Business plan generation device, business plan generation method, and program

Abstract

To generate action plans that more accurately reflect instructions from higher management, and to more accurately execute those action plans. [Solution] The business plan generation device includes an inference unit that uses FAST AI to infer one action plan that an employee should take, or uses SLOW AI to infer another action plan that an employee should take after going through a series of interactive questions to the employee.

Description

Technical Field

[0001] The present disclosure relates to a business plan generation device, a business plan generation method, and a program.

Background Art

[0002] An operation support system described in Patent Document 1, etc. aims to enable support for the operation of an organization according to the management experience of each organization (for example, paragraph 0009 of Patent Document 1). In order to achieve the above object, the operation support system, etc. creates a confirmation form in which financial information and business environment analysis are arranged and displayed, and displays the items in the confirmation form so that they can be edited (for example, claim 1 of Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above-described operation support system, etc., for example, in a company, etc., there are problems such that it may be difficult to formulate an action plan reflecting the management policy of the management level, or it may be difficult to complete the action plan.

[0005] An object of the present disclosure is to accurately generate an action plan reflecting upper-level instructions as compared with the prior art, and to accurately realize the completion of the action plan as compared with the prior art.

Means for Solving the Problems

[0006] To solve the above-mentioned problems, the business plan generation device relating to this disclosure, in order to determine the action plans of multiple employees belonging to a company in stages from the top to the bottom of the company's hierarchical organization, comprises: (1) (1-1) a FAST AI that has in advance learned a first correspondence between a first instruction that an employee belonging to the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction; and (1-2) a SLOW AI that has in advance learned a second correspondence between the first instruction and a set of interactive questions to the employee to guide the employee to take other action plans in response to the instruction; and (2) receiving an actual second instruction from the other employee, (2-1) using the FAST AI to infer the action plan that the employee should take in response to the second instruction, or (2-2) using the SLOW AI The system includes an inference unit that uses AI to infer other action plans that the member should take, after going through a series of interactive questions to the member in response to the second instruction. [Effects of the Invention]

[0007] The business plan generation device described in this disclosure makes it possible to generate action plans that reflect instructions from higher management more accurately than before, and to realize the completion of action plans more accurately than before. [Brief explanation of the drawing]

[0008] [Figure 1] The configuration of the business plan generation system JSS in Embodiment 1 is shown. [Figure 2] The configuration of the business plan generation device JS of Embodiment 1 is shown. [Figure 3] The configuration of the processing unit SY(JS) of Embodiment 1 is shown. [Figure 4] This shows the operation of FAST AI in Embodiment 1. [Figure 5] This shows the operation of SLOW AI in Embodiment 1. [Figure 6] The configuration of terminal TM in Embodiment 1 is shown. [Figure 7]This shows the operation of the business plan generation system JSS of Embodiment 1. [Figure 8] The action plan AP of Section Manager KA in Embodiment 1 is shown. [Figure 9] This shows the sharing of the action plan AP of Section Manager KA in Embodiment 1 with President SY and others. [Figure 10] This demonstrates the operation of the modified business plan generation system JSS. [Figure 11] This demonstrates the operation of the business plan generation system JSS in Embodiment 2. [Figure 12] The hardware configuration of the business plan generation system JSS in Embodiments 1 and 2 is shown. [Figure 13] The hardware configuration based on the software implementation of the business plan generation system JSS in Embodiments 1 and 2 is shown. [Modes for carrying out the invention]

[0009] Embodiment 1 of the business plan generation system related to this disclosure will be described below.

[0010] <Embodiment 1> The business plan generation system JSS of Embodiment 1 will be described below.

[0011] <Configuration of Embodiment 1> Figure 1 shows the configuration of the business plan generation system JSS in Embodiment 1.

[0012] The business plan generation system JSS of Embodiment 1 includes a business plan generation device JS and a plurality of terminals TM, as shown in Figure 1. The business plan generation device JS and the plurality of terminals TM are interconnected via a network NW (e.g., the Internet), as shown in Figure 1.

[0013] As shown in FIG. 1, the business plan generation device JS generates an action plan KK for a subordinate SH (e.g., section chief KA) considering the instructions SJ of other superordinate subordinates SH (e.g., department head BU) for the subordinate SH (e.g., section chief KA) among multiple subordinates SH belonging to company KS, namely, the president SY, directors TO, department heads BU, section chiefs KA, and general employees IP.

[0014] Here, for convenience of explanation, the president SY and directors TO are defined as the management layer KE, while on the other hand, department heads BU, section chiefs KA, and general employees IP are defined as the on-site layer GE.

[0015] The action plan KK is a plan of actions that each subordinate SH should take throughout the year, and includes, without being limited to the appellation, the management policy KH of the president SY, the strategy SR of the directors TO, the measures SS of the department heads BU, the action plan AP of the section chiefs KA, etc. (e.g., shown in FIG. 7).

[0016] The action plan KK should be determined step by step from the upper layer to the lower layer of the hierarchical organization of company KS, that is, from the management layer KE to the on-site layer GE, and more specifically, in the order of president SY → directors TO → department heads BU → section chiefs KA.

[0017] As shown in FIG. 1, the multiple terminals TM are used by the president SY, directors TO, department heads BU, section chiefs KA, and general employees IP.

[0018] 〈Configuration of Business Plan Generation Device JS〉 FIG. 2 shows the configuration of the business plan generation device JS of Embodiment 1.

[0019] The business plan generation device JS of Embodiment 1 has, as shown in FIG. 2, an input / output unit NY(JS), a processing unit SY(JS), a storage unit KI(JS), and a communication unit TU(JS).

[0020] The input / output unit NY(JS) is used, for example, by the administrator (not shown) of the business plan generation device JS to perform input / output for monitoring and controlling the operation of the business plan generation device JS. The input / output unit NY(JS) is, for example, a keyboard, mouse, LCD monitor, or printer.

[0021] The processing unit SY(JS) receives, for example, instructions SJ from an employee (e.g., Section Chief KA) from another employee higher up in the hierarchy (e.g., Department Manager BU), and generates an action plan KK for the employee (e.g., Section Chief KA).

[0022] The memory unit KI(JS) stores, for example, the data necessary for processing by the processing unit SY(JS).

[0023] The communications unit TU(JS) communicates via the network NW. For example, the communications unit TU(JS) receives instructions SJ from another higher-level member SH (e.g., department head BU) to a member SH (e.g., section chief KA) from a terminal TM used by the member SH, and on the other hand, transmits an action plan KK for the member (e.g., section chief KA) to the member SH.

[0024] <Configuration of Processing Unit SY(JS)> Figure 3 shows the configuration of the processing unit SY(JS) of Embodiment 1.

[0025] The processing unit SY(JS) has a FAST AI and a SLOW AI, as shown in Figure 3. The FAST AI and SLOW AI receive, as shown in Figure 3, at least, for example, (1) instructions SJ for the current fiscal year (e.g., FY2024) given by another superior employee SH (e.g., Department Head BU) to an employee of company KS (e.g., Section Chief KA) (e.g., Department Head BU) (e.g., Policy SS from Department Head BU (illustrated in Figure 7)), and (2) a review FK of the execution of the action plan KK (e.g., Action Plan AP from Section Chief KA (illustrated in Figure 7)) of the aforementioned employee SH (e.g., Section Chief KA) for the previous fiscal year (e.g., FY2023).

[0026] On the other hand, FAST AI and SLOW AI infer at least the action plan KK for the current fiscal year (e.g., fiscal year 2024) of the aforementioned employee SH (e.g., Section Chief KA).

[0027] More specifically, FAST AI has pre-learned a first correspondence TK1 between an instruction SJ that an employee SH (e.g., Section Chief KA) of company KS might receive from another employee SH (e.g., Department Manager BU) in a higher position, and a single action plan KK (one) that the aforementioned employee SH (e.g., Section Chief KA) should take in response to that instruction SJ.

[0028] It is desirable that FAST AI has pre-trained the first correspondence TK1, including the retrospective FK described above.

[0029] SLOW AI has pre-learned a second correspondence TK2 between instructions SJ that an employee SH (e.g., section chief KA) of company KS might receive from another employee SH (e.g., department head BU) in a higher position, and a series of interactive questions SM asked to the employee SH (e.g., section chief KA) to guide them to take other action plans KK (other) in response to those instructions SJ.

[0030] <How FAST AI works> Figure 4 shows the operation of FAST AI in Embodiment 1.

[0031] As shown in Figure 4, the FAST AI of Embodiment 1, upon receiving input such as the above-mentioned instruction SJ, reflection FK (illustrated in Figure 3), and other statements such as "a sense of crisis is being fueled" and "they are unwilling to change their attitude," from the terminal TM of Section Chief KA of Company KS, infers the above-mentioned action plan KK (one) (also illustrated in Figure 3), and other statements such as "communication should be improved" and "a cooperative system should be established."

[0032] <How SLOW AI works> Figure 5 shows the operation of SLOW AI in Embodiment 1.

[0033] As shown in Figure 5, the SLOW AI of Embodiment 1 outputs, for example, several interactive questions SM (illustrated in Figure 3) to the terminal TM of Section Manager KA of Company KS, such as "What is the reason for the numerical target to shorten the construction period?", "What are the benefits of shortening the construction period?", etc., and from Section Manager KA's terminal TM, it obtains the following responses HT to the above-mentioned questions SM: "We are referring to the examples of our competitors," "Reduce overtime hours, and place greater emphasis on quality control." Through the above-mentioned exchange of questions SM and responses HT, the SLOW AI infers the following other action plans KK (other) (also illustrated in Figure 3): "The longest construction period should be shortened as the top priority," "We should consider introducing a BIM model," etc.

[0034] <Terminal™ Configuration> Figure 6 shows the configuration of terminal TM in Embodiment 1.

[0035] As shown in Figure 6, the terminal TM of Embodiment 1 includes an input / output unit NY(TM), a processing unit SY(TM), a storage unit KI(TM), and a communication unit TU(TM).

[0036] The input / output unit NY(TM) is used by SH, an employee of company KS, to use terminal TM. Examples of input / output units NY(TM) include keyboards, mice, LCD monitors, and printers.

[0037] The processing unit SY(TM) performs, for example, processing to request the business plan generation device JS to generate an action plan KK for employee SH of company KS.

[0038] The memory unit KI(TM) stores, for example, the data necessary for processing by the processing unit SY(TM).

[0039] The communications unit TU(TM) communicates via the network NW. For example, the communications unit TU(TM) sends instructions SJ for the current year given by a superior subordinate SH (e.g., department head BU) to the business plan generation device JS, and on the other hand, receives the action plan KK for the current year for subordinate SH (e.g., department head KA) from the business plan generation device JS.

[0040] <Correspondence> The processing unit SY(JS) corresponds to the "inference unit," the first correspondence TK1 corresponds to the "first correspondence," the second correspondence TK2 corresponds to the "second correspondence TK2," one action plan KK(one) corresponds to the "one action plan," and another action plan KK(other) corresponds to the "other action plan."

[0041] <Operation of Embodiment 1> Figure 7 shows the operation of the business plan generation system JSS in Embodiment 1.

[0042] The operation of the business plan generation system JSS in Embodiment 1 will be explained with reference to Figure 7.

[0043] Step ST10: As shown in Figure 7, President SY of company KS sends the following information from President SY's terminal TM to the business plan generation device JS: instruction SJ, review FK, and others (also shown in Figure 3): President SY's review FK, company KS's external environment information GKJ, and internal environment information NKJ.

[0044] In the business plan generation device JS, the FAST AI of the processing unit SY (JS) receives inputs of retrospective FK, external environment information GKJ, and internal environment information NKJ from President SY's terminal TM, performs a current situation analysis GB of company KS, and then infers President SY's management policy KH for this year, corresponding to the received retrospective FK, external environment information GKJ, and internal environment information NKJ. Here, the management policy KH is President SY's action plan KK.

[0045] Step ST11: As shown in Figure 7, Director TO of company KS transmits Director TO's review FK and the management policy KH, which is the instruction SJ for this year given by senior staff SH (President SY), from Director TO's terminal TM to the business plan generation device JS.

[0046] In the business plan generation device JS, the SLOW AI of the processing unit SY (JS) receives input from Director TO's terminal TM, namely Director TO's reflection FK and President SY's management policy KH. Based on this input, it infers Director TO's strategy SR for the current year, corresponding to Director TO's reflection FK and President SY's management policy KH, through a series of interactive questions SM to Director TO and multiple responses HT from Director TO (all illustrated, for example, in Figure 5). Here, the strategy SR is Director TO's action plan KK.

[0047] Step ST12: As shown in Figure 7, the department head BU of company KS transmits the strategic plan SR, which is the year's instructions SJ given by the senior staff member SH (Director TO), from the department head BU's terminal TM to the business plan generation device JS.

[0048] In the business plan generation device JS, the FAST AI of the processing unit SY(JS) receives input from the department head BU's terminal TM, specifically the department head BU's retrospective FK and the director TO's strategic SR, and infers the department head BU's measures SS for the current fiscal year, corresponding to the department head BU's retrospective FK and the director TO's strategic SR. Here, measures SS are the action plan KK for the department head BU.

[0049] Step ST13: As shown in Figure 7, Section Chief KA of Company KS sends Section Chief KA's reflection FK and the policy SS, which is the instruction SJ for this fiscal year given by the superior SH (Department Manager BU), from Section Chief KA's terminal TM to the business plan generation device JS.

[0050] In the business plan generation device JS, the SLOW AI of the processing unit SY(JS) receives input from the terminal TM of Section Chief KA, namely Section Chief KA's reflection FK and Department Head BU's policy SS. Based on this input, it infers Section Chief KA's action plan AP for this fiscal year, corresponding to Section Chief KA's reflection FK and Department Head BU's policy SS, through a series of interactive questions SM to Section Chief KA and multiple responses HT from Section Chief KA (all illustrated, for example, in Figure 5). Here, the action plan AP is Section Chief KA's action plan KK.

[0051] <Details of Action Plan AP> Figure 8 shows the action plan AP of Section Manager KA in Embodiment 1.

[0052] The action plan AP reasoned for Section Chief KA, as shown in Figure 8, includes (1) breaking down policy SS (directive SJ from Department Head BU) into steps and processes, (2) defining completion requirements for an action (defined in action plan AP) to be considered complete, (3) designating the person responsible for each action (e.g., person in charge, person implementing it), and (4) determining the goal (deadline) of the action through backcasting.

[0053] In particular, defining the "completion requirements" in (2) above is crucial for accurately managing the progress of an action plan (AP), for example.

[0054] <Sharing of Section Chief KA's action plan AP with President SY and others> Figure 9 shows the sharing of the action plan AP of Section Manager KA in Embodiment 1 with President SY and others.

[0055] In the business plan generation device JS, it is desirable that the communication unit TU (JS) transmits the action plan AP for section chief KA (for example, shown in Figure 7), which has been inferred by the processing unit SY (JS), to the terminals TM of, for example, President SY to Department Head BU, who are subordinates SH to section chief KA, so that President SY to Department Head BU can share section chief KA's action plan AP. This makes it possible for President SY to Department Head BU to verify whether the content of section chief KA's action plan AP matches the content of President SY's management policy KH, Director TO's strategy SR, and Department Head BU's measures SS.

[0056] <Effects of Embodiment 1> As described above, in the business plan generation system JSS of Embodiment 1, the FAST AI or SLOW AI in the processing unit SY(JS) of the business plan generation device JS refers to the pre-learned first correspondence relationship TK1 and the second correspondence relationship TK2 to generate the action plan KK for the current year of employee SH (e.g., section chief KA) of company KS, corresponding to the review FK of the execution of the action plan KK for the previous year of employee SH (e.g., section chief KA) and the instruction SJ from a superior employee SH (e.g., department manager BU). This makes it possible to generate the action plan KK (e.g., section chief KA's action plan AP) of employee SH (e.g., section chief KA) that reflects the instruction SJ (e.g., department manager BU's policy SS) from a superior employee SH (e.g., department manager BU) more accurately than in the past.

[0057] <Variations> Figure 10 shows the operation of a modified version of the business plan generation system JSS.

[0058] As described above, in the business plan generation device JS of Embodiment 1, in step ST10, FAST AI generates the action plan KK (management policy KH) of President SY, in step ST11, SLOW AI generates the action plan KK (strategy SR) of Director TO, in step ST12, FAST AI generates the action plan KK (measures SS) of Department Manager BU, and in step ST13, SLOW AI generates the action plan KK (action plan AP) of Section Manager KA.

[0059] Unlike the business plan generation device JS of Embodiment 1 described above, in the modified business plan generation system JSS, in step ST10, FAST AI or SLOW AI may generate the action plan KK (management policy KH) of President SY; in step ST11, FAST AI or SLOW AI may generate the action plan KK (strategy SR) of Director TO; in step ST12, FAST AI or SLOW AI may generate the action plan KK (measures SS) of Department Manager BU; and in step ST13, FAST AI or SLOW AI may generate the action plan KK (action plan AP) of Section Manager KA.

[0060] Whether FAST AI or SLOW AI should generate the action plan KK may be determined by, for example, the quality and quantity of the employee SH's (e.g., section chief KA's) review of the previous year (FK) and the instructions SJ for this year from higher-level employee SH (e.g., department head BU), and whether it is desirable for the AI ​​to respond to the employee SH's (e.g., section chief KA's) action plan KK for this year after exchanging multiple interactive questions SM and responses HT (both illustrated, for example, in Figure 5), that is, after careful consideration.

[0061] <Embodiment 2> The business plan generation system JSS of Embodiment 2 will be described.

[0062] <Configuration of Embodiment 2> The business plan generation system JSS of Embodiment 2 has a configuration similar to that of the business plan generation system JSS of Embodiment 1 (shown in Figures 1, 2, and 6).

[0063] <Operation of Embodiment 2> Figure 11 shows the operation of the business plan generation system JSS in Embodiment 2.

[0064] The operation of the business plan generation system JSS in Embodiment 2 will be explained with reference to Figure 11.

[0065] To facilitate explanation and understanding, we assume that Section Chief KA, who is one of the employees SH of Company KS, will execute the action plan AP (for example, shown in Figure 7) that was inferred for Section Chief KA in Embodiment 1.

[0066] In addition to the above, SLOW AI, (1A) Assume that the employee SH (e.g., Section Chief KA) has been pre-trained to infer alternative action plans KK (alternative action plans AP2, AP3) that should replace the employee SH's action plan KK (Action Plan AP), corresponding to the instructions SJ (e.g., policy SS) given to him by another employee SH (e.g., department head BU) above him for the current year, (1B) the employee SH's action plan KK (Action Plan AP) for the current year, and (1C) the progress SC of the action plan KK (Action Plan AP) in the short term (e.g., a quarter or half-year less than a year), through a series of interactive questions SM and responses HT with the employee SH (e.g., Section Chief KA).

[0067] Step ST20: Manager KA, in the same manner as in Step ST13 of Embodiment 1, uses his terminal TM to communicate with the business plan generation device JS, and more precisely, with the SLOW AI of the processing unit SY(JS), through which the SLOW AI infers the action plan AP1.

[0068] Here, Action Plan AP1, as shown in Figure 11, is the action plan KK that Section Chief KA should implement over the course of this fiscal year, i.e., fiscal year 2024.

[0069] Step ST21: Section Chief KA will implement Action Plan AP1 for a shorter period than the aforementioned one year, for example, a quarter (3 months).

[0070] Step ST22: Section Chief KA uses his terminal TM to send the policy SS from Department Manager BU, the content of Section Chief KA's action plan AP1, and the progress SC of action plan AP1 to the business plan generation device JS. In the business plan generation device JS, the SLOW AI of the processing unit SY (JS) receives the input of the policy SS, the content of action plan AP1, and the progress SC of action plan AP1, and, through a series of interactive questions SM and responses HT with Section Chief KA, infers an alternative action plan AP2 to replace action plan AP1 and proposes it to Section Chief KA.

[0071] Here, in the interactive series of questions (SM) and responses (HT), it is expected that the section chief (KA) will ask a question (HT) regarding the feasibility of action plan AP2 (for example, whether the completion requirements can be met by the goal (deadline) (as shown in Figure 8)). In that case, just as SLOW AI inferred action plan AP2 as an alternative to action plan AP1, it may also infer action plan AP3 as an alternative to action plan AP2.

[0072] Step ST23: Manager KA will implement action plan AP3, inferred by SLOW AI, over a period shorter than the aforementioned one year, for example, over a quarter.

[0073] From this point onward, Section Chief KA repeats steps ST22 and ST23.

[0074] <Effects of Embodiment 2> As described above, in the business plan generation system JSS of Embodiment 2, SLOW AI receives input such as progress SC for a shorter period (e.g., 3 months, 6 months) than the execution period of action plan AP1 for section chief KA (e.g., 1 year), and infers alternative action plan AP2 and further alternative action plan AP3 that are preferable to action plan AP1. By starting to execute action plan AP3 three months after action plan AP1 is inferred, section chief KA can increase the accuracy of completing department head BU's measures SS compared to continuing to execute action plan AP1 for a full year.

[0075] <Hardware configuration of Embodiments 1 and 2> Figure 12 shows the hardware configuration of the business plan generation system JSS in Embodiments 1 and 2.

[0076] The business plan generation system JSS of Embodiments 1 and 2 includes a processing circuit SYO, as shown in Figure 12, in order to perform the functions described above, and optionally further includes an input circuit NYU and an output circuit SYU.

[0077] The processing circuit SYO is dedicated hardware. The processing circuit SYO implements the functions of the processing unit SY(JS) of the business plan generation device JS and the processing unit SY(TM) of the terminal TM (shown in Figures 2 and 6).

[0078] The processing circuit SYO can 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.

[0079] The input circuit NYU and output circuit SYU exchange inputs and outputs related to the operation of the processing circuit SYO with, for example, the business plan generation device JS and the outside of the terminal TM.

[0080] <Hardware configuration based on software implementation of Embodiments 1 and 2> Figure 13 shows the hardware configuration based on the software implementation of the business plan generation system JSS in Embodiments 1 and 2.

[0081] The business plan generation system JSS of Embodiments 1 and 2 includes a processor PRO and a memory circuit KIO, as shown in Figure 13, and optionally further includes an input circuit NYU and an output circuit SYU.

[0082] Processor PRO is a CPU (Central Processing Unit, also known as a processing unit, arithmetic unit, microprocessor, microcomputer, or DSP (Digital Signal Processing)) that executes programs. Processor PRO implements the functions of the processing unit SY(JS) of the business plan generation device JS and the processing unit SY(TM) of the terminal TM (shown in Figures 2 and 6).

[0083] Processor PRO implements the above-mentioned functions through software, firmware, or a combination of software and firmware. The software and firmware are written as a program PRG and stored in the memory circuit KIO.

[0084] The processor PRO achieves the above-described functions by reading and executing the program PRG described above from the memory circuit KIO. The program PRG described above can also be said to cause the computer to execute the procedures and methods of the processing unit SY(JS) of the business plan generation device JS and the processing unit SY(TM) of the terminal TM.

[0085] Here, memory circuits (KIO) include, for example, non-volatile or volatile semiconductor memories such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), and EEPROM (Electrically Erasable Programmable Read-Only Memory), as well as magnetic disks, flexible disks, optical disks, compact disks, minidiscs, DVDs (Digital Versatile Discs), etc.

[0086] Some of the functions of the processing unit SY(JS) of the business plan generation device JS and the processing unit SY(TM) of the terminal TM may be implemented by the processing circuit SYO (shown in Figure 12), while other functions may be implemented by the processor PRO (shown in Figure 13).

[0087] As described above, the functions of the processing unit SY(JS) of the business plan generation device JS and the processing unit SY(TM) of the terminal TM can be realized by hardware, software, firmware, or a combination thereof.

[0088] The input circuit NYU and output circuit SYU exchange inputs and outputs related to the operation of the processor PRO with, for example, the business plan generation device JS and the terminal TM.

[0089] <Example of structure> The business plan generation device, business plan generation method, and program related to this disclosure have, for example, the following configuration.

[0090] [Item 1] A business plan generation device comprising: (1) (1-1) a FAST AI that has pre-learned a first correspondence between a first instruction that an employee of the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction; and (1-2) a SLOW AI that has pre-learned a second correspondence between the first instruction and a set of interactive questions to the employee to guide the employee toward other action plans that the employee should take in response to the instruction; and (2) an inference unit that receives an actual second instruction from the other employee, (2-1) uses the FAST AI to infer the first action plan that the employee should take in response to the second instruction, or (2-2) uses the SLOW AI to infer other action plans that the employee should take in response to the second instruction through the set of interactive questions to the employee.

[0091] [Item 2] The inference unit receives input from the member who has carried out the action plan for a period shorter than the planned execution period of the action plan, including (1) the second instruction from the other member, (2) the content of the first or second action plan to be carried out by the member, and (3) the progress of the first or second action plan during the short period. Using the SLOW AI, the unit infers an alternative action plan to replace the first or second action plan after going through a series of interactive questions to the member. The business plan generation device described in item 1.

[0092] [Item 3] A business plan generation method comprising: (1) (1-1) a FAST AI that has pre-learned a first correspondence between a first instruction that an employee of the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction; and (1-2) a SLOW AI that has pre-learned a second correspondence between the first instruction and a set of interactive questions to the employee to guide the employee toward other action plans that the employee should take in response to the instruction; and (2) receiving an actual second instruction from the other employee, (2-1) using the FAST AI to infer the first action plan that the employee should take in response to the second instruction, or (2-2) using the SLOW AI to infer other action plans that the employee should take in response to the second instruction through the set of interactive questions to the employee.

[0093] [Item 4] A program for a computer to perform an inference process in which it receives an actual second instruction from the other member, (2-1) uses the FAST AI to infer the first action plan that the member should take in response to the second instruction, or (2-2) uses the SLOW AI to infer the other action plan that the member should take in response to the second instruction, in order to determine the action plans of multiple members belonging to a company in a stepwise manner from the top to the bottom of the company's hierarchical organization, the computer having (1) (1-1) a first correspondence between a first instruction that the member may receive from another member in a higher position, and a first action plan that the member should take in response to the first instruction, and (1-2) a second correspondence between the first instruction and a set of interactive questions to the member to guide the member to take other action plans in response to the first instruction, and (2) receives an actual second instruction from the other member, (2-1) uses the FAST AI to infer the first action plan that the member should take in response to the second instruction, or (2-2) uses the SLOW AI to infer the other action plan that the member should take in response to the second instruction, through the set of interactive questions to the member. [Explanation of Symbols]

[0094] JSS Business Plan Generation System, JS Business Plan Generation Device, TM Terminal, NW Network.

Claims

1. In order to determine the action plans of multiple employees belonging to a company in a stepwise manner from the top to the bottom of the company's hierarchical organization, the system comprises: (1) (1-1) a FAST AI that has pre-learned a first correspondence between a first instruction that an employee belonging to the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction; and (1-2) a SLOW AI that has pre-learned a second correspondence between the first instruction and a series of interactive questions to the employee to guide the employee to take other action plans in response to the instruction; and (2) receiving an actual second instruction from the other employee, (2-1) using the FAST AI to infer the first action plan that the employee should take in response to the second instruction, or (2-2) the SLOW AI A business plan generation device including an inference unit that uses AI to infer other action plans that the employee should take, after the employee has answered a series of interactive questions in response to the second instruction.

2. The inference unit receives input from the member who has carried out the action plan for a period shorter than the planned execution period of the action plan, including (1) the second instruction from the other member, (2) the content of the first or other action plan to be carried out by the member, and (3) the progress of the first or other action plan during the short period. Using the SLOW AI, the unit infers an alternative action plan to replace the first or other action plan after asking the member a series of interactive questions. The business plan generation apparatus according to claim 1.

3. The computer, in order to determine the action plans of multiple employees belonging to a company in a stepwise manner from the top to the bottom of the company's hierarchical organization, comprises: (1) (1-1) a FAST AI that has pre-learned a first correspondence between a first instruction that an employee belonging to the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction; and (1-2) a SLOW AI that has pre-learned a second correspondence between the first instruction and a series of interactive questions to the employee to guide the employee to take other action plans in response to the instruction; and (2) receiving an actual second instruction from the other employee, (2-1) using the FAST AI to infer the first action plan that the employee should take in response to the second instruction, or (2-2) the SLOW AI A business plan generation method that uses AI to perform an inference step of inferring other action plans that the employee should take, after going through a series of interactive questions to the employee in response to the second instruction.

4. The computer comprises (1) (1-1) a FAST AI that has pre-learned a first correspondence between a first instruction that an employee of the company may receive from another employee in a higher position and an action plan that the employee should take in response to the first instruction, and (1-2) a SLOW AI that has pre-learned a second correspondence between the first instruction and a set of interactive questions to the employee to guide the employee to take other action plans in response to the instruction, and (2) receiving an actual second instruction from the other employee, (2-1) using the FAST AI to infer the first action plan that the employee should take in response to the second instruction, or (2-2) using the SLOW AI A program that uses AI to perform an inference process in which it infers other action plans that the member should take, after asking the member a series of interactive questions in response to the second instruction.

Images