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 .
Response to Amendment
Applicant’s amendment filed on February 3, 2026 has been entered. Claims 1-20 are currently pending. Applicant’s amended clams are addressed herein below. (Note: this amendment is not signed by an Attorney of record. It is signed by an Attorney who is not listed as the Attorney of record. The Examiner tried to reach out to an Attorney of record from the list, but no response.)
Specification
The following guidelines illustrate the preferred layout for the specification of a utility application. These guidelines are suggested for the applicant’s use.
Arrangement of the Specification
As provided in 37 CFR 1.77(b), the specification of a utility application should include the following sections in order. Each of the lettered items should appear in upper case, without underlining or bold type, as a section heading. If no text follows the section heading, the phrase “Not Applicable” should follow the section heading:
(a) TITLE OF THE INVENTION.
(b) CROSS-REFERENCE TO RELATED APPLICATIONS.
(c) STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT.
(d) THE NAMES OF THE PARTIES TO A JOINT RESEARCH AGREEMENT.
(e) INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A READ-ONLY OPTICAL DISC, AS A TEXT FILE OR AN XML FILE VIA THE PATENT ELECTRONIC SYSTEM.
(f) STATEMENT REGARDING PRIOR DISCLOSURES BY THE INVENTOR OR A JOINT INVENTOR.
(g) BACKGROUND OF THE INVENTION.
(1) Field of the Invention.
(2) Description of Related Art including information disclosed under 37 CFR 1.97 and 1.98.
(h) BRIEF SUMMARY OF THE INVENTION.
(i) BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S).
(j) DETAILED DESCRIPTION OF THE INVENTION.
(k) CLAIM OR CLAIMS (commencing on a separate sheet).
(l) ABSTRACT OF THE DISCLOSURE (commencing on a separate sheet).
(m) SEQUENCE LISTING. (See MPEP § 2422.03 and 37 CFR 1.821 - 1.825). A “Sequence Listing” is required on paper if the application discloses a nucleotide or amino acid sequence as defined in 37 CFR 1.821(a) and if the required “Sequence Listing” is not submitted as an electronic document either on read-only optical disc or as a text file via the patent electronic system.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a generator” in claims 1-4 and 12-19, and “a discriminator” in claims 1, 12 and 16, and “second discriminator” in claims 8-9 (Note: amended claims still do not have hardware descriptions of “a generator”, and “a discriminator”, and “second discriminator”).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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(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.
Regarding claims 1-20, a “first ANN” (claims 1, 3-4, 9, 12, 14, 16, and 18-19), “second ANN” (claims 1, 12, and 16), and “third ANN” (claim 8) are not described in the originally filed specification. Claim language must be consistent with the original specification.
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 1-6 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Aberman et al. (US 20230015117) in view of Zhou et al. (US 20190220977) and in further view of Park et al. (US 20230281885).
As to claim 1, Aberman discloses a method comprising:
displaying a set of images to a user, the set of images comprising a plurality of non-synthetic images, and a plurality of synthetic images output by a generative adversarial network (GAN) comprising a generator and a discriminator ([0043], [0049]: image editing models 120 can be or can otherwise include various machine-learned models such as neural networks (e.g., deep neural networks) or other types of machine-learned models, [0077]: image editing operator 206 can be generative adversarial network (GAN) operator, [0051]: “processed image data” is interpreted as “synthetic image” and “raw image data” is interpreted as “non-synthetic image”, [0103], Note: it is well known in the art that generative adversarial network (GAN) consists of two neural networks: a generator and a discriminator to create realistic data);
detecting a user response to the set of images ([0035]: image viewed by a user, [0051]: training module may rely on eye-gaze data, [0054]: user can provide user input); and
training the GAN based at least on the user response, including tuning first ANN of the generator based on the gaze of the user ([0051]: training module may rely on eye-gaze data to add efficiency and precision to the training module (e.g., to train the saliency model). Training data may also include the creation of processed image data from raw image data (e.g., to train the image editing operator), [0052]).
Aberman does not expressly teach a generator that comprises a first artificial neural network (ANN) and a discriminator that comprises a second ANN and the user response comprising at least a gaze of the user relative to the set of images.
Zhou teaches a generator that comprises a first artificial neural network (ANN) and a discriminator that comprises a second ANN (Fig. 6, [0050]: generative adversarial network (GAN) includes a generator network G 600 and a discriminator network 610. The generator G 600 is a deep neural network that generates a synthesized output image. The discriminator D 610 is another deep neural network that distinguishes between the synthesized output imager J′ 604 and the real image J 606).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Aberman’s method by incorporating Zhou’s idea of having a generator that comprises an artificial neural network (ANN) and a discriminator that comprises another ANN in order to improve image analysis technique.
Aberman (as modified by Zhou) do not explicitly teach the user response comprising at least a gaze of the user relative to the set of images.
Park teaches the user response comprising at least a gaze of the user relative to the set of images ([0061]: gaze tracking engine 235 identifies a gaze direction and/or gaze area for the user 215 as depicted and/or otherwise represented in the image data 232, [0075]: feedback engine 260 can detect feedback received from a user interface of the user device 220. The feedback can include feedback on the gaze information generated by the gaze tracking engine 235).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Aberman (as modified by Zhou) by adapting Park’s idea of using gaze as a user response in order to provide user flexibility.
As to claim 2, Aberman (as modified by Zhou and Park) teach the method of claim 1, wherein training the GAN based on the user response comprises generating a plurality of maps associated with the gaze of the user relative to the set of images (Aberman: [0104]: training saliency map for each training image indicates location of human eye gaze relative to the training image); and tuning the generator based on the plurality of maps (Aberman: [0039]: use of raw image data, processed image data, saliency maps, and masks also removes confusion from the tuning and makes the tuning more efficient, [0104]).
As to claim 3, Aberman (as modified by Zhou and Park) teach the method of claim 2, wherein tuning the first ANN of the generator based on the plurality of maps comprises performing backpropagation which adjusts weights of the first ANN of the generator in view of a loss function that has increased penalty in one or more salient regions of the plurality of maps (Aberman: [0060]: loss function can be backpropagated through the model(s) to update one or more parameters of the model(s) (e.g., based on a gradient of the loss function), [0061]: model trainer 160 can perform a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained, [0062]: a set of saliency maps).
As to claim 4, Aberman (as modified by Zhou and Park) teach the method of claim 1, further comprising training a machine learning (ML) model with the user response and the set of images, the ML model being trained to receive an input image and generate as output, a map that predicts the gaze of the user relative to the input image, wherein training the GAN based on the user response comprises performing backpropagation which adjusts weights of the first ANN of the generator in view of a loss function that increases penalty in one or more salient regions of the map (Aberman: [0060], [0061]: performing backwards propagation of errors can include performing truncated backpropagation through time. The model trainer 160 can perform a number of generalization techniques (e.g., weight decays, dropouts, etc.) to improve the generalization capability of the models being trained).
As to claim 5, Aberman (as modified by Zhou and Park) teach the method of claim 4, wherein the ML model comprises a convolutional neural network (Aberman: [0049]: convolutional neural network).
As to claim 6, Aberman (as modified by Zhou and Park) teach the method of claim 1, wherein the user response further comprises a response of the user in indicating whether each image in the set of images is output by the GAN (Aberman: [0077]: image editing operator 206 can be generative adversarial network (GAN) operator, [0097]).
As to claim 10, Aberman (as modified by Zhou and Park) teach the method of claim 1, detecting the user response to the set of images is performed at a first computing node, and the user response is received over a network at a second computing node where the GAN is trained (Aberman: Fig. 1A, Park: [0066]: second image processing engine 245, [0074] – [0075]).
As to claim 11, Aberman (as modified by Zhou and Park) teach the method of claim 10, wherein the method is performed periodically to update the GAN using a plurality of user responses (Park: [0076]: training data to update the one or more trained ML model(s) 270).
As to claims 12-15, it is the apparatus performs the operations of claims 1-4, where a system, comprising: a processor; and a memory storing instructions (Aberman: Fig. 1A, [0048]). Please see claims 1-4 for details analysis.
As to claims 16-20, it is a non-transitory computer-readable storage medium storing instructions for performing the functions of claims 1-5. Please see claims 1-5 for details analysis.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over Aberman et al. (US 20230015117) in view of Zhou et al. (US 20190220977) and Park et al. (US 20230281885) and in further view of Anderson (US 20160179201).
As to claim 7, Aberman (as modified by Zhou and Park) teach the method of claim 6, wherein the user response further comprises the response for each image in the set of images (Park: [0061], [0075]).
Aberman (as modified by Zhou and Park) do not explicitly teach an amount of time for the user to provide the response for each image in the set of images.
Anderson teaches an amount of time for the user to provide the response for each image in the set of images ([0030]: image object selection module 210 can infer the user's 110 selection of the image object in response to the user's fixation on the image object for a reference period of time or greater. As such, the user 110 can select a particular image object of the displayed image by looking at the image object for a certain length of time).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method of Aberman (as modified by Zhou and Park) by adapting Anderson’s idea of an amount of time (certain length of time) for the user to provide the response for each image (select a particular image object of the displayed image) in order to provide user more flexibility.
Allowable Subject Matter
Claims 8 and 9 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), 1st paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claims 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
a) Lee (US 20200004333) teaches a generative adversarial network (GAN) comprising a generator and a discriminator ([0140]).
THIS ACTION IS MADE FINAL. 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 AFROZA Y CHOWDHURY whose telephone number is (571)270-1543. The examiner can normally be reached M-F 9am-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nitin Patel can be reached at (571)272-7677. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/AFROZA CHOWDHURY/Primary Examiner, Art Unit 2628