JP6801389B2 - Information processing equipment and information processing programs - Google Patents

Description

The present invention relates to an information processing device and an information processing program.

Patent Document 1 describes a workflow management that can prevent a highly confidential document from being left in a printing device at the printing processing stage of the workflow without the workflow administrator making complicated definitions. The challenge is to provide a device, workflow management program, and workflow system, and a document with high confidentiality and importance is judged as a document that prohibits direct printing based on the document attribute value and user attribute value acquired by the attribute value acquisition unit. , Documents for which direct printing is prohibited are put on standby for printing, and the user terminal of the user in charge of processing the printed document is notified that the printing of the document is waiting, and the notified user prints. When instructed, the document is printed on the printer B specified by the user, and the document that does not prohibit direct printing is disclosed to be printed on the printer A specified in the print process of the workflow without waiting. ..

Patent Document 2 has an object of creating an arbitrary workflow and maintaining such a workflow on a computer server connected to a computer network, and from among the workflow menu options presented in the graphical user interface of the multifunction machine. Automatically and dynamically create the device-specific user interface screens used when running the workflow, the device-specific user interface screens send data entry commands to the graphical user interface, and when the user responds, these methods And systems allow graphical user interfaces to receive workflow data, and these methods and systems are disclosed to send workflow data from a compound machine to a computer device over a computer network to perform the workflow.

Japanese Unexamined Patent Publication No. 2010-061349 JP 2015-154489

Generally, in order to generate a workflow, it is necessary to be familiar with the business targeted by the workflow and to have specialized knowledge of the workflow system. On the other hand, as described in the patent document, an image processing device is used for workflow management, and document processing by the image processing device has an important role in the workflow. ..
An object of the present invention is to provide an information processing apparatus and an information processing program for generating a workflow by using a document or an instruction targeted by the processing apparatus.

The gist of the present invention for achieving such an object lies in the inventions of the following items.
The invention of claim 1, possess a receiving unit that receives an instruction about the processing of the document is used to generate a workflow including a process of the document, the extraction means for extracting the document or information relating to the instruction, the The extraction means extracts the document used in the front step or the back step of the target document, and the extraction means is similar to the target document when the document used in the front step or the back step of the target document cannot be extracted. It is an information processing apparatus that extracts a document to be used and extracts a document used in a pre-process or a post-process of the similar document .

The invention according to claim 2 is the information processing device according to claim 1, wherein the receiving means is connected to or built in a processing device that performs the processing, and receives the instruction from a wireless or wired communication line.

The invention according to claim 3 is the information processing device according to claim 1, wherein the receiving means receives the instruction and then passes the instruction to the processing device that performs the processing.

The invention of claim 4 is the information processing apparatus according to claim 1, wherein when the receiving means receives the instruction, the operator who has given the instruction is added information about the document or the instruction. ..

The invention of claim 5 is the information processing device according to claim 4, wherein the receiving means is the information processing apparatus according to claim 4, which presents the operator with information on the document or the instruction in a selectable manner.

The invention of claim 6, wherein the extraction means according to claim 1, wherein the document used in the previous step or later step of similar documents, given the weight according to the similarity between the target document and the similar documents It is an information processing apparatus according to.

The invention of claim 7 functions as a receiving means for receiving an instruction regarding processing of a document and an extracting means for extracting information regarding the document or the instruction used for generating a workflow including processing of the document. If the extraction means cannot extract the document used in the pre-process or the post-step of the target document, and the extraction means cannot extract the document used in the pre-process or the post-step of the target document, the target. It is an information processing program that extracts a document similar to a document and extracts a document used in a pre-process or a post-process of the similar document .

According to the information processing apparatus of claim 1, information regarding a document or instruction targeted by the processing apparatus used for generating a workflow can be extracted. In addition, the document used in the pre-process or post-process of the target document used for generating the workflow can be extracted. Then, even when the document used in the pre-process or the post-process of the target document cannot be extracted, the document used for generating the workflow can be extracted.

According to the information processing device of claim 2, it is possible to receive an instruction to be accepted via a communication line.

According to the information processing apparatus of claim 3, the instruction can be received before the processing apparatus receives the instruction.

According to the information processing apparatus of claim 4, the operator can add information regarding a document or an instruction.

According to the information processing apparatus of claim 5, the operator can select information regarding a document or an instruction.

According to the information processing apparatus of claim 6 , the weights used for generating the workflow can be extracted according to the degree of similarity between the target document and the similar document.

According to the information processing program of claim 7 , information on a document or instruction targeted by the processing device used for generating a workflow can be extracted. In addition, the document used in the pre-process or post-process of the target document used for generating the workflow can be extracted. Then, even when the document used in the pre-process or the post-process of the target document cannot be extracted, the document used for generating the workflow can be extracted.

It is a conceptual module configuration diagram about the configuration example of this embodiment. It is a flowchart which shows the processing example by this embodiment. It is explanatory drawing which shows the system configuration example using this embodiment. It is a flowchart which shows the processing example by this embodiment. It is explanatory drawing which shows the presentation example of the screen. It is explanatory drawing which shows the data structure example of a job log table. It is explanatory drawing which shows the data structure example of the additional information table. It is a flowchart which shows the processing example by this embodiment. It is explanatory drawing which shows the data structure example of a workflow table. It is explanatory drawing which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is a flowchart which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is a conceptual module configuration diagram about the configuration example of this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is explanatory drawing which shows the processing example by this embodiment. It is a block diagram which shows the hardware configuration example of the computer which realizes this embodiment.

Hereinafter, an example of a preferred embodiment for realizing the present invention will be described with reference to the drawings.
FIG. 1 shows a conceptual module configuration diagram for a configuration example of the present embodiment.
The module generally refers to parts such as software (computer program) and hardware that can be logically separated. Therefore, the module in this embodiment refers not only to the module in the computer program but also to the module in the hardware configuration. Therefore, in the present embodiment, a computer program for functioning as those modules (a program for causing the computer to perform each procedure, a program for causing the computer to function as each means, and each function for the computer). It also serves as an explanation of the program), system and method for realizing the above. However, for convenience of explanation, "remember", "remember", and equivalent words are used, but these words are stored in a storage device or stored when the embodiment is a computer program. It means that it is controlled so that it is stored in the device. Further, the modules may have a one-to-one correspondence with the functions, but in mounting, one module may be configured by one program, a plurality of modules may be configured by one program, and conversely, one module may be configured. May be composed of a plurality of programs. Further, the plurality of modules may be executed by one computer, or one module may be executed by a plurality of computers by a computer in a distributed or parallel environment. It should be noted that one module may include another module. In addition, hereinafter, "connection" is used not only for physical connection but also for logical connection (data transfer, instruction, reference relationship between data, etc.). "Predetermined" means that it is determined before the target process, not only before the process according to the present embodiment starts, but also after the process according to the present embodiment starts. However, if it is before the target process, it is used with the intention that it is determined according to the situation / state at that time or according to the situation / state up to that point. When there are a plurality of "predetermined values", they may be different values, or two or more values (including all values, of course) may be the same. Further, the description "if A, do B" is used to mean "determine whether or not it is A, and if it is determined to be A, do B". However, this excludes cases where it is not necessary to determine whether or not it is A.
In addition, a system or device is configured by connecting a plurality of computers, hardware, devices, etc. by communication means such as a network (including a one-to-one correspondence communication connection), and one computer, hardware, device, etc. It also includes cases where it is realized by such means. "Device" and "system" are used as synonymous terms. Of course, the "system" does not include anything that is nothing more than a social "mechanism" (social system) that is an artificial arrangement.
In addition, for each process by each module or when multiple processes are performed in the module, the target information is read from the storage device, and after the processes are performed, the process results are written to the storage device. is there. Therefore, the description of reading from the storage device before processing and writing to the storage device after processing may be omitted. The storage device here may include a hard disk, a RAM (Random Access Memory), an external storage medium, a storage device via a communication line, a register in a CPU (Central Processing Unit), and the like.

The information processing device 100 according to the present embodiment generates a workflow using a document or an instruction targeted by the image processing device, and as shown in the example of FIG. 1, the data collection module 105, It has a data storage module 120 and an analysis processing module 125.

The data collection module 105 has a processing information collection module 110 and a metadata collection module 115, and is connected to the data storage module 120. The data collection module 105 collects instructions and the like for the image processing device.
The processing information collection module 110 receives instructions regarding image processing of a document. For example, document printing instruction (printing), document fax transmission / reception, document reading (scanning), document reading / mail transmission, document storage (for example, storage device in information processing apparatus 100, USB (Universal Serial) There are storage media such as USB), storage in a document database server or the like connected to the information processing device 100 via a communication line), and the like.
The processing information collection module 110 may be connected to or built into an image processing device that performs image processing, and may receive instructions regarding image processing of a document from a wireless or wired communication line.
Further, the processing information collection module 110 may pass the instruction to the image processing device after receiving the instruction. That is, the processing information collection module 110 first receives the instruction (before the image processing apparatus receives it) and then passes it to the image processing apparatus to ensure that the instruction to the image processing apparatus can be collected. .. Therefore, for example, the processing information collection module 110 may be included in the communication device. The form of the processing information collection module 110 will be described in detail with reference to the example of FIG.

When the processing information collection module 110 receives an instruction, the metadata collection module 115 causes the operator who gave the instruction to add information about the document or the instruction. Information about a document or instruction is also referred to as metadata below. This metadata includes the document itself, the attribute information of the document (document name, creator, document capacity, etc.), the information extracted from the document, the instruction itself, and the attribute information of the instruction (date and time (year, year, when the instruction was given). Month, day, hour, minute, second, second or less, or a combination thereof, including the concept of date), the operator who gave the instruction, the type of instruction, etc.), derived from the instruction The information that has been provided is included.
The metadata collection module 115 may then selectively present information about the document or instruction to the operator. The selectability here means presenting a plurality of pieces of information regarding a document or instruction and allowing the operator to select the information. This makes it possible to obtain the information necessary for workflow generation.

The data storage module 120 is connected to the data collection module 105 and the analysis processing module 125. The data storage module 120 stores information such as instructions and target documents collected by the data collection module 105. Then, the information is supplied to the analysis processing module 125. The instruction to be stored may include link information to the target document. Further, it may be constructed as two databases (database A for storing instructions and database B for storing documents), or as one database (database for storing instructions and documents).

The analysis processing module 125 has a workflow generation module 130, a recommendation generation module 135, and an output module 140, and is connected to the data storage module 120. The analysis processing module 125 uses the information about the document or the instruction to generate a workflow and generate a recommendation or the like.
The workflow generation module 130 generates a workflow including processing of the document from the information about the document or the instruction.
For example, the workflow generation module 130 may generate a workflow by extracting a document used in a pre-process or a post-process of the target document.
Further, when the workflow generation module 130 cannot extract the document used in the pre-process or the post-process of the target document, the workflow generation module 130 extracts a document similar to the target document. Then, the workflow may be generated by extracting the documents used in the pre-process or the post-process of the similar documents.
Then, the workflow generation module 130 attaches weights to the documents used in the pre-process or the post-process of similar documents according to the degree of similarity between the target document and the similar documents, and uses the weights to perform the workflow. It may be generated.

The recommendation generation module 135 generates a recommendation by using a workflow generated by the workflow generation module 130 and including a document similar to the target document. The workflow here may be, in particular, a workflow for an operator (expert, etc.) who has already completed the same processing. Then, as equivalent processing, in particular, a workflow in which the other party is the same may be used. In addition, the recommendation is advice such as points to be noted when processing the document. Specifically, there is advice such as using the designated slip of the other party. Predetermined rules (regulations of predictable personal knowledge) may be used in generating this recommendation.

The output module 140 outputs the workflow generated by the workflow generation module 130, the recommendations generated by the recommendation generation module 135, and the like. To output here, for example, printing with a printing device such as a printer, displaying on a display device such as a display, transmitting an image with an image transmitting device such as a fax, writing to a storage device such as a database, etc. This includes storing in a storage medium such as a memory card and passing it to another information processing device. Further, as a display on the display device, an output as a 3D (Dimensions) image may be included, and further, an audio output to an audio output device such as a speaker, vibration, or the like may be combined.

FIG. 2 is a flowchart showing a processing example according to the present embodiment (information processing device 100).
In step S202, the processing information collection module 110 collects an instruction to the image processing device 300 and a target document.
In step S204, the metadata collection module 115 performs the metadata addition process. The detailed processing of step S204 will be described later using the flowchart shown in the example of FIG.

FIG. 3 is an explanatory diagram showing an example of a system configuration using the present embodiment. In particular, the processing information collection module 110 shown in the example of FIG. 1 and the specific example of the processing in step S202 shown in the example of FIG. 2 are shown. The image processing device 300 is a printer, a multifunction device (an image processing device having any two or more functions such as a scanner, a printer, a copying machine, and a fax machine) installed in an office.
The example shown in FIG. 3A is an example in which the information processing device 100 is built in the stick PC 330. The image processing device 300 has a connection port for inserting the stick PC 330. For example, the stick PC 330 has a USB connector, and the image processing device 300 has a USB port. The stick PC 330 has wireless short-range communication functions such as WiFi, Bluetooth (registered trademark), infrared communication, and visible light communication, and communicates with a personal computer 310, a mobile terminal 320 (including a tablet terminal), and the like. It is possible.
The stick PC 330 receives an instruction (for example, a print instruction) regarding a document transmitted to the image processing device 300 from a personal computer 310, a mobile terminal 320, or the like, and passes it to the image processing device 300. That is, the image processing device 300 receives instructions regarding image processing of documents from the personal computer 310 and the mobile terminal 320 via the stick PC 330. With this configuration, the stick PC 330 (information processing device 100) can reliably collect instructions regarding image processing of documents to the image processing device 300. Of course, the stick PC 330 (or the information processing device 100) may be built in the information processing device 100.

The example shown in FIG. 3A2 is an example in which the information processing device 100 is built in the dedicated processing device 340.
The image processing device 300, the personal computer 310, and the dedicated processing device 340 are connected to each other via a communication line 390. Then, the mobile terminal 320 and the dedicated processing device 340 can communicate with each other by wireless short-range communication. For example, the image processing device 300 may receive an instruction from the dedicated processing device 340, and the personal computer 310 and the mobile terminal 320 may transmit the instruction to the image processing device 300 to the dedicated processing device 340. .. Further, the dedicated processing device 340 may monitor the communication addressed to the image processing device 300 and extract the instruction to the image processing device 300. With this configuration, the dedicated processing device 340 (information processing device 100) can reliably collect instructions regarding image processing of documents to the image processing device 300.

The example shown in FIG. 3B is an example in which the data collection module 105 is built in the personal computer 310 and the mobile terminal 320, and the data storage module 120 and the analysis processing module 125 are built in the management server 350.
The image processing device 300, the personal computer 310, and the management server 350 are connected to each other via the communication line 390.
The management server 350 is a server (on-premises server) installed in-house to manage the image processing device 300. The data collection module 105 in the personal computer 310 and the mobile terminal 320 collects instructions and documents for the image processing device 300, and transmits the collected instructions and documents to the management server 350. The management server 350 performs processing as a data storage module 120 and an analysis processing module 125. Further, the management server 350 may have a function as the metadata collection module 115. In that case, when the personal computer 310 and the mobile terminal 320 give an instruction to the image processing device 300, the management server 350 also communicates with the management server 350, the management server 350 generates metadata, and the personal computer 310 and the mobile terminal The operator of 320 may be allowed to select.

The example shown in FIG. 3C is an example in which the information processing device 100 is built in the data analysis server 380. The data analysis server 380 as a cloud provides the service of the information processing apparatus 100.
The image processing device 300, the personal computer 310, the management server 350, and the communication server 360 are connected to each other via the communication line 390, and the data is also connected via the communication line 390, the firewall device 370, and the communication line 395. It is connected to the analysis server 380.
The data analysis server 380 provides a print service. Specifically, it receives print instructions from the personal computer 310 and the mobile terminal 320, and gives print instructions to the image processing device 300.
However, in order to perform communication beyond the firewall device 370, the personal computer 310 and the mobile terminal 320 access the data analysis server 380, which is a cloud service, from within the office, or manage the dedicated communication server 360. It is necessary to access the dedicated software in the server 350. The communication server 360 is dedicated communication hardware that is accessed from the office side in order to perform communication beyond the firewall device 370. Further, the management server 350 has dedicated communication software accessed from the office side in order to perform communication beyond the firewall device 370. The personal computer 310, the mobile terminal 320, and the image processing device 300 communicate with the data analysis server 380 via the management server 350 or the communication server 360.

FIG. 4 is a flowchart showing a processing example according to the present embodiment (mainly the data collection module 105).
In step S402, the processing information collection module 110 receives the target document information.
In step S404, the metadata collection module 115 extracts the metadata.
In step S406, the metadata collection module 115 extracts candidates for related documents for the target document.
For example, metadata candidates and related document candidates are extracted using the following methods.
(1) Extract metadata candidates from the ruled file naming. For example, there are cases where rules are set in-house (or groups, etc.) for assigning file names. This rule is used to extract metadata candidates. For example, if there is a rule "compose a file name using" date, destination information, document type "", it is assumed that a document with the following file name is printed.
"20161018_Company A_Quotation Request Form 00103.xlsx"
In this case, "20161018" is extracted as the date (date and time), "Company A" is extracted as the destination information, and "Quotation request form" is extracted as the document type as metadata candidates.

(2) Extract related document candidates from past log (history) information and registration files.
For example, as log information, there is the following information (job occurrence date and time, job content, file name of target document, number of target documents, storage destination of target document).
−−−−− −−−−− −−−−−−
・ ・ ・ ・ ・ ・
・ ・ ・ ・ ・ ・
2016.10.10 Document storage 20161018_Company A_Quotation request form 00103.xlsx Page 3 A Server
2016.10.10 Document print 20161018_Company A_Quotation request form 00103.xlsx Page 3 A Server −−−−− −−−−−−−−−−−−−−−
Then, when the document of "20161020_A company_quote 00103.xlsx" is stored as a new job, "20161018_A company_quotation request form 00103.xlsx" is extracted as a candidate of the related document from the log information. In addition, a "superior document" is extracted as a relationship between these documents (hereinafter, also referred to as a related document type). The higher-level document means that the related document is higher than the target document (pre-process in the workflow). Further, this extraction is performed according to a predetermined extraction rule. As a rule for extracting related document candidates, for example, the document names are similar, and in the case of "quotation", "quotation request form" is extracted. Then, as a rule for extracting the relationship between documents, for example, when the date of the candidate document is before the date of the target document, the destination information of the document name is the same, and the target document is "estimate". If it is a "quotation request form", it may be a "superior document".
Furthermore, if there is a document storage of "20161020_A company quotation data.xlsx" as a new job, "20161020_A company_quotation 00103.xlsx" as a candidate for related documents (document with the above document storage) To extract. In this case, the extraction rule of related document candidates is applied to extract the "estimate" when the "estimate data" is included. In addition, "evidence" is extracted as the relationship between these documents, and "reference" is extracted as the second point. In this case, the extraction rule of the relationship between documents is "evidence" if the destination information of the document name is the same and the target document contains "estimate data" and is "estimate", and the runner-up ""Reference" is applied.

(3) Extract keywords from the contents in the document. Extract the keyword as a candidate for metadata. Furthermore, candidates for related documents containing the keyword are extracted.
For example, a paper document is scanned and digitized, and keywords are extracted by structural analysis and character recognition of the document. The keyword here may be a predetermined character string. Then, the file name is given using the keyword. It may also be extracted as a candidate for "document type". For example, the technique described in JP2012-190315 may be used for assigning a file name. Specifically, a feature character string described in Japanese Patent Application Laid-Open No. 2012-190315 (for example, a character string for a human to identify a manuscript, such as a title of a manuscript) or a feature character string. Candidates (character strings that can be characteristic character strings and are determined for each page constituting the manuscript) may be used as file names or file name candidates. Further, as a related document, a document including the keyword may be searched from the above-mentioned log information.

(4) Extract metadata candidates and related document candidates using the statistical data obtained by learning.
Learning is performed using past metadata as learning data (input data). Then, as an output, candidates having a high correlation as "related documents" (for example, candidates whose correlation value is higher or higher than a predetermined threshold value) are extracted. This learning may be processed in the background, and candidates may be extracted using the known metadata (information extracted from the log information) of the document at the time of new registration.

(5) Data being worked on is extracted from the personal computer 310 and the mobile terminal 320, and extracted as metadata candidates and related document candidates.
The information that was being worked on by the personal computer 310 and the mobile terminal 320 at the time of document registration is extracted as metadata. For example, the URL (Uniform Resource Locator) of the website (Website) referred to at the time of document registration may be a candidate for metadata. As a method of obtaining the URL, for example, it may be extracted from the history of the browser and the history of the proxy server. In addition, recently accessed documents may be candidates for related documents. As a method of obtaining the accessed document, for example, it may be extracted from the history of accessing the document database used in the personal computer 310 and the mobile terminal 320.

In step S408, the metadata collection module 115 queries the user. Specifically, the candidate of the related document extracted in step S406 (the candidate of the metadata extracted in step S404 may be included) is displayed, and the user is made to select.
For example, the screen 500 shown in FIG. 5 is presented on a liquid crystal display of a personal computer 310, a mobile terminal 320, or the like used by a user. FIG. 5 is an explanatory diagram showing a presentation example of the screen 500.
The screen 500 is an example of a display screen in a device that gives a print instruction (in the above-mentioned example, a personal computer 310 and a mobile terminal 320).
In the output instruction area 510, for example, the number of copies to be printed, the selection of black-and-white / color printing, and the like can be set.
In the document preview area 520, for example, a reduced image of a document to be printed is displayed.
In the metadata setting area 530, for example, the extracted related document candidates are displayed, and the user interface for modifying the related documents is displayed.
More specifically, at the time of the operation for giving an instruction to the image processing device 300, the software having the function of the data collection module 105 is activated by external cooperation. The instruction to the user interface (particularly, the output instruction area 510 and the document preview area 520) in the image processing device 300 is detected, and the metadata setting area 530 is displayed. The document may be stored in the data storage module 120.

In step S410, the metadata collection module 115 determines whether or not the result of the inquiry to the user is OK, and if it is OK, the process proceeds to step S414, and if not, the process proceeds to step S412.
In step S412, the metadata collection module 115 determines whether or not the process is canceled, ends the process in the case of cancellation (step S499), and returns to step S406 in other cases.
In step S414, the metadata collection module 115 stores the document / metadata in the data storage module 120.
In step S416, the image processing device 300 prints according to the instructions.
In step S418, after the printing is completed, the data collection module 105 updates the meta database in the data storage module 120 with respect to the date and time when the printing is completed.

It should be noted that the processing information collection module 110 may provide a function of adding and correcting metadata for the case where the instruction to the image processing device 300 could not be collected. Further, the metadata already stored in the data storage module 120 may be used as an editing function so that it can be modified later.
The following is an example of the case where the processing information collection module 110 cannot collect the instruction to the image processing device 300.
(1) When the "Scan to Server" function (function to store directly in the document database after scanning) is used.
(2) When the received document by the fax function is directly transferred from the image processing device 300 to the storage.
And so on.

For example, the job log table 600 and the additional information table 700 are generated by the processing of the flowchart shown in the example of FIG.
FIG. 6 is an explanatory diagram showing an example of a data structure of the job log table 600. The job log table 600 includes a metadata ID column 602, a date and time column 604, a login person column 606, a job content column 608, a file name column 610, a document ID column 612, a document type column 614, a document number column 616, and a capacity column 618. It has a storage destination / destination column 620, a print date / time column 622, and the like. Mainly, it is the information generated from the job (instruction) log.
In the present embodiment, the metadata ID column 602 stores information (metadata ID: IDentification) for uniquely identifying the metadata. The date and time column 604 stores the date and time (or the date and time when the job occurred) when the metadata was generated. The login person column 606 stores the login person (user who gave the instruction). The job content column 608 stores the content of the job as an instruction. Job contents include, for example, scanning, printing, faxing, document storage, and the like. The file name field 610 stores the file name of the target document. The document ID column 612 stores information (document ID) for uniquely identifying the document in the present embodiment. As the document ID, for example, a place (URL) in which the document is stored may be used. Further, it may be the same as the file name in the file name field 610. The document type column 614 stores the document type (for example, PDF: Portable Document Format, type of image, text, etc.) of the target document. The document number column 616 stores the number of target documents. The capacity column 618 stores the capacity of the target document. The storage destination / destination column 620 stores the storage destination or the destination of the target document. The print date / time column 622 stores the print date / time of the target document.

FIG. 7 is an explanatory diagram showing an example of a data structure of the additional information table 700. The additional information table 700 has a metadata ID column 702, a date and time column 704, a document type column 706, a version information column 708, a related document column 710, a document-to-document relationship column 712, a destination information column 714, and the like. Information generated in addition to the job history.
The metadata ID column 702 stores the metadata ID. The date and time column 704 stores the date and time related to the document (for example, the date when the document was requested to be created, the date when the document was created, and the like). The document type column 706 stores the type of the document. The types of documents include, for example, a quotation, a delivery note, and a proposal. The version information column 708 stores version information. The related document column 710 stores related documents. The relationship column 712 between documents stores the relationship between documents. Relationships include, for example, higher-level documents, evidence, references, version differences, similar documents, and the like. The other party information column 714 stores the other party information. As the other party information, for example, there is a business partner name, a person in charge name, and the like.

FIG. 8 is a flowchart showing a processing example according to the present embodiment (mainly the workflow generation module 130).
In step S802, the metadata of an arbitrary unprocessed document is fetched. The unprocessed document here is a document stored in the data storage module 120, and is a document that has not yet been targeted for workflow generation.
In step S804, it is determined whether or not there is a previous process document for the document taken out in step S802. If there is no previous process document, the process proceeds to step S806, and if there is, the process returns to step S802. As a relationship with the related document described above, the document that is "upper" may be the pre-process document.

In step S806, a similar document is searched from the feature amount of the metadata of the target document. For example, it may be judged by the position of the target document and the similar document in the feature space centered on the metadata. Specifically, each similarity is calculated by comparing various metadata of each other's documents, and statistical values of similarity (for example, mean value, total value, mode value, median value, etc.). It may be judged whether or not they are similar by comparing with a predetermined threshold value.
In step S808, it is determined whether or not there is a similar document, and if there is, the process proceeds to step S810, and if not, the process proceeds to step S812.
In step S810, it is determined whether or not there is a previous process document for the similar document, and if there is, the process proceeds to step S814, and if not, the process proceeds to step S812.

In step S812, the target document is marked as an orphan document. That is, information indicating that the document is not a processing target is added in this workflow.
In step S814, the strength (<1) of the pre-process document weighted according to the distance is set (≦ 1 when set by a human (user)). The distance here indicates the distance between the target document and the similar document in the feature space, and the close case is similar (the weight is close to 1), and the far case is dissimilar (the weight is close to 1). The weight is a value close to 0).
In step S816, it is determined whether or not there is an unprocessed document. If there is no unprocessed document, the process proceeds to step S818, and if there is, the process returns to step S802.

In step S818, a graph between documents is created in the order of processes. A graph is a graph in graph theory, and is composed of nodes (here, documents) and edges (here, relationships between documents such as pre-processes and post-processes), and here, workflow candidates. It will be. That is, a workflow (candidate) that becomes a graph is created by connecting documents according to the relationship between processes (documents).
In step S820, the intensity of the process is calculated from the relationship between the similarity of documents and the process. For example, the similarity of documents may be used as the intensity of the process.
In step S822, a high-strength relationship is extracted as a workflow.

For example, the workflow table 900 is generated by processing the flowchart shown in the example of FIG.
FIG. 9 is an explanatory diagram showing an example of a data structure of the workflow table 900. The workflow table 900 has a document ID column 902, a pre-process document ID column 904, a similar document number column 906, a similar document ID column 908, a pre-process document ID column 910, a strength column 912, and the like. The document ID column 902 stores the document ID of the target document. The pre-process document ID column 904 stores the document ID of the pre-process document for the target document. The similar document number column 906 stores the number of documents of documents similar to the target document. The combination of the similar document ID column 908, the pre-process document ID column 910, and the strength column 912 follows by the number in the similar document number column 906. The similar document ID column 908 stores the document ID of the similar document. The pre-process document ID column 910 stores the document ID of the pre-process document for the similar document. The strength column 912 stores the strength calculated in step S820.

FIG. 10 is an explanatory diagram showing a processing example according to the present embodiment. A graph between documents generated by the processing of the flowchart shown in the example of FIG. 8 is shown.
The example shown in FIG. 10A shows the case where the target document 1000 has the pre-process document 1010, and shows the case where the pre-process document is present in step S804.
The example shown in FIG. 10B shows an example in which the target document 1000 does not have a pre-process document but has a similar document 1002 and the similar document 1002 has a similar pre-process document 1012. In this case, depending on the strength, a similar pre-process document 1012 can be used as the pre-process document of the target document 1000.
The example shown in FIG. 10C shows an example in the case where the target document 1000 has no pre-process document and no similar document (step S812).

FIG. 11 is an explanatory diagram showing a processing example according to the present embodiment. The workflow generated by the processing of the flowchart shown in the example of FIG. 8 is shown.
In the example of FIG. 8, the target document 1000, the pre-process document 1010, the similar pre-process document 1022, the similar pre-process document 1024, and the pre-process document 1030 are extracted in this order. That is, the pre-process document 1010 is extracted as the pre-process of the target document 1000, and the similar pre-process document 1022 and the similar pre-process document 1024, which are the pre-processes of the similar documents of the pre-process document 1010, are extracted as the pre-process of the pre-process document 1010. The pre-process document 1030 is extracted as a pre-process of the similar pre-process document 1022 and the similar pre-process document 1024. Then, the strength between the similar pre-process document 1022 and the pre-process document 1010 (specifically, the pre-process document 1010 and the document similar to the pre-process document 1010, in which the similar pre-process document 1022 is the pre-process). (Similarity) is the strength between the similar pre-process document 1024 and the pre-process document 1010 (specifically, the pre-process document 1010 and the document similar to the pre-process document 1010, and the similar pre-process document 1024 is the pre-process. Since the degree of similarity with the document is higher than that of the document), the connection between the similar pre-process document 1022 and the pre-process document 1010 is adopted as the workflow. Therefore, the workflow here is composed of the pre-process document 1030, the similar pre-process document 1022, the pre-process document 1010, and the target document 1000 in this order. For example, a request for quotation, a quotation, a purchase order, a delivery note, etc. are applicable in order.

FIG. 12 is a flowchart showing a processing example according to the present embodiment (mainly the recommendation generation module 135).
In step S1202, the metadata of the newly added document is fetched. The target document is the document operated by the user. In other words, in processing this document, recommendations are generated from the workflow that has already been generated.
In step S1204, a similar document is searched from the feature amount of the metadata extracted in step S1202.
In step S1206, it is determined whether or not there is a similar document, and if there is, the process proceeds to step S1210, and if not, the process proceeds to step S1208.
In step S1208, metadata related to the related document is added, and the process ends (step S1299).

In step S1210, it is determined whether or not there is a post-process flow for the similar document, and if there is, the process proceeds to step S1214, and if not, the process proceeds to step S1212.
In step S1212, the information related to the similar document is updated.
In step S1214, the metadata of the post-process document is presented as a recommendation. That is, when processing the target document, by presenting the metadata of the document in the subsequent process in the similar document that has already been processed, it becomes possible to know in advance the processing that must be performed after the target document. For example, the recommendation may be presented in the metadata setting area 530 shown in FIG.

In step S1214, for example, the following recommendations may be presented.
-Present the next task of processing the target document, or present the document that will be the template for the next task. For example, the post-process document itself of a similar document may be presented.
-Present the delivery date (or estimated number of work days) for the next work. For example, the period between the metadata of similar documents (date and time of occurrence) and the metadata of post-process documents (date and time of occurrence) may be calculated, or if there are multiple similar documents, statistical data of the past data may be calculated. All you have to do is extract the value.
・ Present documents, websites, etc. that will be helpful when performing the following work. For example, the "website URL" and "accessed document", which are the metadata of the post-process document of the similar document, may be presented.
・ Present notes. For example, from the metadata of the post-process document of a similar document, the contents that are different from others even in the same work are presented. In particular, present personal knowledge (metadata).

For example, in the ordering business, there are the following personal knowledge. This is knowledge that cannot be obtained without actually experiencing trading operations.
(1) There is a business partner who uses the designated slip of the other party.
-Some slips use the form specified by the company, but there are cases where there is a designated slip of the other party and it is necessary to fill it out.
(2) Some business partners have different persons in charge of issuing purchase orders and persons in charge of delivery destinations.
(3) There are business partners who need to submit an order confirmation for the purchase order.
・ Usually, you can call or email, but there are cases where you are required to submit an order confirmation indicating that the order has been officially received.
(4) It is necessary to confirm with the manufacturer whether it is in stock and how long the delivery date is, and if it is not in stock, it is necessary to take measures such as how many days it will take to arrange it. If similar products exist, In some cases, specifications are sent to multiple vendors for quotations and comparisons are made.
(5) It may be necessary to confirm the "credit limit" of the other party.
-The "credit limit" is the upper limit of the transaction amount set for each customer. For the first time, when starting a transaction, check the creditworthiness of the customer's payment and determine the maximum amount that the transaction can be continued up to. For example, it is judged by "accounts receivable" and "order amount".
(6) It is necessary to confirm the difference between the inventory on the notebook and the actual inventory in response to irregular operations such as emergency orders represented by orders received after the order deadline.
(7) The closing date may differ between the normal month and the end month depending on the other party.
・ If the billing deadline for the end of the month is early, it is necessary to send the bill earlier, and if it is delayed, it will be processed in the next fiscal year and payment will be delayed by one month.

These personal knowledge is extracted from the metadata of the post-process document of similar documents. Specifically, create rules (pattern data) that define these personal knowledge, apply the rules to the metadata of post-process documents of similar documents, and present the matching rules as personal knowledge. Just do it. It should be noted that the match here includes not only an exact match, but also that a predetermined important part matches, and that the match rate is equal to or higher than a predetermined threshold value.

FIG. 13 is an explanatory diagram showing a processing example according to the present embodiment. An example of a user interface when a print instruction is given by a personal computer 310, a mobile terminal 320, or the like is shown. The screen 1300 shows an example of a screen in a display device such as a liquid crystal display in a personal computer 310, a mobile terminal 320, or the like. The screen 500 illustrated in FIG. 5 is a concrete one, and the output instruction area 510 is omitted as it has already been specified. The document preview area 520 corresponds to the preview area 1335, and the user name field 1340 to the related document type field 1370 are displayed in the metadata setting area 530.
The screen 1300 shown in the example of FIG. 13 shows a state in which the Printing tag 1305 is selected. When a print instruction is given, metadata (so-called annotation input) and a preview regarding the print target are displayed on the screen 1300.
On the screen 1300, a status display column 1325, a preview area 1335, a user name column 1340, a print date and time column 1345, a document name column 1350, a document type column 1355, a business partner column 1360, a related document column 1365, a related document type column 1370, The "Update / Print" button 1375 is displayed.
The document to be printed is reduced and displayed in the preview area 1335 according to the operation of the user's print instruction. Then, as described above, the metadata (or metadata candidates) is extracted and displayed. For example, the user name of the user (the person who operated the print instruction, generally the person who is viewing the screen 1300) is displayed in the user name field 1340, and the print instruction is operated in the print date and time field 1345. The date and time of printing is displayed, and the document name of the document to be printed is displayed in the document name field 1350. These are finalized metadata. Then, the document type (estimate) extracted from the file name is displayed in the document type column 1355, and the supplier (for example, the supplier column) extracted from the predetermined column (for example, the supplier column) in the document is displayed in the supplier column 1360. Company A) is displayed, the document related to the document to be printed (201608020_Company A_estimate request form_2016500500) is displayed in the related document column 1365, and the document to be printed is displayed in the related document type column 1370. Display the related document type (upper document) of the related document of. In the document type column 1355, the business partner column 1360, the related document column 1365, and the related document type column 1370, the metadata candidates are displayed in an editable manner. For example, the second and subsequent candidates may be displayed in a selectable manner, or may be edited by a user operation.
When the "update / print" button 1375 is selected, printing and metadata registration are started, and the "printing" display icon 1330 is displayed in the status display field 1325.

FIG. 14 is an explanatory diagram showing a processing example according to the present embodiment. The screen 1300 shown in the example of FIG. 14 shows a state in which the Relations tag 1315 is selected. Documents related to the target document are displayed as a workflow. Specifically, when the "update / print" button 1375 shown in the example of FIG. 13 is selected, the flow screen of the registration result is displayed, the registered document is displayed in red, the next process is displayed in blue, and the right. The following action items are displayed as recommendations below (recommended document presentation area 1480).
The status display field 1325, the "view overview" button 1435, the workflow presentation area 1440, the document type field 1465, the business partner field 1470, the "view update" button 1475, and the recommended document presentation area 1480 are displayed on the screen 1300.
The "selected document" display icon 1430 is displayed in the status display field 1325, and the file name "201609001_A company_estimate_2016500500" of the target document (selected document) is displayed. In this example, it is the document to be printed in the example of FIG. When the "View Summary" button 1435 is selected, the document is reduced and displayed.

In the workflow presentation area 1440, the selected document is displayed as process B “2016901_A company_estimate_2016500500” 1455, and the document related to this document is displayed as a workflow. These related documents are extracted by processing the flowchart shown in the example of FIG. 4 above.
In step B "2016901_A company_estimate_2016500500" 1455, it is indicated by using red that the document is currently being printed (current process).
Process A1 "201600820_A company_estimate request form_2016500500" 1445 indicates that it is a "superior document" in the previous process with respect to process B "201609001_A company_estimate_2016500500" 1455.
Process A2 "20160825_A company_estimation engineering department response form_2016500500" 1450 indicates that it is a document that is "evidence" for process B "201609001_A company_estimate_2016500500" 1455.
Process C "Company A_Order Form" 1460 indicates that it is a document to be created in the next process with respect to Process B "2016901_Company A_Quotation_2016500500" 1455 as a recommendation.
The document type column 1465 and the supplier column 1470 indicate a part of the metadata of the target document (process B “2016901_A company_estimate_2016500500” 1455) that can be modified. The user can change the document type field 1465 and the account field 1470, and if they are changed and the "Update View" button 1475 is selected, they will be associated according to the changed metadata. Extract the document again.
The recommended document presentation area 1480 indicates a document to be created in the next step of the target document. Here, it is shown that it is "Company A purchase order registration", and this document is process C "Company A_order form" 1460 in the workflow presentation area 1440.

FIG. 15 is an explanatory diagram showing a processing example according to the present embodiment. In the example of FIG. 14, a processing example for extracting a workflow similar to the workflow shown in the workflow presentation area 1440 is shown.
The workflow shown in the example of FIG. 15A is the workflow shown in the workflow presentation area 1440 in the example of FIG.
The workflow shown in the example of FIG. 15 (b) is a past workflow and is similar to the workflow shown in the example of FIG. 15 (a). That is, process A1 "20160520_A company_estimation request form_2016200200" 1510 is a "superior document" with respect to process B "201606001_A company_estimate_2016600200" 1515, and process A2 "20160525_A company_estimation engineering department response form_2016600200". 1520 is "evidence" for process B "201606001_A company_estimate_2016200200" 1515, and process B "201660601_A company_estimate_2016200200" 1515 is for process C "201660605_A company_order form_201600200" 1525. The process C "20160605_A company_order form_2016200200" 1525 is a "superior document" with respect to the process D "20160608_A company_delivery note_2016200200" 1530, and the process D "20160608_A company_delivery note_2016600200". 1530 is a "superior document" with respect to process E "20160613_A company_receipt_2016200200" 1535, and process E "20160613_A company_receipt_2016200200" 1535 is in process F "20160625_A company_invoice_2016002200" 1540. On the other hand, it is a "superior document", and the process F "20160625_A company_invoice_2016002200" 1540 indicates that the process G "20160725_A company_receipt_2016600200" 1545 is a "superior document". Process A1 "20160520_A company_Quotation request form_2016200200" 1510, Process B "201606001_A company_Quotation_2016600200" 1515, Process A2 "20160525_A company_Estimation engineering department response form_2016200200" 1520, Process C "20160605_A company_Order form_2016200200" 1525, their relationship is determined to be similar to the workflow shown in the example of FIG. 15 (a). Here, the same document type and business partner (Company A) in each process are extracted. Note that workflows with the same business partner may be extracted. On the contrary, not only the document type and the business partner, but also the workflows that have succeeded in the past may be extracted.

FIG. 16 is an explanatory diagram showing a processing example according to the present embodiment. The screen 1300 shown in the example of FIG. 16 shows a state in which the Relations tag 1315 is selected. A workflow (workflow shown in the example of FIG. 15B) similar to the workflow in the workflow presentation area 1440 of the example of FIG. 14 is displayed.
The "selected document" display icon 1430 is displayed in the status display field 1325 to indicate that there is no currently selected document (it is "unselected").
The workflow shown in the example of FIG. 15B is displayed in the workflow presentation area 1440.
In the document type column 1465 and the supplier column 1470, the metadata in the workflow in the workflow presentation area 1440 is displayed. That is, "not specified" is displayed as the document type column 1465, and "Company A" is displayed as the business partner column 1470. In this workflow, nothing is displayed in the recommended document presentation area 1480 because it has already been completed.

FIG. 17 is a conceptual module configuration diagram for a configuration example of the present embodiment.
FIG. 17 is an explanatory diagram showing another embodiment of the recommendation generation module 135. FIG. 18 is an explanatory diagram conceptually showing a processing example of the recommendation generation module 135 shown in the example of FIG.
An example is shown when the WiFi-compatible product, which is a wireless LAN standard, is applied to support business for each country. For example, wireless LAN standards include "IEEE802.11ad", "IEEE802.11ac", "IEEE802.11n", "IEEE802.11a", "IEEE802.11g", "IEEE802.11b" and the like. These have different communication speeds (maximum) and frequency bands. In this way, there are many types of wireless standards, and regulations and standards differ from country to country. Therefore, it is necessary to apply for each country, and it takes time to obtain approval. In addition, since regulations are revised frequently, it is necessary to always prepare for applications based on the latest information.
Therefore, using this embodiment, we will support the workflow by analyzing and structuring the document contents inside and outside the company using ontology technology, etc., and collecting and presenting related information that will be helpful in the relevant business.

Therefore, the recommendation generation module 135 includes a workflow storage module 1710, a related information reception module 1720, a value extraction module 1730, and a workflow application module 1740.
The workflow storage module 1710 is connected to the value extraction module 1730. The workflow storage module 1710 stores the workflow generated by the workflow generation module 130.
The related information reception module 1720 is connected to the value extraction module 1730, and is also connected to the knowledge storage processing device 1790 via a communication line. The related information reception module 1720 receives information related to the target workflow (or a document in each node of the workflow) from the knowledge storage processing device 1790.
The value extraction module 1730 is connected to the workflow storage module 1710, the related information reception module 1720, and the workflow application module 1740. The value extraction module 1730 extracts knowledge related to the target workflow (or a document in each node of the workflow) from the workflow storage module 1710 and the related information reception module 1720 according to a predetermined rule. .. This predetermined rule is described using the inference rule described by the recursive function of the programming language. For example, it may be one using an inference rule described using a language such as SPARQL. Then, the value extraction module 1730 may pass the knowledge to the workflow application module 1740 or present the knowledge itself.
The workflow application module 1740 is connected to the value extraction module 1730. The workflow application module 1740 assigns and presents the knowledge extracted by the value extraction module 1730 as a recommendation to the target workflow (specifically, a document serving as a node).

The knowledge storage processing device 1790 is connected to the related information reception module 1720 of the recommendation generation module 135 via a communication line. The knowledge accumulation processing device 1790 collects and systematizes knowledge inside and outside the company by using ontology technology and the like, and accumulates the systematized knowledge. The function of the knowledge storage processing device 1790 may be realized as a cloud service.
The knowledge storage processing device 1790 represents the graph structure of the workflow group 1810 and the content analysis result (body of knowledge) 1820 in the workflow storage module 1710 in a database such as SQL (Structured Quality Language). The graph structure may be represented by a method of expressing Semantic Web / ontology technology represented by RDF (Resource Description Framework). For example, content analysis is performed on related documents inside and outside the company and related information of people, and a content analysis result (body of knowledge) 1820 is generated. Specifically, in the content analysis result (body of knowledge) 1820, "wireless communication standard revision information", "country standard revision information", "country laboratory information", "information from WiFi module vendor", "in-house" Information such as "Past application document information" is linked.
The value extraction module 1730 provides value (adds information) to the target workflow by fusing with content analysis. For example, it collects knowledge such as related internal information 1832, related external information 1834, country-specific application information table 1836, and the latest information 1838 such as standards / outer box display standards / approval labels for each country.
As related in-house information 1832, for example, the following is extracted.
・ Past application form, person in charge-For Y country: Product A WiFi application for Y country,
-Xx department A member / evaluation data-Wireless LAN module (802.11b) 2013.05
Further, as related external information 1834, for example, the following is extracted.
-Wireless standard-Latest version: http: // www. .. .. .. ..
・ Application information site-Y country: http: // www. .. .. .. ..
-Z country: http: // www. .. .. .. ..
-Change information-Label change in W country-Application criteria and label change in Z country The country-specific application information table 1836 shows "necessity of local representative" and "presence or absence of certificate expiration date" when applying for WiFi-compatible products in each country. It is a summary of.
The latest information 1838, such as the standards / outer box display standards / approval labels for each country, is the knowledge that the certification mark in Country W has changed.

The workflow application module 1740 adds knowledge of the value information group 1830 to the target workflow. Specifically, the workflow presentation result 1850 is presented.
A workflow showing that the application request form 1852 is prepared, and in connection with it (as the next step), the manual label description requirement 1856, the field laboratory reservation sample arrangement 1860, the application form preparation 1864, etc. are prepared. It is created by the workflow generation module 130. Then, the workflow application module 1740 presents "application to country Y requires 6 weeks" as a recommendation 1854 related to the application request form 1852, as shown in the workflow presentation result 1850, and the manual label description requirement 1856. As a recommendation related to 1858, "The label has changed from the previous application" is presented, and as a recommendation 1862 related to the field laboratory reservation sample arrangement 1860, "A field test is required for application to country Y". Present and present as a recommendation 1866 related to application preparation 1864, such as "Application to country Y requires designation of local registrant".
Here, an example of support for work for each country of WiFi-compatible products has been described, but it may be applied to other work, for example, support for work such as energy-saving standards and food safety standards.

FIG. 19 is an explanatory diagram showing a processing example according to the present embodiment.
In the example shown in FIG. 19A, the relationship between the above-mentioned documents (for example, “evidence” and “reference”) is shown as a workflow. Specifically, Document 1910 is a specification, Document 1920 is a standard (first edition), and Document 1920 is evidence 1915 against Document 1910.
Further, in the content analysis result (body of knowledge) 1820, as shown in the example of FIG. 19 (b1), the knowledge that the standard (first edition) 1960 is evidence 1955 with respect to the specification (first edition) 1950. Represents. The object-oriented class is represented by FIG. 19 (b1), and the instance is represented by FIG. 19 (a1).
After that, when the document 1920 is updated to the document 1930 (standard document (second edition)) 1925 as in the example shown in FIG. 19 (a2), the knowledge accumulation processing device 1790 accepts this and FIG. 19 Update knowledge as in (b2). This is because the workflow group is also a target for the knowledge accumulation processing device 1790. Specifically, the standard (1st edition) 1960 is updated to the standard (2nd edition) 1970 in 1965. Then, as shown in the example of FIG. 19 (b3), the knowledge accumulation processing device 1790 updates the specification (first edition) 1950 to the specification (second edition) 1980 according to the rules. Generate knowledge. In response to this, the workflow application module 1740 generates and presents recommendations to update document 1910 to document 1940, as shown in the example of FIG. 19 (a3).

A hardware configuration example of the information processing apparatus according to the present embodiment will be described with reference to FIG. The configuration shown in FIG. 20 is configured by, for example, a personal computer (PC) or the like, and shows an example of a hardware configuration including a data reading unit 2017 such as a scanner and a data output unit 2018 such as a printer.

The CPU (Central Processing Unit) 2001 includes various modules described in the above-described embodiment, that is, a data collection module 105, a processing information collection module 110, a metadata collection module 115, an analysis processing module 125, and a workflow generation module 130. Control to execute processing according to a computer program that describes the execution sequence of each module such as recommendation generation module 135, output module 140, workflow storage module 1710, related information reception module 1720, value extraction module 1730, workflow application module 1740, etc. It is a department.

The ROM (Read Only Memory) 2002 stores programs, calculation parameters, and the like used by the CPU 2001. The RAM (Random Access Memory) 2003 stores a program used in the execution of the CPU 2001, parameters that are appropriately changed in the execution, and the like. These are connected to each other by a host bus 2004 composed of a CPU bus or the like.

The host bus 2004 is connected to an external bus 2006 such as a PCI (Peripheral Component Interconnect / Interface) bus via a bridge 2005.

A pointing device 2009 such as a keyboard 2008 and a mouse is a device operated by an operator. The display 2010 includes a liquid crystal display device, a CRT (Cathode Ray Tube), and the like, and displays various information as text or image information. Further, it may be a touch screen or the like having the functions of both the pointing device 2009 and the display 2010.

The HDD (Hard Disk Drive) 2011 has a built-in hard disk (which may be a flash memory or the like), drives the hard disk, and records or reproduces programs and information executed by the CPU 2001. The hard disk realizes a function as a data storage module 120 or the like. In addition, various other data, various computer programs, etc. are stored.

The drive 2012 reads out the data or program recorded on the removable recording medium 2013 such as the mounted magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, and transfers the data or program to the interface 2007, the external bus 2006. , Bridge 2005, and RAM 2003 connected via the host bus 2004. The removable recording medium 2013 can also be used as a data recording area.

The connection port 2014 is a port for connecting the external connection device 2015, and has a connection portion such as USB and IEEE1394. The connection port 2014 is connected to the CPU 2001 and the like via the interface 2007, the external bus 2006, the bridge 2005, the host bus 2004, and the like. The communication unit 2016 is connected to the communication line and executes data communication processing with the outside. The data reading unit 2017 is, for example, a scanner, and executes a document reading process. The data output unit 2018 is, for example, a printer, and executes a document data output process.

The hardware configuration of the information processing device shown in FIG. 20 shows one configuration example, and the present embodiment is not limited to the configuration shown in FIG. 20, and the module described in the present embodiment is executed. Any configuration may be possible. For example, some modules may be configured with dedicated hardware (for example, an Applied Special Integrated Circuit (ASIC)), and some modules are in an external system and connected by a communication line. Further, a plurality of systems shown in FIG. 20 may be connected to each other by a communication line so as to cooperate with each other. In addition to personal computers, it is also incorporated in mobile information and communication devices (including mobile phones, smartphones, mobile devices, wearable computers, etc.), information appliances, robots, copiers, fax machines, scanners, printers, multifunction devices, etc. You may.

In the above-described embodiment, the example of searching the front-end process document in the workflow generation is shown, but the back-end process document may be searched, or both the front-end process document and the back-end process document are searched. You may. When the latest document is the target document, the pre-process document may be searched, and when the initial document is the target document, the post-process document may be searched.

The described program may be stored in a recording medium and provided, or the program may be provided by a communication means. In that case, for example, the program described above may be regarded as an invention of "a computer-readable recording medium on which the program is recorded".
The "computer-readable recording medium on which a program is recorded" means a computer-readable recording medium on which a program is recorded, which is used for program installation, execution, program distribution, and the like.
The recording medium is, for example, a digital versatile disc (DVD), which is a standard established by the DVD Forum, "DVD-R, DVD-RW, DVD-RAM, etc.", and DVD + RW. Standards such as "DVD + R, DVD + RW, etc.", compact discs (CD), read-only memory (CD-ROM), CD recordable (CD-R), CD rewritable (CD-RW), etc., Blu-ray discs (CD-RW) Blu-ray® Disc), optomagnetic disc (MO), flexible disc (FD), magnetic tape, hard disk, read-only memory (ROM), electrically erasable and rewritable read-only memory (EEPROM® )), Flash memory, random access memory (RAM), SD (Secure Digital) memory card and the like.
Then, the whole or a part of the program may be recorded on the recording medium and stored, distributed, or the like. Further, by communication, for example, a wired network used for a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, an intranet, an extranet, or wireless communication. It may be transmitted using a transmission medium such as a network or a combination thereof, or may be carried on a carrier.
Further, the program may be a part or all of other programs, or may be recorded on a recording medium together with a separate program. Further, the recording may be divided into a plurality of recording media. Further, it may be recorded in any mode as long as it can be restored, such as compression and encryption.

100 ... Information processing device 105 ... Data collection module 110 ... Processing information collection module 115 ... Metadata collection module 120 ... Data storage module 125 ... Analysis processing module 130 ... Workflow generation module 135 ... Recommendation generation module 140 ... Output module 300 ... Image processing Device 310 ... Personal computer 320 ... Mobile terminal 330 ... Stick PC
340 ... Dedicated processing device 350 ... Management server 360 ... Communication server 370 ... Firewall device 380 ... Data analysis server 390 ... Communication line 395 ... Communication line 1710 ... Workflow storage module 1720 ... Related information reception module 1730 ... Value extraction module 1740 ... Workflow application module 1780 ... Recommendation (recommended document) presentation area 1790 ... Knowledge accumulation processing device

Claims (7)

A reception means that accepts instructions regarding document processing,
Used to generate a workflow including a process of the document, have a extracting means for extracting the document or information relating to the instruction,
The extraction means extracts the document used in the pre-process or the post-process of the target document.
When the extraction means cannot extract the document used in the pre-process or the post-process of the target document, the extraction means extracts a document similar to the target document and the document used in the pre-process or the post-process of the similar document. Information processing device that extracts . The information processing device according to claim 1, wherein the receiving means is connected to or built in a processing device that performs the processing, and receives the instruction from a wireless or wired communication line. The information processing device according to claim 1, wherein the receiving means passes the instruction to a processing device that performs the processing after receiving the instruction. The information processing device according to claim 1, wherein when the reception means receives the instruction, the operator who has given the instruction is added information about the document or the instruction. The information processing device according to claim 4, wherein the receiving means selectively presents information related to the document or the instruction to the operator. The information processing apparatus according to claim 1 , wherein the extraction means weights a document used in a pre-process or a post-process of the similar document according to the degree of similarity between the target document and the similar document. Computer,
A reception means that accepts instructions regarding document processing,
To function as an extraction means for extracting information about the document or instructions used to generate a workflow that includes processing the document .
The extraction means extracts the document used in the pre-process or the post-process of the target document.
When the extraction means cannot extract the document used in the pre-process or the post-process of the target document, the extraction means extracts a document similar to the target document and the document used in the pre-process or the post-process of the similar document. Information processing program that extracts .