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 .
Response to Arguments
Applicant’s arguments and amendment have persuasively overcome the claim objections, the 112 rejections related to 112f, additional 112 rejections, and the 101 rejection for lacking a statutory category.
The remaining issues are addressed below.
Double Patenting
Applicant argues:
Applicant requests that the Double Patenting Rejection be held in abeyance until the other rejections are resolved.
Examiner responds:
The examiner cannot hold rejections in abeyance.
112a
Applicant argues:
The claims do not engage in unlimited functional claiming but rather recite specific structural elements (processor, computer-readable storage media) that perform defined operations, which is consistent with proper claim drafting practice.
Examiner responds:
The issue raised is not a lack of a processor, the issue is that the claim is directed to a result rather than the steps taken to achieve the result. See, for example, MPEP 2173.05(g) “For instance, a single means claim covering every conceivable means for achieving the stated result was held to be invalid under 35 U.S.C. 112, first paragraph because the court recognized that the specification, which disclosed only those means known to the inventor, was not commensurate in scope with the claim.” (The examiner knows that the present claim is not a single means claim, but the same reasoning applies because there is a “claim covering every conceivable means for achieving the stated result.”
Applicant argues:
An "aesthetic score" is clearly defined in the Application, e.g., "The aesthetic score is configured to specify an amount of visual aesthetics exhibited by the input digital image." Application, [0003].
Examiner responds:
The examiner does not understand the relevance of this argument.
112b
Applicant argues:
The specification clarifies that "the input digital image 122, for instance, as an example of previously unseen digital data is processed by the machine-learning model 118." … to describe the image being received for processing, not a user-entry requirement.
Examiner responds:
It is not clear to the examiner how receiving an input digital image differs from receiving a digital image. Is the inputting a separate step? Should the image be previously unseen?
Applicant argues:
The specification states that "the aesthetic score is configured to specify an amount of visual aesthetics exhibited by the input digital image." Application, [0003]. The specification further explains that the machine-learning model "learns a function 'F' ... mapping images to a scalar score 'F(x)E [0,1],' where higher values in this instance indicate higher aesthetics." Application, [0039].
Examiner responds:
Specification, [0073] “it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described.”
Applicant argues:
The aesthetic score is a quantified numerical value output by the machine-learning model
Examiner responds:
Applicant may wish to amend the claim accordingly.
Applicant argues:
Regarding the term "describing," the specification provides concrete examples: "The user interaction data describes user interaction with the training digital images, e.g., a view count, number of appreciations (e.g., 'likes'), number of purchases, and so forth."
Examiner responds:
Applicant may wish to amend the claim accordingly.
Applicant argues:
A person of ordinary skill in the art would understand "describing" to mean that the user interaction data contains information about user interactions with the training digital images.
Examiner responds:
This is not precise enough to be definite.
Applicant argues:
which is consistent with the first dictionary definition cited by the Examiner, applying to things previously mentioned in a parallel way.
Examiner responds:
From the office action: “Here, none of the uses comport with the first meaning because none of the uses provide parallel lists”
Applicant argues:
Claims 6, 12, and 16 each recite "aesthetics learning as a classification of the learning signal into respective buckets," which mirrors the specification's definition.
Examiner responds:
The language in the specification is not a definition. Rather, Specification, [0073] “it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described.”
Applicant argues:
A person of ordinary skill in the art would understand this term.
Examiner responds:
Applicant may wish to submit evidence to support this statement.
Applicant argues:
Regarding claim 20's recitation of "an amount of visual aesthetics exhibited by the input digital image," the specification explains that "an aesthetics detection service is employed to address these and other technical challenges by generating an aesthetic score that is usable to quantify an amount of visual aesthetics exhibited by a respective digital image using machine learning, automatically and without user intervention." Application, [0014].
Examiner responds:
The language in the specification is not a definition. Rather, Specification, [0073] “it is to be understood that the invention defined in the appended claims is not necessarily limited to the specific features or acts described.”
101
Applicant argues:
because the specific technical steps recited cannot practically be performed in the human mind.
Examiner responds:
Which steps is Applicant referring to?
Applicant’s remaining arguments are that various claim elements cannot be performed mentally. However, both the guidance cited in the rejection and Recentive Analytics, Inc. v. Fox Corp., 134 F. 4th 1205 (Fed. Cir. 2025) show that generic artificial intelligence techniques, such as the techniques here, are mental processes.
102/103
Applicant argues:
However, this describes using images that a user has interacted with as training images, but not using aggregated user engagement statistics as training signals for the model itself.
Examiner responds:
The claim is not limited to “aggregated user engagement statistics”
Applicant’s other arguments over the prior art rely on importing limitations from the specification, but that is not how claims are interpreted. Rather, the claimed “user inter action data describing user interaction” is broad enough to encompass Shaji’s teachings.
Claim Objections
Claim 20 is objected to because of the following informalities:
Claim 20 does not recite “non-transitory.”
Claim 20 recites “instruction” in the singular (instead of the plural)
Appropriate correction is required.
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 1-20 (all claims) are rejected on the ground of nonstatutory double patenting as being unpatentable over the claims of each of U.S. Patent No. US 10489688 B2, US 10515443 B2, US 11069030 B2, US 11532036 B2, and US 12211129 B2 in view of the prior art as applied below.
Both the pending claims and the conflicting patents are all directed to aesthetic scores of images. Therefore, all of the conflicting patents are directed to the same problem as the present application. Further, any differences between the present claims and the claims in any of the conflicting patents are obvious in view of the prior art as applied below. It would have been obvious to one of ordinary skill in the art, before the effective filing date, to combine the below prior art with any of the conflicting patents for implementation details (especially as the patent claims lack implementation details). Based on the findings herein, this is an example of “(A) Combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
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 (all claims) 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(s) 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 inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claim 1 recites “generating, by the processing device, an aesthetic score of the input digital image using a machine-learning model,” but this is unlimited functional claiming because of the wide variety of different architectures and ways that this could be performed. MPEP 2173.05(g).
Claims 10 and 20 recite corresponding training language, and this is similarly rejected.
Dependent claims are likewise 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 1-20 (all claims) 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.
Claim 1 recites “receiving … an input,” but it is not clear if “input” is a distinct requirement from the receiving. For example, is “input” intended to require that it was entered by a user?
Claims 1, 10 and 20 recite “aesthetic score,” but this is new terminology. MPEP 2173.05(a). In particular, claim 2 recites “aesthetic score is configured to specify an amount” and claims 10 and 20 recite the same language, but without the “is.” Whether something is “configured to specify an amount” is subjective (one person might think that the color green is good, and someone else may not assign it meaning). MPEP 2173.05(b)(IV). Additionally, it is unclear if “aesthetic score” is intended to be specific to the training data, or if it applies generally.
Claims 1, 10 and 20 recite “describing,” but this is subjective because different people can have different opinions as to what is meant. MPEP 2173.05(b)(IV).
Claims 1, 3, 4, 9, 10, and 18-20 recite “respectively,” but it is not clear how this term is meant. https://www.dictionary.com/browse/respectively defines respectively as:
Adverb
(of two or more things) referring or applying to two or more things previously mentioned in a parallel or sequential way.
Joe and Bob escorted Betty and Alice, respectively.
in precisely the order given; sequentially.
Here, none of the uses comport with the first meaning because none of the uses provide parallel lists. From context, it does not appear that the intent of “respectively” is the second meaning.
Claims 6, 12, and 16 recite “aesthetics learning,” but this is new terminology. MPEP 2173.05(a). In particular, is this limited to the claimed bucketing of classifications?
Claims 9 and 18 recite “associated,” but this is subjective because it lacks an objective relationship. MPEP 2173.05(b)(IV).
Claims 11, 14, and 17 recite “the train,” but this lacks sufficient antecedent basis. MPEP 2173.05(e).
Claim 20 recites “an amount of visual aesthetics exhibited by the input digital image,” but this is subjective. MPEP 2173.05(b)(IV).
Claim 20 recites “collecting training data,” but it is unclear what this means. Is the intent that the processing device merely loads data, does the processing device need to generate the data, does the processing device need to facilitate the user interactions?
Claim 10 recites corresponding language and is likewise rejected.
Dependent claims are likewise rejected.
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 (all claims) are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Step 1: Claim 1 (and its dependents) recite a method, and processes satisfy Step 1 of the eligibility test.
Claim 10 (and its dependents) recite a system, and machines satisfy Step 1 of the eligibility test.
Claim 20 will recite a non-transitory computer-readable storage medium, and manufactures satisfy Step 1 of the eligibility test. (See the above claim objection)
Step 2A, prong one: All of the elements of claims 1-20 are a mental process because a person can look at an image and assign an aesthetic score. Further, the various models are also mental processes, see example 47, claim 2, element (d) (from the July 2024 AI subject matter eligibility examples). MPEP 2106.04(a)(2)(III)(C) explains that use of a generic computer or in a computer environment is still a mental process. In particular, this section begins by citing Gottschalk v. Benson, 409 US 63 (1972). “The Supreme Court recognized this in Benson, determining that a mathematical algorithm for converting binary coded decimal to pure binary within a computer’s shift register was an abstract idea.” In Benson the Supreme Court did not separately analyze the computer hardware at issue; the specifics of what hardware was claimed is only included in an appendix to the decision.
Because there are no additional elements, no further analysis is required for Step 2A, prong two or Step 2B.
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)(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.
(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.
Claims 1-8, 10-16, 19, and 20 (all claims except those rejected under 103 below) are rejected under 35 U.S.C. 102(a)(1) and/or (a)(2) as anticipated by US20180039879A1 (“Shaji”).
1. A method comprising:
receiving, by a processor, an input digital image; (Shaji, claim 1, “receiving an image for scoring;”)
generating, by the processor, an aesthetic score of the input digital image using a machine-learning model, the machine-learning model trained using training digital images and user interaction data describing user interaction with the training digital images, respectively; and (Shaji, claim 1, “applying the machine-learned model to the received image thereby assigning an aesthetic score to the received image, wherein the learned features are inputs to the machine-learned model.” See also, claim 9 detailing the user interaction.)
outputting, by the processor, the aesthetic score. (Shaji, [0081] “a ranking may be generated, and displayed in display 602, that shows a user the best rated images.” Shaji’s ranking teaches the claimed aesthetic score.)
2. The method of claim 1, wherein the aesthetic score is configured to specify an amount of visual aesthetics exhibited by the input digital image. (Shaji, claim 1, “wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score;”)
3. The method of claim 1, wherein the user interaction data describes, respectively, a number of appreciations of the training digital images and a number of views of the training digital images. (Shaji, [0068] “As another example, training images may come from known information about a user (or segment), such as, for example, recently visited or most visited images, “likes,” or other data that may be collected for a given user (or segment) indicative of a user's (or segment's) aesthetic preference.” See also, claim 9 detailing an additional user interaction.)
4. The method of claim 1, further comprising training the machine-learning model using training data including the training digital images and the user interaction data describing user interaction with the training digital images, respectively. (Shaji, claim 1, “updating the base neural network to generate a personalized neural network based on the received second set of training images … executing the personalized neural network on the received image to generate learned features.”)
5. The method of claim 4, wherein the training includes generating aesthetics classification labels as a learning signal based on the training data. (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
6. The method of claim 5, wherein the generating aesthetics classification labels includes:
generating a learning signal based on the training data; and (Shaji, claim 1, “updating the base neural network to generate a personalized neural network based on the received second set of training images … executing the personalized neural network on the received image to generate learned features.”)
generating the aesthetics classification labels through aesthetics learning as a classification of the learning signal into respective buckets. (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
7. The method of claim 4, wherein the training includes generating candidate aesthetics scores and confidence estimates of the candidate aesthetics scores. (Shaji, [0081] “The user may also receive a confidence score, which indicates the degree of certainty based on the current ranking.” Shaji’s ranking teaches the claimed aesthetics score.)
8. The method of claim 7, wherein the generating the candidate aesthetics scores and the confidence estimates of the candidate aesthetics scores includes:
generating aesthetics classifications using a classifier; and (Shaji, [0033] “In some embodiments, a personalization layer can receive as input, the output of a multi-label, multi-class classifier”)
generating the candidate aesthetics scores and the confidence estimates based on the aesthetics classifications. (Shaji, Fig. 5.)
10. A system comprising:
a processor; and (Shaji, Fig. 2, processor 205)
a computer-readable storage medium storing instructions that, in response to execution by the processor, causes the processor to perform operations including: (Shaji, Fig. 2, memory 220)
collect training data including training digital images and user interaction data describing user interaction with the training digital images, respectively; and (Shaji, claim 1, “updating the base neural network to generate a personalized neural network based on the received second set of training images … executing the personalized neural network on the received image to generate learned features.”)
train a machine-learning model using the training data to generate an aesthetic score based on an input digital image, the aesthetic score configured to specify an amount of visual aesthetics exhibited by the input digital image. (Shaji, claim 1, “wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score;”)
11. The system of claim 10, wherein the train includes learning signal extraction to generate aesthetics classification labels as a learning signal based on the training data. (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
12. The system of claim 11, wherein the learning signal extraction module includes:
generation of a learning signal based on the training data; and (Shaji, claim 1, “updating the base neural network to generate a personalized neural network based on the received second set of training images … executing the personalized neural network on the received image to generate learned features.”)
generation of the aesthetics classification labels through aesthetics learning as a classification of the learning signal into respective buckets. (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
13. The system of claim 12, wherein the learning signal is based on a number of appreciations of the training digital images and a number of views of the training digital images. (Shaji, [0068] “As another example, training images may come from known information about a user (or segment), such as, for example, recently visited or most visited images, “likes,” or other data that may be collected for a given user (or segment) indicative of a user's (or segment's) aesthetic preference.” See also, claim 9 detailing an additional user interaction.)
14. The system of claim 10, wherein the train includes an aesthetic classification module that is configured to generate candidate aesthetics scores and confidence estimates of the candidate aesthetics scores. (Shaji, [0081] “The user may also receive a confidence score, which indicates the degree of certainty based on the current ranking.” Shaji’s ranking teaches the claimed aesthetics score.)
15. The system of claim 14, wherein the aesthetic classification module includes:
generation of aesthetics classifications using a classifier; and a (Shaji, [0033] “In some embodiments, a personalization layer can receive as input, the output of a multi-label, multi-class classifier”)
generation of the candidate aesthetics scores and the confidence estimates based on the aesthetics classifications. (Shaji, Fig. 5.)
16. The system of claim 15, wherein the machine-learning system is configured to generate the aesthetics classifications based on aesthetics classification labels generated through aesthetics learning as a classification of a learning signal into respective buckets based on the training data. (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
19. The system of claim 10, wherein the user interaction data describes relative amounts of user interaction with the training digital images, respectively. (Shaji, [0068] “As another example, training images may come from known information about a user (or segment), such as, for example, recently visited or most visited images, “likes,” or other data that may be collected for a given user (or segment) indicative of a user's (or segment's) aesthetic preference.” See also, claim 9 detailing an additional user interaction.)
20. One or more computer-readable storage media storing instruction that, in response to execution by a processor, causes the processor to perform operations comprising:
collecting training data including training digital images and user interaction data describing user interaction with the training digital images, respectively; and (Shaji, claim 1, “updating the base neural network to generate a personalized neural network based on the received second set of training images … executing the personalized neural network on the received image to generate learned features.”)
training a machine-learning model using the training data to generate an aesthetic score based on an input digital image, the aesthetic score configured to specify an amount of visual aesthetics exhibited by the input digital image. (Shaji, claim 1, “wherein a more aesthetically-pleasing image is given a higher aesthetic score and a less aesthetically-pleasing image is given a lower aesthetic score;”)
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.
Claims 9, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over US20180039879A1 (“Shaji”) in view of “Cross-validation (statistics),” March 8, 2024, Wikipedia, retrieved from https://en.wikipedia.org/w/index.php?title=Cross-validation_(statistics)&oldid=1212658624 (“Wikipedia”)
9. The method of claim 4, wherein the training includes generating training aesthetic scores using confidence-filtered and cross-validated model predictions by:
outputting candidate aesthetic scores and confidence estimates for the training images (Shaji, [0081] “The user may also receive a confidence score, which indicates the degree of certainty based on the current ranking.” Shaji’s ranking teaches the claimed aesthetics score.)
generating filtered scores by filtering the candidate aesthetic scores based on the confidence estimates; (Shaji, [0081] “In some embodiments, a ranking may be generated, and displayed in display 602, that shows a user the best rated images.”)
assigning aesthetics classification labels by discretizing the filtered scores into a plurality of classes associated, respectively, with a plurality of buckets; and (Shaji, claim 9, “receiving input from the user indicating one or more of: (i) that the user prefers an image of the one or more candidate images; (ii) that the user dislikes an image of the one or more candidate images; and (iii) that the user prefers one image over another image of the one or more candidate images.”)
training the machine-learning model based on aesthetic scores and confidence estimates generated based on the aesthetics classification labels. (Shaji, claim 9. Claim 9 recites that the above training data is used to train.)
Shaji is not relied on for the below claimed language.
However, Wikipedia teaches training images that are generated using cross-validation (Wikipedia, “In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (called the validation dataset or testing set).” See also the “Applications” section, specifying use with images.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to apply the teachings of Wikipedia to the teachings of Shaji such that Shaji’s training images are cross-validated for the purpose of “The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias[10] and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).” Wikipedia. Note that Shaji, claim 9, teaches a known dataset that is used for training.
Based on the above, this is an example of “combining prior art elements according to known methods to yield predictable results.” MPEP 2143.
Claims 17 and 18 are rejected as per claim 9.
Conclusion
The patents (and their pre-grant publications) cited for double patenting are also considered pertinent to applicant's disclosure, particularly US20190026609A1.
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 DAVID ORANGE whose telephone number is (571)270-1799. The examiner can normally be reached Mon-Fri, 9-5.
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/DAVID ORANGE/Primary Examiner, Art Unit 2663
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