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 .
Response to Amendment
Applicant’s response, filed 8/11/2025, to the last office action has been entered and made of record.
In response to the amendments to the claims, they are acknowledged, supported by the original specification, and no new matter is added.
Amendments to the claims have necessitated a new ground of rejection over the newly applied prior art. Please see the updated interpretations and rejections as fully disclosed below.
Response to Arguments
Applicant’s arguments with respect to the amended claims have been considered but are not persuasive. Applicant argues that the prior art does not disclose classifying the one or more text features indicating a partition between a first and second document within the image file. Tanvir discloses on Page 7/19 that the different page numbers are identified so that each can be correctly classified within a document set. Thus, this allows each page to be classified and grouped with its associated document set. As such, Tanvir does meet the limitations of the claims as disclosed below.
Additionally, the applicant’s additional arguments are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument. As such, the newly found prior art of Kawamura (US Pub 2017/0068442) is used in combination with Tanvir and Guha, and together meets the limitations of the amended independent claims, including a further interpretation of the determining / locating of the document partition based on the extracted one or more text features within the image file.
Based on these facts, this action is made FINAL.
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, 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.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Tanvir (Qaisar Tanvir. “Multi Page Document Classification using Machine Learning and NLP”. 2021 August 07) in view of Kawamura (US Pub 2017/0068442) and Guha (Guha et al. “IIRM: Intelligent Information Retrieval Model for Structured Documents by One-Shot Training Using Computer Vision - Arabian Journal for Science and Engineering”. 2022 March 31).
Regarding claim 10, Tanvir discloses a system for management of electronic files, the system comprising:
a processor; and a memory comprising instructions that, when executed, cause the processor to (Tanvir Page 5/19, embodied within the database to perform the machine learning engine):
access, from a database of a plurality of electronic documents, an electronic image file (Tanvir Page 5/19-6/19, access a package file that includes multiple electronic documents);
extract, by a trained machine learning model, one or more text features from the image file (Tanvir Page 6/19-7/19, the machine learning model determines the boundaries between the different documents including when one document set ends and another starts);
classify the one or more text features as indicative of a partition between a first document and a second document within the image file (Tanvir Page 7/19, the machine learning model classifies the different documents as being first, last, or other in the set);
locate, by the trained machine learning model Tanvir Page 7/19, once the package is processed, the predictions is used to identify the boundaries of the documents).
Tanvir does not disclose where Kawamura teaches locate the document partition location within the image file is based on the extracted one or more text features (Kawamura Paragraph [0027] the electronic document includes metadata that includes the different chapter locations within the document);
display, on a display device, the plurality of electronic documents and an indication of the determined document partition location within the image file (Kawamura Fig. 3A and Paragraph [0037], the content of the electronic document is displayed and separately shows the chapter and the page number associated with it).
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the system of Tanvir with the teachings of Kawamura to locating the document partition based on the text features and display them in order to allow a user to view and know the different chapters and its page number, as well as when one portion ends and where another begins.
Tanvir in combination with Kawamura additionally does not disclose where Guha teaches to receive feedback data corresponding to an accuracy of the document partition location within the image file and adjust, based on the feedback data, a parameter of the trained machine learning model (Guha Page 6/17, the user is able to specify the page number during training to improve the performance of the search).
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the system of Tanvir and Kawamura with the teachings of Guha to include user interaction of an image file in order to allow a user to assist in training and improve the machine learning algorithm.
Regarding claim 11, Tanvir discloses the system of claim 10 wherein the extracted one or more text features comprise a page number, a title, a formatting feature, a signature block, or a document identifier of the image file (Tanvir Page 7/19, the page number is extracted and used to determine a predicted boundary between the documents).
Regarding claim 12, Tanvir discloses the system of claim 10 wherein the processor is further caused to: identify a text feature different the one or more text features for extraction, add a text feature different the one or more text features for extraction, or remove a text feature from the one or more text features (Tanvir Page 6/19, the text features are identified in the machine learning engine to predict the page number in the document).
Regarding claim 13, Tanvir discloses the system of claim 10 wherein the processor is further caused to: associate a weighted value to the extracted one or more text features, wherein the document partition location within the image file is further based on the weighted value (Tanvir Page 6/19, a confidence score is given and used to determine the first and last page of the document within the package).
Regarding claim 14, the combination of Tanvir, Kawamura, and Guha together discloses the system of claim 13 wherein the adjusted parameter of the machine learning model comprises the associated weighted value to the extracted one or more text features (Guha Page 6/17, the adjusted parameter learns from the data samples provided).
Regarding claim 15, Tanvir discloses the system of claim 10 wherein the document partition location within the image file comprises an indicator of a last page of a first document of the image file and a first page of a second document of the image file (Tanvir Page 7/19, the boundaries of the documents are identified and predicted, which includes the last page of one document, and the beginning of the next document).
Regarding claim 16, the combination of Tanvir, Kawamura, and Guha together discloses the system of claim 10 wherein the processor is further caused to: generate a graphical user interface displaying at least a portion of a content of the image file and an indicator of the document partition location within the image file (Guha Page 6/17, the user is able to specify the page number of the selected document during training).
Regarding claim 17, the combination of Tanvir, Kawamura, and Guha together discloses the system of claim 10 wherein the feedback data comprises a correct indicator or an incorrect indicator of the document partition location within the image file (Guha Page 6/17, the user is able to provide the correct page number of the selected document during training).
Regarding claims 1-9 and 18-20, the rationale provided in the rejection of claims 10-17 is provided herein. In addition, the system of claims 10-17 corresponds to the method of claims 1-9 and the non-transitory computer-readable storage media (Tanvir Page 5/19, embodied within the database to perform the machine learning engine), and performs the steps disclosed herein.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/VINCENT RUDOLPH/ Supervisory Patent Examiner, Art Unit 2671