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
This action is in response to the applicant's communication filed on 03/14/2025. In virtue of this communication, claims 2-21 filed on 03/14/2025 are currently pending in the instant application.
Claim 1 has been cancelled in a preliminary amendment filed on 03/14/2025.
Information Disclosure Statement
The information Disclosure statement (IDS) form PTO-1449, filed on 01/14/2024 are in compliance with the provisions of CFR 1.97. Accordingly, the information disclosed therein was considered by the examiner.
Drawings
The drawings received on 12/11/2024 have been reviewed by Examiner and they are acceptable.
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 2 (system), 9 (method), and 17 (method) rejected on the ground of non-statutory double patenting as being unpatentable over parent co-owned claims 1(system), 9(method), and 14 (computer readable medium) of U.S. Patent No. 12,183,099. The conflicting claims are not identical because the embodiments of co-owned claims 1, 9, and 14 omit steps not explicitly required by the embodiment of instant claims. However, the conflicting claims are not patentably distinct from each other because:
· Instant claims 2, 9, and 17 and co-owned claims 1, 9, and 14 recite common subject matter;
· Instant claims 2, 9, and 17, which recite the open ended transitional phrase “comprising,” does not preclude the difference in steps recited by co-owned claims 1, 9, and 14, and
· the elements of instant claims 2, 9, and 17 are obvious over co-owned claims 1, 9, and 14, and completely anticipate the subject matter of instant claim, and “anticipation is the epitome of obviousness” Connell v. Sears, Roebuck & Co.,722 F.2d 1542, 1548, 220 USPQ 193, 198 (Fed. Cir. 1983) (citing In re Fracalossi, 681F.2d 792, 215 USPQ 569 (CCPA 1982)).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 2-3, 8-9, 12, 14-15, 17, 20 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gupta et al. (US 2019/0332849).
As per claim 2, A computer system, comprising: a processor; and a non-transitory computer-readable medium having stored thereon computer-executable instructions that are executable by the processor to cause the computer system to perform operations comprising: (Gupta, ¶[0020-0021] discloses computer processors and memory. ¶[0113-0114]) “accessing a current item of digital content;”(Gupta, ¶[0018] discloses a new image is received for detection of duplicates or near duplicates. ¶[0080] discloses he query image (e.g., image file) is analyzed to detect the face and extract the face image data from the overall profile image.)
“generating an authenticity score by comparing the current item of digital content to a plurality of prior items of digital content” (Gupta, [0035], discloses analyzing user profile images searching for duplicate profile images, ¶[0050] discloses convert the image data (e.g., pixel data, RGB data) to a feature vector 506 that represents the profile image 502. ¶[0051] discloses Once two images are converted to respective feature vectors 506, the feature vectors 506 may be compared to determine if the images are similar (or exactly the same) or if the images are dissimilar. two images are considered similar if the images are the same or if one image is a transformation of the other ¶[0085] discloses determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity). ¶[0090])
“wherein the authenticity score indicates a likelihood that the current item of digital content was derived from one or more of the prior items of digital content in the plurality of prior items of digital content,”(Gupta, ¶[0036] discloses a near-duplicate image, also referred to herein simply as a “near duplicate,” of a given image is an image that is of the same person as the given image. The near-duplicate image may be derived from the given image by altering the given image ¶[0051] discloses two images are considered similar if the images are the same or if one image is a transformation of the other (e.g., by cropping, rotating, or changing brightness); otherwise, the images are dissimilar. ¶[0061] discloses a larger distance between two face embeddings means that the faces are likely not of the same person ¶[0085] discloses the comparison includes determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity), and if the distance is below a predetermined threshold, the two vectors are considered to be associated with near-duplicate images.)
“wherein the comparing comprises: providing the current item of digital content and the plurality of prior items of digital content as input to a machine learning model;”(Gupta, ¶[0018], discloses prior indexing and subsequent query processing , then ¶[0050] discloses a profile image is input to a neural network 504 in order to convert the image data (e.g., pixel data, RGB data) to a feature vector 506 that represents the profile image further ¶[0051]. )
“wherein the current item of digital content and the plurality of prior items of digital content complete a forward pass through one or more intermediate layers of the machine learning model;”(Gupta, ¶[0054] and ¶[0056] discloses When an input (e.g., the profile image 502) is presented to the neural network 504, the input is propagated forward through the neural network, layer by layer, until the input reaches the output layer )
“and wherein the authenticity score is based on an output of the machine learning model;”(Gupta, ¶[0047] discloses the extraction is performed by a neural network, and the result is a feature vector,¶[0050] discloses the vectorization of a profile image by a neural network, according to some example embodiments. A profile image 502 is input to a neural network 504 in order to convert the image data (e.g., pixel data, RGB data) to a feature vector 506 that represents the profile image. ¶[0058] discloses the distance, in the hyperspace of the feature vectors, is calculated to determine if the feature vectors correspond to the same image. When the vector distance is small, the feature vectors correspond to the same image; otherwise, the feature vectors correspond to different images. In some example embodiments, a threshold distance is utilized for determining similarity, where two feature vectors are considered to be of the same image when the distance is equal to or less than the threshold distance. See ¶[0085] and ¶[0093]. )
“and determining whether the authenticity score for the current item of digital content satisfies a threshold authenticity value.” (Gupta, ¶[0058] discloses a threshold distance is utilized for determining similarity, where two feature vectors are considered to be of the same image when the distance is equal to or less than the threshold distance.¶[0085] discloses the comparison includes determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity), and if the distance is below a predetermined threshold, the two vectors are considered to be associated with near-duplicate images.)
As per claim 3, The computer system of claim 2, “wherein the machine learning model comprises a neural network, the neural network comprising the one or more intermediate layers.” (Gupta, ¶[0053] discloses One type of machine learning is embodied in a neural network. ¶[0054] discloses A deep neural network (DNN) is a stacked neural network. A DNN uses a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. Higher-level features are derived from lower-level features to form a hierarchical representation. The layers following the input layer may be convolution layers that produce feature maps that are filtering results of the inputs and are used by the next convolution layer. ¶[0056] discloses use of backpropagation can include propagation and updating weights. When an input (e.g., the profile image 502) is presented to the neural network 504, the input is propagated forward through the neural network, layer by layer, until the input reaches the output layer. The output of the neural network is then compared to the desired output. ¶[0093] discloses the feature extractor 804 is a neural network .)
Claim 15 and 20 have been analyzed and are rejected for the reasons indicated in claim 3 above.
As per claim 8, The computer system of claim 2, “wherein accessing the current item of digital content comprises receiving the current item of digital content from a user.”(Gupta, ¶[0037] discloses The social network requests users to add a profile image by a user interface.)
Claim 14 has been analyzed and is rejected for the reasons indicated in claim 8 above.
As per claim 9, A computer-implemented method, comprising: “accessing, by a computer system, a current item of digital content;” (Gupta, ¶[0018] discloses a new image is received for detection of duplicates or near duplicates. ¶[0080] discloses he query image (e.g., image file) is analyzed to detect the face and extract the face image data from the overall profile image.)
“generating, by the computer system, an authenticity score by comparing a portion of the current item of digital content to a plurality of prior items of digital content,” (Gupta, [0035], discloses analyzing user profile images searching for duplicate profile images, ¶[0050] discloses convert the image data (e.g., pixel data, RGB data) to a feature vector 506 that represents the profile image 502. ¶[0051] discloses Once two images are converted to respective feature vectors 506, the feature vectors 506 may be compared to determine if the images are similar (or exactly the same) or if the images are dissimilar. two images are considered similar if the images are the same or if one image is a transformation of the other. ¶[0080] discloses the query image (e.g., image file) is analyzed to detect the face and extract the face image data from the overall profile image. ¶[0085] discloses determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity), (candidate distance scoring))
“wherein the authenticity score indicates a likelihood that the portion of the current item of digital content was derived from one or more of the prior items of digital content in the plurality of prior items of digital content,” ¶[0051] discloses two images are considered similar if the images are the same or if one image is a transformation of the other (e.g., by cropping, rotating, or changing brightness); otherwise, the images are dissimilar. ¶[0093] discloses the feature extractor 804 generates the feature vector based on the face data. the feature extractor 804 is a neural network as described above with reference to FIG. 5. Which indicate possible portion derivation.)
“wherein the comparing comprises: providing the portion of the current item of digital content and the plurality of prior items of digital content as input to a machine learning model;”(Gupta, ¶[0018] discloses prior indexing and subsequent query processing, further see Figure 5, ¶[0093] discloses face input vectorization by neural network.)
“wherein the portion of the current item of digital content and the plurality of prior items of digital content complete a forward pass through one or more intermediate layers of the machine learning model;”(Gupta, ¶[0056] discloses the input is propagated forward through the neural network, layer by layer, until the input reaches the output layer. ¶[0093] discloses face input processing.)
“and wherein the authenticity score is based on an output of the machine learning model;”(Gupta, ¶[0050] discloses profile image is input to a neural network in order to convert the image data (e.g., pixel data, RGB data) to a feature vector 506 that represents the profile image. ( learned outputs). ¶[0085] discloses output distance score.)
“and determining, by the computer system, whether the authenticity score for the current item of digital content satisfies a threshold authenticity value.” (Gupta, ¶[0058] discloses a threshold distance is utilized for determining similarity, where two feature vectors are considered to be of the same image when the distance is equal to or less than the threshold distance.¶[0085] discloses the comparison includes determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity), and if the distance is below a predetermined threshold, the two vectors are considered to be associated with near-duplicate images.)
As per claim 12, The computer-implemented method of claim 9, further comprising: “isolating, by the computer system, the portion from the current item of digital content in order to make less than an entirety of the current item of digital content available to the machine learning model.” (Gupta, ¶[0080] discloses he query image (e.g., image file) is analyzed to detect the face and extract the face image data from the overall profile image. By focusing the analysis on the face.¶[0093] discloses isolated data (face) neural input.)
As per claim 17, A computer-implemented method, comprising: “receiving, by a computer system, a current item of digital content from a user;” (Gupta, ¶[0018] discloses a new image is received for detection of duplicates or near duplicates. ¶[0037] discloses user supplies images.)
“isolating, by the computer system, a portion of the current item of digital content from a remainder of the current item of digital content;” (Gupta, ¶[0080] discloses the query image (e.g., image file) is analyzed to detect the face and extract the face image data from the overall profile image. By focusing the analysis on the face.)
“generating, by the computer system, an authenticity score by comparing the portion of the current item of digital content to a plurality of prior items of digital content,”(Gupta, ¶[0080] discloses selected face data, ¶[0085] discloses determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity).)
“wherein the authenticity score indicates a likelihood that the current item of digital content was derived from one or more of the prior items of digital content in the plurality of prior items of digital content,” (Gupta, ¶[0051] discloses two images are considered similar if the images are the same or if one image is a transformation of the other (e.g., by cropping, rotating, or changing brightness); otherwise, the images are dissimilar. ¶[0093] discloses the feature extractor 804 generates the feature vector based on the face data. the feature extractor 804 is a neural network as described above with reference to FIG. 5.)
“by providing the portion of the current item of digital content and the plurality of prior items of digital content as input to a machine learning model that comprises one or more intermediate layers through which the portion of the current item of digital content and the plurality of prior items of digital content pass,” (Gupta, ¶[0018] discloses ¶[0018] discloses prior indexing and subsequent query processing, further see Figure 5, ¶[0054] and ¶[0056] disclose discloses the input is propagated forward through the neural network, layer by layer, until the input reaches the output layer. ¶[0093] discloses face data input to neural network.)
“and wherein the authenticity score is based on an output of the machine learning model;” (Gupta, ¶[0050] discloses learned vectors. ¶[0085] discloses output distance score.)“and determining, by the computer system, whether the authenticity score for the current item of digital content satisfies a threshold authenticity value.”(Gupta, ¶[0058] discloses a threshold distance is utilized for determining similarity, where two feature vectors are considered to be of the same image when the distance is equal to or less than the threshold distance.¶[0085] discloses the comparison includes determining a distance between the two vectors (e.g., Euclidean distance or cosine similarity), and if the distance is below a predetermined threshold, the two vectors are considered to be associated with near-duplicate images.)
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries 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.
Claim(s) 4 and 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Spiegel et al. (US 2020/0401617).
As per claim 4, The computer system of claim 2, However Gupta does not explicitly disclose the following which would have been obvious in view of Spiegel from similar filed of endeavor “wherein providing the current item of digital content and the plurality of prior items of digital content as input to a machine learning model comprises providing the current item of digital content as a paired input with each respective prior item of digital content of the plurality of prior items of digital content.”(Spiegel, ¶[0141] discloses input to a Siamese network two neural which learns to differentiate between two inputs. A Siamese network consists of two identical neural networks, each taking one of the two input images. The last layers of the two networks are then fed to a contrastive loss function, which calculates the similarity between the two images. The output from the Siamese network may thus be used to compare between images and to find a match between a query image and a reference image. ¶[0142] disclose a set of reference images 20 of a specific route may be created. The paired comparison is applied to each inherited candidate.)
Claim 16 has been analyzed and is rejected for the reasons indicated in claim 4 above.
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Spiegel technique of visual monitoring system into Gupta technique to provide the known and expected uses and benefits of Spiegel technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Spiegel into Gupta in order to provide accurate information of existing images. (Refer to Spiegel paragraph [0063].)
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Waxler et al. (US 2019/0294629).
As per claim 5, The computer system of claim 2, wherein the operations further comprise: “transmitting one or more electronic requests to a private data store, wherein the private data store stores one or more of the prior items of digital content; receiving, from the private data store, an electronic permission authorization in response to the electronic request; and “accessing the one or more of the prior items of digital content in response to the electronic permission authorization.” (Waxler, ¶[0106] discloses secured database, ¶[0153] discloses once the owner 132 approves (714) the transfer request, the requester 124 can be notified (716) to start the image download. During the download, the protocol SDK begins with an initial authentication request that provides access to a given image repository. Once authenticated, subsequent requests (718 and 720) use individual image URLs to retrieve pixel or metadata information.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Waxler technique of image query for authentication into Gupta technique to provide the known and expected uses and benefits of Waxler technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Waxler into Gupta in order to provide an efficient system that prevent security risks (Refer to Waxler paragraph [0003].)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Klare et al. (US 2016/0132720).
As per claim 6, The computer system of claim 2, wherein the operations further comprise: however Gupta does not explicitly disclose the following which would have been obvious in view of Klare “executing a web crawlers on the Internet, wherein the web crawler identifies one or more of the prior items of digital content in one or more web pages of the Internet.” (Klare, ¶[0045] discloses when executed by a processor connected to the crawler servers 202, may cause one or more of the servers 202 to crawl image URL's based on a seed page. the one or more servers 202 starts by accessing a seed page, which may be indicated by a seed URL, and then searching the page for images or videos contained on the page .An image or video that is found on the page is downloaded by a crawler server 202 and stored in a database or storage system. ¶[0046] discloses the crawler server 202 follows all links to other network addresses, such as web URL's, located on the seed page, and reiterates the process of scraping all images from each page visited. ¶[0051] discloses The feature vector generated to represent the query image may thus be compared to the feature vectors generated to represent the images previously ingested, which are stored in the gallery files.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Klare technique of image searching and identification into Gupta technique to provide the known and expected uses and benefits of Klare technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Klare into Gupta in order to provide efficient and scalable system for image queries (Refer to Klare paragraph [0004].)
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Klare et al. (US 2016/0132720), further in view of Waxler et al. (US 2019/0294629).
As per claim 19, The computer-implemented method of claim 17, However Gupta does not explicitly disclose the following which Would have been obvious in view of Waxler form similar filed of endeavor further comprising: “accessing, by the computer system, a first one or more of the prior items of digital content from a private data store;” (Waxler, ¶[0153] discloses initial authentication request that provides access to a given image repository.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Waxler technique of image query for authentication into Gupta technique to provide the known and expected uses and benefits of Waxler technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Waxler into Gupta in order to provide an efficient system that prevent security risks (Refer to Waxler paragraph [0003].)
However Gupta as modified by Waxler does not explicitly disclose the following which would have been obvious sin view of Klare form similar filed of endeavor “and executing, by the computer system, a web crawler on the Internet, wherein the web crawler identifies a second one or more of the prior items of digital content in one or more web pages of the Internet.” (Klare, ¶[0045] discloses when executed by a processor connected to the crawler servers 202, may cause one or more of the servers 202 to crawl image URL's based on a seed page. the one or more servers 202 starts by accessing a seed page, which may be indicated by a seed URL, and then searching the page for images or videos contained on the page .An image or video that is found on the page is downloaded by a crawler server 202 and stored in a database or storage system. ¶[0046] discloses the crawler server 202 follows all links to other network addresses, such as web URL's, located on the seed page, and reiterates the process of scraping all images from each page visited. ¶[0051] discloses The feature vector generated to represent the query image may thus be compared to the feature vectors generated to represent the images previously ingested, which are stored in the gallery files.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Klare technique of image searching and identification into Gupta as modified by Waxler technique to provide the known and expected uses and benefits of Klare technique over fake image detection technique of Gupta as modified by Waxler. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Klare into Gupta as modified by Waxler in order to provide efficient and scalable system for image queries (Refer to Klare paragraph [0004].)
Claim(s) 13 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Burge et al.(US 2018/0101742).
As per claim 13, The computer-implemented method of claim 12, “wherein: the item of digital content is an image;”(Gupta, ¶[0018] discloses when a new image is received. ¶[0080] discloses extracting face image data from overall profile image.)
However Gupta does not explicitly disclose the following which would have been obvious in view of Burge from similar filed of endeavor “and isolating the portion from the current item of digital content comprises cropping the portion from the image.” (Burge, ¶[0066] discloses the system crops the received image of a face to a predefined size. ¶[0070] discloses the system enrolls the cropped image of the face through a deep neural network (DNN) to generate a sparse feature vector.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Burge technique of image search and face recognition into Gupta technique to provide the known and expected uses and benefits of Burge technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Burge into Gupta in order to provide reliable, efficient, and effective face recognition. (Refer to Burge paragraph [0004].)
Claim 18 has been analyzed and is rejected for the reasons indicated in claim 13 above.
Claim(s) 10 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta et al. (US 2019/0332849), further in view of Boiman et al. (US 2008/0208828).
As per claim 10, The computer-implemented method of claim 9, However Gupta does not explicitly disclose the following which would have been obvious in view of Burge from similar filed of endeavor “wherein the portion of the item of digital content comprises a plurality of fragments of the item of digital content associated with indications of how the plurality of fragments are relatively arranged in the item of digital content.” (Boiman, ¶[0049] discloses a region LR under consideration in a query, surrounding a data point P, such as exemplary region LR.sub.Pk or LR.sub.Pa of FIG. 2, may be represented as an ensemble of patches by breaking the region down into an ensemble of many (e.g., hundreds) of small patches at multiple scales with their relative geometric positions.)
Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to combine Boiman technique of image similarity detection into Gupta technique to provide the known and expected uses and benefits of Boiman technique over fake image detection technique of Gupta. The proposed combination would have constituted a mere arrangement of old elements with each performing their known function, the combination yielding no more than one would expect from such an arrangement. Therefore, it would have been obvious to a person of ordinary skill in the art to incorporate Boiman into Gupta in order to accurately measure similarities and irregularities in images (Refer to Boiman paragraph [0004].)
As per claim 11, The computer-implemented method of claim 10, wherein: “the current item of digital content is an image;”(Gupta, ¶[0018] discloses receiving new image.)
“the fragments are pixel patches of the image;”( Boiman, ¶[0099] discloses) The spatial image descriptors of the small (e.g., 7.times.7) spatial patches Firstly, the spatial gradient magnitude may be computed for each pixel in the patch.) “and the indications of how the plurality of fragments are relatively arranged comprise distance vectors specifying a distance and direction of one pixel patch to another”.(Boiman, ¶[0086] discloses in FIG. 9, is defined by the summation of vectors V1 and V2. As shown in FIG. 9, vector V1 defines a distance and direction between patch q.sup.1y.sub.P1 and point P1.)
Allowable Subject Matter
Claims 7 and 21 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 and on the pending conditions of the rejected and objected matter set forth in this action.
The following is a statement of reasons for the indication of allowable subject matter: the prior art of record, alone or in combination, fails to teach or suggest the limitations set forth by the equations of each of claims 7 and 21.
Contact
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAGHAYEGH AZIMA whose telephone number is (571)272-1459. The examiner can normally be reached Monday-Friday, 9:30-6:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vincent Rudolph can be reached at (571)272-8243. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SHAGHAYEGH AZIMA/Examiner, Art Unit 2671
Prosecution Insights