DETAILED ACTION
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 .
The action is responsive to the following communication: an application filed on 08/09/2023 where:
Claims 1-20 are currently pending.
Allowable Subject Matter
Claims 3, 7-9, 12-18 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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, 2, 4, 5, 6, 10, 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Rangarajan et al. (US 12,300,008, hereinafter Rangarajan) in view of Grefenstette (US 6,289,304).
Regarding claim 1, Rangarajan teaches A method for extracting an area of interest in a document, the method being performed by a computing device and comprising:
extracting one or more target pages (see figs 3-5, and Col. 15, lines 1-15, In this case, the scanned image detection model is trained to extract features from an input page image, and output a target prediction of the location) from a document, the document comprising a plurality of pages (Col. 8, lines 30-40, Although the example of FIGS. 3-5 illustrates operations 220-240 being applied to a single image or page, this workflow could be applied to document image files having multiple pages).
Rangarajan does not explicitly teach: extracting an area of interest including a plurality of sentences from a target page based on a first part-of-speech characteristic and a sentence characteristic of the target page.
However, Grefenstette teaches: extracting an area of interest including a plurality of sentences from a target page based on a first part-of-speech characteristic (Col. 7, lines 40-65, fig. 3, steps S4-S7, and the POS data from box s4 can be used to obtain a verb group (characteristic) annotated version of the sentence.) and a sentence characteristic (Col. 5, lines 15-30, Sentence break criteria applicable to languages such as French and English often treat tokens that are strings of one or more appropriate sentence-ending punctuation marks as sentence breaks, such as a period, a question mark, an exclamation point, etc. Such tokens may be referred to herein as "sentence-final" (sentence characteristic)).) of the target page.
Therefore, the Applicant's claimed invention would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Rangarajan to include extracting an area of interest including a plurality of sentences from a target page based on a first part-of-speech characteristic and a sentence characteristic of the target page as taught by Grefenstette. The motivation/suggestion would have been to further enhance/improve the method since doing so would allow for the ability to remove and produce a reduced version as output text according to the level of reduction currently requested by the user.
Regarding claim 2, Rangarajan and Grefenstette teach: The method of claim 1, wherein the extracting of the one or more target pages includes: generating a second part-of-speech characteristic and a page characteristic for each of the plurality of pages (Grefenstette, fig. 3, Step S6, Noun group annotation); and extracting the one or more target pages using the second part-of-speech characteristic and the page characteristic (Grefenstette, fig. 3, Step S6, Noun group annotation. Col. 8, lines 1-25, the verb group annotated current sentence buffer can be similarly passed through a computer-based noun group annotator, which inserts noun group markings into the current sentence buffer).
Regarding claim 4, Rangarajan and Grefenstette teach: The method of claim 2, wherein the page characteristic includes one or more characteristics of a number of words in a page and whether a number is included in a beginning word of a sentence within the page (Grefenstette, Col. 8, lines 35-60. By selecting a reduction level, the signal from the user input device will indirectly indicate how much text should be retained or, conversely, how much should be removed. And see fig. 4).
Regarding claim 5, Rangarajan and Grefenstette teach: The method of claim 2, wherein the extracting of the one or more target pages further includes generating an image characteristic for each of the plurality of pages (Rangarajan, see figs 3-5, and Col. 15, lines 1-15, In this case, the scanned image detection model is trained to extract features from an input page image. Col. 8, lines 30-40, Although the example of FIGS. 3-5 illustrates operations 220-240 being applied to a single image or page, this workflow could be applied to document image files having multiple pages).
Regarding claim 6, Rangarajan and Grefenstette teach: The method of claim 5, wherein the image characteristic includes one or more characteristics of a font size of a text area (Rangarajan, the user may adjust the size and areas covered by the bounding boxes, fig. 6 steps, 630, 635 and 645, See fig. 17) and an arrangement form of the text area in a page (Rangarajan, The user may also modify any tags/labels associated with each of the bounding boxes.).
Regarding claim 10, Rangarajan and Grefenstette teach: The method of claim 1, wherein the first part-of-speech characteristic include one or more characteristics of a distribution ratio of a noun, a distribution ratio of a verb, and a distribution ratio of an adjective (Grefenstette, Col. 5, lines 40-45, A "word group type" is one of a set of types applicable to groups of tokens in text. Examples include verb groups, noun groups, prepositional phrase groups, and subclause groups (which might include other groups).).
Regarding claim 11, Rangarajan and Grefenstette teach: The method of claim 1, wherein the sentence characteristic includes one or more characteristics of whether a number is included in a beginning word of a sentence, whether a punctuation mark is present in the sentence, and a number of words in the sentence (Grefenstette, Col. 8, lines 35-60. By selecting a reduction level, the signal from the user input device will indirectly indicate how much text should be retained or, conversely, how much should be removed. And see fig. 4).
Claim 19 is rejected for reasons similar to claim 1 above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANDREW H LAM whose telephone number is (571)270-7969 and fax number is 571-270-8969. The examiner can normally be reached on 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Benny Tieu can be reached on 571-272-7490. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW H LAM/
Primary Examiner, Art Unit 2682