JP7789120B2 - Company rule revision device, company rule revision method, company rule revision program, and recording medium - Google Patents

Description

This disclosure relates to a company rule revision device, a company rule revision method, a company rule revision program, and a recording medium.

In conventional standards information management devices, when revision information indicating the revision details of a standard to be revised is input to an input unit, an update unit updates a database of revision information for the standard to be revised based on the revision information. A notification unit then notifies relevant parties of the revision information regarding the shipped products that correspond to the revised standard (see, for example, Patent Document 1).

Japanese Patent Application Laid-Open No. 2021-163242

In conventional standards information management devices like the one described above, the revision information database is updated when revision information is entered, and relevant parties are notified of the revision information. However, the revision content itself is created manually, and the actual revision work requires a great deal of effort.

This disclosure has been made to solve the above-mentioned problems, and aims to provide a company rule revision device, company rule revision method, company rule revision program, and recording medium that can reduce the effort required for revising company rules.

The company rule revision device according to the present disclosure includes a data acquisition unit that acquires current company rule data, which is data on the current company rules that are the subject of revision, and revision summary data, which is data that outlines the revisions to the current company rules, and a revised company rule generation unit that generates revised company rules, which are the company rules after revision, by inputting the current company rule data and revision summary data acquired by the data acquisition unit into artificial intelligence.
The company rule revision method disclosed herein includes a data acquisition step of acquiring current company rule data, which is data on the current company rules that are to be revised, and revision summary data, which is data that shows an overview of the revisions to the current company rules, and a revised company rule generation step of generating revised company rules, which are the company rules after revision, by inputting the current company rule data and revision summary data acquired by the data acquisition step into artificial intelligence.

This disclosure can reduce the effort required to revise company rules.

1 is a block diagram showing a company rule revision system according to embodiment 1. FIG. 4 is a flowchart showing a company rule revision process performed by the company rule revision device of FIG. 1 . FIG. 10 is a block diagram showing a modified example of the company rule revision device according to the first to seventh embodiments. This is a configuration diagram showing a first example of a processing circuit that realizes each function of the company rule revision device and artificial intelligence of embodiments 1 to 7. This is a configuration diagram showing a second example of a processing circuit that realizes each function of the company rule revision device and artificial intelligence of embodiments 1 to 7.

Hereinafter, embodiments will be described with reference to the drawings.
Embodiment 1.
1 is a block diagram showing a company rule revision system according to embodiment 1. In the figure, the company rule revision system includes a company rule revision device 10 and an artificial intelligence 20.

The company rules revision device 10 has two functional blocks: a data acquisition unit 11 and a revised company rules generation unit 12. The company rules revision device 10 can be, for example, a personal computer.

The data acquisition unit 11 acquires input data. The input data includes current company rules data, revision summary data, and explanatory data. The user inputs the current company rules data, revision summary data, and explanatory data into the company rules revision device 10.

Current company rules data is data on current company rules that are subject to revision. Company rules include employee work rules, wage rules, detailed wage regulations, etc. Wage rules are attached to employee work rules. Furthermore, detailed wage regulations stipulate the details of wage rules. In this way, company rules are often divided into multiple layers.

Multiple internal rules are interrelated. Each internal rule is made up of multiple clauses. The number of internal rules to be revised may be one, two, or more. The internal rules to be revised may be all clauses of each internal rule, or only some of the clauses contained in each internal rule.

The revision summary data is data that shows an overview of the revisions to the current company rules. Specifically, the revision summary data includes text data that explains the overview of the revisions.

Various levels of granularity are possible regarding the detailed outline of the revisions. In other words, the outline of the revisions may be close to the articles of the revised company rules, or it may only indicate the direction of the revised company rules, and various patterns are envisioned. The revised company rules are the revised company rules obtained by revising the current company rules.

Explanatory data is data explaining the current company rules. For example, explanatory data is data that explains each article of the current company rules. The article-by-article explanation explains the content of each article contained in the current company rules in simple language. The article-by-article explanation also explains the purpose of the current company rules, their scope of application, and other information necessary for implementing the company rules.

The revised company rules generation unit 12 generates revised company rules by inputting the current company rules data, revision summary data, and explanatory data acquired by the data acquisition unit 11 into the artificial intelligence 20.

The artificial intelligence 20 is provided, for example, on a server independent of the company rule revision device 10. The company rule revision device 10 is capable of communicating with the artificial intelligence 20.

The artificial intelligence 20 has, as functional blocks, a trained model storage unit 21 and an inference unit 22. The trained model storage unit 21 stores trained models. The trained models are models that take current company rules data, revision summary data, and explanatory data sent from the company rules revision device 10 as inputs and output revised company rules.

The inference unit 22 inputs the current company rules data, revision summary data, and explanatory data input by the company rules revision device 10 into the trained model stored in the trained model storage unit 21. The inference unit 22 also sends the revised company rules data output from the trained model to the company rules revision device 10.

The trained model may be a model trained by machine learning, for example, through supervised learning, reinforcement learning, or unsupervised learning.

Furthermore, the trained model may be a model obtained by performing learning according to, for example, deep learning, genetic programming, functional logic programming, or other known algorithms or methods.

Specifically, the trained model may be a model called a Neural Network (NN) model, a Convolutional Neural Network (CNN) model, a Recurrent Neural Network (RNN), a Variational Autoencoder (VAE), a Generative Adversarial Network (GAN), a Diffusion Model, a Transformer model, a Large Language Model (LLM), a Visual Language Model (VLM), a Bidirectional Encoder Representations from Transformers (BERT), a Generative Pre-trained Transformer (GPT), or a Contrastive Language Image Pre-training (CLIP).

Note that the above models are not exclusive; for example, LLM, VLM, BERT, and GPT are included in the Transformer model. Also, for example, the Transformer model is included in the NN model.

In addition, multiple types of learning algorithms and models may be combined. Trained models also include models known as multimodal models, which are trained by combining multiple different types of data.

Figure 2 is a flowchart showing the company rule revision process performed by the company rule revision device 10 of Figure 1. When the company rule revision process begins, in step S101, the company rule revision device 10 acquires current company rule data, revision summary data, and explanation data.

Next, in step S102, the company rule revision device 10 inputs the current company rule data, revision summary data, and explanatory data into the artificial intelligence 20. Then, in step S103, the company rule revision device 10 acquires and saves the output from the artificial intelligence 20. Once the revised company rule has been created in this way, the company rule revision device 10 terminates processing.

The company rule revision device 10 creates a format for revised company rules as necessary. The format may include a revision history showing the revised parts, a comparison table of company rules before and after the revision, etc. The revision history, comparison table, etc. may be included in the output from the artificial intelligence 20.

The format of the revised company rules may also be created by an external device (not shown). In this case, the company rules revision device 10 outputs the revised company rules data obtained from the artificial intelligence 20 to the external device.

As a concrete example of a company rule revision, we will explain the case where the company rule to be revised is a travel expense rule. In this example, the travel expense rule stipulates that actual expenses must be paid for accommodation during business trips. However, it also stipulates that the upper limit for accommodation expenses is 10,000 yen.

Furthermore, the explanation of the travel expenses regulations states that the upper limit for travel expenses is set at 10,000 yen, taking into account the average accommodation prices at accommodation facilities nationwide.

In addition, the summary of the revisions to the travel expense regulations states that, due to the large differences in accommodation fees between regions, the upper limit on accommodation fees will be revised to a rate that takes into account the circumstances of each region.

By inputting the current company rules data, revision summary data, and explanatory data containing the above-mentioned content into the artificial intelligence 20, the artificial intelligence 20 can output revised company rules that change the upper limit on accommodation costs for each region, for example, based on data on average accommodation rates suitable for business trips in each region.

In addition, travel expense rules may also stipulate an upper limit on subsidies for food and beverage expenses during business trips. In this case, even if the summary of revisions to the travel expense rules does not include any mention of revisions to food and beverage expenses, the artificial intelligence 20 can suggest revisions to the provisions regarding food and beverage expenses based on data on average food and beverage expenses by region, just as with accommodation expenses.

On the other hand, if there are circumstances under which revisions to food and beverage expenses are not desired, the AI 20 can output revised company rules that do not change the provisions regarding food and beverage expenses by including a note in the summary of the revisions that the provisions regarding food and beverage expenses will not be revised. In other words, the summary of the revisions can include items that are prohibited from being revised, preventing unnecessary revisions and eliminating the need to check the output content.

The company rule revision method of embodiment 1 includes a data acquisition step and a revised company rule generation step.

The data acquisition step is a step for acquiring current company regulations data, revised summary data, and explanatory data. The revised company regulations generation step is a step for generating revised company regulations by inputting the current company regulations data, revised summary data, and explanatory data acquired in the data acquisition step into the artificial intelligence 20.

With this company rule revision device 10 and company rule revision method, revised company rules are generated by acquiring current company rule data and revision summary data and inputting them into the artificial intelligence 20. Therefore, by using current company rule data that is normally prepared within the company, specific revised company rules can be created simply by preparing revision summary data, reducing the effort required for revising company rules.

Furthermore, in embodiment 1, in addition to the current company rules data and revision summary data, explanatory data is acquired and input into the artificial intelligence 20, thereby generating revised company rules. Therefore, by using explanatory data that is normally prepared within the company, more appropriate revised company rules can be created, further reducing the effort required to revise company rules.

The company rule revision program of embodiment 1 is a program that causes a computer to execute the company rule revision method described above.

Furthermore, the recording medium of embodiment 1 is a computer-readable recording medium that stores a company rule revision program that causes a computer to execute the company rule revision method of embodiment 1.

Embodiment 2.
Next, a company rule revision device 10 according to embodiment 2 will be described. The configuration of the company rule revision system according to embodiment 2 is the same as that shown in Fig. 1. The basic flow of the company rule revision process according to embodiment 2 is the same as that shown in Fig. 2.

In embodiment 2, the data acquisition unit 11 acquires related rule data in addition to current company rule data, revision summary data, and explanatory data. The user inputs the current company rule data, revision summary data, explanatory data, and related rule data into the company rule revision device 10.

Related rules data is data on rules that are related to the current company rules that are subject to revision, but are not subject to revision.

The revised company rules generation unit 12 generates revised company rules by inputting the current company rules data, revision summary data, and explanatory data, as well as related rule data, into the artificial intelligence 20.

The data acquisition step in embodiment 2 is a step for acquiring related rules data in addition to current company regulations data, revision summary data, and explanatory data.

The revised company rules generation step in embodiment 2 is a step in which revised company rules are generated by inputting related rule data into the artificial intelligence 20 in addition to current company rules data, revision summary data, and explanatory data.

Other configurations and methods in embodiment 2 are the same as those in embodiment 1.

With this company rule revision device 10 and company rule revision method, revised company rules are generated by acquiring related rule data and inputting it into the artificial intelligence 20. This makes it possible to generate revised company rules without creating any contradictions with rules that are not subject to revision.

For example, if the company rules to be revised are detailed wage rules, the related rule data may be data on the wage rules. Because the wage rules are higher-level rules than the detailed wage rules, the detailed wage rules are bound by the content of the wage rules. Therefore, by using data on the wage rules as related rule data, the detailed wage rules can be revised so as not to contradict the content of the wage rules.

The company rule revision program of embodiment 2 is a program that causes a computer to execute the company rule revision method of embodiment 2.

Furthermore, the recording medium of embodiment 2 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 2.

In the second embodiment, the rules not subject to revision included in the related rule data are not limited to company rules, but may also be external rules such as related laws and regulations. For example, if the company rules subject to revision are employee work rules, the related rule data may be laws such as the Labor Standards Act.

Furthermore, in embodiment 2, if the rule not subject to revision included in the related rule data is a statute, in addition to the related rule data, explanatory data explaining the content of the statute may be acquired and input to the artificial intelligence 20. The explanatory data for statutes is published by the relevant government ministries and agencies, and is therefore easy to obtain. By using explanatory data for statutes, the appropriateness of revised company rules can be improved.

In addition, laws and regulations related to company rules, their explanatory text, etc. may be entered into the artificial intelligence 20 in advance, either manually or automatically. In this case, when laws and regulations, their explanatory text, etc. are revised, the revisions may be automatically reflected in the trained model.

Embodiment 3.
Next, a company rule revision device 10 according to embodiment 3 will be described. The configuration of the company rule revision system according to embodiment 3 is the same as that shown in Fig. 1. The basic flow of the company rule revision process according to embodiment 3 is the same as that shown in Fig. 2.

In embodiment 3, the data acquisition unit 11 acquires company information data in addition to current company rules data, revision summary data, and commentary data. The user inputs the current company rules data, revision summary data, commentary data, and company information data into the company rules revision device 10.

Corporate information data is data that shows information about the company whose internal rules are to be revised. Examples of corporate information include corporate philosophy and management strategy.

The revised company rules generation unit 12 generates revised company rules by inputting company information data into the artificial intelligence 20 in addition to current company rules data, revision summary data, and explanatory data.

The data acquisition step in embodiment 3 is a step of acquiring company information data in addition to current company regulations data, revision summary data, and explanatory data.

The revised company rules generation step in embodiment 3 is a step in which revised company rules are generated by inputting company information data into the artificial intelligence 20 in addition to current company rules data, revision summary data, and explanatory data.

Other configurations and methods in embodiment 3 are the same as those in embodiment 1.

With this company rule revision device 10 and company rule revision method, revised company rules are generated by acquiring company information data and inputting it into the artificial intelligence 20. This improves the validity of revised company rules within a company.

The company rule revision program of embodiment 3 is a program that causes a computer to execute the company rule revision method of embodiment 3.

Furthermore, the recording medium of embodiment 3 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 3.

Embodiment 4.
Next, a company rule revision device 10 according to embodiment 4 will be described. The configuration of the company rule revision system according to embodiment 4 is the same as that shown in Fig. 1. The basic flow of the company rule revision process according to embodiment 4 is the same as that shown in Fig. 2.

In embodiment 4, the revised company rules generation unit 12 generates revised company rules and revised explanations by inputting current company rules data, revision summary data, and explanation data into the artificial intelligence 20. The revised explanations are explanations of the company rules after the revision.

The revised company rules generation step in embodiment 4 is a step in which the current company rules data, revised summary data, and explanatory data acquired in the data acquisition step are input into the artificial intelligence 20 to generate revised company rules and revised explanatory data.

Other configurations and methods in embodiment 4 are the same as those in embodiment 1.

With this company rule revision device 10 and company rule revision method, revision explanations are generated, so the revision explanation data can be used as explanation data the next time the company rules are revised, reducing the effort required for the next company rule revision.

The company rule revision program of embodiment 4 is a program that causes a computer to execute the company rule revision method of embodiment 4.

Furthermore, the recording medium of embodiment 4 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 4.

Embodiment 5.
Next, a company rule revision device 10 according to embodiment 5 will be described. The configuration of the company rule revision system according to embodiment 5 is the same as that shown in Fig. 1. The basic flow of the company rule revision process according to embodiment 5 is the same as that shown in Fig. 2.

In embodiment 5, the revised company rules generation unit 12 generates revised company rules and related department information by inputting current company rules data, revision summary data, and explanatory data into the artificial intelligence 20. The related department information is information about internal departments related to the revised company rules.

The revised company rules generation step in embodiment 5 is a step in which the current company rules data, revision summary data, and explanatory data acquired in the data acquisition step are input into the artificial intelligence 20 to generate revised company rules and related department information.

Other configurations and methods in embodiment 5 are the same as those in embodiment 1.

This company rule revision device 10 and company rule revision method generates related department information, making it possible to more reliably disseminate revised company rules to all related departments within the company.

For example, the department in charge of travel expense regulations is primarily the general affairs department, but it is also thought that the accounting department is relevant. In this case, the relevant department information generated indicates that the departments relevant to the revised company regulations are the general affairs department and the accounting department.

The company rule revision program of embodiment 5 is a program that causes a computer to execute the company rule revision method of embodiment 5.

Furthermore, the recording medium of embodiment 5 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 5.

Embodiment 6.
Next, a company rule revising device 10 according to a sixth embodiment will be described. The configuration of the company rule revising system according to the sixth embodiment is the same as that shown in Fig. 1. The basic flow of the company rule revising process according to the sixth embodiment is the same as that shown in Fig. 2.

In embodiment 6, the revised company rules generation unit 12 inputs current company rules data, revision summary data, and explanatory data into the artificial intelligence 20, thereby generating multiple different proposed company rules revisions as revised company rules.

The revised company rules generation step in embodiment 6 is a step in which the current company rules data, revision summary data, and explanatory data acquired in the data acquisition step are input into the artificial intelligence 20 to generate multiple different proposed company rules revisions as revised company rules.

Other configurations and methods in embodiment 6 are the same as those in embodiment 1.

With this company rule revision device 10 and company rule revision method, multiple company rule revision proposals are generated, allowing the person in charge to select the most appropriate company rule revision proposal and use it as the final revised company rule. This allows the appropriateness of revised company rules within a company to be improved simply by selecting one of the multiple company rule revision proposals.

The company rule revision program of embodiment 6 is a program that causes a computer to execute the company rule revision method of embodiment 6.

Furthermore, the recording medium of embodiment 6 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 6.

The revised company rules generation unit 12 may generate multiple company rules revision proposals and difference explanation information by inputting the current company rules data, revision summary data, and explanatory data into the artificial intelligence 20. The difference explanation information is information that explains the differences between multiple company rules revision proposals. The differences between multiple company rules revision proposals may include, for example, at least one of the advantages and disadvantages of each company rules revision proposal.

Embodiment 7.
Next, a company rule revising device 10 according to embodiment 7 will be described. The configuration of the company rule revising system according to embodiment 7 is the same as that shown in Fig. 1. The basic flow of the company rule revising process according to embodiment 7 is the same as that shown in Fig. 2.

In embodiment 7, the revised company rules generation unit 12 generates revised company rules and revision explanation information by inputting current company rules data, revision summary data, and explanation data into the artificial intelligence 20. The revision explanation information is information explaining the revised company rules to internal personnel related to the revised company rules. Examples of personnel include people in managerial positions.

The revised company rules generation step in embodiment 7 is a step in which the current company rules data, revision summary data, and explanatory data acquired in the data acquisition step are input into the artificial intelligence 20 to generate revised company rules and revised explanatory information.

Other configurations and methods in embodiment 7 are the same as those in embodiment 1.

This company rule revision device 10 and company rule revision method generates revision explanation information, making it easy for the person in charge to understand the content of the revised company rules. As a result, if the person in charge is, for example, a manager, the manager's approval for the revised company rules can be obtained quickly.

The company rule revision program of embodiment 7 is a program that causes a computer to execute the company rule revision method of embodiment 7.

Furthermore, the recording medium of embodiment 7 is a computer-readable recording medium that records a company rule revision program that causes a computer to execute the company rule revision method of embodiment 7.

Note that the above embodiments 1 to 7 may be implemented in any suitable combination.

For example, in embodiments 3 to 7, association rule data similar to that in embodiment 2 may be acquired and input to the artificial intelligence 20.

Furthermore, in embodiments 2 and 4 to 7, company information data similar to that in embodiment 3 may be acquired and input to the artificial intelligence 20.

In addition, in embodiments 2, 3, and 5 to 7, revised explanations may be generated in the same manner as in embodiment 4.

Also, in embodiments 2, 3, 4, 6, and 7, related department information may be generated in the same manner as in embodiment 5.

Furthermore, in embodiments 1 to 3 and 5 to 7, the acquisition and input of commentary data may be omitted.

Furthermore, in embodiments 1 to 7, the current company rules data acquired by the data acquisition unit 11 and the data acquisition step may include data on multiple company rules that do not have a hierarchical relationship. In this case, only revision summary data related to some of the multiple company rules may be acquired.

Examples of multiple internal company rules that do not have a hierarchical relationship include travel expense rules and housing allowance rules. In this case, only the revision summary data for the travel expense rules may be acquired and input into the artificial intelligence 20. In this case, for the housing allowance rules, a revision to the upper limit of housing subsidies can be proposed based on data on average rental prices for rental housing by region, similar to the revision of food and beverage expenses described in embodiment 1.

Furthermore, in embodiments 1 to 7, the current company rules data acquired by the data acquisition unit 11 and the data acquisition step may include all current company rules. In this case, the revised company rules generation unit 12 and the revised company rules generation step may generate revised company rules and relationship explanation information. The relationship explanation information is information that explains the relationship between the revised company rules and other company rules.

Here, Figure 3 is a block diagram showing a modified version of the company rule revision device 10 of embodiments 1 to 7. In this modified version, artificial intelligence 20 is provided in the company rule revision device 10.

In this way, in embodiments 1 to 7, the artificial intelligence 20 may be provided in the company rule revision device 10, and the same effects as in embodiments 1 to 7 can be obtained.

The functions of the company rule revision device 10 and the artificial intelligence 20 in embodiments 1 to 7 are realized by processing circuits. Figure 4 is a configuration diagram showing a first example of a processing circuit that realizes the functions of the company rule revision device 10 and the artificial intelligence 20 in embodiments 1 to 7. The processing circuit 100 in the first example is dedicated hardware.

Furthermore, the processing circuit 100 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or a combination of these. Furthermore, each function of the company rule revision device 10 and the artificial intelligence 20 may be realized by a separate processing circuit 100, or all functions may be realized together by the processing circuit 100.

Furthermore, Figure 5 is a configuration diagram showing a second example of a processing circuit that realizes the functions of the company rule revision device 10 and artificial intelligence 20 in embodiments 1 to 7. The processing circuit 200 of the second example includes a processor 201 and memory 202.

The processor 201 may be, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a microprocessor, a microcontroller, or a DSP (Digital Signal Processor).

In the processing circuit 200, each function of the company rule revision device 10 and the artificial intelligence 20 is realized by software, firmware, or a combination of software and firmware. The software and firmware are written as programs and stored in the memory 202. The processor 201 realizes each function by reading and executing the programs stored in the memory 202.

The programs stored in memory 202 can also be said to cause the computer to execute the procedures or methods of each of the above-mentioned components. Here, memory 202 refers to non-volatile or volatile semiconductor memory, such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), or EEPROM (Electrically Erasable and Programmable Read Only Memory). Magnetic disks, flexible disks, optical disks, compact disks, minidisks, DVDs, etc. also fall under memory 202.

Note that some of the functions of the above-mentioned components may be implemented using dedicated hardware, and some may be implemented using software or firmware.

In this way, the processing circuit can realize the functions of each of the above-mentioned parts through hardware, software, firmware, or a combination of these.

The above describes preferred embodiments in detail, but the present invention is not limited to the above-described embodiments, and various modifications and substitutions can be made to the above-described embodiments without departing from the scope of the claims.

The various aspects of this disclosure are summarized below as appendices.

(Appendix 1)
A company rule revision device comprising: a data acquisition unit that acquires current company rule data, which is data on the current company rules that are the subject of revision, and revision summary data, which is data that shows an outline of the revisions to the current company rules; and a revised company rule generation unit that generates revised company rules, which are the company rules after revision, by inputting the current company rule data and the revision summary data acquired by the data acquisition unit into artificial intelligence.
(Appendix 2)
The data acquisition unit further acquires commentary data that is data explaining the current company rules,
The company rules revision device described in Appendix 1, wherein the revised company rules generation unit generates the revised company rules by further inputting the explanatory data into the artificial intelligence.
(Appendix 3)
The company rule revision device described in Appendix 2, wherein the commentary data is data of article-by-article commentary for each article of the current company rules.
(Appendix 4)
The company rules revision device described in Appendix 2 or Appendix 3, wherein the revised company rules generation unit further generates a revised commentary, which is an explanation of the revised company rules, by further inputting the commentary data into the artificial intelligence.
(Appendix 5)
The data acquisition unit further acquires related rule data, which is data on rules that are not subject to revision and are related to the current company rules;
A company rule revision device described in any one of Appendix 1 to Appendix 4, wherein the revised company rule generation unit generates the revised company rule by further inputting the related rule data into the artificial intelligence.
(Appendix 6)
The data acquisition unit further acquires company information data, which is data on information of companies that have the current company rules;
The company rules revision device described in any one of Appendix 1 to Appendix 5, wherein the revised company rules generation unit generates the revised company rules by further inputting the company information data into the artificial intelligence.
(Appendix 7)
A company regulations revision device described in any one of Appendix 1 to Appendix 6, wherein the revised company regulations generation unit further generates related department information, which is information regarding internal departments related to the revised company regulations, by inputting the current company regulations data and the revision summary data into artificial intelligence.
(Appendix 8)
The revised company rules generation unit inputs the current company rules data and the revision summary data into artificial intelligence to generate multiple different company rules revision proposals as the revised company rules. A company rules revision device described in any one of Appendix 1 to Appendix 7.
(Appendix 9)
A company regulations revision device described in any one of Appendix 1 to Appendix 8, in which the revised company regulations generation unit further generates revision explanation information, which is information explaining the revised company regulations to internal personnel related to the revised company regulations, by inputting the current company regulations data and the revision summary data into artificial intelligence.
(Appendix 10)
A method for revising company rules, comprising: a data acquisition step of acquiring current company rules data, which is data on the current company rules to be revised, and revision summary data, which is data showing an outline of the revisions to the current company rules; and a revised company rules generation step of generating revised company rules, which are the company rules after revision, by inputting the current company rules data and the revision summary data acquired by the data acquisition step into artificial intelligence.
(Appendix 11)
A company rule revision program that causes a computer to execute the company rule revision method described in Appendix 10.
(Appendix 12)
A computer-readable recording medium having recorded thereon a company rule revision program that causes a computer to execute the company rule revision method described in Appendix 10.

10. Company rules revision device, 11. Data acquisition unit, 12. Revised company rules generation unit, 20. Artificial intelligence.

Claims (11)

a data acquisition unit that acquires current company rules data, which is data on the current company rules to be revised, and revision summary data, which is data that shows an outline of the revision content of the current company rules; and a revised company rules generation unit that generates revised company rules, which are the company rules after revision, by inputting the current company rules data and the revision summary data acquired by the data acquisition unit into an artificial intelligence ,
The data acquisition unit further acquires commentary data that is data explaining the current company rules,
The revised company rules generation unit is a company rules revision device that generates the revised company rules by further inputting the explanatory data into the artificial intelligence . 2. The company rule revision device according to claim 1 , wherein the commentary data is data of article-by-article commentary on each article of the current company rules. The company rule revision device according to claim 1 or claim 2 , wherein the revised company rule generation unit further generates a revised commentary that is a commentary of the revised company rule by further inputting the commentary data into the artificial intelligence. The data acquisition unit further acquires related rule data, which is data on rules that are not subject to revision and are related to the current company rules;
3. The company rule revising device according to claim 1 , wherein the revised company rule generating unit generates the revised company rule by further inputting the related rule data into the artificial intelligence. The data acquisition unit further acquires company information data, which is data on information of companies that have the current company rules;
3. The company rule revising device according to claim 1 , wherein the revised company rule generating unit generates the revised company rule by further inputting the company information data into the artificial intelligence. The company regulations revision device described in claim 1 or claim 2, wherein the revised company regulations generation unit further generates related department information, which is information regarding internal departments related to the revised company regulations, by inputting the current company regulations data and the revision summary data into artificial intelligence . The company rule revision device described in claim 1 or claim 2 , wherein the revised company rule generation unit generates multiple different company rule revision proposals as the revised company rules by inputting the current company rule data and the revision summary data into artificial intelligence. The company rules revision device described in claim 1 or claim 2, wherein the revised company rules generation unit further generates revision explanation information, which is information explaining the revised company rules to internal personnel related to the revised company rules, by inputting the current company rules data and the revision summary data into artificial intelligence . A company rule revising method including: a data acquisition step in which a data acquisition unit in a company rule revising device acquires current company rule data , which is data on the current company rule that is the subject of revision, revision summary data , which is data showing an outline of the revision contents to the current company rule , and commentary data, which is data explaining the current company rule; and a revised company rule generation step in which a revised company rule generation unit in the company rule revising device inputs the current company rule data, revision summary data, and commentary data acquired in the data acquisition step into artificial intelligence, thereby generating revised company rules, which are the company rules after revision. A company rule revision program that causes a computer to execute the company rule revision method according to claim 9 . A computer-readable recording medium having recorded thereon a company rule revision program for causing a computer to execute the company rule revision method according to claim 9 .