DETAILED ACTION
This action is in response to the Amendment dated 17 July 2026. Claim 1, 13 and 20 are amended. No claims have been added or cancelled. Claims 1-20 remain pending and have been considered below.
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 .
Allowable Subject Matter
Claims 2-5, 10, 14-17 and 19 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.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-10 and 12-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-5, 7, 9, 13, 15, 16 and 17 of U.S. Patent No. 10,353,950, claims 1, 2, 4-6, 8-10 and 12 of U.S. Patent No. 10,664,519, claims 1, 3, 4, 7, 8, 10, 11, 14, 15, 17 and 18 of U.S. Patent No. 11,461,386, and claims 1-8, 10-14 and 16-20 of U.S. Patent No. 12,038,977. Although the claims at issue are not identical, they are not patentably distinct from each other because the features of the claims listed above of the instant application are anticipated by or are obvious variants of the identified claims of the four reference patents (e.g. one of ordinary skill in the art, namely a software developer, would recognize that OCR is a type of image processing/analysis).
Claim Rejections - 35 USC § 103
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.
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, 6-9, 12, 13, 18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lester (US 2017/0249339 A1) in view of Hamel et al. (US 2014/0314319 A1) and further in view of Dejean et al. (US 2012/0039536 A1).
As for independent claim 1, Lester teaches a method comprising:
receiving, by a computing system, a query image [(e.g. see Lester paragraph 0067 and Figs. 6A-B) ”In FIG. 6A, the user interface 600 includes a control section 601 and an output section 602. The user interface 600 includes a blank canvas 603 for receiving an input image and selecting a subset of the input image using one or more input tools (e.g., image upload control 604). Search results responsive to an image search query are provided for display via the output section 602. In some aspects, the control section 601 includes a search control (e.g., 605) to initiate the reverse image search. In other aspects, the reverse image search may be initiated independent of the search control 605”].
receiving, by the computing system, a first user input from a user selecting a portion of the query image [(e.g. see Lester paragraph 0069 and Fig. 6C) ”In FIG. 6C, the cropping operation 606b is applied to the input image 606a to determine a selected image subset. The cropped operation 606b may include a user selection using a bounding box that is adjustable in two dimensions for identifying the selected image subset. In this example, the bounding box is focusing on an object resembling a banana”].
obtaining, by the computing system, one or more first search results and a suggested search query; and providing, by the computing system, a first user interface for display to the user, the first user interface comprising the one or more first search results and the suggested search query [(e.g. see Lester paragraphs 0064, 0068, 0070 and Figs. 6B-D numeral 608) ”the search results may be provided for display within the output section 602 in real-time (or on-the-fly) based on an image comparison to the cropped raw image 606c using the image search engine 256. The listing of images 609 may be displayed in a particular order based on a prioritization of angle differences between compared feature vectors … the processor 236 using the image search engine 256 provides search suggestions to a user. The user information such as a user profile may be used by the image search engine 256 to identify weighted contributors such as prioritization of keywords, geography, time of day associated with the image search, or the like … the image search engine 256 may provide search suggestions 608 based on usage data of the user. For example, the search suggestions 608 may include possible search results based on filtering rules associated with certain image search patterns”].
Lester does not specifically teach based on a first optical character recognition (OCR) operation performed to detect text in a first area of the query image associated with the portion of the query image and a second OCR operation performed to detect further text in the second area of the query image, wherein a first processing power associated with the first OCR operation is greater than a second processing power associated with the second OCR operation. However, in the same field of invention or solving similar problems, Hamel teaches:
based on a first optical character recognition (OCR) operation performed to detect text in a first area of the query image associated with the portion of the query image and a second OCR operation performed to detect further text in the second area of the query image, wherein a first processing power associated with the first OCR operation is greater than a second processing power associated with the second OCR operation [(e.g. see Hamel paragraphs 0042, 0059, 0060, 0107) ”the methods and systems according to embodiments of the invention generally involve two independent OCR processes operating in parallel and characterized by specific and generally different processing speeds and accuracy rates. More particularly, one of the OCR processes, referred to as a "fast OCR process", aims at presenting a text-based representation of the portion of the working area to the user as quickly as possible, at the expense of potentially sacrificing some accuracy in the process. In contrast, the other OCR process, referred to as a "high-precision OCR process", aims at providing a text-based representation of the portion of the working area that is as accurate as possible, at the risk of sacrificing some speed … the step 104 of performing the fast OCR process is preferably carried out by processing 142 the region of interest in a prioritized manner … performing 106 the high-precision OCR process. Preferably, the high-precision OCR process is performed on more than the region of interest 26 of the image 20, for example on the entire image 20 … As used herein, the term "prioritized manner" is meant to indicate that the fast OCR process treats all or part of the textual content inside the region of interest before, more rapidly and/or with more processing resources than other textual content in the image”].
Therefore, considering the teachings of Lester and Hamel, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add based on a first optical character recognition (OCR) operation performed to detect text in a first area of the query image associated with the portion of the query image and a second OCR operation performed to detect further text in the second area of the query image, wherein a first processing power associated with the first OCR operation is greater than a second processing power associated with the second OCR operation, as taught by Hamel, to the teachings of Lester because it allows processed content to be presented to the user as quickly as possible (e.g. see Hamel paragraph 0060).
Lester and Hamel do not specifically teach second OCR operation, performed in a second area of the query image and which excludes the first area of the query image. However, in the same field of invention or solving similar problems, Dejean teaches:
second OCR operation, performed in a second area of the query image and which excludes the first area of the query image [(e.g. see Dejean paragraphs 0034, 0036 and Figs. 3 and 4) ”After the re-zoning engine 30 identifies the new text zones, it invokes the OCR engine 16 to apply the second character recognition pass 32 at least in the new text zones. Optionally, the OCR engine 16 performs the second pass 32 as a parameterized character recognition according to the textual pattern template for the new text zone that is generated by the textual pattern template builder component 36 of the re-zoning engine 30 … The second pass 32 performs character recognition at least on the new text zones identified by the re-zoning engine 30 based on the image of the paginated document 10”]. Examiner notes that, as depicted in Figs. 3 and 4, the second pass OCR zones (e.g. bounded text in Fig. 4) excludes the first pass OCR zones (e.g. bounded text in Fig. 3).
Therefore, considering the teachings of Lester, Hamel and Dejean, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add second OCR operation, performed in a second area of the query image and which excludes the first area of the query image, as taught by Dejean, to the teachings of Lester and Hamel because it improves and optimizes OCR recognition and output (e.g. see Dejean paragraph 0042).
As for dependent claim 6, Lester, Hamel and Dejean teach the method as described in claim 1 and Lester further teaches:
wherein receiving, by the computing system, the first user input from the user selecting the portion of the query image comprises cropping the query image to obtain a cropped query image and the cropped query image includes one or more entities [(e.g. see Lester paragraph 0069 and Fig. 6C) ”In FIG. 6C, the cropping operation 606b is applied to the input image 606a to determine a selected image subset. The cropped operation 606b may include a user selection using a bounding box that is adjustable in two dimensions for identifying the selected image subset. In this example, the bounding box is focusing on an object resembling a banana”].
As for dependent claim 7, Lester, Hamel and Dejean teach the method as described in claim 6 and Lester further teaches:
further comprising identifying one or more entities from the cropped query image using a neural network [(e.g. see Lester paragraphs 0032, 0070) ”when the cropped raw image is received by the server 130, the processor 136, using the neural network 234, extracts the feature vector of the cropped raw image, and compares the cropped image feature vector to each of the precomputed feature vectors in the collection of images 254 to return the listing of the plurality of images … the image search engine 256 identifies a listing of images 609 containing content that is visually similar to the cropped raw image 606c by filtering images in the extracted feature space. For example, the listing of images 609 includes images of bananas that are visually similar to the banana depicted in the cropped raw image 606c. In this embodiment, the search results may be provided for display within the output section 602 in real-time (or on-the-fly) based on an image comparison to the cropped raw image 606c using the image search engine 256. The listing of images 609 may be displayed in a particular order based on a prioritization of angle differences between compared feature vectors”].
As for dependent claim 8, Lester, Hamel and Dejean teach the method as described in claim 6 and Lester further teaches:
wherein cropping the query image includes cropping the query image based on the first user input defining an area of interest within the query image [(e.g. see Lester paragraph 0056, 0069) ”The user selection may identify a two-dimensional bounding box representing what portion of the input image the user would like to crop out from the input image (e.g., the selected image subset). The bounding box may be a user interface for selecting a portion of the input image such that content within the bounding box is extracted from the input image … The cropped operation 606b may include a user selection using a bounding box that is adjustable in two dimensions for identifying the selected image subset”].
As for dependent claim 9, Lester, Hamel and Dejean teach the method as described in claim 1, but Lester does not specifically teach the following limitation. However, Hamel teaches:
wherein the first OCR operation includes implementing a first OCR engine that detects the text within the first area of the query image and the second OCR operation includes implementing a second OCR engine that detects the further text within the second area of the query image, wherein the first OCR engine has a higher processing power than the second OCR engine [(e.g. see Hamel paragraphs 0042, 0059, 0060, 0107) ”the methods and systems according to embodiments of the invention generally involve two independent OCR processes operating in parallel and characterized by specific and generally different processing speeds and accuracy rates. More particularly, one of the OCR processes, referred to as a "fast OCR process", aims at presenting a text-based representation of the portion of the working area to the user as quickly as possible, at the expense of potentially sacrificing some accuracy in the process. In contrast, the other OCR process, referred to as a "high-precision OCR process", aims at providing a text-based representation of the portion of the working area that is as accurate as possible, at the risk of sacrificing some speed … the step 104 of performing the fast OCR process is preferably carried out by processing 142 the region of interest in a prioritized manner … performing 106 the high-precision OCR process. Preferably, the high-precision OCR process is performed on more than the region of interest 26 of the image 20, for example on the entire image 20 … As used herein, the term "prioritized manner" is meant to indicate that the fast OCR process treats all or part of the textual content inside the region of interest before, more rapidly and/or with more processing resources than other textual content in the image”].
The motivation to combine is the same as that used for claim 1.
As for dependent claim 12, Lester, Hamel and Dejean teach the method as described in claim 1 and Lester further teaches:
further comprising: receiving, by the computing system, a query input from the user which causes a search engine to search for the query image, wherein receiving, by the computing system, the query image, is responsive to the query input from the user [(e.g. see Lester paragraph 0067) ”In FIG. 6A, the user interface 600 includes a control section 601 and an output section 602. The user interface 600 includes a blank canvas 603 for receiving an input image and selecting a subset of the input image using one or more input tools (e.g., image upload control 604). Search results responsive to an image search query are provided for display via the output section 602. In some aspects, the control section 601 includes a search control (e.g., 605) to initiate the reverse image search. In other aspects, the reverse image search may be initiated independent of the search control 605”].
As for independent claim 13, Lester, Hamel and Dejean teach a system. Claim 13 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
As for dependent claim 18, Lester, Hamel and Dejean teach the system as described in claim 13; further, claim 18 discloses substantially the same limitations as claim 9. Therefore, it is rejected with the same rational as claim 9.
As for independent claim 20, Lester, Hamel and Dejean teach a non-transitory computer-readable storage device. Claim 20 discloses substantially the same limitations as claim 1. Therefore, it is rejected with the same rational as claim 1.
Claim 11 is rejected under 35 U.S.C. 103 as being unpatentable over Lester (US 2017/0249339 A1) in view of Hamel et al. (US 2014/0314319 A1) and further in view of Dejean et al. (US 2012/0039536 A1), as applied to claim 9 above, and further in view of Yadav et al. (US 2017/0287346 A1).
As for dependent claim 11, Lester, Hamel and Dejean teach the method as described in claim 9, but do not specifically teach wherein the second OCR engine includes a shallower neural network than a neural network of the first OCR engine. However, in the same field of invention or solving similar problems, Yadav teaches:
wherein the second OCR engine includes a shallower neural network than a neural network of the first OCR engine [(e.g. see Yadav paragraph 0019, 0022-0025) ”Optical character recognition (“OCR”) is performed 16 on the text regions to determine if there are any indications of presence of any anchor points in this frame or in the frames nearby … Shallow Models: … SIFT (scale invariant feature transform) and SURF (speeded up robust features) are extracted from the training images to create a bag-of-words model on the features. For example, 256 clusters in the bag-of-words model can be used. Then a support vector machine (SVM) classifier is trained using the 256 dimensional bag-of-features from the training data … Deep Models: … CNNs have been extremely effective in automatically learning features from images. CNNs process an image through different operations such as convolution, max-pooling etc. to create representations that are analogous to human brains. CNNs have recently been very successful in many computer vision tasks, such as image classification, object detection, segmentation etc. Motivated by that, CNN for classification is used 22 to determine the anchor points”].
Therefore, considering the teachings of Lester, Hamel, Dejean and Yadav, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to add wherein the second OCR engine includes a shallower neural network than a neural network of the first OCR engine, as taught by Yadav, to the teachings of Lester, Hamel and Dejean because it enables a quick summary to be generated and allows for quick navigation for the user (e.g. see Yadav abstract).
Response to Arguments
Applicant's arguments, filed 17 July 2026, have been fully considered but they are not persuasive.
Applicant argues that [“Hamel fails to disclose or suggest [the amended limitations] as recited in independent claim 1.” (Page 9).].
The argument described above, in paragraph number 10, with respect to the newly added limitations to the independent claims has been considered, but is moot in view of the new grounds of rejection.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER J FIBBI whose telephone number is (571)-270-3358. The examiner can normally be reached Monday - Thursday (8am-6pm).
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/CHRISTOPHER J FIBBI/Primary Examiner, Art Unit 2174
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