Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Information Disclosure Statement
Applicants’ Information Disclosure Statement, filed 08/28/2025, has been received, entered into the record, and considered. See attached form PTO-1449.
Claim Rejections – 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-16 are rejected under 35 U.S.C. 101 because the claimed invention are directed to non-statutory subject matter.
Claims 1, 15, 16 recite an apparatus/method/medium, comprising: “displaying a type of a document, which is represented by image data and identified by analysis processing on the image data and a file name of a file including the image data, which is generated based on a rule set in association with the corresponding type on a single screen; and allowing for accepting of correction of the type and the file name”.
These limitations are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other than reciting "a memory, a processor", nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, but for the
" a memory, a processor " language, “displaying a type of a document …; and allowing for accepting of correction of the type and the file name” in the context of this claim encompasses steps that can be performed mentally, with the aid of pen and paper to analyze information, gathering data and organizing data.
If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment, and opinion).
This judicial exception is not integrated into a practical application. In particular, the claims recite additional element – using “a processor, a memory" to “displaying a type of a document …; and allowing for accepting of correction of the type and the file name”, these limitations amount to data gathering which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
“displaying a type of a document …; and allowing for accepting of correction of the type and the file name”; these limitation are mere generic transmissions and presentations of collected and analyzed data which is considered to be insignificant extra solution activity (MPEP 2106.05(g).
Claims 1, 16 merely recite an apparatus, a non-transitory computer readable medium comprising a processor, memory; performing steps of method claim 15.
“The processor, the memory" are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of displaying a type of a document …; and allowing for accepting of correction of the type and the file name) such that they amount no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. (see MPEP 2106.05(f)). The claim is directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and in combination, they do not add significantly more to the exception. Considered separately and as an ordered combination, the claimed elements do not recite additional elements that: improve a computer itself; improve another technology or technical field; improve to the functioning of the computer itself.
The limitations “displaying a type of a document …; and allowing for accepting of correction of the type and the file name” amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Dependent claims 2-14 merely add further details of the abstract steps recited in claim 1 without including an improvement to another technology or technical field, an improvement to the functioning of the abstract idea to a particular technology environment. Therefore, dependent claims 2-14 are also directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 1, 4, 15, 16 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by SUZUKI et al. (US Pub No. 2015/0302277).
As to claims 1,15, 16, SUZUKI teaches an image processing apparatus comprising: at least one memory that stores instructions (i.e. FIG. 1 illustrates a hardware configuration of an image reading device that is an example of an imageprocessing apparatus according to an embodiment of the present invention, [0025]); and
at least one processor that executes the instructions (i.e. The CPU 101 executes a program stored in the ROM 102 or the HDD 104 by using the RAM 103 as a work area, to control the entire image reading device 100 and realize various functions such as those described below with reference to FIG. 2, [0027]) to:
display a type of document, which is represented by image data and identified by analysis processing on the image data and a file name of a file including the image data, which is generated based on a rule on a single screen (i.e. When the user specifies an area 204 a and an area 204 b in the image display area 201 of the area specification receiving screen 200, the OCR processing unit 150 performs an OCR process on each of the images in the areas 204 a, 204 b. Here, assuming that the priority level of the area 204 a is higher, “◯◯ Company Limited” obtained from the area 204 a is arranged first, and “New Business Office” obtained from the area 204 b is arranged next, thereby obtaining “◯◯ Company Limited New Business Office” as the candidate of a file name based on the result of OCR, [0096]); and
allow for accepting of correction of the type and the file name (i.e. PLEASE SELECT FILE NAME FROM THE CANDIDATES, See FIG. 9; In the example of FIG. 8, when an OCR process is performed on each of the areas 211 through 213, the character strings of “: ◯◯ Company Limi”, “◯◯ Company Limited”, and “name: ◯◯ Company”, are obtained. The user may select which one of these character strings are to be used, from a character string selection screen 220 as illustrated in FIG. 9. The buttons 221 through 223 respectively correspond to the character strings obtained from the areas 211 through 213, respectively, [0111]).
As per claim 4, SUZUKI teaches the image processing apparatus according to claim 1, wherein
the file name includes a character string extracted from the image data (i.e. When the user specifies an area 204 a and an area 204 b in the image display area 201 of the area specification receiving screen 200, the OCR processing unit 150 performs an OCR process on each of the images in the areas 204 a, 204 b. Here, assuming that the priority level of the area 204 a is higher, “◯◯ Company Limited” obtained from the area 204 a is arranged first, and “New Business Office” obtained from the area 204 b is arranged next, thereby obtaining “◯◯ Company Limited New Business Office” as the candidate of a file name based on the result of OCR, [0096]).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claims 2, 3, 5-10, 12, 14 are rejected under 35 U.S.C. 103 as being unpatentable over SUZUKI et al. (US Pub No. 2015/0302277), in view of Rezvani et al. (US Pub No. 2021/0064866).
As per claim 2, SUZUKI the image processing apparatus according to claim 1, wherein:
the single screen further displays a folder that is set in association with the type into which the image data is saved (i.e. Here, the management information is, for example, the file name when storing the image data as a file. In this case, the management information setting unit 161 may attach an appropriate extension to the character string passed from the integrated control unit 120. The management information may be other arbitrary data used for managing the image data in association with the image data, such as a name of the directory in which the image data is to be stored, a value of an item in the property of the image data, a value of an appropriate item associated with the image data when storing the image data in a database, etc., [0052]), and
accepting of correction of a folder name of the folder is allowed (i.e. Furthermore, the purpose of the image or image data in which management information is set, is not limited to storage. The image or image data may be sent, together with the management information, to a storage in an external network, an external database, etc., by an appropriate communication means such as an e-mail, without being stored in the image reading device 100 in a fixed manner, [0118]).
SUZUKI implicitly teaches "displays a folder" (i.e. managing the image data in association with the image data, such as a name of the directory in which the image data is to be stored, [0052]; The image or image data may be sent, together with the management information, to a storage in an external network, an external database, [0118]).
SUZUKI does not seem to specifically teach "a folder that is set in association with the type".
Rezvani teaches this limitation (i.e. The device may classify and provide extracted information in an organized format (e.g., the classification model of the service may use a different classifier for each network vendor to classify the phone bill invoices according to network vendors (e.g., invoices from Vendor A are sent to folder A and invoices from Vendor B are sent to folder B). Each classifier may include an OCR template that is designed to extract particular information from each document, such as metadata related to the vendor, amount due, due date, and invoice number. The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B, [0029]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
It would have been obvious to one of ordinary skill of the art having the teaching of SUZUKI, Rezvani before the effective filing date of the claimed invention to modify the system of SUZUKI to include the limitations as taught by Rezvani. One of ordinary skill in the art would be motivated to make this combination in order to allow a user to classify the document(s) using the trained classification model, in view of Rezvani ([0023]), as doing so would give the added benefit of having the device routed the documents into different folders according to the mappings provided by the classifiers used by the classification model, as taught by Rezvani ([0023]).
As per claim 3, Rezvani teaches the image processing apparatus according
to claim 2, wherein
a user is not allowed to correct the type of the document, the folder name, and the file name on the single screen (i.e. Document classification workflow 300 further shows invoice classification 306, which involves arranging invoice group 330 into subgroups 340, 342, 344 based on differences in vendors, [0052]; Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; Output 312 includes different destinations, such as destination A, destination B, and destination C. These destinations may be located at the device performing document classification workflow 300 and/or at one or more remotely positioned devices (e.g., a server), [0055]).
As per claim 5, Rezvani teaches the image processing apparatus according to claim 2, wherein
in a case wherein one piece of information out of the type of the document, the folder name, and the file name is designated by a user, the single transitions to another screen that allows the user to correct the one piece of information designated by the user (i.e. In some cases, the classification model may be unable to classify some documents and assign these unclassified documents to a separate folder for user review. A user may manually classify these documents, which in turn further provides additional data used by machine learning to enhance the automatic classification by the model. Thus, increasing the quantity of sample documents used to train the classification model may increase the accuracy of the classification model and/or the variety of document types that the classification model can classify, [0080]; A classifier may enable mapping documents to particular folders or other target destinations. As shown in FIG. 5A, “classifier 1” is shown as a folder that a user can assign sample documents to “classifier 1” to train the classification model to learn how to subsequently route similar documents to the sample documents placed in “classifier 1”, [0072]).
As per claim 6, Rezvani teaches the image processing apparatus according to claim 5, wherein
in a case where the correction of the one piece of information ends, the other screen transitions to the single screen on which the correction of the one piece of information is reflected (i.e. Data validation 310 may involve validating the data extracted during data extraction 308. In some instances, manual validation 360 may be used. This may involve the device requesting manual approval from a user for one or more documents via an interface. Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B), [0029]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
As per claim 7, Rezvani teaches the image processing apparatus according to claim 5, wherein
in the displaying, a list of candidates of the folder name that can be designated by the user is displayed on the other screen after the transition in a case where the folder name is designated on the single screen (i.e. Data validation 310 may involve validating the data extracted during data extraction 308. In some instances, manual validation 360 may be used. This may involve the device requesting manual approval from a user for one or more documents via an interface. Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B), [0029]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
As per claim 8, SUZUKI teaches the image processing apparatus according to claim 5, wherein
in the displaying, a character string used in the file name is displayed in a state that allows for the correction by the user on the other screen after the transition in a case where the file name is designated on the single screen (i.e. PLEASE SELECT FILE NAME FROM THE CANDIDATES, See FIG. 9; In the example of FIG. 8, when an OCR process is performed on each of the areas 211 through 213, the character strings of “: ◯◯ Company Limi”, “◯◯ Company Limited”, and “name: ◯◯ Company”, are obtained. The user may select which one of these character strings are to be used, from a character string selection screen 220 as illustrated in FIG. 9. The buttons 221 through 223 respectively correspond to the character strings obtained from the areas 211 through 213, respectively, [0111]).
As per claim 9, Rezvani teaches the image processing apparatus according to claim 5, wherein
in the displaying, a list of candidates of the type of the document that can be designated by the user is displayed on the other screen after the transition in a case where the type of the document is designated on the single screen (i.e. Data validation 310 may involve validating the data extracted during data extraction 308. In some instances, manual validation 360 may be used. This may involve the device requesting manual approval from a user for one or more documents via an interface. Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B), [0029]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
As per claim 10, Rezvani teaches the image processing apparatus according to claim 5, wherein
in a case wherein the correction of the type of the document by the user ends on the other screen after the transition in a case where the type of document is designated on the single screen, the single screen on which the correction of the type of the document is reflected is displayed (i.e. Data validation 310 may involve validating the data extracted during data extraction 308. In some instances, manual validation 360 may be used. This may involve the device requesting manual approval from a user for one or more documents via an interface. Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B), [0029]), and
the folder name and the file name corrected based on the correction of the type of the document are displayed on the single screen on which the correction of the type of the document is reflected (i.e. Classifiers section 506 may include classifiers added by a user via an addition option associated with classifiers section 506. A classifier may enable mapping documents to particular folders or other target destinations. As shown in FIG. 5A, “classifier 1” is shown as a folder that a user can assign sample documents to “classifier 1” to train the classification model to learn how to subsequently route similar documents to the sample documents placed in “classifier 1”. In addition, “classifier 2” as well as other classifiers can be added and trained to route particular documents based on the sample documents placed within each classifier. Classification section 506 also includes other options, such an undo option (e.g., undo last assignment option), a preview a document option or a set of documents assigned to a classifier, a delete a classifier option, and a delete one or more documents option, [0072]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
As per claim 12, Rezvani teaches the image processing apparatus according to claim 2, wherein
in a case where there is information out of the type of the document, the folder name, and the file name that satisfies a predetermined condition, the single screen transitions to another screen that allows a user to correct the information satisfying the predetermined condition (i.e. In some cases, the classification model may be unable to classify some documents and assign these unclassified documents to a separate folder for user review. A user may manually classify these documents, which in turn further provides additional data used by machine learning to enhance the automatic classification by the model. Thus, increasing the quantity of sample documents used to train the classification model may increase the accuracy of the classification model and/or the variety of document types that the classification model can classify, [0080]; A classifier may enable mapping documents to particular folders or other target destinations. As shown in FIG. 5A, “classifier 1” is shown as a folder that a user can assign sample documents to “classifier 1” to train the classification model to learn how to subsequently route similar documents to the sample documents placed in “classifier 1”, [0072]).
As per claim 14, Rezvani teaches the image processing apparatus according
to claim 2, wherein
in a case where there are a plurality of pages of the image data, the single screen displays the type of the document, the folder name, and the file name by a page unit (i.e. Data validation 310 may involve validating the data extracted during data extraction 308. In some instances, manual validation 360 may be used. This may involve the device requesting manual approval from a user for one or more documents via an interface. Automatic validation based on predefined rules 362 may be performed by one or more computing devices, [0054]; The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B), [0029]).
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over SUZUKI et al. (US Pub No. 2015/0302277), in view of Rezvani et al. (US Pub No. 2021/0064866), as applied to claims above, and further in view of Drory et al. (US Pub No. 2009/0248615).
As per claim 11, Rezvani teaches the image processing apparatus according to claim 2 wherein
information out the type of the document, the folder name, and the file name that satisfies a predetermined condition is displayed in a highlighted manner on the single screen (i.e. The device may classify and provide extracted information in an organized format (e.g., As such, the classification model of the service may use a different classifier for each network vendor to classify the phone bill invoices according to network vendors (e.g., invoices from Vendor A are sent to folder A and invoices from Vendor B are sent to folder B). Each classifier may include an OCR template that is designed to extract particular information from each document, such as metadata related to the vendor, amount due, due date, and invoice number. The device may classify and provide extracted information in an organized format (e.g., a CSV file) for user review as well as store the invoices in folders arranged according to vendors (e.g., a first folder for Vendor A, a second folder for Vendor B, [0029]; Document preview 508 may correspond to an area where any document selected for a preview by the user may be shown. This may enable a user to review each unclassified document by clicking on the document, which can further allow the user to decide which classifier to assign each unclassified document to. In addition, document preview 508 can be used to review documents placed into each classifier, [0073]).
Although SUZUKI, Rezvani do not seem to specifically teach "... a highlighted ...", Drory teaches this limitation (See Suggested Folder in Figs. 1A, 1B).
It would have been obvious to one of ordinary skill of the art having the teaching of SUZUKI, Rezvani, Drory before the effective filing date of the claimed invention to modify the system of SUZUKI, Rezvani to include the limitations as taught by Drory. One of ordinary skill in the art would be motivated to make this combination in order to show a suggested folder in view of Drory ([0044]), as doing so would give the added benefit of adding a file name and file type, and the file is saved by activating the save button once a folder is chosen in the suggested folder, as taught by Drory ([0049]).
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over SUZUKI
et al. (US Pub No. 2015/0302277), in view of Rezvani et al. (US Pub No. 2021/0064866), as applied to claims above, and further in view of Kilpatrick et al. (US Pat No. 10,437,776).
As per claim 13, Rezvani teaches the image processing apparatus according to claim 2, wherein
in a case where there is information out of the type of the document, the folder name, and the file name that has a certainty lower than a predetermined threshold, the single screen transitions to another screen that allow a user to correct the information having the certainty lower than the predetermined threshold (i.e. various types of machine learning techniques may be used to train one or more classification models. For instance, artificial neural networks, decision trees, support vector machines, and Bayesian networks are all example techniques that may be used to train a classification model, [0028]; When training is performed via user interface 600, the classification model may use machine learning to analyze and classify the documents within document section 606 according to the classifiers added to the classification model by the user. Machine learning may involve extracting and comparing some metadata within a document relative to metadata of documents within the different classifiers of the classification model. Text may be extracted via OCR templates associated with the classifiers, [0079]; Machine learning may also enable the classification model to learn how to classify documents based on similarities in structure of the documents, [0022]).
Rezvani implicitly teaches the term "threshold" (i.e. artificial neural networks, decision trees, support vector machines, and Bayesian networks are all example techniques that may be used to train a classification model, [0028]; Machine learning may also enable the classification model to learn how to classify documents based on similarities in structure of the documents, [0022]).
Although SUZUKI, Rezvani do not expressly state the term "threshold", Kilpatrick teaches this term (i.e. Of the six folders shown in the data structure 300, the folder A138 has the highest score of 0.9, the folder A1478 has the second highest score of 0.8, and the folder A104 has the third highest score of 0.6. The folders A294 and A392 each has a score of 0.2, and the folder A832 has the lowest score of 0.1, col. 10, line 65 to col. 11, line 37; the folders associated with respective scores that exceed the first threshold are selected, col. 1, lines 43-53).
It would have been obvious to one of ordinary skill of the art having the teaching of SUZUKI, Rezvani, Kilpatrick before the effective filing date of the claimed invention to modify the system of SUZUKI, Rezvani to include the limitations as taught by Kilpatrick. One of ordinary skill in the art would be motivated to make this combination in order to select the folders associated respective scores that exceed the first threshold, in view of Kilpatrick (col. 1, lines 43-53), as doing so would give the added benefit of displaying the suggested folders for uploading, as taught by Kilpatrick (col. 11, line 38 to col. 12, line 2).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ghosh et al. (US Pub. 2023/0214357) – discloses managing and organizing files in large-scale enterprise level file systems, wherein the context of the digital data and media content can be determined by using Natural Language Processing (NLP) to extract the substantive content of the digital data and Artificial Intelligence (AI) to properly classify the substantive content.
Matsumoto et al. (US Pat. 10,984,232) discloses setting file names for scan images.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MIRANDA LE whose telephone number is (571)272-4112. The examiner can normally be reached M-F 7AM-5PM.
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/MIRANDA LE/ Primary Examiner, Art Unit 2153
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