Information processing device and information processing method

Abstract

An information processing device (10) comprises a reception unit (11) that receives generation request information for an individual manual that is a work manual that has been personalized to a user, an acquisition unit (12) that, in response to the reception of the generation request information for the individual manual, acquires information related to a work manual that pertains to the user, characteristic information for the user, and situation information, a determination unit (13) that determines a work risk level for the user on the basis of the acquired characteristic information and situation information, and an output unit (14) that, on the basis of the determined risk level, uses the information related to the work manual to output input text for a prompt for instructing generation of an individual manual for the user.

Description

Information processing device and information processing method

[0001] The present disclosure relates to an information processing device and an information processing method.

[0002] Responding to security measures is essential to protect your company and yourself from all kinds of incidents both inside and outside the company. For this reason, various business manuals, including security manuals, are prepared regarding security measures.

[0003] However, when it comes to security measures, the magnitude of the risk is thought to vary depending on the knowledge and behavioral characteristics of individuals. Therefore, in order to minimize and smooth out the risk, strict security rules are often put in place based on people who are prone to making mistakes. However, such strict security rules may be excessively restrictive for some individuals, and may actually impair work efficiency.

[0004] JP 2011-060288 A

[0005] Patent Document 1 describes a technology for providing an individualized security policy. However, although the technology in Patent Document 1 focuses on the characteristics of individual users, it does not focus on the situation of each individual user at that time (for example, level of fatigue, workload, whether in a hurry, etc.), and does not go so far as to consider assessing the level of risk taking into account the above-mentioned situation.

[0006] Meanwhile, in recent years, various types of content have been generated using generative artificial intelligence (AI) models. A generative AI model is a model that can generate content (generation results) in response to a prompt containing input information, according to any one or a combination of instructions, context, questions, and output formats indicated by the prompt, and return the generated content as response information.

[0007] Therefore, the present disclosure aims to utilize a generative AI model to obtain an individual manual that is appropriately personalized according to the user's characteristics and situation, thereby improving overall efficiency.

[0008] The information processing device of the present disclosure includes a reception unit that receives request information for generating an individual manual, which is a business manual personalized for a user; an acquisition unit that acquires information related to the business manual related to the user, characteristic information of the user, and situation information of the user in response to the reception of the request information for generating the individual manual; a determination unit that determines the degree of business risk for the user based on the acquired characteristic information and situation information; and an output unit that outputs input text to a prompt to instruct the generation of an individual manual for the user using information related to the business manual based on the determined degree of risk.

[0009] According to the present disclosure, by utilizing a generative AI model, it is possible to obtain an individual manual that is appropriately personalized according to the user's characteristics and situation, thereby improving overall efficiency.

[0010] FIG. 1 is a configuration diagram of an entire system including an information processing device. FIG. 2 is a flow diagram of processing executed by the information processing device. FIG. 3 is a flow diagram showing detailed content of a process for determining the degree of risk in business. FIG. 4 is a diagram for explaining machine learning of information related to incidents. FIG. 5 is a diagram showing a method for outputting precautions according to risk level. FIG. 6 is a diagram showing an example of an instruction sentence. FIG. 7 is a diagram showing another example system configuration. FIG. 8 is a diagram showing an example hardware configuration of an information processing device.

[0011] Hereinafter, an embodiment of an information processing device and an information processing method according to the present disclosure will be described with reference to the drawings. In the following embodiment, a form will be described in which a large language model (LLM) that is mainly used for text generation is used as an example of a generative AI model.

[0012] [Configuration of a system including an information processing device] Fig. 1 shows a configuration diagram of a system 1 including an information processing device 10 according to the present disclosure. As shown in Fig. 1, the system 1 includes an external server 30A on which a large-scale language model (LLM) 30 runs, the information processing device 10, and an in-house server 20A on which an in-house system runs. The in-house server 20A includes an in-house database (in-house DB) 20 that pre-stores various data to be processed in the in-house system (such as information related to business manuals, which will be described later), and a learning model 21 on which information related to incidents, which will be described later, has been machine-learned. The information processing device 10 can employ various information processing devices (such as a smartphone, a mobile phone, a smartwatch, a wearable device, a laptop computer, a desktop computer, or a server) as its hardware.

[0013] An RAG (Retrieval-Augmented Generation) system application is installed on the information processing device 10, and the RAG system operates. The RAG system is a type of prompt extension technology used for, for example, corporate information linkage between LLMs. Specifically, when a generation request is made to an LLM using an instruction statement (a sentence input to a prompt) based on a content generation request, the system searches for related information (reference information) in advance and requests the LLM to generate the obtained information along with the instruction statement. The present disclosure corresponds to an invention that extends and improves the functionality of the RAG system, and has an aspect of enriching and optimizing the instruction statement (a sentence input to a prompt) for the LLM 30 using information obtained from an internal DB 20 and a learning model 21 that cannot be accessed from an external server 30A.

[0014] The above-mentioned prompt refers to information indicating an instruction or question input to a generative AI model (LLM30 in this embodiment) in an interactive system such as a dialogue with the generative AI model or a command line interface (CLI), and corresponds to an "input sentence" input to the generative AI model. The prompt input to the LLM30 in this embodiment is text data including, for example, instructions to be executed by the LLM30, tasks to be executed by the LLM30, background / context to be considered by the LLM30 (e.g., roles, conditions), questions to be answered by the LLM30, and information indicating the output format of response information from the LLM30. Furthermore, input information to be used as the target of instructions / tasks to be executed by the LLM30 may be added to the prompt. Examples of such input information include data files with file names including a predetermined extension, such as text data, image data, application-related data, audio data, video data, and still image data. The above-mentioned application-related data refers to data such as document data, table data, and graph data that can be processed by a default application program.

[0015] In order to realize the functions according to the present disclosure, the information processing device 10 includes a reception unit 11, an acquisition unit 12, a determination unit 13, and an output unit 14. The function of each unit will be described below.

[0016] The reception unit 11 is a functional unit that receives generation request information for an individual manual, which is a business manual personalized for a user. The reception process may be performed by input by the user or by reception from another device. Alternatively, a predetermined button (e.g., an individual manual generation request button) may be provided, and the generation request information may be received when the button is operated (clicked).

[0017] The "information requesting the generation of an individual manual" refers to information for requesting the information processing device 10 to generate an individual manual, which is a business manual appropriately personalized according to the characteristics and circumstances of a certain user. For example, the information requesting the generation of an individual manual may include a directive related to the request for the generation of an individual manual, and may further include the "business manual name" used in acquiring information related to the (1) business manual related to the user, or the information related to the business manual itself. The information requesting the generation of an individual manual may also include at least one of the user's "user identification information (such as an employee ID)" used in acquiring the user's (2) characteristic information and (3) user situation information, or the (2) user characteristic information itself or the (3) user situation information itself. Specific examples of the (1) information related to the business manual, (2) user characteristic information, and (3) user situation information will be described later.

[0018] The acquisition unit 12 is a functional unit that acquires (1) information related to the business manual, (2) user characteristic information, and (3) user situation information related to the user in response to receiving request information for the generation of an individual manual.

[0019] The information about the business manual (1) above may be acquired from the in-house DB 20 by the acquisition unit 12 searching the in-house DB 20 using the name of the business manual entered by the user as a key. The information about the business manual may include, for example, information about a manual for preventing the erroneous sending of in-house emails, which includes various precautions such as: - Carefully check the address, subject, and text - Open attached files before sending.

[0020] The above (2) user characteristic information may be, for example, characteristic information of a person who is prone to making mistakes (for example, disorganized data in the PC, not taking measures to prevent mistakes, not taking notes, negative thinking, etc.), or may be information related to security (for example, security literacy level, information on security qualifications, etc.). Security information is used, for example, to change technical terms contained in individual manuals to corresponding general terms according to literacy level.

[0021] The above (3) user status information includes, for example, information on the user's level of fatigue, workload, whether the user is in a hurry, time elapsed since the start of work, etc. This information may be information input by the user or information obtained from work status data in the in-house DB 20, etc.

[0022] The determination unit 13 shown in FIG. 1 is a functional unit that determines the business risk level for a user based on the acquired user characteristic information and situation information. Here, "risk level" refers to the degree of likelihood that a user will cause a business incident. The following describes an embodiment in which the risk level is determined based on the frequency with which a user causes a business incident. The risk level may be expressed using multiple levels, such as "high," "medium," and "low," or may be a score expressed as a specific numerical value (e.g., a numerical value ranging from 0 to 1, a numerical value ranging from 0 to 100, etc.). Examples of incidents include various mistakes that can occur in the course of work, missed executions or communication, missed deadlines, and violations of company rules (especially security-related rules).

[0023] In this embodiment, the determination unit 13 obtains a score according to the frequency of incidents for a user using a learning model 21 that has been machine-learned based on information about incidents that have occurred in the course of work for each of various employee characteristics and situations. Examples of the "information about incidents that have occurred in the course of work for each of various employee characteristics and situations" include, for example, the details of the incident that occurred (such as data in a PC not being organized, not taking measures to prevent mistakes, etc.), the date and time of the incident, etc., for each "characteristic" and "situation (in this example, normal or abnormal)," as shown in FIG. 4 .

[0024] In the above machine learning, a large amount of information on various incidents as shown in Fig. 4 is input to the learning model 21, and the learning model 21 is made to find and learn patterns and regularities, etc., regarding how frequently incidents occur under what characteristics and circumstances from the input information on incidents. By inputting the characteristics and circumstances of a certain user into the learning model 21 through such machine learning, the learning model 21 is generated in advance so that it outputs a score (for example, a numerical value ranging from "0" indicating the lowest degree of occurrence frequency to "1" indicating the highest degree of occurrence frequency) according to the frequency of incidents corresponding to the characteristics and circumstances of the user.

[0025] The determination unit 13 inputs user characteristic information and situation information into the learning model 21 that has been machine-learned as described above, and thereby obtains a score corresponding to the frequency of incidents for the user, output from the learning model 21. The score obtained in this manner may be determined as the user's risk level. However, in this embodiment, the determination unit 13 determines a risk level corresponding to the risk level as the user's risk level based on predetermined criteria for classifying risk levels into multiple risk levels and the score obtained as described above. Details will be described in the processing description based on Figures 2 and 3. For example, when a score corresponding to the incident frequency is obtained as a value ranging from "0," which indicates the lowest occurrence frequency, to "1," which indicates the highest occurrence frequency, the following describes an example in which the risk level is determined to be "high," "medium," or "low" based on the result of comparing the score with predetermined criteria H (e.g., 0.7) for determining a "high" risk level and criteria L (e.g., 0.3) for determining a "low" risk level.

[0026] The output unit 14 is a functional unit that outputs an instruction sentence (an input sentence to a prompt) to the LLM 30 to instruct the generation of an individual manual for the user using information related to the business manual based on the determined risk level ("risk level" in this embodiment). In addition, the output unit 14 receives and outputs an individual manual personalized for the user as a generation result output from the LLM 30 in response to the input sentence to the prompt.

[0027] In this embodiment, the output unit 14 further includes in the input text an instruction to include in the individual manual a notice to the user that is to be output in an output method according to the risk level. As the "notes to the user," for example, the content of an incident that occurred in the past (for example, an omission of an addressee) may be output as a notice.

[0028] As shown in FIG. 5, regarding the output method according to the risk level, for example, in the case of a "high" risk level, the warnings may be detailed, for example, by describing detailed details of the impact of the incident (for example, details of damage suffered by the company (loss of credibility, slump in stock prices, etc.)). In addition, in the case of a "medium" risk level, some of the warnings may be highlighted in red, for example. In this case, the content to be highlighted may be specified in advance, or may be specified by the LLM 30 or the like based on past cases. By outputting warnings to users using an appropriate output method according to the risk level in this way, it is possible to prevent situations in which unnecessarily detailed warnings or warnings are displayed in an unnecessarily conspicuous manner for users with a low risk level.

[0029] Furthermore, the output unit 14 may include in the input text an instruction to include in the individual manual an instruction to control the frequency of checking the user's work based on the user's situation information. For example, when the user's situation is "abnormal" (when the user is very tired, when the workload is abnormally high, etc.), an instruction to include in the individual manual an instruction to increase the frequency of checking the user's work (i.e., increase the number of checkpoints) may be included. Controlling the frequency of checking the user's work based on the user's situation information in this way can prevent incidents from occurring.

[0030] [Regarding Processing Executed in Information Processing Device] Processing executed in the information processing device 10 (processing related to the information processing method of the present disclosure) will be described below with reference to the flow diagram of FIG. 2 .

[0031] 2 is triggered by the reception unit 11 receiving a request for generating an individual manual for a user. In response to the reception of the request for generating an individual manual, the acquisition unit 12 acquires (1) information related to the business manual, (2) user characteristic information, and (3) user situation information related to the user (step S1). The information related to the business manual (1) may be acquired from the in-house DB 20 by searching the in-house DB 20 using the name of the business manual entered by the user as a key. The information related to the user (2) user characteristic information and (3) user situation information may be acquired directly from the information entered by the user, or may be acquired from the in-house DB 20 by searching the in-house DB 20 using user identification information (such as an employee ID) entered by the user as a key.

[0032] Next, the determination unit 13 executes the process of determining the user's work-related risk level (FIG. 3) (step S2). As described above, this process uses the learning model 21, which is machine-learned based on information about work-related incidents (such as the content of the incident (failure to confirm the addressee), the date and time of the incident, etc.) for various employee characteristics and situations.

[0033] First, the determination unit 13 inputs the user's characteristic information and situation information into the learning model 21 (step S21 in FIG. 3 ). Then, the determination unit 13 obtains a score (e.g., a numerical value ranging from "0," which indicates the lowest occurrence frequency, to "1," which indicates the highest occurrence frequency) corresponding to the frequency of incidents for the user, output from the learning model 21 (step S22). The score thus obtained may be determined as the user's risk level. Here, the determination unit 13 determines the risk level as "high," "medium," or "low" according to the results of comparing the score with the aforementioned criterion H (e.g., 0.7) for determining a "high" risk level and the criterion L (e.g., 0.3) for determining a "low" risk level, which are used to classify risk levels into "high," "medium," and "low," as follows: If the score is equal to or greater than the criterion H (YES in step S23), the determination unit 13 determines the risk level to be "high" (step S24). Furthermore, if the score is less than the standard H (NO in step S23) and less than the standard L (YES in step S25), the determination unit 13 determines the risk level to be "low" (step S26). On the other hand, if the score is less than the standard H (NO in step S23) and is equal to or greater than the standard L (NO in step S25), the determination unit 13 determines the risk level to be "medium" (step S27). The risk level determined in this manner is passed to the output unit 14.

[0034] The above-mentioned standard H (e.g., 0.7) and standard L (e.g., 0.3) may be values ​​predetermined by the system designer or may be values ​​set by an operator of the information processing device 10. As an example of the score, another example may be adopted, such as an integer ranging from "0" indicating the lowest occurrence frequency to "100" indicating the highest occurrence frequency.

[0035] Returning to FIG. 2 , in the next step S3, the output unit 14 outputs an input sentence (an instruction sentence to the LLM 30) to a prompt for instructing the generation of an individual manual using information related to the business manual. For example, an instruction sentence (FIG. 6) containing multiple items such as a role, task, condition, input information, and example precautions may be as follows: "Role: You are a security officer. Task: Please correct the manual for mis-sent emails. Conditions: If the risk level is "medium," write the precautions in red. If the risk level is "high," display the precautions in detail... Input information: Manual for mis-sent emails - Check the recipients (To, Cc) - Check the email title... Example precautions: - The recipient is not entered - The email title is for a different matter..."

[0036] Thereafter, the LLM 30 outputs the individual manual (a manual personalized for the user) generated based on the input sentences as the generation result, and the output unit 14 acquires and outputs the generation result (individual manual) from the LLM 30 (step S4). This allows the user to check the contents of the individual manual personalized for them.

[0037] According to the embodiment described above, it is possible to obtain an individual manual that is appropriately personalized according to the characteristics and circumstances of the user by utilizing the generative AI model (LLM 30). As a result, it is possible to prevent an inconvenience such as a business manual based on uniform rules leading to excessive restrictions and impairing business efficiency, and to improve overall efficiency.

[0038] (Modification of System 1) System 1 is not limited to the configuration shown in Fig. 1 described above, and may have a configuration in which LLM 30 is implemented in information processing device 10, as shown in Fig. 7. This configuration can be realized by installing an application that executes the functions of LLM 30 in information processing device 10. Furthermore, although Figs. 1 and 7 show examples in which in-house DB 20 is implemented outside of information processing device 10 (for example, on a network), in-house DB 20 may also be implemented in information processing device 10.

[0039] The gist of the present disclosure lies in the following [1] to [7]. [1] An information processing device comprising: a reception unit that receives request information for generating an individual manual that is a business manual personalized for a user; an acquisition unit that acquires information related to the business manual related to the user, characteristic information of the user, and situation information of the user in response to the reception of the request information for generating the individual manual; a determination unit that determines a level of business risk for the user based on the acquired characteristic information and situation information; and an output unit that outputs an input sentence to a prompt for instructing the generation of an individual manual for the user using the information about the business manual based on the determined level of risk. [2] The information processing device described in [1], wherein the determination unit uses a learning model that has been machine-learned based on information about incidents that have occurred in the course of work for each of the characteristics and situations of various employees, and determines, in response to input of the characteristic information and situation information of the user, as the level of risk, a score corresponding to the frequency of occurrence of the incidents for the user, which is output from the learning model. [3] The information processing device according to [2], wherein the determination unit determines a risk level corresponding to the determined risk level based on predetermined criteria for classifying risk levels into a plurality of risk levels and the score. [4] The information processing device according to [3], wherein the output unit includes in the input text an instruction to include in the individual manual a message to the effect that notes for the user will be output using an output method corresponding to the risk level. [5] The information processing device according to any one of [1] to [4], wherein the output unit includes in the input text an instruction to include in the individual manual a message to control the frequency of checking the user's work based on the user's situation information. [6] The information processing device according to any one of [1] to [5], wherein the output unit receives and outputs the individual manual as a generation result output from a generative AI model in response to input of the input text to the prompt.[7] An information processing method comprising the steps of: an information processing device receiving request information for generating an individual manual, which is a business manual personalized for a user; the information processing device acquiring information about the business manual related to the user, characteristic information about the user, and situation information about the user in response to receiving the request information for generating the individual manual; the information processing device determining a business risk level for the user based on the acquired characteristic information and situation information; and the information processing device outputting an input sentence to a prompt for instructing the generation of an individual manual for the user using information about the business manual based on the determined risk level.

[0040] [Explanation of Terms, Explanation of Hardware Configuration (FIG. 8), etc.] The block diagrams used in the description of the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using a single device that is physically or logically coupled, or may be realized using two or more physically or logically separated devices that are directly or indirectly connected (e.g., wired, wireless, etc.) and these multiple devices. The functional block may be realized by combining the single device or multiple devices with software.

[0041] Functions include, but are not limited to, judgment, determination, assessment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, consideration, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs transmission is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on how these functions are implemented.

[0042] For example, an information processing device according to an embodiment of the present disclosure may function as a computer that executes the processes of the present disclosure. Fig. 8 is a diagram illustrating an example of a hardware configuration of an information processing device 10 according to an embodiment of the present disclosure. The information processing device 10 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, a bus 1007, etc.

[0043] In the following description, the term "apparatus" can be interpreted as a circuit, a device, a unit, etc. The hardware configuration of the information processing device 10 may be configured to include one or more of the apparatuses shown in the drawings, or may be configured to exclude some of the apparatuses.

[0044] Each function of the information processing device 10 is realized by loading specified software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communication via the communication device 1004, and control at least one of reading and writing data in the memory 1002 and storage 1003.

[0045] The processor 1001 controls the entire computer by running, for example, an operating system, and may be configured as a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, a register, etc.

[0046] The processor 1001 also reads programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes in accordance with these. The programs used are those that cause a computer to execute at least some of the operations described in the above-described embodiments. While the various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

[0047] The memory 1002 is a computer-readable recording medium and may be configured by, for example, at least one of a read-only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), a random access memory (RAM), etc. The memory 1002 may also be called a register, a cache, a main memory (primary storage device), etc. The memory 1002 can store executable programs (program codes), software modules, etc. for implementing a wireless communication method according to an embodiment of the present disclosure.

[0048] Storage 1003 is a computer-readable recording medium, and may be composed of at least one of, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, a magnetic strip, etc. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other appropriate medium including at least one of memory 1002 and storage 1003.

[0049] The communication device 1004 is hardware (transmission / reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as, for example, a network device, a network controller, a network card, a communication module, etc. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc. to realize at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0050] The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that receives input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that outputs to the outside. The input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

[0051] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

[0052] The information processing device 10 may also be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

[0053] The notification of information is not limited to the aspects / embodiments described in the present disclosure and may be performed using other methods. For example, the notification of information may be performed by physical layer signaling (e.g., Downlink Control Information (DCI) and Uplink Control Information (UCI)), higher layer signaling (e.g., Radio Resource Control (RRC) signaling, Medium Access Control (MAC) signaling, broadcast information (Master Information Block (MIB) and System Information Block (SIB))), other signals, or a combination thereof. Furthermore, the RRC signaling may be referred to as an RRC message, and may be, for example, an RRC Connection Setup message, an RRC Connection Reconfiguration message, or the like.

[0054] Each aspect / embodiment described in the present disclosure may be implemented using any of the following standards: LTE (Long Term Evolution), LTE-Advanced (LTE-A), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (x is, for example, an integer or a decimal number)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.17 (WiMAX (registered trademark)), IEEE 802.19 (WiMAX (registered trademark)), IEEE 802.20 (WiMAX (registered trademark)), IEEE 802.21 (Wi-Fi (registered trademark)), IEEE 802.22 (WiMAX (registered trademark)), IEEE 802.23 (WiMAX (registered trademark)), IEEE 802.24 (WiMAX (registered trademark)), IEEE 802.25 (WiMAX (registered trademark)), IEEE 802.26 (WiMAX (registered trademark)), IEEE 802.27 (WiMAX (registered trademark)), IEEE 802.28 (WiMAX (registered trademark)), IEEE 802.29 (WiMAX (registered trademark)), IEEE 802.30 (WiMAX (registered trademark)), IEEE 802.31 (Wi-Fi (registered trademark)), IEEE 802.32 (WiMAX (registered trademark)), IEEE 802.33 (WiMAX (registered trademark)), IEEE 802.34 ( The present invention may be applied to at least one of systems using 802.20, Ultra-Wideband (UWB), Bluetooth, or other suitable systems, and next-generation systems that are extended, modified, created, or defined based on these systems. It may also be applied to a combination of multiple systems (e.g., a combination of LTE and / or LTE-A with 5G).

[0055] The order of the procedures, sequences, flowcharts, etc. of each aspect / embodiment described in this disclosure may be changed unless it is consistent. For example, the methods described in this disclosure present elements of various steps using an example order, and are not limited to the particular order presented.

[0056] Input and output information may be stored in a specific location (for example, memory) or may be managed using a management table. Input and output information may be overwritten, updated, or added to. Output information may be deleted. Input information may be transmitted to another device.

[0057] The determination may be made based on a value represented by one bit (0 or 1), a Boolean value (true or false), or a numerical comparison (e.g., comparison with a predetermined value).

[0058] The aspects / embodiments described in this disclosure may be used alone, in combination, or switched depending on the implementation. Notification of predetermined information (e.g., notification that "X is true") is not limited to explicit notification, but may be implicit (e.g., not notifying the predetermined information).

[0059] Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended to be illustrative and does not have any limiting meaning on the present disclosure.

[0060] Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

[0061] Software, instructions, information, etc. may also be transmitted or received over a transmission medium. For example, if software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and / or wireless technologies (such as infrared, microwave), then these wired and / or wireless technologies are included within the definition of transmission media.

[0062] The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0063] Note that terms described in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of a channel and a symbol may be a signal (signaling). Furthermore, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, a cell, a frequency carrier, etc.

[0064] As used in this disclosure, the terms "system" and "network" are used interchangeably.

[0065] Furthermore, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values ​​from a predetermined value, or may be expressed using other corresponding information. For example, a radio resource may be indicated by an index.

[0066] The names used for the above-described parameters are not intended to be limiting in any way. Furthermore, the mathematical expressions using these parameters may differ from those explicitly disclosed in this disclosure. The various channels (e.g., PUCCH, PDCCH, etc.) and information elements may be identified by any suitable names, and therefore the various names assigned to these various channels and information elements are not intended to be limiting in any way.

[0067] As used in this disclosure, the terms "determining" and "determining" may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may also include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in memory), and the like. Furthermore, "judgment" and "decision" can include regarding resolving, selecting, choosing, establishing, comparing, etc. as having been "judged" or "decided." In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." Furthermore, "judgment (decision)" can be interpreted as "assuming," "expecting," "considering," etc.

[0068] As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

[0069] As used in this disclosure, any reference to an element using a designation such as "first," "second," etc. does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient method of distinguishing between two or more elements. Thus, a reference to a first and a second element does not imply that only two elements may be employed or that the first element must in some way precede the second element.

[0070] When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Furthermore, when the term "or" is used in this disclosure, it is not intended to be an exclusive or.

[0071] In this disclosure, where articles are added by translation, such as a, an, and the in English, the disclosure may include that the nouns following these articles are in the plural form.

[0072] In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "coupled" may also be interpreted in the same way as "different."

[0073] 1...system, 10...information processing device, 11...reception unit, 12...acquisition unit, 13...decision unit, 14...output unit, 20...in-house DB, 21...learning model, 20A...in-house server, 30...LLM, 30A...external server, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims

1. An information processing device comprising: a reception unit that receives request information for generating an individual manual, which is a business manual personalized for a user; an acquisition unit that acquires information about the business manual related to the user, characteristic information about the user, and situation information about the user in response to the reception of the request information for generating the individual manual; a determination unit that determines the degree of business risk for the user based on the acquired characteristic information and situation information; and an output unit that outputs input text to a prompt to instruct the generation of an individual manual for the user using information about the business manual based on the determined degree of risk.

2. The information processing device described in claim 1, wherein the determination unit uses a learning model that has been machine-learned based on information regarding incidents that occur in the course of work for various employee characteristics and situations, and determines the degree of risk as a score corresponding to the frequency of occurrence of the incidents for the user, output from the learning model, in response to input of the user's characteristic information and situation information.

3. The information processing device described in claim 2, wherein the determination unit determines a risk level corresponding to the determined risk degree based on predetermined criteria for classifying the risk degree into multiple risk levels and the score.

4. The information processing device described in claim 3, wherein the output unit includes in the input text an instruction to include in the individual manual a notice to the user that is to be output in an output method according to the risk level.

5. The information processing device according to claim 1, wherein the output unit includes in the input text an instruction to include in the individual manual an instruction to control the frequency of checking the user's work based on the user's status information.

6. The information processing device according to claim 1, wherein the output unit receives and outputs the individual manual as a generation result output from the generative AI model in response to input of the input sentence to the prompt.

7. An information processing method comprising: a step in which an information processing device receives request information for generating an individual manual, which is a business manual personalized for a user; a step in which the information processing device acquires information about the business manual related to the user, characteristic information about the user, and situation information about the user in response to receiving the request information for generating the individual manual; a step in which the information processing device determines a level of business risk for the user based on the acquired characteristic information and situation information; and a step in which the information processing device outputs an input sentence to a prompt for instructing the generation of an individual manual for the user using information about the business manual based on the determined level of risk.

Images