DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the claims filed on 04/29/2024.
Claims 1-20 are currently pending and have been examined.
Information Disclosure Statement
Information Disclosure Statements received 04/29/2024 and 04/08/2025 have been reviewed and considered.
Claim Rejections- 35 U.S.C. § 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. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the subject matter eligibility (SME) analysis described in MPEP 2106.03, the instant claims fall within the four statutory categories of invention identified by 35 U.S.C. 101. In the instant case, claims 1-17 are directed to a method, claims 18-19 are directed to a system, and claim 20 is directed to a manufacture. Claims 1, 18, and 20 are parallel in nature, therefore, the analysis will use claim 1 as the representative claim.
In Step 2A Prong One, it must be considered whether the claims recite a judicial exception. Claim 1, as exemplary, recites abstract concepts including: receiving a product image, ... analyzing the product image to determine particular parameters of the product image, the analyzing including ... to detect features of the product image; determining, based on the determined particular parameters of the product image using ... parameters of existing product images, whether the product image is suitable; and providing output based on whether the product image is suitable.
These identified limitations recite the abstract idea of “analyzing a product image and providing output based on whether the product image is suitable”, which falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas as assessing a product image for suitability is a fundamental economic practice long prevalent in our system of commerce. Accordingly, claims 1, 18, and 20 recite an abstract idea. See MPEP 2106.04.
In Step 2A Prong Two, examiners evaluate integration into a practical application by: (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception(s); and (2) evaluating those additional elements individually and in combination to determine whether they integrate the exception into a practical application.
Instant claims 1, 18, and 20 recite additional elements including: a computer; a digital raster image defined by an array of pixels; an image analysis algorithm; a trained product image model; a computer system comprising: at least one processor and a memory storing instructions; and a non-transitory computer-readable storage medium storing instruction thereon. The computer, computer system, and non-transitory computer-readable storage medium is recited at a high-level of generality such that it amounts to no more than “apply it” or mere instruction to implement the abstract idea on a computer. Similarly, specifying that the product image is a “digital raster image” and the analysis is performed “using an image analysis algorithm” and trained “product image model”, without reciting details describing how these analysis are achieved by these features, also amount to no more than “apply it”. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it". See Electric Power Group, LLC v. Alstom, S.A., 830 F.3d 1350, 1356, 119 USPQ2d 1739, 1743-44 (Fed. Cir. 2016). See MPEP 2106.05(f). The combination of these additional elements amounts to no more than implementing the identified abstract idea in a generic computer environment. Claims 1, 18, and 20 are thus directed to an abstract idea.
Under Step 2B of the SME analysis, if it is determined that the claims recite a judicial exception that is not integrated into a practical application of that exception, it is then necessary to evaluate the additional elements individually and in combination to determine whether they provide an inventive concept (i.e., whether the additional elements amount to significantly more than the exception itself).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) individually and in combination are merely being used to apply the abstract idea to a general computer components. For the same reason, the elements are not sufficient to provide an inventive concept. As explained in MPEP 2106.05(f), implementing an abstract idea with a generic computer does not add significantly more in Step 2B. Therefore, the additional elements, alone or in ordered combination, there is no inventive concept in the claim, and thus claims 1, 18, and 20 are not patent eligible.
Dependent claim(s) 2-3, 7-11, 15-17, and 19 do not aid in the eligibility of the independent claims. These claims merely further define the abstract idea without reciting any further additional elements. Thus dependent claims 2-3, 7-11, 15-17, and 19 are also ineligible.
Dependent claims 4-6 recites additional elements including: wherein the image analysis algorithm includes a segmentation process to locate a pixel boundary between a product and a background in the product image. Similar to the additional elements identified above, the claim does not contain any description of the mechanism for accomplishing this result, and consequently amounts to no more than instruction to apply the abstract idea with a generic computer. Accordingly, claim(s) 4-6, considered both individually and as a combination, is/are ineligible.
Dependent claim 12-14 recite additional elements including: wherein the product image model is or uses a machine learning model; wherein the machine learning model is or uses a neural network; and training the product image model ... wherein training the product image model includes providing parameters of existing product images as inputs to the neural network. These additional elements do not integrate the abstract idea into a practical application because they merely amount to no more than a general link of the use of the abstract idea to a particular technological environment or field of use (i.e., implementation via existing machine learning technology). Even in combination, these additional elements do not act to integrate the abstract idea into a practical application and do not amount to significantly more than the abstract idea itself. Thus claims 12-14 are also ineligible.
Claim Rejections - 35 U.S.C. § 102
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-4 and 9-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Pyati (US 2019/0311301 A1).
Claim 1, Pyati discloses a computer-implemented method comprising:
receiving a product image, the product image being a digital raster image defined by an array of pixels (¶ [0104] “Graphical user interface 300D includes a user interface element, photos and videos section 320, for adding, uploading, editing, deleting, and otherwise managing a set of photos, videos, or other image data for the item listing”; ¶ [0105] Examiner notes that features such as image resolution are exclusive to raster images and therefore necessarily include a digital raster image defined by pixels.);
analyzing the product image to determine particular parameters of the product image, the analyzing including using an image analysis algorithm to detect features of the product image (¶ [0106] “the online marketplace may also be capable of evaluating the content of the photos and videos themselves using computer-vision (CV) techniques. For example, the online marketplace may process the photos and videos to extract lower-level image features, such as points, edges, or regions of interest.”);
determining, based on the determined particular parameters of the product image using a product image model trained based on parameters of existing product images, whether the product image is suitable (¶ [0104] “a machine learning system capable of evaluating an item listing based on current inputs and evaluating the item listing by substitute one or more of the current inputs”; ¶ [0132] “ For example, the computing system can receive attribute values for attributes of previous item listings, extract feature values and features from the attributes and attribute values, and build a feature vector for each previous item listing using the extracted features and feature values. The system can apply the feature vectors and target objective to a machine learning algorithm to generate the machine learning model.”); and
providing output based on whether the product image is suitable (¶ [0104] “Photo and video section 320 also includes photo and videos recommendation 322, a user interface element that offers suggestions to the user on how the user can change the set of photos or videos for the item listing ... to achieve a target objective (e.g., maximizing a selling price for the item)”).
Claim 2 – Pyati discloses the method of 1. Pyati further discloses wherein the detected features are used to determine the particular parameters of the product image (¶¶ [0104]-[0106])
Claim 3 – Pyati discloses the method of 1. Pyati further discloses wherein the product image model is trained for at least one of a particular product type (¶ [0040]; FIG. 2), a particular merchant, or a particular region.
Claim 4 – Pyati discloses the method of 1. Pyati further discloses wherein the image analysis algorithm includes a segmentation process to locate a pixel boundary between a product and a background in the product image (¶ [0108]).
Claim 9 – Pyati discloses the method of 1. Pyati further discloses, wherein the output is based on an image resolution of the product image (¶ [0105]).
Claim 10 – Pyati discloses the method of 1. Pyati further discloses, wherein one of the determined particular parameters of the product image corresponds to a portion of a product that is in view in the product image (¶ [0106])
Claim 11 – Pyati discloses the method of 1. Pyati further discloses, wherein the determined particular parameters of the product image are input into the product image model to produce an estimate of the quality of the product image (¶ [0094]).
Claim 12 – Pyati discloses the method of 11. Pyati further discloses, wherein the product image model is or uses a machine learning model (¶ [0104]).
Claim 13 – Pyati discloses the method of 12. Pyati further discloses wherein the machine learning model is or uses a neural network (¶ [0069]; ¶ [0133]).
Claim 14 – Pyati discloses the method of 13. Pyati further discloses, comprising: training the product image model based on the parameters of the existing product images, wherein training the product image model includes providing parameters of the existing product images as inputs to the neural network (¶¶ [0068]-[0069] description of supervised learning methods, including neural networks, being trained using pre-labeled data such as previous listings).
Claim 15 – Pyati discloses the method of 1. Pyati further discloses,, wherein the determination as to whether the product image is suitable includes a determination as to the consistency of the product image with other product images (¶ [0095])
Claim 16 – Pyati discloses the method of claim 1. Pyati further discloses wherein the output includes an indication as to how the product image could be modified to improve the product image (¶ [0104] “suggestions to the user on how the user can change the set of photos or videos for the item listing”)
Claim 17 – Pyati discloses the method of claim 16. Pyati further discloses wherein the output is a recommendation for improving the quality of the product image (¶ [0104] see suggestions to achieve a target objection such as maximizing a selling price for the item).
Claim 18, which is directed to a system, recites limitations that are parallel in nature as those addressed above for method claim 1. Claim 18 is therefore rejected for the same reasons as set forth above for claim 1.
Claim 19 – Pyati discloses the system of claim 18. Pyati further discloses: wherein the output is based on at least one of a background of the product image, whether the product image is blurry, whether the entire product is in focus in the product image, an image resolution of the product image (¶ [0105]), or a particular parameter of the product image corresponding to a portion of the product that is in view in the product image (¶ [0106]).
Claim 20, which is directed to a non-transitory computer-readable medium, recites limitations that are parallel in nature as those addressed above for method claim 1. Claim(s) 20 is therefore rejected for the same reasons as set forth above for claim 1.
Claim Rejections - 35 U.S.C. § 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.
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 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 factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
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.
Claims 5-6 are rejected under 35 U.S.C. 103 as being unpatentable over Pyati in view of Jain et al. (US 2020/0286151 A1).
Claim 5 – Pyati discloses the method of claim 4. Pyati does not explicitly disclose wherein the output is based on the image background. However, Jain – which like Pyati is directed to providing recommendations to improve item listing images – further teaches: wherein the output is based on the background of the product image (¶ [0078] “a suggested background image 404”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the output based on the background as taught by Jain in the method of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversion of those listings (Jain ¶ [0022]).
Claim 6 – The combination of Pyati in view of Jain teaches the method of claim 5. Pyati does not teach the following limitations, however Jain further teaches: wherein the output is based on a consistency of the background with backgrounds of other product images (Jain ¶ [0046] “the scene compatibility score 212 allows each of these candidate background content 130 items to be compared, e.g., to identify images that have better scene compatibility with a product being listed than other images.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the background consistency as taught by Jain in the method of Pyati in order to reduce the reliance on human users to select and upload digital visual content for inclusion with listings that optimizes conversion of those listings (Jain ¶ [0022]).
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Pyati in view of Glasgow (US 2014/0104379 A1).
Claim 7 – Pyati discloses the method of claim 1. Pyati does not disclose limitations associated with whether an image is blurry, however Glasgow – which like Pyati is directed to generating recommendations for product images – further teaches, wherein the output is based on whether the product image is blurry (Glasgow ¶ [0059] “the system could examine a photograph to determine ... whether appropriate sharpness (i.e., not blurry) has been achieved”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the image blurriness as taught by Glasgow in the method of Pyati so that photographs are taken to effectively list an item for sale (Glasgow ¶ [0018]).
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Pyati in view of Ertle et al. (US 2019/0122384 A1).
Claim 8 – Pyati discloses the method of claim 1. Pyati does not explicitly disclose wherein the output is based on whether the entire product is in focus, however Ertle –which is also directed to determining how product images could be improved – further teaches: wherein the output is based on whether the entire product is in focus in the product image (Ertle ¶ [0074] “For example, the user may be prompted to move, rotate, or adjust the focus of the image capturing device to better capture the product. As another example, the user may be prompted to more closely align the item captured by the image capturing device with a field (or outline) displayed on the user interface, so as to assist the user in properly capturing the item within the image data. As yet another example, the user may be prompted via the user interface to capture the image of the product within the a target area, such as overlay 605”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have included the output based on whether the entire product is in focus, as taught by Ertle, in the method of Pyati because there is a need for an improved image recognition system that is capable of efficiently obtaining image data to be used as training and verification data for machine learning techniques such that the system may detect objects within image data with a certain level of confidence (Ertle ¶ [0004]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Grimes (2018/0018729 A1) is directed to processing product images and consumer data to generate product recommendation.
K. Schwarz, P. Wieschollek and H. P. A. Lensch (NPL Reference U) uses deep learning to predict the aesthetic quality of any image.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to KENNEDY A GIBSON-WYNN whose telephone number is (571)272-8305. The examiner can normally be reached M-F 8:30-5:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jeffrey Smith can be reached at 571-272-6763. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.G.W./Examiner, Art Unit 3688
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688