Information processing device, information processing method, and information processing program

Abstract

The prompts input into the trained AI model contain multiple processes but are created as a single sentence, so if an invalid answer is returned, it is difficult to determine which stage of the process is the cause. [Solution] To solve the above problem, an information processing device is provided that has a prompt setting unit that sets a prompt, and a split prompt generation unit that inputs the set prompt into a trained AI model and generates split prompts by dividing the set prompt.

Description

[Technical Field]

[0001] The present invention relates to an information processing device that acquires desired information from a file data group containing various information using a trained AI model. [Background technology]

[0002] In recent years, the use of large-scale language models (hereinafter sometimes abbreviated as LLM) to acquire predetermined information from a group of file data has become widespread. For example, Patent Document 1 discloses a technology that uses LLM to collect character information related to predetermined items from a variety of printed materials with no fixed format, such as advertising flyers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7430437 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology of Patent Document 1 collects one or more pieces of character information relating to one or more specified items from a group of character strings printed on a flyer read by OCR by providing the LLM with prompts including instructions for specifying the items to be collected and various other instructions regarding the collection.

[0005] As in the example above, in most cases, the prompt given to LLM contains instructions for multiple processes leading up to the final desired information. For example, a prompt such as "Please create a table showing the average life expectancy for each prefecture in the Kanto region" contains multiple processes: "Extract prefectures in the Kanto region," "Obtain the average life expectancy for each prefecture," and "Create a table of the results."

[0006] However, even though multiple processes are included, the prompts entered are often created as a single sentence as described above. Therefore, if the results obtained from the created prompt are undesirable or incorrect information is returned, it is difficult to determine which stage of the process is causing the problem and which process needs to be corrected to improve the situation. [Means for solving the problem]

[0007] Therefore, in order to solve the above problems, the present invention provides the following information processing device etc. That is, the information processing device has a prompt setting unit that sets a prompt, and a split prompt generation unit that inputs the set prompt to a trained AI model and generates split prompts by dividing the set prompt.

[0008] In addition to the above features, the present invention provides an information processing device further comprising a divided prompt setting unit that sets the generated divided prompt.

[0009] In addition to the above features, the present invention provides an information processing device further having a division processing step generation unit that inputs the generated division prompt or the set division prompt into the trained AI model and generates a division processing step corresponding to the generated division prompt or the set division prompt.

[0010] In addition to the above features, the present invention provides an information processing device further comprising a division processing step result acquisition unit that acquires the processing results of the generated division processing steps.

[0011] In addition to the above features, the split prompt generation unit provides an information processing device that inputs the set prompt into the trained AI model and generates split prompts by splitting the set prompt, together with the corresponding split processing steps.

[0012] In addition to the above features, the present invention provides an information processing device that further has a sample setting unit that sets correct answer sample file data that is input to the trained AI model together with the set prompt and serves as a correct answer example for obtaining the answer required by the set prompt, and correct answer information contained in the correct answer sample file data.

[0013] In addition to the above features, the present invention provides an information processing device further comprising a correct answer information explanation prompt setting unit that sets a correct answer information explanation prompt that explains the correct answer information that has been set.

[0014] In addition to the above features, the split prompt generation unit provides an information processing device that inputs the set correct answer sample file data and correct answer information together with the set prompt into a trained AI model to generate a split prompt.

[0015] In addition to the above features, the splitting processing step generation unit provides an information processing device that inputs the set correct sample file data and correct answer information into the trained AI model to generate splitting processing steps.

[0016] In addition to the above features, the information processing device further includes a split processing step evaluation unit that compares the processing result of the split processing step generated by inputting the set correct answer sample file data and correct answer information into the trained AI model, or the split processing step generated from a split prompt generated by inputting the set correct answer sample file data and correct answer information into the trained AI model, with the correct answer information, and evaluates the generated split processing step.

[0017] The present invention also provides an information processing device having a prompt setting unit that sets a prompt, and a processing step generation unit that inputs the set prompt into the trained AI model and generates divided processing steps based on the set prompt.

[0018] In addition to the above features, the present invention provides an information processing device further comprising a processing step result acquisition unit that acquires the processing results of the generated processing steps.

[0019] In addition to the above features, the present invention provides an information processing device further having a processing step prompt generation unit that inputs the generated processing step into a trained AI model and generates a processing step prompt that instructs the trained AI model to use the generated processing step.

[0020] The present invention also provides an information processing method executed by an information processing device, which includes a prompt setting step for setting a prompt, and a split prompt generation step for inputting the set prompt into a trained AI model and generating split prompts by dividing the set prompt.

[0021] Also provided is an information processing program that causes an information processing device to execute a prompt setting step of setting a prompt, and a split prompt generation step of inputting the set prompt into a trained AI model and generating split prompts by dividing the set prompt.

[0022] The present invention also provides an information processing method executed by an information processing device, the information processing method including a prompt setting step for setting a prompt, and a processing step generation step for inputting the set prompt into the trained AI model and generating divided processing steps based on the set prompt.

[0023] Also provided is an information processing program that causes an information processing device to execute a prompt setting step of setting a prompt, and a processing step generation step of inputting the set prompt into the trained AI model and generating divided processing steps based on the set prompt. [Effects of the Invention]

[0024] This invention makes it possible to clarify the prompts input into a trained AI model and the intermediate processing that leads to the answer obtained from that input, allowing for review and correction to obtain the desired answer. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a block diagram illustrating an example of a functional configuration of an information processing apparatus according to a first embodiment. [Figure 2] Conceptual diagram showing an example of a split prompt [Figure 3] Conceptual diagram showing an example of a viewer displaying correct sample file data [Figure 4] FIG. 1 is a conceptual diagram showing an example of the hardware configuration for realizing an information processing apparatus according to a first embodiment. [Figure 5] FIG. 1 is a flow chart showing an example of a process flow of an information processing apparatus according to a first embodiment; [Figure 6] FIG. 10 is a block diagram illustrating an example of a functional configuration of an information processing apparatus according to a second embodiment. [Figure 7] FIG. 10 is a flow chart showing an example of a process flow of an information processing apparatus according to a second embodiment of the present invention; DETAILED DESCRIPTION OF THE INVENTION

[0026] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the present invention should not be limited to these embodiments and can be embodied in various forms without departing from the spirit and scope of the present invention.

[0027] Example 1 <Summary> The present invention is an information processing device that inputs set prompts into a trained AI model and generates prompts for each intermediate processing step included in the prompts. This makes it possible to clarify the intermediate processing leading up to the final result, and to judge the appropriateness of the prompts for each step and the accuracy of the intermediate results, as well as to review and correct each step to obtain a more accurate answer.

[0028] The following describes the functions and processing flow of the information processing device, as well as the hardware. The functional blocks of this system described below can be implemented as a combination of hardware and software. Specifically, if a computer is used, these include hardware components such as a CPU (central processing unit), main memory, bus, or secondary storage device (such as a hard disk drive, nonvolatile memory, or storage media such as CDs or DVDs, and drives for reading these media), input devices used to input information, printers, display devices, and other external peripherals, as well as interfaces for these external peripherals, communication interfaces, driver programs and other application programs for controlling the hardware, and user interface applications. The CPU processes data entered from input devices and other interfaces and stored in memory or on a hard disk in accordance with a program deployed in main memory, processing and storing the data, and generating commands to control the hardware and software. Alternatively, the functional blocks of this system can be implemented using dedicated hardware.

[0029] Furthermore, this invention can be realized not only as a system but also as a method. Furthermore, a part of such an invention can be configured as software. Furthermore, a program used to cause a computer to execute such software, and a recording medium on which the program is fixed, are naturally included within the technical scope of this invention (the same applies throughout this specification).

[0030] <Functional configuration> Fig. 1 is a block diagram showing an example of the functional configuration of an information processing device according to Example 1. As shown in Fig. 1, the information processing device 100 includes a prompt setting unit 101, a divided prompt generation unit 102, a divided prompt setting unit 103, a divided processing step generation unit 104, and a divided processing step result acquisition unit 105.

[0031] In this embodiment, the trained AI model is a machine learning model that has been trained in advance to handle general-purpose tasks. A typical example is the well-known LLM, but it may also be a different model such as a small language model or a multimodal language model that can handle images and other formats.

[0032] <Prompt settings section> The prompt setting unit 101 has a function of setting a prompt. A prompt is an instruction to a trained AI model, and is used to extract and acquire information such as numerical values, character strings, image data, and audio data from the target file data, and to obtain information obtained by performing calculations and processing on the extracted information (total number, average information, maximum information, coordinate information, spatial relationships such as distance and collision, 3D model information, 3D graphic information, image, specific part of the image, image content, character information, etc.) as an answer from the trained AI model.

[0033] The target file data may be stored in a storage device provided in the information processing device, or may be stored in a storage device connected to the information processing device via a communication line, etc. The file data may be in a variety of file formats, including, but not limited to, Word files, Portable Document Format (PDF) files, Excel files, image files, Comma Separated Values ​​(CSV) files, text files, Building Information Modeling (BIM) files, 2D CAD (Computer Aided Design) files, and 3D model files.

[0034] For example, when targeting CSV files related to the facilities of multiple buildings and obtaining the "total number of elevators" from the file data, the desired information would be the number of elevators in each building extracted from each file and the sum of those numbers. In addition to numerical calculations, the desired information could also be the results of processing such as 3D spatial processing or 2D graphic processing. For example, when targeting 3D model files of multiple buildings and obtaining the "collision points between columns and beams," the desired information would be the 3D spatial information obtained by extracting all column and beam models from each file, calculating whether there are any collisions between them, and extracting the collision points.

[0035] Furthermore, for example, if you are looking for "inconsistencies between elevations and floor plans" in 2D drawing files of multiple buildings, you can extract all of the architectural design information for elevations and floor plans contained in each file, check whether there are any inconsistencies when they are reconstructed into 3D solids, and then document the inconsistencies into text, which will become the desired information.

[0036] The prompt to be set is expressed in natural language, for example, "Tell me the distance between pillars." Such a prompt may be input by the user, or may be set in part or in whole by the information processing device, or may be set again by the user after modifying what has been set by the information processing device.

[0037] Prompts can not only instruct the trained AI model on the desired answer, but also include conditions for leading to an appropriate answer and examples to refer to. For example, when setting a prompt to instruct the model to extract a "column object" that is part of a BIM model, "Extract the value of the 'Column Object'. The 'Column Object' is part of the BIM model of a building, and is a long, vertical, 3D structure that extends vertically from the ground." Set the prompt as follows:

[0038] Furthermore, the prompt is not limited to one, but multiple prompts can be set. For example, in addition to the instruction to extract the "column object" mentioned above, instructions to extract the "beam object" and the "type and number of elevators" can also be set. In the case of extracting the "beam object", "Extract the value of 'Beam Object'. 'Beam Object' is a part of the BIM model of a building, and has a 3D structure that is horizontal and more than 2m off the ground." Also, if you want to extract "type and number of elevators," "Please extract the type and number of elevators in the BIM model. If not applicable, please leave blank." It is set by sentences such as:

[0039] <Split prompt generation part> The split prompt generation unit 102 has a function of inputting the set prompt into a trained AI model and generating split prompts by dividing the set prompt. The set prompt may be an instruction that leads to an answer through multiple processes, and the split prompt generation unit generates the split prompts by using the trained AI model to infer instructions for each of the multiple processes included in the prompt.

[0040] Figure 2 is a conceptual diagram showing an example of a segmented prompt. Here, the prompt "Tell me the distance between the pillars" 201 is shown. When this prompt is input to the trained AI model, the trained AI model infers the processes included in the input prompt and generates segmented prompts corresponding to each process.

[0041] For example, as shown in the figure, if the set prompt is "Find the distance between the columns" 201, the step-by-step processes to be performed to find the distance between the columns are inferred, and according to each inferred process, a split prompt "Find the columns" 202, a split prompt "Find the surface of each column" 203, and a split prompt "Draw a line connecting the surfaces and find its length" 204 are generated. These split prompts correspond to each of the step-by-step processes A to C, from extracting a column object 206 from BIM data 205 of the building shown in the figure (Process A), obtaining data on the surface of the extracted column object (Process B), and obtaining the length 207 of the line connecting the surfaces of adjacent columns as the distance between the columns (Process C).

[0042] Furthermore, the configuration may be such that the user can reconfigure the prompts to be set in the prompt setting unit based on the generated divided prompts.

[0043] By dividing the main prompt set earlier and generating split prompts in this way, it is possible to visualize the prompts (split prompts) that instruct the intermediate processing leading to the result, and evaluate the appropriateness of the processing and split prompts at each stage.

[0044] <Split prompt setting section> The split prompt setting unit 103 has a function of setting the generated split prompt. For example, the user can modify the generated split prompt. For example, in the above example, the split prompt "Please find the pillar" can be modified to "Please find the pillar. Note that "pillar" also means pillar."

[0045] The system may also be configured to allow split prompts to be further split. This splitting can be achieved by a user creating split prompts that are further split, or by inputting the split prompts into trained AI models to generate further split prompts. By enabling corrections and adjustments on a split prompt basis in this way, it is possible to set and correct instructions for the trained AI model in greater detail, resulting in more accurate answers.

[0046] <Split processing step generation unit> The division processing step generation unit 104 has the function of inputting the generated division prompt or the set division prompt into the trained AI model and generating a division processing step according to the generated division prompt or the set division prompt.

[0047] A segmentation processing step is a specific process that executes the instructions of a segmentation prompt. For example, a segmentation processing step corresponding to the segmentation prompt "Find the pillars" shown in Figure 2 would involve calling a trained AI model to extract pillar objects from the target BIM (Building Information Modeling) file or CAD (Computer Aided Design) file. Furthermore, a segmentation processing step corresponding to "Find the surface of each pillar" would involve calling a trained AI model to extract surface data for the pillar objects. Similarly, a segmentation processing step corresponding to "Draw a straight line connecting the surfaces and find its length" would involve generating a script (a simple program) that obtains the length of the line connecting the surfaces of adjacent pillars and executing that script to obtain the distance between the pillars.

[0048] The generation of split processing steps as described above requires appropriately dividing the entire process and determining the processing to be executed for each division, which has the problem that only engineers with specialized knowledge and experience can create them themselves, but by generating them by inputting split prompts into a trained AI model, anyone can create split processing steps themselves. Furthermore, because the split prompts are also generated by a trained AI model, specialized knowledge is further unnecessary when generating split processing steps.

[0049] As described above, the division processing steps generated by the trained AI model may be processes executed by script execution or processes via trained AI. Furthermore, the division processing steps may use the processing results of the preceding and following division processing steps, or the processing results of one division processing step may be provided to multiple other division processing steps. Furthermore, some or all of the division processing steps may be configured by referencing existing processing blocks for performing predetermined processing, or the division processing steps may be configured by modifying or adjusting existing processing blocks.

[0050] The split prompt generation unit described above can also be configured to input the set prompt into a trained AI model to generate split prompts by dividing the set prompt, and to generate split processing steps corresponding to each generated split prompt. As described above, the split processing step generation unit also generates split processing steps by inputting the split prompt into a trained AI model, so by configuring the split prompt generation unit to have this function as well, it is possible to generate corresponding split prompts and split processing steps together.

[0051] <Division processing step result acquisition unit> The division processing step result acquisition unit 105 has a function of acquiring the processing results of the division processing step generated. For example, in the example shown in FIG. 2, the extracted pillars are the processing results first, and the surface data of the extracted pillars are the next processing results. Then, the length of the line connecting the surface data of each extracted pillar is acquired as the processing result. In this case, the final processing result of the division processing step becomes the answer to the prompt "Find the distance between the pillars." Alternatively, the processing result of the division processing step may be further calculated or processed, and the result may be used as the final answer to the prompt.

[0052] By obtaining the processing results of each segmentation process step, the correctness and validity of the intermediate process results leading to the final answer can be evaluated. If the result obtained in any of the segmentation process steps is not valid, the segmentation process step that produced that result and the segment prompt that generated that segmentation process step can be corrected or adjusted to make the final answer more appropriate.

[0053] <Sample setting section> The sample setting unit has a function of setting correct answer sample file data that is input into the trained AI model together with the set prompt and serves as a correct answer example for obtaining the answer required by the set prompt, and correct answer information contained in the correct answer sample file data.

[0054] The split prompt generation unit generates a split prompt by inputting the set correct answer sample file data and correct answer information into the trained AI model along with the set prompt. The split processing step generation unit generates a split processing step by inputting the set correct answer sample file data and correct answer information into the trained AI model. In this case, the correct answer sample file data and correct answer information can be set for each split prompt input into the trained AI model, or they can be set for some of the split prompts.

[0055] In addition, when the division processing step result acquisition unit acquires the processing results of the division processing step using a trained AI model, the results can be configured to be input into the trained AI model.

[0056] Correct sample file data is file data that serves as a model when obtaining desired information through a set prompt, and contains correct information that serves as a correct example. For example, if the desired information is the illuminance distribution in a room, BIM data that shows the room with lighting and other equipment installed would be correct sample file data. This correct sample file data and the lighting shown in it are the correct information.

[0057] In addition, not only is one piece of correct answer information set for correct answer sample file data, but also correct answer information may be set for each piece of correct answer sample file data. For example, if the correct answer sample file data is a 3D model file for multiple architectural structures including information on columns and beams, and the desired information is the "collision points between columns and beams" in each architectural structure, the actual number of "collision points between columns and beams" in each architectural structure is not limited to "1," and each collision point between a column and a beam is specified as correct answer information for each piece of correct answer sample file data. Note that the correct answer information in this case may be coordinate information on a 3D image showing the collision points, or may be image information that clarifies the collision points by, for example, showing them with a surrounding line.

[0058] The sample setting unit can also be configured to set the correct answer information by specifying specific information on the data in a viewer that displays the set correct answer sample file data. While the user's operation of specifying the correct answer information, such as a 3D space model, 2D vector data, image, table data, numerical values, or text, requires expertise, providing a viewer as in this configuration is beneficial because it makes it easy to set the correct answer information, such as a 3D model, 3D space, or 2D graphic part, an image area, or a cell in table data.

[0059] Figure 3 is a conceptual diagram showing an example of a viewer displaying correct sample file data. As shown in Figure 3(a), the viewer 301 displays BIM data representing an interior room in which lighting and other equipment are arranged as correct sample file data. This BIM data includes various information about the dimensions and materials of the displayed interior room, as well as about objects such as each piece of equipment (ceiling light 302, bracket light 303, bench 304, counter 305), and each of these can be extracted.

[0060] Here, if the desired information is the illuminance distribution in a room and you want to specify ceiling light 302 as the correct information from this correct sample file data, you can specify it as the correct information by, for example, surrounding ceiling light 302 with an encircling line 306 as shown in FIG. 3(b). You can also similarly specify bracket light 305 as the correct information. By specifying the correct information in this way, information such as the illuminance and position of each of the ceiling light and bracket light is identified, and this becomes the correct information for obtaining the illuminance distribution in the room. Note that the correct information in this case can be specified not only by surrounding it but also by directly selecting the 3D object.

[0061] The correct answer information can be set in various ways depending on the correct answer sample file data. For example, if the correct answer sample file data is an image file, by selecting a specific range in the viewer that opens the image file, that range can be set as the correct answer information to be extracted. Also, by selecting a specific layer in the viewer that opens a CAD file, that layer can be set as the correct answer information to be extracted.

[0062] It is also possible to set content that does not explicitly exist in the data. For example, by opening a BIM file in a viewer and selecting the space between specific objects in the viewer, the distance between those objects can be set as the correct information to be extracted. Also, by opening spreadsheet data such as Excel in the viewer and selecting a specific cell, that cell can be set as the correct information to be extracted.

[0063] As described above, the correct answer information to be set may be in any format, such as numerical information, a three-dimensional spatial model, a two-dimensional figure, an image, or text. The results of a calculation process may also be set as the correct answer information. The correct answer information may not only provide information on the desired final result, but also information on the process leading up to that result. For example, in the example of extracting "collision points between columns and beams" from the aforementioned three-dimensional model, the correct answer information for the process may include (1) the column and beam models included in each file, (2) whether or not there is a collision between them, and (3) information on the three-dimensional space from which the collision points are extracted.

[0064] The sample setting unit can also be configured to set specific information in the data as incorrect information in a viewer that displays the correct sample file data. 3D models often contain objects that are confusable with correct information. For example, if you want to extract an "air conditioning duct," there may be similar objects such as "water supply and drainage piping" and "wiring rack" located near the object. In this case, by specifying "air conditioning duct" as the correct answer and also "water supply and drainage piping" and "wiring rack" as incorrect answers, the trained AI can avoid excessive pipe detection and accurately obtain the desired information.

[0065] It is also possible that a file containing only incorrect answer information without correct answer information is set as correct answer sample file data.

[0066] <Correct Answer Information Explanation Prompt Setting Section> The correct answer information explanation prompt setting unit has a function of setting a correct answer information explanation prompt that explains the correct answer information that has been set. In 3D models, there are often objects that are confusable with the object of the correct answer information. For example, when you want to extract an "air conditioning duct," there may be "water supply and drainage piping" or "wiring rack" placed near that object, and these are mutually similar objects. In this case, instead of just specifying "air conditioning duct" as the correct answer information, In this sample file, the "air conditioning duct" is the correct answer for the desired information. This is because it is a long, thin cylindrical model, and its end is connected to the "air intake." By setting a prompt to explain the correct answer information in this way, it is possible to prevent the trained AI model from detecting incorrect information, generate accurate segmentation prompts and segmentation processing steps, and improve the accuracy of the results obtained when the trained AI model is used to obtain the processing results of the segmentation processing steps. Furthermore, if multiple pairs of correct sample file data and correct answer information are input as samples to the trained AI model, a correct answer information explanation prompt may be set for each pair.

[0067] Furthermore, if incorrect information is specified, an explanation for this incorrect information may be included in the correct information explanation prompt.

[0068] Furthermore, the correct answer sample file data and correct answer information may be set by the user, or may be set in advance by a person who provides the information processing device to the user.

[0069] In addition, the split prompt generation unit can be configured to input the set correct answer sample file data and correct answer information together with the set prompt into a trained AI model to generate a split prompt.

[0070] For example, by using the BIM file shown in Figure 2 as the correct sample file data and setting the column object in the figure as the correct information, it is possible to contribute to the generation of more appropriate split prompts such as, "Please find the column by referring to the column object specified in the correct information. Also, please note that the specified vertical object is not a column but a piping duct." Also, by selecting the space between adjacent column objects and setting the distance between those column objects as the correct information to be extracted, it is possible to generate a split prompt that properly extracts the information necessary to measure the distance between the columns.

[0071] By inputting the correct sample file data and correct answer information into the trained AI model along with the prompt, it is possible to generate an appropriate splitting prompt for obtaining the desired information. The splitting processing steps generated based on the splitting prompt generated in this way will also be more appropriate splitting processing steps for obtaining the desired information.

[0072] The split processing step generation unit can also be configured to input the set correct answer sample file data and correct answer information into a trained AI model to generate split processing steps. As described above, instead of inputting correct answer information, etc. when generating split prompts, it is also effective to input correct answer information, etc. when generating split processing steps. Also, correct answer information, etc. may be input at both the split prompt generation stage and the split processing step generation stage. The correct answer information, etc. to be input may be the same, or different correct answer information, etc. may be input at each stage. Also, correct answer information, etc. may be set for each split prompt and input to generate split processing steps. Furthermore, when a trained AI model is used to obtain the processing results of the split processing steps, the correct answer information, etc. may also be input into the trained AI model to obtain the processing results.

[0073] <Division processing step evaluation part> The system can also be configured to further include a split processing step evaluation unit that compares the processing results of a split processing step generated by inputting correct answer sample file data and correct answer information set in a trained AI model, or a split processing step generated from a split prompt generated by inputting correct answer sample file data and correct answer information set in a trained AI model, with the correct answer information, and evaluates the generated split processing step.

[0074] The evaluation of the division processing steps may be performed on the processing result of the final division processing step among the generated division processing steps, or on the processing results of all the generated division processing steps. By comparing the processing result with the correct answer information in this way, it is possible to evaluate how accurate the division processing steps are.

[0075] Furthermore, by configuring the segmentation processing step generation unit to input the evaluation results into the trained AI model, segmentation processing steps can be generated based on the evaluation results, which can be useful for generating more accurate segmentation processing steps. For example, it can be configured to continue regenerating segmentation processing steps until the processing results and correct answer information match.

[0076] <Hardware configuration> 4 is a conceptual diagram showing an example of the configuration of hardware that realizes the information processing device of the first embodiment. As shown in the figure, the information processing device 400 has a CPU 401 that performs various arithmetic processing, a RAM 402 that is a volatile storage medium, a storage 403 such as a flash memory or HDD that is a non-volatile storage medium, a communication interface 404, and an input / output interface 405. The RAM 402 reads programs that perform various arithmetic processing to cause the CPU 401 to execute the programs, and also provides a work area (working area) for the programs. In addition, a plurality of addresses are assigned to the RAM 402, and the programs executed by the CPU 401 can exchange data and perform processing by identifying and accessing the addresses.

[0077] Here, the functions of the prompt setting unit 101, the split prompt generation unit 102, the split prompt setting unit 103, the split processing step generation unit 104, and the split processing step result acquisition unit 105 of the information processing device 100 in Figure 1 are mainly realized by the CPU 401 and RAM 402 in Figure 4. Also, the functions of the sample setting unit, the correct answer information explanation prompt setting unit, and the split processing step evaluation unit (not shown) are mainly realized by the CPU 401 and RAM 402 in Figure 4. Also, when an external database that stores file data to be used for obtaining desired information or setting correct answer sample file data exists, or when an external trained AI model is used, each function is realized by mutually exchanging signals and information via the communication interface 404 and the input / output interface 405.

[0078] <Processing flow> 5 is a flow diagram simply illustrating an example of a processing flow of the information processing device according to the first embodiment. First, a prompt is set (S501: prompt setting step). Then, the set prompt is input to a trained AI model to generate split prompts by dividing the set prompt (S502: split prompt generating step). Then, the generated split prompts are set (S503: split prompt setting step). Then, the generated split prompt or the set split prompt is input to the trained AI model to generate split processing steps corresponding to the generated split prompt or the set split prompt (S504: split processing step generating step). Then, the processing results of the generated split processing steps are acquired (S505: split processing step result acquiring step).

[0079] <Effects> According to the information processing apparatus of this embodiment, it is possible to evaluate and correct the processes along the way to the final result.

[0080] <Example 2> <Summary> While the information processing apparatus of the first embodiment generates divided processing steps from divided prompts, the present embodiment is an information processing apparatus that generates divided processing steps (processing steps) from a prompt without passing through divided prompts.

[0081] <Functional configuration> Fig. 6 is a block diagram showing an example of the functional configuration of an information processing device of this embodiment. As shown in Fig. 6, an information processing device 600 has a prompt setting unit 601, a processing step generation unit 602, a processing step result acquisition unit 603, and a processing step prompt generation unit 604. The prompt setting unit has the same function as the configuration of the same name in the first embodiment, so a description thereof will be omitted.

[0082] <Processing step generation section> The processing step generation unit 602 has a function of inputting the set prompt to the trained AI model and generating divided processing steps based on the set prompt. In the first embodiment, the divided prompt is input to the trained AI model to generate divided processing steps, but in this configuration, the set prompt is input to generate processing steps.

[0083] The reason why it is called a "processing step" is that the prompt from which it is generated is different from that in Example 1, but the technical significance is the same as that of the "split processing step", and the trained AI model infers the multiple processes included in the prompt to generate individual processing steps. The generated steps may be processes using a static script or processes via a trained AI model. Furthermore, since the generated processing steps are the same as the split processing steps in Example 1, a repeated explanation will be omitted.

[0084] <Processing step result acquisition unit> The processing step result acquisition unit 603 has a function of acquiring the processing results of the generated processing steps. The function of this configuration is the same as that of the divided processing step result acquisition unit of the first embodiment. By acquiring the processing results of each processing step, the correctness and validity of the results of the intermediate processing can be evaluated. If the result obtained in any processing step is inappropriate, the processing step that brought about that result and the prompt that generated that processing step can be corrected or adjusted, thereby making the final answer more appropriate.

[0085] <Processing step prompt generation part> The processing step prompt generation unit 604 has a function of inputting the generated processing step into a trained AI model and generating a processing step prompt, which is a prompt that instructs the trained AI model to use the generated processing step.

[0086] The processing step prompt that instructs the trained AI model to execute the generated processing step is a prompt that the generated processing step is generated by the trained AI model, and is a prompt that corresponds to the processing step. By generating such a processing step prompt, it is possible to evaluate whether each generated processing step and its corresponding processing step prompt are correctly associated, which can contribute to obtaining a more accurate answer.

[0087] Furthermore, similar to the split prompt setting unit of Example 1, the unit can be configured to have a function that allows a user or the like to set the generated processing step prompt, and the user can modify or adjust the generated processing step prompt.

[0088] The generated processing step prompt may be input to a trained AI model to generate further processing steps. The processing results of the regenerated processing steps are obtained, and the generation of processing step prompts and processing steps is repeated based on the obtained results, which can contribute to obtaining more accurate answers.

[0089] <Other configurations> As in Example 1, when using a trained AI model to generate processing steps or processing step prompts or to obtain processing step processing results, it is also possible to configure the trained AI model to input correct sample file data and correct answer information (sample setting unit).

[0090] It can also be configured to compare the acquired processing results with the set correct answer sample file data and correct answer information to evaluate the processing steps (processing step evaluation unit). It can also be configured to set a correct answer information explanation prompt that explains the set correct answer information (correct answer information explanation prompt setting unit). In this way, using the correct answer sample file and correct answer information helps to generate more appropriate processing steps and obtain the desired answer with high accuracy.

[0091] <Hardware configuration> The information processing device of this embodiment can be realized by the hardware configuration shown in Fig. 4 according to the first embodiment. The functions of a prompt setting unit 601, a processing step generation unit 602, a processing step result acquisition unit 603, and a processing step prompt generation unit 604 of an information processing device 600 of the second embodiment shown in Fig. 6 are mainly realized by the CPU 401 and RAM 402 of Fig. 4.

[0092] <Processing flow> FIG. 7 is a flow diagram illustrating a simplified example of the processing flow of the information processing device according to the second embodiment. First, a prompt is set (S701: prompt setting step). Then, the set prompt is input to the trained AI model, and divided processing steps are generated based on the set prompt (S702: processing step generation step). Then, a determination is made as to whether or not to generate a processing step prompt (S703: determination step). If it is determined that a processing step prompt should be generated, the generated processing step is input to the trained AI model, and a processing step prompt that instructs the trained AI model to use the generated processing step is generated (S704: processing step prompt generation step), and a processing step is further generated again (S702). On the other hand, if it is determined that a processing step prompt should not be generated, the processing result of the generated processing step is acquired (S705: processing step result acquisition step).

[0093] <Effects> According to the information processing apparatus of this embodiment, it is possible to evaluate and correct the processes along the way to the final result. [Explanation of symbols]

[0094] 100, 400, 600: Information processing equipment 101, 601: Prompt setting section 102: Split prompt generation unit 103: Split prompt setting section 104: Division processing step generation unit 105: Division processing step result acquisition unit 602: Processing step generation unit 603: Processing step result acquisition unit 604: Processing step prompt generation unit 401:CPU 402:RAM 403:Storage 404: Communication interface 405: Input / output interface

Claims

1. a prompt setting unit for setting a prompt; a split prompt generation unit that inputs the set prompt into a trained AI model and generates split prompts by dividing the set prompt; An information processing device having the above.

2. The information processing apparatus according to claim 1 , further comprising a divided prompt setting unit that sets the generated divided prompt.

3. 3. The information processing device according to claim 2, further comprising a division processing step generation unit that inputs the generated division prompt or the set division prompt into the trained AI model and generates a division processing step corresponding to the generated division prompt or the set division prompt.

4. The information processing apparatus according to claim 3 , further comprising a division processing step result acquisition unit that acquires the processing results of the generated division processing steps.

5. The split prompt generation unit 3. The information processing device according to claim 1, wherein the set prompt is input to the trained AI model to generate split prompts obtained by splitting the set prompt, together with corresponding split processing steps.

6. 2. The information processing device according to claim 1, further comprising a sample setting unit that sets correct answer sample file data that serves as a correct answer example for obtaining an answer required by the set prompt, and correct answer information included in the correct answer sample file data.

7. The information processing apparatus according to claim 6 , further comprising a correct answer information explanation prompt setting unit that sets a correct answer information explanation prompt that explains the correct answer information that has been set.

8. The information processing device according to claim 6 , wherein the division prompt generation unit inputs the set correct answer sample file data and correct answer information together with the set prompt into a trained AI model to generate a division prompt.

9. The information processing device according to claim 8 , wherein the division processing step generation unit inputs the set correct sample file data and correct answer information into the trained AI model to generate division processing steps.

10. The information processing device according to claim 9, further comprising a split processing step evaluation unit that compares the processing result of the split processing step generated by inputting the set correct answer sample file data and correct answer information into the trained AI model, or the split processing step generated from a split prompt generated by inputting the set correct answer sample file data and correct answer information into the trained AI model, with the correct answer information, to evaluate the generated split processing step.

11. a prompt setting unit for setting a prompt; a processing step generation unit that inputs the set prompt into the trained AI model and generates divided processing steps based on the set prompt; An information processing device having the same.

12. The information processing apparatus according to claim 11 , further comprising a processing step result acquisition unit that acquires the processing result of the generated processing step.

13. The information processing device according to claim 11 or 12, further comprising a processing step prompt generation unit that inputs the generated processing step into a trained AI model and generates a processing step prompt that instructs the trained AI model to use the generated processing step.

14. The information processing device according to claim 13 , wherein the processing step generation unit inputs the generated processing step prompt to the trained AI model to generate a processing step corresponding to the processing step prompt.

15. An information processing method executed by an information processing device, a prompt setting step for setting a prompt; A split prompt generation step of inputting the set prompt into a trained AI model to generate split prompts by dividing the set prompt; An information processing method comprising:

16. a prompt setting step for setting a prompt; A split prompt generation step of inputting the set prompt into a trained AI model to generate split prompts by dividing the set prompt; An information processing program that causes an information processing device to execute the above.

17. An information processing method executed by an information processing device, a prompt setting step for setting a prompt; a processing step generation step of inputting the set prompt into the trained AI model and generating divided processing steps based on the set prompt; An information processing method comprising:

18. a prompt setting step for setting a prompt; a processing step generation step of inputting the set prompt into the trained AI model and generating divided processing steps based on the set prompt; An information processing program that causes an information processing device to execute the above.

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