Information processing system and information processing program

Abstract

To provide an information processing system and information processing program for predicting printing specifications according to an orderer.SOLUTION: An estimation unit 107 inputs new order information, including information on an orderer of new printed matter, and information on content of information to be printed on the new printed matter, or information on specification items of the new printed matter, to a trained model pre-trained to output information on specifications of printed matter upon input of order information, including information on an orderer of printed matter, and information on content of information to be printed on the printed matter, or information on specification items of the printed matter, so as to output information on specifications of the new printed matter corresponding to the new order information.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an information processing system and an information processing program. [Background technology]

[0002] Patent document 1 discloses a print ordering system that places an order for printing with a factory, which identifies the type and amount of paper used at the factory based on information collected from the factory to which the order is placed, and sends the identified type and amount of paper to the supplier of the paper used. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-26535 Summary of the Invention [Problem to be solved by the invention]

[0004] When ordering printed products from a printing company, it is difficult for the client to create a purchase order because the product specifications are so detailed.

[0005] In cases where large owner-operated companies order printed products on a case-by-case basis, the client is unable to create the order form themselves, so sales representatives from the printing company are dispatched or stationed on-site to work together with the client to create the product, taking into account the client's intentions.

[0006] There are various types of printing products that are ordered periodically, such as monthly or for regular events, seasonal printing products, and printing products that are ordered periodically to make up for shortages. In such cases, customers may request, "I want to make another printing product like the one I made at that time." Even in such cases, a purchase order must be created from scratch, which does not reduce the burden on sales staff.

[0007] Therefore, an object of the present disclosure is to provide an information processing system and an information processing program that can predict the specifications of printed matter according to an orderer. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, the information processing system of the first aspect includes a processor, and when the processor inputs order information including information about the person ordering the printed matter and information about the content of the information to be printed on the printed matter or information about the specification items of the printed matter, the processor outputs information about the specifications of the new printed matter corresponding to the new order information by inputting new order information including information about the person ordering the new printed matter and information about the content of the information to be printed on the new printed matter or information about the specification items of the new printed matter into a trained model that has been pre-trained to output information about the specifications of the printed matter.

[0009] An information processing system according to a second aspect is the information processing system according to the first aspect, wherein the information about the orderer includes the name of the orderer or the type of business of the orderer.

[0010] In the information processing system of the third aspect, in the information processing system of the first aspect, the information regarding the content of the information to be printed on the printed matter includes information regarding the timing of the information to be printed, information regarding the event of the information to be printed, information regarding the purpose of the information to be printed, or information regarding the product to be printed.

[0011] An information processing system according to a fourth aspect is the information processing system according to the first aspect, wherein the order information and the new order information further include information relating to an order cycle.

[0012] An information processing system according to a fifth aspect is the information processing system according to the first aspect, wherein the order information and the new order information further include information relating to the number of orders.

[0013] An information processing system according to a sixth aspect is the information processing system according to the first aspect, wherein the information relating to the specifications includes an image representing the printed matter.

[0014] An information processing system according to a seventh aspect is the information processing system according to the first aspect, wherein the processor extracts the new order information from input text or speech using a large-scale language model that has been trained in advance, and outputs information regarding the specifications of the new printed material corresponding to the new order information by inputting the extracted new order information into the trained model.

[0015] The information processing program of the eighth aspect causes a computer to input new order information including information about the orderer of a printed matter and information about the content of the information to be printed on the new printed matter or information about the specification items of the printed matter into a trained model that has been pre-trained to output information about the specifications of a printed matter when order information including information about the orderer of a new printed matter and information about the content of the information to be printed on the new printed matter or information about the specification items of the new printed matter is input. [Effects of the Invention]

[0016] According to the first aspect, it is possible to predict the specifications of the printed matter according to the orderer.

[0017] According to the second aspect, it is possible to predict the specifications of the printed matter based on the name of the orderer or the type of business of the orderer.

[0018] According to the third aspect, the specifications of the printed matter can be predicted based on information about the time of the information to be printed, information about the event, information about the purpose, or information about the product.

[0019] According to the fourth aspect, it is possible to predict the specifications of printed matter in consideration of the order cycle.

[0020] According to the fifth aspect, it is possible to predict the specifications of printed matter based on the number of orders.

[0021] According to the sixth aspect, it is possible to predict an image representing a printed matter according to the orderer.

[0022] According to the seventh aspect, the specifications of the printed matter can be predicted from the input text or voice.

[0023] According to the eighth aspect, it is possible to predict the specifications of the printed matter according to the orderer. [Brief explanation of the drawings]

[0024] [Figure 1] 1 is a diagram showing a schematic configuration of an information processing system according to an embodiment of the present invention; [Figure 2] FIG. 2 is a block diagram showing the main configuration of an electrical system of a printing device in the information processing system according to the present embodiment. [Figure 3] 2 is a block diagram showing the configuration of the main electrical system of a management device and a client computer in the information processing system according to the present embodiment. FIG. [Figure 4] FIG. 2 is a functional block diagram showing the functional configuration of a client computer in the information processing system according to the present embodiment. [Figure 5] FIG. 10 is a diagram illustrating an example of a trained neural network model. [Figure 6] 10 is a flowchart showing an example of the flow of a learning process performed in a client computer of the information processing system according to the present embodiment. [Figure 7] 10 is a flowchart showing an example of the flow of an estimation process performed in a client computer of the information processing system according to the present embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0025] An example of this embodiment will be described in detail below with reference to the drawings. In this embodiment, an information processing system in which a management device, a printing device, a client computer, etc. are connected to each other via communication lines such as various networks will be described as an example. Figure 1 is a diagram showing the schematic configuration of an information processing system 10 according to this embodiment.

[0026] 1, an information processing system 10 according to this embodiment includes a management device 11, a printing device 12, and a client computer 14. The management device 11, the printing device 12, and the client computer 14 are connected to each other via a communication line 18 such as a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, or an intranet. The management device 11, the printing device 12, and the client computer 14 are each capable of transmitting and receiving various data to and from each other via the communication line 18. In this embodiment, the client computer 14 issues a printing instruction to the printing device 12 via the management device 11, and the printing device 12 prints in accordance with the print instruction.

[0027] Although FIG. 1 shows one management device 11, one printing device 12, and one client computer 14, there may be a plurality of each, or there may be a plurality of any of them.

[0028] The printing device 12 according to this embodiment has multiple functions, such as a printing function for performing print processing, a post-processing function for performing post-processing on printed paper, etc. The multiple functions may include a reading function for reading an original to obtain image information representing the original, a copying function for copying an image recorded on the original onto paper, a facsimile function for sending and receiving various data via a telephone line (not shown), a transfer function for transferring original information such as image information read by the reading function, etc., and a storage function for storing original information such as read image information.

[0029] In the following description, the facsimile function may be referred to as fax, the reading function as scan, the printing function as print, and the copying function as copy.

[0030] FIG. 2 is a block diagram showing the main configuration of the electrical system of the printing device 12 in the information processing system 10 according to this embodiment.

[0031] As shown in Fig. 2, the printing device 12 according to this embodiment includes a control unit 20. The control unit 20 may include a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory).

[0032] Meanwhile, the printing device 12 according to this embodiment includes a hard disk drive (HDD) 26 that stores various data, application programs, and the like. The printing device 12 is also connected to a user interface 22 and includes a display control unit 28 that controls the display of various operation screens and the like on the display of the user interface 22. The printing device 12 is also connected to the user interface 22 and includes an operation input detection unit 30 that detects operation instructions input via the user interface 22. In the printing device 12, the HDD 26, the display control unit 28, and the operation input detection unit 30 are electrically connected to a system bus 42. While the printing device 12 according to this embodiment uses the HDD 26 as a storage unit, this is not a limitation and a non-volatile storage unit such as a flash memory may also be used. Furthermore, while this embodiment uses a touch panel capable of displaying and inputting operations as the user interface 22, this is not a limitation and a user interface having a display and an operation unit that are separate may also be used.

[0033] The printing device 12 according to this embodiment also includes a print control unit 34 that controls the printing process performed by the printing unit 24, the transport of paper to the printing unit 24 by the transport unit 25, and the post-processing performed by the post-processing unit 46. The printing device 12 may also include a read control unit that controls the optical image reading operation performed by the document reading unit and the document feeding operation performed by the document transport unit. The printing device 12 also includes a communication line I / F (interface) unit 36 ​​that is connected to the communication line 18 and transmits and receives communication data to and from other external devices, such as the client computer 14, connected to the communication line 18. The printing device 12 may also include a facsimile I / F (interface) unit that is connected to a telephone line (not shown) and transmits and receives facsimile data to and from a facsimile device connected to the telephone line. The printing device 12 may also include a transmission / reception control unit that controls the transmission and reception of facsimile data via the facsimile I / F unit 38. In the printing device 12, the print control unit 34 and the communication line I / F unit 36 ​​are electrically connected to a system bus 42.

[0034] With the above configuration, the printing device 12 according to this embodiment uses the control unit 20 to control the display of information such as operation screens and various messages on the display of the user interface 22 via the display control unit 28. The printing device 12 also uses the control unit 20 to control the operation of the printing unit 24, conveying unit 25, and post-processing unit 46 via the print control unit 34, and to control the sending and receiving of communication data via the communication line I / F unit 36. Furthermore, the printing device 12 uses the control unit 20 to grasp the operation content of the user interface 22 based on the operation information detected by the operation input detection unit 30, and executes various controls based on this operation content.

[0035] In this embodiment, an example of an application stored in the HDD 26 includes an application that executes a function such as printing.

[0036] Next, the configuration of the main electrical parts of the management device 11 and the client computer 14 according to this embodiment will be described. Fig. 3 is a block diagram showing the configuration of the main electrical parts of the management device 11 and the client computer 14 in the information processing system 10 according to this embodiment. Note that the management device 11 and the client computer 14 have general computer configurations, and therefore, the following description will be given using the management device 11 as a representative.

[0037] As shown in FIG. 3 , the management device 11 according to this embodiment includes a CPU 11A as an example of a processor, a ROM 11B, a RAM 11C, a storage 11D, an operation unit 11E, a display unit 11F, and a communication line I / F (interface) unit 11G. The CPU 11A controls the overall operation of the management device 11. The ROM 11B stores various control programs and parameters in advance. The RAM 11C is used as a work area when the CPU 11A executes various programs. The storage 11D stores various data and application programs. The operation unit 11E is used to input various information. The display unit 11F is used to display various information. The communication line I / F unit 11G is connected to a communication line 18 and transmits and receives various data to and from other devices connected to the communication line 18. The communication line I / F unit 11G may also be configured to be capable of directly communicating with each device using various well-known wireless communications. The above-described components of the management device 11 are electrically connected to one another via a system bus 111. In the management device 11 according to this embodiment, the storage 11D is used as a storage unit, and examples of storage include non-volatile storage units such as HDDs (Hard Disk Drives) and flash memories.

[0038] With the above configuration, the management device 11 according to this embodiment uses the CPU 11A to access the ROM 11B, RAM 11C, and storage 11D, to obtain various data via the operation unit 11E, and to display various information on the display unit 11F. In addition, the management device 11 uses the CPU 11A to control the transmission and reception of communication data via the communication line I / F unit 11G.

[0039] In the information processing system 10 configured as above, the management device 11 manages a series of manufacturing processes. The series of manufacturing processes includes, for example, a production process, a prepress process, a plate making process, a printing process, a processing process, and a delivery process.

[0040] Next, a description will be given of the functional configuration of the client computer 14. Fig. 4 is a block diagram showing an example of the functional configuration of the client computer 14.

[0041] As shown in FIG. 4, the client computer 14 is functionally configured to include a collection unit 101, a learning data storage unit 102, a learning unit 103, a model storage unit 104, a reception unit 105, an extraction unit 106, an estimation unit 107, and an output unit 108.

[0042] The collection unit 101 collects learning data including pairs of order information for past printed matter and information on the specifications of the printed matter corresponding to the order information.

[0043] The order information includes information about the person ordering the printed material. Specifically, the information about the person ordering the printed material includes the name of the person ordering the printed material or the type of business of the person ordering the printed material. Examples of the type of business of the person ordering the printed material include government agencies, retail stores, publishers, advertising agencies, finance, etc.

[0044] The collection unit 101 acquires information about the orderer of the printed matter from the order history information held by the management device 11.

[0045] The order information may include information about the content of the information to be printed on the printed matter. Specifically, the information about the content of the information to be printed includes information about the time of the information to be printed, information about the event of the information to be printed, information about the purpose of the information to be printed, or information about the product to be printed. Information about the time of the information to be printed includes, for example, the season (spring, summer, fall, winter), the beginning of the year, the end of a heatwave, the beginning of the new year, etc. Information about the event of the information to be printed includes, for example, a new car exhibition, a new store opening, a travel sale, etc.

[0046] Information about the purpose of the information to be printed includes, for example, pamphlets, flyers, menus, catalogs, greeting cards (New Year's cards, summer greetings, late summer greetings, winter greetings), etc. Information about the products to be printed includes cars, sushi, seasonal vegetables, travel, clothes, etc.

[0047] The collection unit 101 acquires information about the content of information to be printed on the printed matter from the order history information held by the management device 11.

[0048] The order information may also include information about the specifications of the printed matter. Specifically, the information about the specifications of the printed matter includes a combination of the specifications of the printed matter and the values ​​of the specifications. The specifications of the printed matter may include, for example, the background, the size of the printed matter, the text color, special colors, the number of copies, the packaging method, and the delivery date.

[0049] The collection unit 101 acquires information about the specification items of the printed matter from the order history information held by the management device 11.

[0050] The order information may also include information regarding the order cycle, such as monthly, weekly, yearly, seasonal, etc.

[0051] The collection unit 101 acquires information about the order cycle from the order history information held by the management device 11.

[0052] The order information may also include information about the number of orders placed, such as the number of times printed materials with the same specifications have been ordered in the past, or the number of times the same printed material has been ordered in the past.

[0053] The collection unit 101 acquires information about the number of orders from the order history information held by the management device 11.

[0054] The information about the specifications of the printed matter includes a specification sheet. The specification sheet includes, for example, the following information:

[0055] Design: Specify the design of the item to be printed. Provide logos and images if necessary. Manuscript ·parts ·cover ·Main text ·design ·background ·character ·image Illustrations ·graph ·table ·Calibration Preflight ·Proofreading and printing completed ·direction Vertical / horizontal writing Vertical / Horizontal Size: Specify the size of your print, for example A4 for a flyer or B2 for a poster. ·size Standard size Dimensions (mm / inch) Vertical / Horizontal Height / Width Paper: Specify the type and weight of paper you are using. Common choices include matte, glossy, and cardstock. Grain Vertical grain / Horizontal grain ·With the grain / against the grain Paper Coated paper / glossy paper / high-quality paper / plain paper kilograms of paper Thickness Color Mode: Specifies the color mode to be used for printing. Generally, CMYK is used, but in special cases, Pantone colors may be required. Color Mode Text color Ink, toner Printing Method: Specifies the specific printing method used to create the print, e.g., offset printing, digital printing, screen printing, etc. ·surface Single / double sided ·Printing method Offset printing, digital printing, screen printing Finishing: If your print requires finishing, specify requirements such as lamination, UV coating, or foil stamping. Finishing / processing Binding: saddle stitching, side stitching, walnut Folding: W-shaped quarter fold / outer tri-fold / roll tri-fold / roll quarter fold / gate fold Sides: Short side, long side ·direction: Finishing Gloss PP processing / foil stamping / die cutting ·Quantity: Specify the exact quantity of prints you require. Number of copies (quantity) Delivery date: Specify the date you would like the printed materials delivered. It is important to set a schedule with ample margin. ·deadline ·date Early / mid / late month ·Weekend / end of month · Budget: It is effective to clearly set a budget for the printed material and ask for proposals within that range. Pickup Method: Specify how you would like to receive your prints, such as in person, by mail, or by delivery. Delivery method Boxes / wrapping / pallets

[0056] The information about the specifications of the printed matter may include an image representing the printed matter, such as an image that schematically represents the printed matter or an image that represents an example of the printed matter.

[0057] The management device 11 has information about printing orders and history information for managing manufacturing information. The manufacturing information includes actual specifications for manufacturing and images representing printed matter.

[0058] The collection unit 101 collects information on the specifications of the printed matter from the history information held by the management device 11.

[0059] The learning data storage unit 102 stores a plurality of pieces of learning data collected by the collection unit 101.

[0060] The learning unit 103 receives order information as an input and constructs a neural network model for estimating information relating to the specifications of printed matter that corresponds to the order information, based on a plurality of learning data.

[0061] Specifically, the trained neural network model accepts input of order information including at least one of the name of the orderer, the type of business of the orderer, information on the time of the information to be printed, information on the event of the information to be printed, information on the purpose of the information to be printed, information on the product to be printed, information on specification items, information on the order cycle, and information on the number of orders, and outputs information on the specifications of the printed matter, which represents at least one of a specification sheet corresponding to the order information and an image representing the printed matter (see Figure 5). Deep learning can be used as an example of a learning algorithm, and a neural network model may be constructed so that when order information of the learning data is input, information on the specifications of the printed matter of the learning data is output.

[0062] More specifically, the order information of the learning data is used as input, and information regarding the specifications of the printed matter is estimated as the output of the model, and the estimated information regarding the specifications of the printed matter is compared with the information regarding the specifications of the printed matter in the learning data to calculate the error in the information regarding the specifications of the printed matter, and the parameters of the model may be updated so as to minimize the value of the error.

[0063] Here, the correlation between the various information included in the order information and the information regarding the specifications of the printed matter will be described.

[0064] First, the type of printed material can be narrowed down based on the name or industry of the client. For example, orders from government agencies tend to include printing of envelope addresses, orders from retail stores tend to include printing of leaflets, fliers, and posting products, and orders from publishers tend to include printing of books. Furthermore, orders from advertising agencies tend to include printing of booklets or pamphlets, and orders from financial companies tend to include printing of securities. In this way, there is a correlation between the name or industry of the client and information regarding the specifications of the printed material.

[0065] Furthermore, as in the following example, there is a correlation between information about the content of information printed on a printed matter and information about the specifications of the printed matter.

[0066] "Pamphlet distributed at last year's new car exhibition" "A flyer for a new store opening" Autumn Fish Sushi Menu New spring seasonal vegetable menu "Summer Luxury Travel Brochure" "This fall's new clothing catalog" "Travel sale flyer during the winter off-season" "New Year's greeting cards (New Year's cards) for customers" "Summer greeting card (summer greetings) for customers" "A greeting card to customers to wish them well after the heatwave (late summer greetings)" "New Year's greeting card (winter greeting card) for customers"

[0067] If the order cycle has characteristics, it is often the case that the printed matter is published periodically, such as catalogs, pamphlets, business cards, company magazines, newspapers, etc. In this way, there is a correlation between information about the order cycle and information about the specifications of the printed matter.

[0068] If the number of orders is large, there is a high possibility that the order is for a printed product that has been ordered many times. In this way, there is a correlation between information about the number of orders and information about the specifications of the printed product.

[0069] The model storage unit 104 stores a trained neural network model.

[0070] The reception unit 105 receives text data describing an order for printed matter from a user who is an orderer, or voice data uttered regarding an order for printed matter. For example, voice data such as "I'm ordering the pamphlet distributed at last year's new car exhibition. I'd like to use this year's seasonal color as the background and include an advertisement on part of the page. I'd like the size to be A4, with a dark text color, and the logo to be printed in special colors with C as an O value, M as an O value, Y as an O value, and K as an O value. Please deliver 10,000 copies to the address XXXX. I would like them to be wrapped and delivered in two installments, this month and next month."

[0071] The extraction unit 106 uses a trained large-scale language model to extract various pieces of information contained in the order information from text data written regarding the order of printed matter or from speech data uttered regarding the order of printed matter.

[0072] Specifically, the extraction unit 106 inputs the text data or voice data received by the reception unit 105 and a prompt sentence instructing extraction of various information included in the order information into a trained large-scale language model. The extraction unit 106 extracts various information included in the order information from the output of the trained large-scale language model. An example of the large-scale language model may be generative AI (artificial intelligence). The large-scale language model may be trained to extract various information included in the order information from the text data or voice data. An example of a learning algorithm in this case may be fine-tuning of the generative AI.

[0073] The order information includes information about the person who ordered the printed matter. Specifically, the information about the person who ordered the printed matter includes the name of the person who ordered the printed matter or the type of business of the person who ordered the printed matter. The extraction unit 106 extracts, for example, the name of the person who ordered the printed matter and the type of business of the person who ordered the printed matter, such as "government agency."

[0074] The order information may also include information about the content of the information to be printed on the printed matter. Specifically, the information about the content of the information to be printed includes information about the time of the information to be printed, information about the event of the information to be printed, information about the purpose of the information to be printed, or information about the product to be printed. The extraction unit 106 extracts, for example, information about the time of the information to be printed ("spring"), information about the event of the information to be printed ("new car exhibition presentation"), information about the purpose of the information to be printed ("pamphlet"), and information about the product to be printed ("car").

[0075] The order information may also include information related to the specification items of the printed matter. For example, the extraction unit 106 extracts a combination of the specification items of the printed matter and the value of the item, such as "size of printed matter is A4, number of copies is 10,000."

[0076] The order information may also include information about the order cycle, for example, the extraction unit 106 extracts the information about the order cycle, such as "every year."

[0077] The order information may also include information about the number of orders placed. The extraction unit 106 extracts, for example, information about the number of orders placed, such as "three times."

[0078] The trained large-scale language model may be stored in the model storage unit 104 or in an external device.

[0079] The estimation unit 107 uses a trained neural network model stored in the model storage unit 104 to estimate information related to the specifications of the printed matter corresponding to the extracted order information.

[0080] Specifically, the various information contained in the extracted order information is converted into a data structure (e.g., scalar values, vectors, etc.) that can be input into a trained neural network model, and then input into the trained neural network model, and information regarding the specifications of the printed material is estimated from the output of the trained model.

[0081] The output unit 108 outputs to the user the specifications included in the information relating to the estimated specifications of the printed matter and an image representing the printed matter.

[0082] At this time, the user who is the orderer checks the specifications and the image representing the printed matter included in the information on the estimated specifications of the printed matter, and if the specifications differ from those of the printed matter that the user is trying to order, the specifications are changed by an operation of the user or the person in charge of ordering. At this time, the collection unit 101 may acquire the changed specifications as the correct specifications, and collect pairs of the order information and the information on the specifications of the printed matter including the acquired specifications as learning data.

[0083] Next, specific processing performed in the information processing system 10 according to this embodiment configured as described above will be described.

[0084] First, in the client computer 14, the CPU 14A reads out the learning program from the ROM 14B or the storage 14D, loads it into the RAM 14C, and executes it, thereby performing the learning process shown in FIG.

[0085] In step S100, the CPU 14A functions as the collection unit 101 to collect learning data including pairs of order information related to printing and information on the specifications of the printed matter corresponding to the order information, and stores the learning data in the learning data storage unit 102.

[0086] In step S102, the CPU 14A, as the learning unit 103, inputs the order information based on multiple learning data, learns a neural network model for estimating information related to the specifications of the printed matter corresponding to the order information, stores the model in the model memory unit 104, and terminates the learning process.

[0087] Next, in the client computer 14, the CPU 14A reads out the information processing program from the ROM 14B or the storage 14D, expands it into the RAM 14C, and executes it, thereby performing the estimation process shown in Fig. 7. At this time, it is assumed that the user has input text data written regarding the order of printed matter or voice data uttered regarding the order of printed matter.

[0088] In step S110, CPU 14A functions as reception unit 105 and receives text data entered by the user that is written regarding the order for printed matter, or voice data uttered regarding the order for printed matter.

[0089] In step S112, the CPU 14A, as the extraction unit 106, uses a trained large-scale language model to extract various information contained in the order information from text data written regarding the ordering of printed matter or from voice data spoken regarding the ordering of printed matter.

[0090] In step S114, CPU 14A functions as estimation unit 107 and uses the trained neural network model stored in model storage unit 104 to estimate information relating to the specifications of the printed matter corresponding to the extracted order information.

[0091] In step S116, CPU 14A outputs information relating to the estimated specifications of the printed matter to the user as output unit 108, and the estimation process ends. The user can use the information relating to the estimated specifications of the printed matter to place an order for the printed matter.

[0092] By carrying out the process in this manner, it is possible to predict the specifications of the printed matter according to the orderer.

[0093] <Modification> The present invention is not limited to the above-described embodiment, and various modifications and applications are possible without departing from the spirit and scope of the present invention.

[0094] For example, the trained neural network model may estimate multiple pieces of information related to the specifications of printed matter corresponding to the extracted order information. In this case, the multiple estimated results of the information related to the specifications of printed matter may be ranked. In this case, pairs of order information and multiple ranked pieces of information related to the specifications of printed matter corresponding to the order information may be collected as training data, and the neural network model may be trained using the collected data.

[0095] In addition, although an example has been described in which various pieces of order information are accepted and information regarding the specifications of the printed matter is estimated, it is also possible to accept only a portion of the various pieces of order information rather than accepting all of the various pieces of order information and estimate information regarding the specifications of the printed matter.

[0096] In addition, although the example has been described in which the client computer 14 learns a model and estimates information related to the specifications of the printed matter, it may also be configured as being divided into a learning device that learns the model and an estimation device that estimates information related to the specifications of the printed matter.

[0097] Furthermore, in the above embodiments, the term "processor" refers to a processor in a broad sense, and includes general-purpose processors (e.g., CPUs, etc.) and dedicated processors (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0098] Furthermore, the operations of the processors in the above embodiments may not only be performed by a single processor, but may also be performed by multiple processors located at physically separate locations working together. Furthermore, the order of the operations of the processors is not limited to the order described in the above embodiments, and may be changed as appropriate.

[0099] Furthermore, although the "system" in this embodiment is described as being composed of multiple devices as an example, it may also be composed of a single device that has some of the functions of multiple devices.

[0100] The processing performed by the printing device 12 according to the above embodiment may be software-based, hardware-based, or a combination of both. The processing performed by the printing device 12 may also be stored as a program on a storage medium and distributed.

[0101] Furthermore, the present disclosure is not limited to the above, and it goes without saying that various modifications can be made without departing from the spirit of the present disclosure.

[0102] Furthermore, the information relating to the specifications of the printed matter may include information relating to the cost of post-processing.

[0103] The following additional notes are provided regarding the above-described embodiments. (((1))) a processor, the processor comprising: When order information including information about an orderer of a printed matter and information about the content of information to be printed on the printed matter or information about specification items of the printed matter is input, new order information including information about an orderer of a new printed matter and information about the content of information to be printed on the new printed matter or information about specification items of the new printed matter is input to a trained model that has been trained in advance to output information about the specifications of the printed matter, and information about the specifications of the new printed matter corresponding to the new order information is output. Information processing system.

[0104] (((2))) The information about the client includes the name of the client or the business type of the client, The information processing system according to (((1))).

[0105] (((3))) The information processing system according to ((1)) or ((2))), wherein the information relating to the content of the information to be printed on the printed matter includes information relating to the time of the information to be printed, information relating to the event of the information to be printed, information relating to the purpose of the information to be printed, or information relating to the product to be printed.

[0106] (((4))) The information processing system according to any one of ((1))) to ((3))), wherein the order information and the new order information further include information relating to an order cycle.

[0107] (((5))) The information processing system according to any one of ((1))) to ((4))), wherein the order information and the new order information further include information relating to the number of orders.

[0108] (((6))) the information about the specifications includes an image representing the printed matter; The information processing system according to any one of (((1))) to (((5))).

[0109] (((7))) The processor extracts the new order information from the input text or speech using a large-scale language model that has been trained in advance, and outputs information regarding the specifications of the new printed matter corresponding to the new order information by inputting the extracted new order information into the trained model. The information processing system according to any one of (((1))) to (((6))).

[0110] (((8))) When order information including information about an orderer of a printed matter and information about the content of information to be printed on the printed matter or information about specification items of the printed matter is input, new order information including information about an orderer of a new printed matter and information about the content of information to be printed on the new printed matter or information about specification items of the new printed matter is input to a trained model that has been trained in advance to output information about the specifications of the printed matter, and information about the specifications of the new printed matter corresponding to the new order information is output. An information processing program that causes a computer to perform certain tasks.

[0111] According to (((1))), it is possible to predict the specifications of printed matter according to the client.

[0112] According to (((2))), the specifications of the printed matter can be predicted based on the name of the client or the client's industry.

[0113] According to (((3))), the specifications of the printed matter can be predicted based on information about the time of the information to be printed, information about the event, information about the purpose, or information about the product.

[0114] According to (((4))), it is possible to predict the specifications of printed materials based on the order cycle.

[0115] According to (((5))), it is possible to predict the specifications of printed materials based on the number of orders.

[0116] According to (((6))), it is possible to predict an image that represents a printed matter according to the customer.

[0117] According to (((7))), it is possible to predict the specifications of a printed matter from input text or voice.

[0118] According to (((8))), it is possible to predict the specifications of printed matter according to the client. [Explanation of symbols]

[0119] 10 Information Processing Systems 11 Management device 12 Printing device 14 client computers 24 Printing Department 46 Post-processing section 101 Collection Department 102 Learning data storage unit 103 Learning Department 104 Model Memory Unit 105 Reception 106 Extraction part 107 Estimation part 108 Output section

Claims

1. a processor, the processor comprising: When order information including information about an orderer of a printed matter and information about the content of information to be printed on the printed matter or information about specification items of the printed matter is input, new order information including information about an orderer of a new printed matter and information about the content of information to be printed on the new printed matter or information about specification items of the new printed matter is input to a trained model that has been trained in advance to output information about the specifications of the printed matter, and information about the specifications of the new printed matter corresponding to the new order information is output. Information processing system.

2. The information about the client includes the name of the client or the business type of the client, The information processing system according to claim 1 .

3. 2. An information processing system according to claim 1, wherein the information regarding the content of the information to be printed on the printed matter includes information regarding the timing of the information to be printed, information regarding the event of the information to be printed, information regarding the purpose of the information to be printed, or information regarding the product to be printed.

4. The information processing system according to claim 1 , wherein the order information and the new order information further include information regarding an order cycle.

5. 2. The information processing system according to claim 1, wherein the order information and the new order information further include information regarding the number of orders.

6. the information about the specifications includes an image representing the printed matter; The information processing system according to claim 1 .

7. The processor extracts the new order information from the input text or speech using a large-scale language model that has been trained in advance, and outputs information regarding the specifications of the new printed matter corresponding to the new order information by inputting the extracted new order information into the trained model. The information processing system according to claim 1 .

8. When order information including information about an orderer of a printed matter and information about the content of information to be printed on the printed matter or information about specification items of the printed matter is input, new order information including information about an orderer of a new printed matter and information about the content of information to be printed on the new printed matter or information about specification items of the new printed matter is input to a trained model that has been trained in advance to output information about the specifications of the printed matter, and information about the specifications of the new printed matter corresponding to the new order information is output. An information processing program that causes a computer to perform certain tasks.

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