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 .
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show the target body position information in FIG. 3 as described in the specification in Paragraph [0081]. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 1-20 are rejected under 35 U.S.C. because the claimed invention is directed to an abstract idea (mathematical calculations) without significantly more. The flow chart in MPEP 2106, Subject Matter Eligibility Test for Products and Processes, will be referred to establish ineligible subject matter.
Regarding claim 11, Step 1: the claim recites an electron device, which would be categorized as a process under the four recognized statutory categories. Step 2A Prong One: However, the claim is further directed to the abstract ideas (mathematical calculations) of determining a target loss function, which is a mathematical calculation (see MPEP 2106.04(a)(2)). Step 2A Prong Two: Additional elements include generic computer elements (memory and one or more processors), sample data (sample image, first and second cropped image), and using the sample data and target loss function to perform model training on a preset image cropping model to obtain a target image cropping model. The addition of generic computer elements, input, and applying the process as computer instruction does not integrate the judicial exception into a practical application (see MPEP 2106.05(d)). Step 2B: The additional claim elements do not amount to significantly more than the judicial exception, as explained above. Therefore, the claim is ineligible.
Regarding claims 12-14, additional limitations do not amount to significantly more and therefore the claims are all ineligible.
Regarding claims 1-10 and 15-20, the rationale provided in rejection of claim 11-14 is incorporated herein. In addition, the electronic device of claims 11-14 corresponds to the method of claims 1-10, the device of claim 15, as well as the non-transitory machine-readable information storage mediums of claims 16-20, and performs the steps disclosed herein. Therefore, the claims are ineligible.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the investor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application as filed, had possession of the claimed invention.
Regarding claim 1-20, the claims recite computer-implemented functions including using sample data and a target loss function to perform model training on a preset image cropping model to obtain a target image cropping model.
Applicants are respectfully reminded, for computer-implemented functional claims, “examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented that claimed subject matter.” MPEP § 2161.01(I).
As the initial matter, the Examiner notes that claims 1, 11, and 16 are each an originally-filed claim. However, originally-filed claims 1, 11, and 16 do not disclose how 1, 11, and 16 themselves, or how “using the sample data and the target loss function to perform model training on a preset image cropping model to obtain a target image cropping model” itself, is done and so does not provide the necessary written description support for pending 1, 11, 16. Accord Ariad, 598 F.3d at 1349 (indicating original claim language does not necessarily satisfy the written description requirement for the claimed subject matter). That is to say, originally-filed claim 1, 11, and 16 itself, or themselves, do not provide an algorithm that performs the function " using the sample data and the target loss function to perform model training on a preset image cropping model to obtain a target image cropping model " in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
In addition, the examiner also notes that claims 4, 14, and 19 are each an originally-filed claim. However, originally-filed claims 4, 14, and 19 do not disclose how 4, 14, and 19 themselves, or how “inputting the sample image into a target detection model to obtain output comprising target body position information” itself, is done and so does not provide the necessary written description support for pending 4, 14, and 19. Accord Ariad, 598 F.3d at 1349 (indicating original claim language does not necessarily satisfy the written description requirement for the claimed subject matter). That is to say, originally-filed claim 4, 14, and 19 itself, or themselves, do not provide an algorithm that performs the function “inputting the sample image into a target detection model to obtain output comprising target body position information” in sufficient detail that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Furthermore, Applicant's specification does not describe an algorithm that performs the
function “using the sample data and the target loss function to perform model training on a preset image cropping model to obtain a target image cropping model” in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. For example, Applicant's specification discloses “Moreover, the cropped image obtained using the trained target image cropping model can effectively avoid problems such as character truncation and text truncation, improving the cropping accuracy effectively and thus improving the user experience effectively. Also, the target image cropping model in the solution of the present disclosure can also crop images in various sizes, meeting the multi-size requirements of different scenarios effectively, and thus improving the user experience.” as seen in Par. [0082] of the applicant's originally filed specification. However, such disclosure merely details about the scheme, thus is not an algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Applicant's specification also does not describe an algorithm that performs the
function “inputting the sample image into a target detection model to obtain output comprising target body position information” in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. For example, Applicant's specification discloses “That is to say, in the example, the target detection model can be used to detect the position of the target body in the sample image to obtain specific position information of each target body in the sample image (that is, the target body position information described above), so that the model can extract effective features based on the target body position information for use in evaluating the cropped image, thereby providing strong support for improving the cropping accuracy of the model” as seen in Par. [0075] of the applicant's originally filed specification. However, such disclosure merely details about the scheme, thus is not an algorithm (e.g., the necessary steps and/or flowcharts) that performs the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Applicant is also reminded, "if the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph, for lack of written description must be made." MPEP § 2161.01(I).
Therefore, because an algorithm for the function “using the sample data and the target loss function to perform model training on a preset image cropping model to obtain a target image cropping model” is not disclosed in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter, and in accordance with MPEP § 2161.01, claims 1, 4, 11, 14, 16, and 19 are rejected for lack of written description.
Claims 2-3, 5-10, 12-13, 15, 17-18, and 20 recite limitations in claims 1, 11, and 13, thus are also rejected.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2, 4, 6, 8-10. 12, 15, 17 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 2, 12, and 17, each recite “after determining that the sample data comprises two or more first cropped images” and “after determining that the sample data comprises two or more second cropped images”, but does not disclose how two or more first cropped images and two or more second crop images are both able to be obtained simultaneously. It is unclear how the invention determines whether the sample data comprises two or more first cropped images and two or more second cropped images, when in claim 1 it is stated that the sample data comprises: a sample image, a first cropped image, and a second cropped image, not two or more images.
Regarding claim 4, it recites “inputting the sample image into a target detection model to obtain output comprising target body position information.” The phrase “target body position information” lacks clear scope because claim 4 is dependent on claim 3 which also recites “obtaining target body position information”. It is unclear whether this is a separate target body position information or the same as recited in claim 3.
Regarding claim 6, it recites the first branch comprising a first feature alignment module, a first graph attention network (GAT) and a first multilayer perceptron (MLP) AND/OR the second branch comprises a second feature alignment module, a second graph attention network (GAT) and a second multilayer perceptron (MLP). However, claim 5, on which claim 6 depends on, recites a first branch AND second branch, making claim 6 contradictory to claim 5. It is unclear how in one scenario only the first branch comprises the three modules that process on the global feature of the sample image and a global feature of the first cropped image and, in another scenario, only the second branch comprises the three modules that process on the global feature of the sample image and a global feature of the second cropped image, when in claim 5 it is stated that both branches process on the global feature for the sample and respective cropped images.
Regarding claim 8, it recites the first cropped image is obtained by horizontally cropping the sample image AND/OR the second cropped image is obtained by vertically cropping the sample image. However, claim 1, on which claim 8 depends on, recites a first cropped image obtained by cropping the sample image in a first manner, AND a second cropped image obtained by cropping the sample image in a second manner, making claim 8 contradictory to claim 1. It is unclear how in one scenario only the first cropped image is obtained horizontally and, in another scenario, only the second cropped image is obtained vertically, when in claim 1 it is stated that both cropped images are obtained by a cropping manner.
Regarding claim 9, it recites “inputting the image to be cropped into a target image cropping model to obtain a target cropped image”. The phrase “a target image cropping model” lacks clear scope because claim 9 is dependent on claim 1 which also recites “obtain a target image cropping model”. It is unclear whether this is a separate target image cropping model or the same as recited in claim 1.
Claim 10, 15, and 20 recite limitations in claim 9, thus are also rejected.
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, 3, 9, 11, 13, 15-16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Hou (CN 109919156).
Regarding claim 11, Hou (CN 109919156) discloses an electronic device (Hou teaches “a computing device” Page 5/43) comprising
at least one processor (Hou teaches “a processor” Page 5/43) and
a memory connected in communication with the at least one processor (Hou teaches “a memory, a processor and a computer program stored on the memory and capable of running on the processor” Page 5/43)
wherein the memory stores an instruction executable by the at least one processor, and the instruction, when executed by the at least one processor, enables the at least one processor to execute (Hou teaches “the processor executes the steps of training method or image cutting method of image cutting prediction model when executing the program” Page 5/43) to execute
obtaining sample data comprising: a sample image, a first cropped image obtained by cropping the sample image in a first manner (Hou teaches “an original image, can be manually determined the key part of the original image, and manually cutting the original image for multiple times, each cutting to generate a group of cutting sample information.” Page 8/43) and
a second cropped image obtained by cropping the sample image in a second manner (Hou teaches “an original image, can be manually determined the key part of the original image, and manually cutting the original image for multiple times, each cutting to generate a group of cutting sample information.” Page 8/43)
determining a target loss function (Hou teaches “calculating the loss function” Page 2/43), wherein the target loss function is used to
constrain a difference between a first predicted attribute value of the first cropped image and a first theoretical attribute value of the first cropped image (Hou teaches “a loss function calculating module for calculating loss function according to the information of the predicted image output by the image cutting prediction model and the information of the cutting sample” Page 4/43. Examiner notes that “information of the predicted image output” maps to predicted attribute value, and “information of the cutting sample” maps to “theoretical attribute value”), and
constrain a difference between a second predicted attribute value of the second cropped image and a second theoretical attribute value of the second cropped image (Hou teaches “a loss function calculating module for calculating loss function according to the information of the predicted image output by the image cutting prediction model and the information of the cutting sample” Page 4/43; “…manually cutting the original image for multiple times, each cutting to generate a group of cutting sample information.” Page 8/43) and
using the sample data and the target loss function to perform model training on a preset image cropping model to obtain a target image cropping model (Hou teaches “training module, used for using the loss function to train the image cutting prediction model” Page 4/43; “the pre-trained image cutting prediction model to predict the characteristic information of the original image to obtain the prediction image of the original image information” Page 7/43)
wherein the first predicted attribute value and the second predicted attribute value are obtained using the preset image cropping model (Hou teaches “In one embodiment of the present invention, the information of the predicted image according to the image cutting prediction model output” Page 3/43).
While Hou (CN 109919156) does disclose constraining a difference between a first predicted attribute value of the first cropped image and a first theoretical attribute value of the first cropped image (Hou teaches “a loss function calculating module for calculating loss function according to the information of the predicted image output by the image cutting prediction model and the information of the cutting sample” Page 4/43), it does not fully disclose that it constrains a difference between a second predicted attribute value of the second cropped image and a second theoretical attribute value of the second cropped image, but it would have been obvious to have done so. The reason is the image cropping model can perform on multiple different cropped images, each crop having their own theoretical attribute value (“…manually cutting the original image for multiple times, each cutting to generate a group of cutting sample information.” Page 8/43). Therefore, the image cropping model constrains a difference for theoretical attribute value and predicted attribute value for each cropped image. Thus, it would have been obvious to one of ordinary skill in the art at the time of the invention by the applicant to constrain a difference between a second predicted attribute value of the second cropped image and a second theoretical attribute value of the second cropped image, because it allows for performing the constraining step for both cropped images without having to continually run the model.
Regarding claim 13, Hou (CN 109919156) discloses the electronic device according to claim 11 wherein the instruction, when executed by the at least one processor, further enables the at least one processor to execute using the sample data and the target loss function to perform model training (Hou teaches “training module, used for using the loss function to train the image cutting prediction model” Page 4/43), by
obtaining target body position information of the sample image (Hou teaches “for an original image, can be manually determined the key part of the original image” Page 8/43),
inputting the sample image, the target body position information, the first cropped image and the second cropped image into the preset image cropping model, to obtain output comprising first predicted attribute value and second predicted attribute value (Hou teaches “an original image, can be manually determined the key part of the original image, and manually cutting the original image for multiple times, each cutting to generate a group of cutting sample information” Page 8/43;).
obtaining a loss value of the target loss function based on the first predicted attribute value and the second predicted attribute value (Hou teaches “a loss function calculating module for calculating loss function according to the information of the predicted image output by the image cutting prediction model and the information of the cutting sample” Page 4/43; “…each cutting to generate a group of cutting sample information.” Page 8/43) and
adjusting an adjustable parameter in the preset image cropping model based on the loss value, to obtain the target image cropping model (Hou teaches “when training the image cutting prediction model, adjusting the parameter of the image cutting prediction model according to the result of the loss function” Page 11/43).
Regarding claims 1, 3, 16 and 18, the rationale provided in the rejection of claim 11 and 13 is incorporated herein. In addition, the electronic device of claim 11 and 13 corresponds to the method of claim 1 and 3, in addition to the non-transitory computer-readable medium of claim 16 and 18, and performs the steps disclosed herein.
Regarding claim 9, the rationale taught in claim 1 is applied herein. In addition, Hou (CN 109919156) discloses a method for processing an image (Hou teaches “The image cutting prediction model obtained by the embodiment of the invention can be used for image cutting process” Page 8/43), comprising
obtaining an image to be cropped (Hou teaches “suitable for each kind of image of different content” Page 8/43) and
inputting the image to be cropped into a target image cropping model to obtain a target cropped image (Hou teaches “The image cutting prediction model obtained by the embodiment of the invention can be used for image cutting process” Page 8/43),
Regarding claim 15 and 20, the rationale provided in the rejection of claim 9 is incorporated herein. In addition, the electronic device of claim 15 corresponds to the method of claim 11 and the non-transitory computer-readable medium of claim 20 corresponds to that of claim 16.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Hou (CN 109919156) in view of Li (Pub. # 20192914360)
Regarding claim 5, Hou (CN 109919156) discloses the method of claim 1, wherein the preset image cropping model comprises a first branch and a second branch (Hou teaches “…each prediction channel corresponding to a range of one image aspect ratio” Page 4/43)
Hou (CN 109919156) fails to disclose or teach where Li (Pub. # 20192914360) does disclose wherein the first branch is used to process a global feature of the sample image and a global feature of the first cropped image to obtain the first predicted attribute value (Li teaches “In the forward pass, the feature map of the input image is extracted with the convolution block. Then the feature corresponding to the cropping window is cropped out from the feature map and fused using a global average pooling (GAP) layer. The fused feature is sent to a block consisting of two fully-connected layers and an LSTM layer [51] with two outputs” Page 6/16; Fig. 4. Examiner notes that the global averaging pooling captures the global features from the fused feature maps of the sample and cropped image which are then processed after) and
the second branch is used to process the global feature of the sample image and a global feature of the second cropped image to obtain the second predicted attribute value (Examiner notes the art also discusses a “Siamese architecture” that can be used to replicate a second branch. Page 5/16; Fig. 3.).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Hou (CN 109919156) to incorporate the teachings of Li (Pub. # 20192914360) by including: processing the global feature of the sample image and first cropped image to obtain the first predicted attribute value in order to better train the model based on attribute/aesthetics and create a model further in line with human decision making (See Li (Pub. # 20192914360) Page 1/16).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Hou (CN 109919156) in view of Li (Pub. # 20192914360) as applied to claim 5, and further in view of “Application of Deep Learning to Three Problems in Image Analysis and Image Processing: Automatic Image Cropping, Remote Heart Rate Estimation and Quadrilateral Detection (2022)” (Hereinafter: Cheng (2022))
Regarding claim 7, the combination of Hou (CN 109919156) and Li (Pub. # 20192914360) together discloses the method of claim 5, wherein the preset image cropping model further comprises a shared backbone network, an output of the shared backbone network serves as inputs of the first branch and the second branch, and the shared backbone network is configured to
obtain the global feature of the first cropped image based on the first cropped image and obtain the global feature of the second cropped image based on the second cropped image (Li teaches “The two networks in the architecture share weights, and output aesthetic scores for the original professional image Ii and the corresponding random cropped images Ci respectively” Page 5/16; Fig. 3).
Hou (CN 109919156) and Li (Pub. # 20192914360) fails to disclose or teach where Cheng (2022) does disclose obtaining the global feature of the sample image based on the sample image and target body position information of the sample image (Cheng teaches “a crop evaluation model that takes an original uncropped image and the coordinates of the crop candidates produced by the sliding window algorithm as input, and predicts their scores” Page 30/107. Examiner notes that the coordinates are based on Rule of Thirds to get a target area of the original uncropped image).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Hou (CN 109919156) to incorporate the teachings of Li (Pub. # 20192914360) and Cheng (2022) by including: a shared backbone network, an output of the shared backbone network serves as inputs of the first branch and the second branch, and the shared backbone network is configured to obtain the global feature of the sample image based on the sample image and target body position information of the sample image; obtain the global feature of the first cropped image based on the first cropped image; and obtain the global feature of the second cropped image based on the second cropped image in order to help in getting a better and more appropriate selection of the sample image and contributes to the overall model performance (See Cheng (2022) Page 22/107; Fig. 2.2) and better train the model based on attribute/aesthetics and create a model further in line with human decision making (See Li (Pub. # 20192914360) Page 1/16)
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Hou (CN 109919156) in view of Selena (WO 2026006237)
Regarding claim 8, Hou (CN 109919156) discloses the method of claim 1 (Hou teaches “The cropped sample is at least one image obtained by cropping the original image with at least one image aspect ratio; each of the image aspect ratios being within a range of the respective image aspect ratios” Page 3/43).
Hou (CN 109919156) fails to disclose or teach where Selena (WO 2026006237) does disclose that the first cropped image is obtained by horizontally cropping the sample image and/or, the second cropped image is obtained by vertically cropping the sample image (Selena teaches “In some embodiments, the recomposition module 208 is a machine-learning model that is trained using pairs of recomposed ground truth images that are cropped versions of original input images. For example, the recomposed ground truth images may be cropped horizontally and/or cropped vertically to remove pixels from the original input images” Paragraph 89).
Therefore, it would have been obvious to a person of ordinary skill in the art prior to the effective filing date of the claimed invention to have modified Hou (CN 109919156) to incorporate the teachings of Selena (WO 2026006237) by including: horizontal and vertical cropping in order to “…remove portions of the output image to further improve the image, for example, by cropping from the top or bottom to center the subject vertically (recomposition)” (See Selena (WO 2026006237) Paragraph 0091).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure is Hui (WO 2020034663) and Cheng (CN 117666897).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMMAD A KHAN whose telephone number is (571)272-0283. The examiner can normally be reached Monday - Thursday, 7 a.m. 6 p.m. ET..
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.
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Mohammad Ali Khan
Examiner
Art Unit 2671
/M. Ali Khan/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
Prosecution Insights