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 .
Claim Rejections - 35 USC § 112
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 1-13 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.
The term “similarity” in claims 1, 3, 6, 10-11, and 13 is a relative term which renders the claim indefinite. The term “similarity” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The term similarity is indefinite in this context because the specification provides multiple exemplary embodiments of similarity, but it never strictly defines the similarity beyond the ordinary meaning. As such, people of ordinary skill in the art can use a variety of metrics to judge the similarity between various portraits and paintings as such, the term is relative and indefinite. The dependent claims of 2, 4, 5, 7-9, and 12 inherit this issue, and they are similarly rejected.
The term “reliability” in claim 7 is a relative term which renders the claim indefinite. The term “reliability” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. In the context of claim 7, the disclosed reliability is some form of probability over the correctness of the identification. As such, people of ordinary skill in the art can use a variety of metrics or ways to measure the reliability of any prediction with a simple binary scale of 0% or 100% being within a BRI. This leaves the term as relative and indefinite.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite a mental process being performed by generic computer elements. This judicial exception is not integrated into a practical application because the generic computer elements are not significantly more. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the elements recited are generic, the claims recite machine learning in a very generic manner without significant details on what would make it anything more than a recitation of the term along with the recitation of a memory configured to store which is standard to computers along with a proccing system that executes instructions being a more generic recitation than merely stating a processor which executes instructions. As such, these elements are not significantly more.
In regards to claim 1, A method for verification of persons in portrait paintings, the method comprising: providing a reference image set including images of at least two different portrait paintings, wherein the portrait paintings depict a same reference person (A person of ordinary skill in the art can obtain images of two different portraits of the same person); providing a contrary image set including images of at least two other portrait paintings, wherein the other portrait paintings respectively depict a contrary person (A person of ordinary skill can also have images of a different set of portraits showing a different person); providing at least one image of a portrait painting to be examined, wherein the portrait painting depicts a person to be verified (A person can have an additional portrait that has a different, unknown person or be looking at one in a gallery); determining image-specific features via a method of machine learning, wherein at least one feature is allocated to each image (The recitation of machine learning here is generic, and it is performing a task that a human being can do mentally like simply observing and identifying specific features in a variety of images); determining a first distribution of similarities by using the features of the images from the reference and/or the contrary image set depicting the same person (A person of ordinary skill in the art can create some form of distribution of the various similarities whether that be from reference image features or from the contrary images); determining another distribution of similarities by using the features of the images from the reference image and/or the contrary image set depicting different persons (A person of ordinary skill in the art can create a similar distribution based off of a different set of images); determining at least one parameter required for evaluating a criterion for distinguishing the reference person from the contrary person depending on the first and/or the other distribution of similarities (A person of ordinary skill in the art can determine some form of criterion that is used to distinguish the people in various sets of portraits); determining a degree of similarity by using the features of the image to be examined and each image from the reference image set; and verifying whether the person to be verified is identical to the reference person when the degree of similarity meets the criterion (A person of ordinary skill can determine the degree of similarity by checking the various features against one another and verify this via their degree meeting some kind of criterion, threshold, or other metric).
In regards to claim 2, wherein providing the images takes place by import via an interface or from a data base and/or by recording the portrait painting via an imaging system (A person of ordinary skill can take images from a data base or record the images in a rudimentary fashion using pen and paper).
Further, wherein providing the images takes place by import via an interface or from a data base and/or by recording the portrait painting via an imaging system (This is insignificant extra-solution activity as how the images are acquired does not materially impact the process of identifying the images.)
In regards to claim 3, the image-specific features are determined as vectors in a vector space (A person of ordinary skill in the art could make the features correspond to values in a vector); and the first and/or the other distribution and/or the degree of similarity is determined based on differences between the vectors (A person of ordinary skill in the art can simply look at the differences in values between two vectors and make a determination based off of that).
In regards to claim 4, wherein a value from a value range of the first and/or the other distribution is determined as the parameter for evaluating the criterion (A person of ordinary skill in the art can use a value from a range of values to evaluate another value or thing).
In regards to claim 5, wherein: a first probability distribution is generated from the first distribution (A person of ordinary skill in the art can create some form of distribution of the probabilities from a distribution); and another probability distribution is generated from the other distribution (A person of ordinary skill in the art can determine some other distribution from another distribution).
In regards to claim 6, wherein: determining the degree of similarity includes determining at least one plausibility value (A person of ordinary skill in the art can figure out some value for plausibility on how similar two portraits are); and the plausibility value is established as a ratio between the probabilities allocated to a similarity value in the first and the other probability distribution (A ratio can simply be a fraction of some kind, and a person of ordinary skill in the art could divide two numbers).
In regards to claim 7, wherein: a reliability value is determined; and the reliability value indicates a probability of correctness of a result (A person of ordinary skill in the art can determine the reliability in some manner whether it be a simple, yes, the answer is accurate or no, it is inaccurate).
In regards to claim 8, wherein: the method of machine learning is trained by: providing a first training image set including portrait photographs, wherein at least one photograph-specific basic truth is allocated to each image (A person of ordinary skill can look at photographs and determine some kind of truth about said photo); pre-training a neural network using the first training image set (A person of ordinary skill can use a set of images to train themselves to notice certain stylistic choices or similarities); providing a second training image set including images of portrait paintings, wherein at least one painting-specific basic truth is allocated to each image (A person of ordinary skill can look at a set of images and determine some kind of truth about the paintings whether that be specific patterns or common elements within the painting); and specializing the pre-trained neural network using the second training image set (A person could further hone and specialize their skillset by simply looking over a second pair of images); and the method of machine learning is trained to determine image-specific features for images of portrait paintings (A person of ordinary skill in the art can determine specific features for images from these kinds of paintings).
In regards to claim 9, wherein the other distribution is determined from a difference between features of images of the reference image set and images of the contrary image set (A person of ordinary skill in the art can make a distribution of some kind from the differences between the features of the two paintings).
In regards to claim 10, it is similar to claim 1, and it is similarly rejected for most of the claim. There are the additional features from claims 5 and 6 that were incorporated into this claim. The additional features have already been rejected under 101, and these would be rejected for the same reasons.
In regards to claim 11, it is similar to claim 1, outside the recitation of the generic computer elements that make up the physical structure, and it is similarly rejected.
In regards to claim 12, comprising an imaging system configured to record the at least one image of the portrait painting to be examined (A person of ordinary skill in the art can record a portrait via a pen and paper).
In regards to claim 13, it is similar to claim 1, and it is similarly rejected.
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.
Claims 1-4, 7, 9, 11, and 13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ramya et al. (“Computerized Face Recognition in Renaissance Portrait Art”) from the IDS, hereinafter referred to as Ramya.
In regards to claim 1, Ramya discloses a method for verification of persons in portrait paintings, the method comprising: providing a reference image set including images of at least two different portrait paintings, wherein the portrait paintings depict a same reference person (Page 86 first new paragraph of the right column, The reference discloses the use of reference images which would be more than one image with a known identity); providing a contrary image set including images of at least two other portrait paintings, wherein the other portrait paintings respectively depict a contrary person (Page 86 first new paragraph of the right column, the section labelled dataset from pages 90-91 along with table 2 and figure 2, The paragraph discloses reference images which are used. The dataset section details the reference images used in the research which details a number of different artists from the 15th to 18th centuries as shown in table 2 who depict various different people as shown in figure 2 which would constitute multiple contrary image sets); providing at least one image of a portrait painting to be examined, wherein the portrait painting depicts a person to be verified (Page 86 first new paragraph of the right column, Discloses a test image of a person to be identified); determining image-specific features via a method of machine learning, wherein at least one feature is allocated to each image (Page 94 under the section titled conclusions, Discloses that the feature space is identified using machine learning); determining a first distribution of similarities by using the features of the images from the reference and/or the contrary image set depicting the same person (Page 89, the feature combination section and Figure 5, The feature combination section discloses that the match section combines a variety of the features to form a first probability distribution based around if the figures match); determining another distribution of similarities by using the features of the images from the reference image and/or the contrary image set depicting different persons (Page 89, the feature combination section and Figure 5, The same section and figure show a second distribution called the no match distribution); determining at least one parameter required for evaluating a criterion for distinguishing the reference person from the contrary person depending on the first and/or the other distribution of similarities (The Hypothesis Testing Section on page 90, Discloses that there is a statistic gathered which distinguishes relevant people from less relevant people); determining a degree of similarity by using the features of the image to be examined and each image from the reference image set; and verifying whether the person to be verified is identical to the reference person when the degree of similarity meets the criterion (Figure 1 and the section entitled identity verification on page 90, Figure 1 shows the process disclosed can be used for the identification particularly in section b with the section on identity verification section discloses a verification system that verifies if the individual is the same).
In regards to claim 2, Ramya discloses wherein providing the images takes place by import via an interface or from a data base and/or by recording the portrait painting via an imaging system (The section labelled dataset from pages 90-91, The disclosed data set of portraits is acquired and they must be processed with some form of imaging system to have digital version to run through this test).
In regards to claim 3, Ramya discloses the image-specific features are determined as vectors in a vector space (The section labelled Feature Extraction on page 88, The section discloses that certain features are characterized as vectors in a vector space); and the first and/or the other distribution and/or the degree of similarity is determined based on differences between the vectors (The section labelled Feature Extraction on page 88, The similarity is calculated based off of the differences between the vectors specifically the Euclidean distance between the features which is a difference between the vectors).
In regards to claim 4, Ramya discloses wherein a value from a value range of the first and/or the other distribution is determined as the parameter for evaluating the criterion (The Hypothesis Testing Section on page 90, The calculation of the p value requires such values to be used).
In regards to claim 7, Ramya discloses wherein: a reliability value is determined; and the reliability value indicates a probability of correctness of a result (The Hypothesis Testing Section on page 90, The described p value is described as probability values that indicate the probability of correctness.).
In regards to claim 9, Ramya discloses wherein the other distribution is determined from a difference between features of images of the reference image set and images of the contrary image set (Figure 1 and the section entitled introduction on page 86, Both the figure and the introduction allow for the two distributions as shown in the training section, the features are selected and a distribution of features is generated from the various reference images and contrary images, similarly the reference image of the non-training phase is also compared using the training model derived from the image set).
In regards to claim 11, claim 11 is similar to claim 1 for the most part, and these elements are rejected similarly under Ramya. Ramya discloses the additional elements, a system comprising: a memory configured to store instructions; a processing system configured to execute the instructions, wherein the instructions include (Title, The title discloses that the method utilizes a computer which would imply a processor that executes instructions along with a memory): and an imaging interface configured to obtain the at least one image of the portrait painting to be examined (Figure 1 and the section entitled introduction on page 86, The figure and the introduction allow for a test image to be used for identification. Since imaging interface and obtain are rather broad terms, the system obtaining the image from a databank or via some other means is within a BRI of the claims).
In regards to claim 13, it is similar to claim 1, and it is similarly rejected.
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.
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, 8, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ramya et al. (“Computerized Face Recognition in Renaissance Portrait Art”) from the IDS, hereinafter referred to as Ramya, in view of Gupta et al. (“Deep learning based identity verification in renaissance paintings”), from the IDS, hereinafter referred to as Gupta.
In regards to claim 5, Ramya does not explicitly disclose wherein: a first probability distribution is generated from the first distribution; and another probability distribution is generated from the other distribution.
Gupta does disclose wherein: a first probability distribution is generated from the first distribution (Section 3.4, The last paragraph describes that the similarity distributions are generated from each other); and another probability distribution is generated from the other distribution (Section 3.4, The last paragraph describes that the similarity distributions are generated from each other).
It would have been prima facie obvious to combine the teachings of the prior arts as incorporating the elements of Gupta would lead to a predictable increase in accuracy. Generating the probability distributions based off of the distributions made on the similarities would allow for the probability distributions to be more accurate and to more accurately reflect the likelihood of each figure’s identity. As such, it would be prima facie obvious to combine.
In regards to claim 6, Ramya discloses wherein: determining the degree of similarity includes determining at least one plausibility value (The Hypothesis Testing Section on page 90, The described p value is described as probability values).
Ramya does not explicitly disclose and the plausibility value is established as a ratio between the probabilities allocated to a similarity value in the first and the other probability distribution.
However, Gupta does disclose and the plausibility value is established as a ratio between the probabilities allocated to a similarity value in the first and the other probability distribution (Section 3.5 specifically the equation under 5, The p values or plausibility values derived in Gupta are directly shown to be a ratio between the probabilities).
In regards to claim 8, Ramya does not explicitly disclose any element of this claim.
Gupta does disclose wherein: the method of machine learning is trained by: providing a first training image set including portrait photographs, wherein at least one photograph-specific basic truth is allocated to each image (Section 3.3, Describes that the model is pretrained on one data set of images where it learns specific style features from those images); pre-training a neural network using the first training image set (Section 3.3, Explicitly states that “the pre-trained network base model has knowledge about the original dataset”); providing a second training image set including images of portrait paintings, wherein at least one painting-specific basic truth is allocated to each image (Section 3.3, The same method is applied to the pictures gathered as described in section 3.3); and specializing the pre-trained neural network using the second training image set (Section 3.3, The Siamese network is trained using contrastive loss on the gathered images which is within the BRI); and the method of machine learning is trained to determine image-specific features for images of portrait paintings (Section 3.3, This is done to pick out specific details which are then used as feature vectors).
In regards to claim 10, Claim 10 shares most of its elements with claim 1 which Ramya covers. However, the additional elements added, wherein: a first probability distribution is generated from the first distribution and another probability distribution is generated from the other distribution, in determining the degree of similarity, at least one plausibility value is determined, and the plausibility value is established as a ratio between the probabilities allocated to a similarity value in the first and the other probability distribution. Ramya does not explicitly disclose these additional elements.
Gupta does disclose wherein: a first probability distribution is generated from the first distribution and another probability distribution is generated from the other distribution (Section 3.4, The last paragraph describes that the similarity distributions are generated from each other), in determining the degree of similarity, at least one plausibility value is determined, and the plausibility value is established as a ratio between the probabilities allocated to a similarity value in the first and the other probability distribution (Section 3.5 specifically the equation under 5., The p values or plausibility values derived in Gupta are directly shown to be a ratio between the probabilities).
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Ramya et al. (“Computerized Face Recognition in Renaissance Portrait Art”) from the IDS, hereinafter referred to as Ramya, in view of Gupta et al. (“Deep learning based identity verification in renaissance paintings”), from the IDS, hereinafter referred to as Gupta as applied to claims 5-6, 8, and 10 above, and further in view of Fang et al. (US 20200226724 A1), hereinafter referred to as Fang.
In regards to claim 12, neither Gupta nor Ramya disclose comprising an imaging system configured to record the at least one image of the portrait painting to be examined.
Fang does disclose comprising an imaging system configured to record the at least one image of the portrait painting to be examined (Paragraphs 27, 45, 147, and 151, All of these paragraphs disclose that a camera could be used to capture images which would include portraits and painted portraits with paragraph 45 explicitly stating that, “images captured by a computing device, such as with a camera integrated into computing device”).
It would be prima facie obvious to combine these arts as Fang is simply applying a known method to achieve predictable results. Fang’s method of acquiring images is by using a camera to acquire pictures of a painting or other image. Cameras are a known element which have a known purpose, to acquire pictures of objects that they are pointed at. The claim is combining the camera that takes pictures of paintings with a device that is made to recognize painting portraits. Doing so leads to the predictable result of the device can now take pictures of portraits, so as such, this is prima facie obvious.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CONOR AIDAN O'MALLEY whose telephone number is (571)272-0226. The examiner can normally be reached Monday - Friday 9:00 am. - 5:00 pm. EST.
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, Andrew Moyer can be reached at 5722729523. 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.
CONOR AIDAN. O'MALLEY
Examiner
Art Unit 2675
/CONOR A O'MALLEY/Examiner, Art Unit 2675
/ANDREW M MOYER/Supervisory Patent Examiner, Art Unit 2675
Prosecution Insights