Information processing method, program, and information processing system

Abstract

Provided are an information processing method, program, and information processing system for obtaining answers from a trained model while reducing the input burden on the user. [Solution] In an information processing system including a server and a user terminal connected to each other so that they can communicate with each other via a network, an information processing method executed by the server includes an information acquisition step S102 for acquiring information stored in a storage unit, input information generation steps S104 to S112 for generating input information to be input to a trained model based on the information acquired in the information acquisition step, and input information output steps S112 to S116 for outputting the input information.

Description

[Technical field]

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

[0002] Cited Document 1 discloses an information processing method related to natural language processing, which outputs, in response to a question input by a user, question candidates corresponding to the question and options for the user to request output of other question candidates on a display screen, accepts a user's selection from the question candidates and options output on the display screen, and outputs an answer based on the selection. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-135135 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, in the case of the above-mentioned prior art, since the question input by the user is required, if the user does not think of asking a question, naturally no answer is obtained. In other words, even if an attempt is made to improve business efficiency by using answers using an information processing method, if the user does not think of asking a question in the first place, no answer using the information processing method is obtained, and in this respect there is room for improvement.

[0005] Taking into consideration the above-mentioned facts, the present invention aims to obtain answers from a trained model while reducing the input burden on the user. [Means for solving the problem]

[0006] According to one embodiment of an information processing method, the information processing method is executed by an information processing device, and includes an information acquisition step of acquiring information stored in a storage unit, an input information generation step of generating input information to be input to a trained model based on the information acquired in the information acquisition step, and an input information output step of outputting the input information.

[0007] According to a program of one embodiment, an information processing device is caused to execute an information processing method including an information acquisition step of acquiring information stored in a storage unit, an input information generation step of generating input information to be input to a trained model based on the information acquired in the information acquisition step, and an input information output step of outputting the input information.

[0008] According to one embodiment, the information processing system is executed by an information processing device, and includes an information acquisition unit that acquires information stored in a storage unit, an input information generation unit that generates input information to be input to a trained model based on the information acquired by the information acquisition unit, and an input information output unit that outputs the input information. Effect of the Invention

[0009] According to one embodiment, answers can be obtained from a trained model while reducing the input burden on the user. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of a configuration of an information processing system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of a server according to an embodiment. [Diagram 3] FIG. 2 is a diagram illustrating an example of a functional configuration of a server according to an embodiment. [Figure 4] FIG. 2 is a diagram illustrating an example of a processing flow of an information processing system according to an embodiment. [Diagram 5] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 6] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 7] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 8] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 9] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 10] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 11] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. [Figure 12] 11A and 11B are diagrams illustrating an example of an input and output screen of a user terminal according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] An embodiment of an information processing system according to the present invention will be described below with reference to Figures 1 to 12. In each drawing, the same or equivalent components and parts are given the same reference numerals. Also, the dimensional ratios in the drawings are exaggerated for the convenience of explanation and may differ from the actual ratios.

[0012] (System Overview) First, an overview of the information processing system 10 according to the present embodiment will be described. The information processing system 10 according to the present embodiment is an information processing system for obtaining a processing result using a trained model 222. The information processing system 10 obtains a processing result based on a prompt obtained by inputting a prompt as input information to a large-scale language model as a trained model 222 that has been trained with a large amount of text data from articles, books, websites, etc. on the Internet. The information processing system 10 is configured to generate at least a part of the prompt to be input to the trained model 222 based on information on the content that the user U wants to process, and present this to the user U to assist the input. Therefore, even if the user U cannot input an appropriate prompt for the processing result he or she wants to obtain, the trained model 222 can obtain the desired processing result.

[0013] (System Configuration) Fig. 1 is a diagram showing an example of a configuration of an information processing system 10 according to the present embodiment. As shown in Fig. 1, the information processing system 10 according to the present embodiment includes a server 12 and a user terminal 14, which are communicably connected to each other via a network N. The network N is, for example, a wired LAN (Local Area Network), a wireless LAN, the Internet, a public line network, a mobile data communication network, or a combination of these.

[0014] The user terminal 14 is an example of an information processing device that is operated by a user U to input and display various information. The user terminal 14 may be a PC (Personal Computer), a smartphone, a tablet terminal, a server device, a microcomputer, a wearable device, or a combination of these.

[0015] The server 12 is an example of an information processing device that acquires information input from the user terminal 14, processes the information, and outputs the results. The server 12 may be a PC (Personal Computer), a smartphone, a tablet terminal, a server device, a microcomputer, or a combination of these. The specific configuration and operation of the server 12 will be described later.

[0016] (Hardware configuration) 2 is a block diagram showing a hardware configuration of the server 12. The server 12 includes a processor 120, a memory 122, a storage 124, a communication I / F 126, an input / output I / F 128, and a drive device 134, which are communicatively connected to each other via a bus B.

[0017] The processor 120 controls each component of the server 12 and realizes the functions of the server 12 by expanding various programs stored in the storage 124 into the memory 122 and executing them. The programs executed by the processor 120 include, but are not limited to, an OS (Operating System) and a program 220 described below. The execution of these programs by the processor 120 realizes a part of the state visualization method according to this embodiment. The processor 120 is, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), a GPU (Graphics Processing Unit), an ASIC (Application Specific Integrated Circuit), a DSP (Digital Signal Processor), or a combination of these.

[0018] The memory 122 is, for example, a Read Only Memory (ROM), a Random Access Memory (RAM), or a combination thereof. The ROM is, for example, a Programmable ROM (PROM), an Erasable Programmable ROM (EPROM), an Electrically Erasable Programmable ROM (EEPROM), or a combination thereof. The RAM is, for example, a Dynamic RAM (DRAM), a Static RAM (SRAM), a Magnetoresistive RAM (MRAM), or a combination thereof.

[0019] The storage 124 stores an OS, various programs described below, and various data. The storage 124 is, for example, a flash memory, a hard disk drive (HDD), a solid state drive (SSD), a storage class memory (SCM), or a combination of these.

[0020] The communication I / F 126 is an interface for connecting the server 12 to external devices including the user terminal 14 and the image capturing device 16 via the network N and for controlling communication. The communication I / F 126 is, for example, an adapter conforming to Bluetooth (registered trademark), Wi-Fi (registered trademark), ZigBee (registered trademark), Ethernet (registered trademark), or optical communication (for example, Fibre Channel), but is not limited thereto.

[0021] The input / output I / F 128 is an interface for connecting an input device 132 and an output device 130 to the server 12. The input device 132 is, for example, a mouse, a keyboard, a touch panel, a microphone, a scanner, a camera, various sensors, an operation button, or a combination of these. The output device 130 is, for example, a display, a projector, a printer, a speaker, a vibrator, or a combination of these.

[0022] The drive device 134 reads and writes data from and to the disk medium 136. The drive device 134 is, for example, a magnetic disk drive, an optical disk drive, a magneto-optical disk drive, or a combination of these. The disk medium 109 is, for example, a compact disc (CD), a digital versatile disc (DVD), a floppy disk (FD), a magneto-optical disk (MO), a Blu-ray (registered trademark) disc (BD), or a combination of these.

[0023] In this embodiment, the program may be written into memory 122 or storage 124 during the manufacturing stage of server 12, or may be provided to server 12 via network N, or may be provided to server 12 via a non-transitory computer-readable recording medium such as disk media 136.

[0024] Moreover, the hardware configuration of the user terminal 14 is substantially the same as the hardware configuration of the server 12 described above, and therefore a detailed description thereof will be omitted.

[0025] (Functional configuration) Next, the functional configuration of the server 12 will be described. Fig. 3 is a diagram showing an example of the functional configuration of the server 12. When executing various programs, the server 12 realizes various functions using the above-mentioned hardware resources. The server 12 has a communication unit 20, a storage unit 22, and a control unit 24 as the functional configuration realized by the server 12. Each functional configuration is realized by the processor 120 reading and executing a program 220 stored in the memory 122 or the storage 124.

[0026] The communication unit 20 is realized by the communication I / F 126. The communication unit 20 transmits and receives information to and from the user terminal 14 via the network N. The communication unit 20 receives information input from the user terminal 14. In addition, the communication unit 20 transmits information to the user terminal 14, and receives a request by the user U from the user terminal 14.

[0027] The storage unit 22 is realized by the memory 122 and the storage 124. In the storage unit 22, a program 220, a trained model 222, a prompt DB 224, a user information DB 226, and a processing result DB 228 are stored.

[0028] The trained model 222 is composed of at least one trained machine learning model. As described above, the trained model 222 is, for example, a large-scale language model, which is trained with a large amount of text data from Internet articles, books, websites, etc. When text data called a prompt is input, the trained model 222 executes information processing according to the prompt, generates various data including the text data as a result, and outputs the data as a processing result RS.

[0029] In addition, when the trained model 222 receives information stored in the storage unit, it generates a prompt that matches the information. That is, the trained model 222 acquires information about the user U (details of which will be described later) stored in the user information DB 226 as part of the storage unit, and predicts the next action that the user U can take and the information that the user U will need from the information. Based on the result of this prediction, the trained model 222 generates a prompt for executing the required process or the process that the trained model 222 can support. Specifically, when information about the user U that "is a member of the intellectual property department" is acquired from the user information DB 226 as information about the user U, the trained model 222 predicts, based on the information, that the next action that the member of the intellectual property department can take is, for example, "prior art research on new business," "research on the patent application status of competitors," "confirmation of the rights status of the company's patent," "translation for a foreign patent application," "communication with a patent office," and "preparation for an inventor interview." Then, as an example, a prompt is generated saying "Please investigate and summarize the latest patent information and technology trends in the XX field" in order to perform a prior art search in the field of the new business using the trained model 222 from the inferred result of "prior art search on new business". In addition, the "XX field..." in the above-mentioned prompt may be left blank or a placeholder so that the user U can input further information about which field it is. This further information input by the user U corresponds to the "additional input information" described in claim 6 (see AD in FIG. 11, the two-dot chain line indicating the template TP in the figure is added for explanation and is not actually displayed. In addition, the additional input information AD is text data other than the template TP). In other words, the trained model 222 is capable of generating not only all of the prompts but also at least a part of the prompts that leave room for inputting additional information, and at least a part of the prompt corresponds to the template TP of the prompt (see FIG. 10). The template TP in which the additional input information AD is input corresponds to the prompt.

[0030] In addition, the specific example described above is configured to estimate the next action that the user U may take and the information that the user U will need from the information of the user U stored in the user information DB 226 as part of the storage unit, but is not limited to this. The next action that the user U may take and the information that the user U will need may be estimated from information in a database as part of the storage unit in which various information linked to other applications such as a calendar application, an office work application, and a customer management system is stored. That is, at least one of the next action that the user U may take and the information that the user U will need may be estimated based on the schedule information of the user U linked to the calendar application. Specifically, when schedule information related to a meeting is acquired from the schedule information, the next action that the user U may take from the meeting and the information that the user U will need, such as "creating minutes," "researching the agenda," "sending an email to contact attendees," and "latest information on legal amendments related to the agenda," are estimated, and at least a part of the prompts for performing processing that the trained model 222 can support for each estimation result is generated. In addition to this, at least one of the next action that the user U may take and the information that the user U will need may be estimated based on business negotiation information linked to the customer management system. Specifically, when business negotiation information is acquired, the next action that user U can take and information that user U will require are inferred from the information, such as "send a follow-up call or email," "conduct a demo or presentation," "prepare and review a contract," "analyze customer needs and readjust proposals," "basic customer information," "history of past interactions," "current stage of business negotiations," "detailed business negotiation information," "competitor information," and "customer decision-making process." Then, at least a part of a prompt is generated for performing a process that can be supported by the trained model 222 for each inference result (for example, a process that can be supported for the inference result "send a follow-up call or email" is "create a draft email," etc.).

[0031] The user information 226 is a database that stores various information of the user U who has registered to use the information processing system 10. Specific examples of the various information of the user U include user identification information for unique identification, user preference information such as interest information, user history information including past usage, and user attribute information such as occupation and job title.

[0032] The processing result DB 228 is a database that stores information indicating the results of executing a process based on a prompt (processing result RS). The processing result RS is various information including text information and image information, and the processing result DB 228 stores the processing result RS, the prompt, and information acquired from the storage unit in a mutually linked manner.

[0033] The control unit 24 is realized by the processor 120 reading and executing the program 220 from the memory 122 (see FIG. 2) and cooperating with other hardware configurations. The control unit 24 includes an information acquisition unit 242, an information processing unit 244, and an output unit 246.

[0034] The information acquisition unit 242 acquires various information required for processing in the information processing unit 244. Specifically, it acquires information stored in a storage unit including the memory unit 22 and a database connected to an external communication-enabled device, information on the contents that the user U wants to process, and additional input information. In this embodiment, the information on the contents that the user U wants to process is information selected by the user U from among the category information of processing displayed on the screen (see FIG. 5, which corresponds to "Suggest how to use the generated AI based on your calendar", "Suggest how to use the generated AI based on linked customer management software", "Suggest how to use the generated AI based on the most recently used file", and "Suggest how to use the generated AI based on email / message") (see FIG. 6, which corresponds to "Suggest how to use the generated AI based on your calendar" selected by "you", that is, "information on the contents that the user U wants to process"). Note that the information on the contents that the user U wants to process is not limited to this, and may be information specified by text data, or information from various sensors such as voice input, gestures, hardware operations, eye tracking, and biosensors.

[0035] To acquire additional information, the information acquisition unit 242 displays a prompt template TP having blanks or placeholders (blanks in the example of FIG. 10) as shown in Fig. 10, and acquires additional input information AD (see Fig. 11) input by the user U to fill in the blanks in the template TP. Note that the additional input information AD is not limited to text data, and may be information such as images, videos, sounds, and UI operation results.

[0036] The information processing unit 244 performs information processing using the trained model 222. Specifically, as described above, various information stored in the storage unit is acquired, and from the information, the next action (movement) that a subject related to the information, such as the user U, etc., can take and information that the user U needs are predicted. Furthermore, based on the above-mentioned prediction result, the information processing unit 244 generates a prompt or a prompt template TP for the trained model 222 to execute the necessary processing or the processing that can be supported. In other words, the information processing unit 244 corresponds to the "input information generating unit" recited in claim 9. Furthermore, the information processing unit 244 executes information related to the content that the user U wants to process and information processing according to the prompt, and generates various data including text data as a result, and outputs the data as a processing result RS.

[0037] The output unit 248 controls so as to output the processing result of the information processing unit 244 to the user terminal 14. In other words, the information processing unit 244 corresponds to the "input information output unit" recited in claim 9.

[0038] (Processing Executed by Information Processing System 10) Next, the operation of the information processing system 10 will be described. Fig. 4 is a flow chart showing an example of the flow of processing by the information processing system 10. The processor 120 reads out the program 220 stored in the storage 124, expands it in the memory 122, and executes it, thereby performing processing. Note that when the processor 120 receives operation information for terminating the operation of the information processing system 10, or information on the end of an operation from the user terminal 14 during the determination processing being executed (these are simply referred to as "termination operation"), the processor 120 ends the processing based on the program 220 being processed.

[0039] The processor 120 outputs information regarding the content that the user U wants to process that can be processed by the server 12 to be displayed on the user terminal 14 (see Figure 5), and also acquires input information regarding the content that the user U wants to process (step S100, see Figure 6).

[0040] The processor 120 acquires information from the storage unit (step S102) and analyzes the acquired information (step S104). The processor 120 then determines whether or not the next possible action of the subject related to the information can be inferred from the analysis result (step S106). If the next possible action cannot be inferred (step S106: NO), the processor 120 moves the process to step S110 described later. On the other hand, if the next possible action can be inferred (step S106: YES), the processor 120 outputs the inference result (the inference results correspond to "research" in FIG. 7, "draft email" in FIG. 8, and "write minutes" in FIG. 9), and generates a prompt or template TP based on the next possible action selected by the user U from the inference results (for example, by operating the "use generation AI with this method" button in FIG. 7-FIG. 9) (step S108). The process of step S102 described above corresponds to the "information acquisition step" recited in claim 1.

[0041] The processor 120 determines whether or not other necessary information can be inferred from the results of the analysis in step S104 (step S110). If the necessary information cannot be inferred (step S110: NO), the processor 120 moves the process to step S114, which will be described later. On the other hand, if the necessary information can be inferred (step S110: YES), the processor 120 generates a prompt or template TP related to the necessary information (acquisition, assistance, etc.) (step S112). The processes from step S104 to step S112 described above correspond to the "input information generating step" recited in claim 1.

[0042] The processor 120 determines whether or not a prompt or template TP has been generated (step S114). If not (step S114: NO), the processor 120 transitions to step S100. On the other hand, if generated (step S114: YES), the processor 120 outputs the prompt or template TP to be displayed on the user terminal 14 (step S116), and also outputs an additional input information screen IE (see FIG. 10) to be displayed on the user terminal 14 (step S118). The processes from step S112 to step S116 correspond to the "input information output step" recited in claim 1.

[0043] The processor 120 determines whether or not additional input information AD has been input by the user U and acquired (step S120). If additional input information AD has not been acquired (step S120: NO), the processor 120 inputs a prompt or template TP to the trained model 222 in the absence of additional input information AD, performs processing (step S126), and proceeds to step S124. On the other hand, if additional input information AD has been acquired (step S120: YES), the processor 120 inputs the prompt or template TP, including the additional input information AD, to the trained model 222, and performs processing (step S122).

[0044] The processor 120 acquires the processing result RS (step S124), and outputs the processing result RS to the user terminal 14 for display (step S128, see FIG. 12). The processor 120 then determines whether or not an instruction to end the processing based on the program 220 has been given (step S130). If no instruction has been given (step S130: NO), the processor 120 transitions to step S100. On the other hand, if an instruction has been given (step S130: YES), the processor 120 ends the processing based on the program 220. The processes from step S120 to step S126 described above correspond to the "processing steps" recited in claim 7.

[0045] (Effects of one embodiment) According to the information processing system 10 of the present embodiment, since it has an information acquisition step of acquiring information stored in a storage unit, an input information generation step of generating at least a part of a prompt to be input to the trained model 222 based on the information acquired in the information acquisition step, and an input information output step of outputting a prompt, the user U can use a prompt suited to the situation or state of the user U without having to think about it from scratch. This allows the user U to obtain an answer from the trained model while reducing the input burden on the user U.

[0046] In addition, the input information generating step infers the next action from the information acquired in the information acquiring step, and generates at least a part of the prompt from the inference result, so that the user U can use a prompt that is more suited to the situation or state of the user U without having to think about it from scratch. This allows the user U to obtain an answer from the trained model 222 while further reducing the input burden on the user U.

[0047] Furthermore, the input information generating step infers necessary information from the information acquired in the information acquiring step and generates at least a part of the prompt from the inferred result, so that the user U can use a prompt suited to the situation or state of the user U without having to think about it from scratch. This allows the user U to obtain an answer from the trained model 222 while further reducing the input burden on the user U.

[0048] Furthermore, the input information generating step estimates at least one of the next action and the required information based on the schedule information stored in the storage unit, and generates at least a part of the prompt from the estimation result, so that the user U can use a prompt that more specifically matches the situation or state of the user U without having to think about it from scratch. This allows the user U to obtain an answer from the trained model 222 while further reducing the input burden on the user U.

[0049] Furthermore, the input information generation step estimates at least one of the next action and the required information based on the business negotiation information stored in the storage unit, and generates at least a part of the prompt from the estimation result, so that the user U can use a prompt that more specifically matches the situation or state of the user U without having to think about it from scratch. This allows the user U to obtain an answer from the trained model 222 while further reducing the input burden on the user U.

[0050] Furthermore, the input information generating step presents at least a part of the generated prompt and acquires additional input information AD to be input to the trained model 222, and the input information outputting step outputs at least a part of the prompt and the additional input information AD, so that the user U etc. can instruct the trained model 222 on processing that is more suited to his / her own situation or condition. This makes it easier for the user U etc. to obtain the processing result he / she desires.

[0051] Furthermore, since the method includes a processing step of acquiring the processing results by inputting the output results from the input information output step into a trained model, the answer obtained from the trained model can be used.

[0052] In the above embodiment, the next action that the user U may take and the information that the user U needs are estimated based on various information linked to the calendar application and the customer management system application. However, the present invention is not limited to this, and the next action that the user U may take and the information that the user U needs may be estimated based on various information linked to other applications such as an office work application, an email application, and a message application. Specifically, in the case of various information linked to an office work application for document creation, spreadsheets, and presentations, "document creation and correction," "data analysis and reporting," "creation and implementation of presentations," "schedule management and task management," "collaboration and feedback collection," "resource planning and management," "basic information on the file," "metadata on the contents of the file," "usage history and access history," "related files and materials," "instructions and feedback," and "templates and standard formats" are estimated as the next action that the user U may take and the information that the user U needs. Then, at least a part of the prompts for performing processing that the trained model 222 can support for each estimation result is generated. Specifically, a process of supporting proofreading of the document is performed for the estimation result of "document creation and correction." Specifically, typos and grammatical errors are pointed out in the document provided by the user. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please proofread the document (specified by the user as additional input information). Please point out any typos and grammatical errors."

[0053] In addition, for the estimation results of "data analysis and reporting", a process is performed to support the creation of a pivot table of data, for example. Specifically, a pivot table that displays a monthly summary of sales is created based on a data set provided by the user. An example of a prompt (including a template TP) generated in the input information generation step to execute this process is "Please create a pivot table that displays a monthly summary of sales from the data set (specified by the user as additional input information)."

[0054] Furthermore, for the estimated result of "creating and implementing a presentation", a process is performed to support improvement of slide design, for example. Specifically, the process provides improvement measures to make the presentation materials provided by the user more visually attractive. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "How can I make the presentation slides (specified by the user as additional input information) more visually attractive?"

[0055] Furthermore, for the estimation result of "schedule management and task management", a process is performed to support the creation of a task list and the setting of a reminder, for example. Specifically, a list for managing the progress, deadlines, and resources of a task is created based on a project plan provided by the user, and a reminder is set. An example of a prompt (including a template TP) generated in the input information generation step to execute this process is "Please create a task list for the project (specified by the user as additional input information) and the deadlines for each task using a spreadsheet software, and set a reminder."

[0056] In addition, for the estimated result of "joint editing and feedback collection", a process is performed to support the setting of an online document for joint editing, as an example. Specifically, the user saves the document file in a storage unit, generates a link for joint editing, and shares it with the team. An example of a prompt (including a template TP) generated in the input information generation step to execute this process is "Save the document file (specified by the user as additional input information) in the storage unit, create a link for joint editing, and share it with the team."

[0057] Furthermore, for the estimation results of "resource planning and management", a process is performed to support the creation of a resource allocation table, for example. Specifically, a resource (personnel and budget) allocation table is created using a spreadsheet software based on the project plan provided by the user. An example of a prompt (including a template TP) generated in the input information generation step to execute this process is "Please create a resource (personnel and budget) allocation table using a spreadsheet software based on the project plan (specified by the user as additional input information)."

[0058] Furthermore, as an example, a process is performed to assist in the extraction of file metadata from the estimation result of "basic file information." Specifically, basic information such as file name, creation date, and last update date for all files in the folder is listed. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please list the file name, creation date, and last update date for all files in the folder (specified by the user as additional input information)."

[0059] In addition, for the estimation result of "metadata related to the contents of the file", a process is performed to assist in summarizing the contents of the document, for example. Specifically, the main sections and their contents are summarized based on the document file provided by the user. An example of a prompt (including the template TP) generated in the input information generation step to perform this process is "Please summarize the main sections and contents of the document file (specified by the user as additional input information)."

[0060] Furthermore, for the estimated results of "usage history and access history", a process is performed to assist in checking the editing history of a file, for example. Specifically, the editing history of a sheet in a specific spreadsheet is displayed, and it is possible to check who changed what data and when. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please display the editing history of the sheet in the spreadsheet (specified by the user as additional input information) and tell me who changed what data and when."

[0061] Furthermore, for the estimation result of "related files and materials," a process is performed to support the creation of a link collection of related materials, for example. Specifically, a link collection of files and materials related to the project is created. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please create a link collection of files and materials related to the project (specified by the user as additional input information)."

[0062] In addition, for the estimated result of "instructions and feedback," a process is performed to support the collection and organization of feedback, as an example. Specifically, feedback on the report is collected from the team, and the main points are organized and compiled into a list. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please collect feedback on the report (specified by the user as additional input information) from the team, organize the main points, and compile them into a list."

[0063] Furthermore, for the inferred result of "template or standard format," a process is performed to support customization of the template, as an example. Specifically, a standard proposal template is used to create a customized version for future projects. An example of a prompt (including template TP) generated in the input information generation step to execute this process is "Please use the proposal template (specified by the user as additional input information) to create a customized version for future projects." Although several specific examples have been given above, the inferred result, the process that can be supported, and the prompt (including template TP) are not limited to these.

[0064] <Additional Notes> The present embodiment includes the following disclosure.

[0065] (Appendix 1) An information processing method executed by an information processing device, An information acquisition step of acquiring information stored in the storage unit; an input information generation step of generating at least a portion of input information to be input to the trained model based on the information acquired in the information acquisition step; an input information output step of outputting the input information; An information processing method comprising:

[0066] (Appendix 2) The input information generating step predicts a next action from the information acquired in the information acquiring step, and generates at least a part of the input information from a result of the prediction. 2. An information processing method as described in Appendix 1.

[0067] (Appendix 3) The input information generating step infers necessary information from the information acquired in the information acquiring step, and generates at least a part of the input information from an inference result. 10. The information processing method according to claim 1 or 2.

[0068] (Appendix 4) The input information generating step estimates at least one of a next action and necessary information based on the schedule information stored in a storage unit, and generates at least a part of the input information from an estimation result. An information processing method according to any one of Supplementary Note 1 to Supplementary Note 3.

[0069] (Appendix 5) The input information generating step estimates at least one of a next action and necessary information based on the business negotiation information stored in a storage unit, and generates at least a part of the input information from an estimation result. An information processing method according to any one of Supplementary Note 1 to Supplementary Note 4.

[0070] (Appendix 6) The input information generating step includes presenting at least a portion of the generated input information and acquiring additional input information to be input to the trained model; The input information output step outputs at least a part of the input information and the additional input information. An information processing method according to any one of Supplementary Note 1 to Supplementary Note 5.

[0071] (Appendix 7) A processing step of acquiring a processing result by inputting an output result in the input information output step into the trained model, An information processing method according to any one of Supplementary Notes 1 to 6.

[0072] (Appendix 8) In the information processing device, An information acquisition step of acquiring information stored in the storage unit; an input information generation step of generating at least a portion of input information to be input to the trained model based on the information acquired in the information acquisition step; an input information output step of outputting the input information; A program for executing an information processing method including the steps of:

[0073] (Appendix 9) An information processing system executed by an information processing device, an information acquisition unit that acquires information stored in the storage unit; an input information generation unit that generates at least a portion of input information to be input to a trained model based on the information acquired by the information acquisition unit; an input information output unit that outputs the input information; An information processing system having the above configuration.

[0074] The embodiments disclosed herein are illustrative in all respects and should not be considered as limiting. The scope of the present invention is indicated by the claims, not by the above meaning, and is intended to include all modifications within the meaning and scope of the claims. The present invention is not limited to the above-mentioned embodiments, and various modifications are possible within the scope of the claims, and embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of the present invention. [Explanation of symbols]

[0075] 10. Information Processing Systems 220 Programs 222 trained models 242 Information Acquisition Department TP template (at least part of the input information) AD Additional input information RS Processing Result

Claims

1. An information processing method executed by an information processing device, An information acquisition step of acquiring user information stored in a storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. Information processing methods.

2. An information processing method executed by an information processing device, An information acquisition step of acquiring schedule information stored in a storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. Information processing methods.

3. An information processing method executed by an information processing device, An information acquisition step of acquiring business negotiation information stored in the storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. Information processing methods.

4. The input information generating step includes presenting at least a portion of the generated input information and acquiring additional input information to be input to the trained model; The input information output step outputs at least a part of the input information and the additional input information.

3. The information processing method according to claim 1.

5. The input information generating step includes presenting at least a portion of the generated input information and acquiring additional input information to be input to the trained model; The input information output step outputs at least a part of the input information and the additional input information. The information processing method according to claim 3.

6. A processing step of acquiring a processing result by inputting an output result in the input information output step into the trained model, 3. The information processing method according to claim 1.

7. A processing step of acquiring a processing result by inputting an output result in the input information output step into the trained model, The information processing method according to claim 3.

8. In the information processing device, An information acquisition step of acquiring user information stored in a storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. A program for executing an information processing method.

9. In the information processing device, An information acquisition step of acquiring schedule information stored in a storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. A program for executing an information processing method.

10. In the information processing device, An information acquisition step of acquiring business negotiation information stored in the storage unit; an input information generation step of predicting at least one of the next action that the user may take and information that the user will need based on the information acquired in the information acquisition step, and generating at least a part of input information to be input to the trained model so that the trained model outputs a processing result required for the prediction result or a processing result that leads to support; an input information output step of outputting the input information; Including, The estimation and generation process of the input information generation step is performed using a trained model. A program for executing an information processing method.

11. An information processing system executed by an information processing device, an information acquisition unit that acquires user information stored in the storage unit; an input information generation unit that predicts at least one of the next action that the user may take and information that the user will need based on the information acquired by the information acquisition unit, and generates at least a part of input information to be input to the trained model so that the trained model outputs a processing result that is required for the prediction result or a processing result that leads to support; an input information output unit that outputs the input information; having The estimation and generation process of the input information generation unit is performed using a trained model. Information processing system.

12. An information processing system executed by an information processing device, an information acquisition unit that acquires the schedule information stored in the storage unit; an input information generation unit that predicts at least one of the next action that the user may take and information that the user will need based on the information acquired by the information acquisition unit, and generates at least a part of input information to be input to the trained model so that the trained model outputs a processing result that is required for the prediction result or a processing result that leads to support; an input information output unit that outputs the input information; having The estimation and generation process of the input information generation unit is performed using a trained model. Information processing section.

13. An information processing system executed by an information processing device, an information acquisition unit that acquires the business negotiation information stored in the storage unit; an input information generation unit that predicts at least one of the next action that the user may take and information that the user will need based on the information acquired by the information acquisition unit, and generates at least a part of input information to be input to the trained model so that the trained model outputs a processing result that is required for the prediction result or a processing result that leads to support; an input information output unit that outputs the input information; having The estimation and generation process of the input information generation unit is performed using a trained model. Information processing section.

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