a radar chart, a cross tabulation, and a scalar chart.

17. The personnel data interpretation support system is a computer. a personnel-related data accumulation step in which personnel-related data including statistically processable n-dimensional (n≧2) data is classified into a plurality of items related to personnel management and accumulated; an itemized personnel-related data acquisition step for acquiring personnel-related data by item; a distribution type-specific template storage step for storing template messages to be presented to users in advance according to distribution type identification information for identifying the n-dimensional distribution type of the personnel-related data acquired by item; a distribution type identification information receiving step of receiving, in accordance with instructions, distribution type identification information indicating to which distribution type the distribution of the acquired item-specific personnel-related data corresponds; a fixed phrase acquisition step of acquiring a fixed phrase stored based on the acquired distribution type identification information; a template output step for outputting the acquired template in association with the acquired itemized personnel-related data; An operating program that executes the above.

18. The personnel data interpretation support system, which is the computer, a target value holding step of holding target values ​​that are target values ​​for at least some of the items; a distribution type target fixed sentence storage step for storing fixed sentences to be presented to a user in advance according to distribution type identification information and the target value; Execute The operating program of claim 17, wherein the template acquisition step further executes a target-dependent template acquisition substep that acquires a stored template based on distribution type identification information and, if there is a target value assigned to the item from which the distribution type identification information was obtained, the target value.

19. The personnel data interpretation support system, which is the computer, a cluster analysis rule holding step for holding a cluster analysis rule for performing cluster analysis on the personnel-related data acquired by each item; a cluster analysis step of performing cluster analysis on the personnel-related data acquired by each item based on the personnel-related data acquired by each item and the stored cluster analysis rules; a cluster analysis result output step for outputting the cluster analysis result; 19. The operating program according to claim 17 or 18, further causing the program to execute the following:

20. The personnel data interpretation support system, which is the computer, a correlation analysis rule holding step for holding correlation analysis rules for performing correlation analysis on the personnel-related data acquired by item; a correlation analysis step of performing correlation analysis on the personnel-related data acquired by each item based on the personnel-related data acquired by each item and the correlation analysis rule stored; a correlation analysis result output step for outputting the correlation analysis result; 19. The operating program according to claim 17 or 18, further causing the program to execute the following:

21. The personnel data interpretation support system, which is the computer, a feature word analysis rule holding step for holding feature word analysis rules for analyzing feature words in natural language data when the personnel-related data acquired by item includes the natural language data; a feature word analysis step of analyzing the natural language data for feature words based on the natural language data and a stored feature word analysis rule; a feature word analysis result output step for outputting the feature word analysis result; 19. The operating program according to claim 17 or 18, further causing the program to execute the following:

22. The operating program of claim 17, wherein the plurality of items include at least one of information on compliance and ethics, information on costs, information on diversity, information on leadership, information on organizational culture, information on health management, information on productivity, information on recruitment, transfers, and turnover, information on skills and abilities, information on succession planning, and information on the workforce.

23. The operating program of claim 22, wherein the plurality of items further include at least one of information regarding the number of personnel and personnel composition, information regarding overtime hours, information regarding paid leave utilization rate, information regarding training, information regarding skills, information regarding competencies, and information regarding goal setting and evaluation.

24. 18. The operating program according to claim 17, wherein the distribution type identification information is distribution type identification information for at least one of a bar graph, a histogram, a pie chart, a line graph, a scatter plot, a radar chart, a cross tabulation, and a scalar chart.

Images