DETAILED ACTION
Claim(s) 1,4,5,7,14,21 and 15,16,17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation:
Claim(s) 2,3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Timoshenko (Identifying Customer Needs from User-Generated Content):
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of VAHDAT et al. (US 2022/0101144 A1):
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Kim et al. (Improving Cross-Modal Retrieval with Set of Diverse Embeddings):
Claim(s) 9,10,11,12,13 and 18,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Piety et al. (US 5,637,781):
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/11/2026 has been entered.
Response to Arguments
II. Rejections under 35 USC 103
A. Independent Claim 1
Applicant's arguments filed 4/10/2026 have been fully considered but they are not persuasive.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 10, 2nd para, 1st S: “active learning”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., remarks, page 10, 2nd para, 2nd S: “selecting particular images from an unlabeled
dataset to be rated for labeling to improve the model accuracy in a subsequent training”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicant’s arguments, see remarks, page 10, 3rd para, filed 4/10/2026, with respect to the rejection(s) of claim(s) 1 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103:.
Claim(s) 1,4,5,7,14,21 and 15,16,17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation, wherein PARAMESWARN teaches the claimed “uncertainty score” via fig. 4:425” UNCERTAINTY SCORING”:
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Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claim(s) 1,4,5,7,14,21 and 15,16,17 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation:
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Re 1. (Currently Amended), Burnap teaches A computer-implemented method, comprising:
receiving1 an (“Ratings”, pg. 20, 5th para, 1st S) input via a (“SUV” (a vehicle), pg. 20, 1st S) user (fig. 1: “CONSUMER EVALUATION”) interface relating to a (car-design) concept;
automatically obtaining (via an automatic, fed device via Fig. 1: “Augmenting Aesthetic Design with Machine Learning”2)nd para, 2nd S: fig. 3: “DESIGN CONCEPT TESTING” at said smaller training set) of images based on the input;
obtainingnd param 2nd S) via the (“SUV” (a vehicle), pg. 20, 1st S) user interface for each image from the first set (resulting in “a smaller set”, pg. 3, 2nd para, 2nd S) of images to label each of the images from the first set of images (or likewise “to predict ratings for new images and for generated images.”, pg. 7, 1 para, 4th S);
trainingst S: fig. 2: trained: “GENERATIVE MODEL (MACHINE)”: “PREDICTIVE MODEL (MACHINE)”) model (i.e., a “training” “encoder”, pg. 13, 4th para, 4th S: fig. 2: “ENCODER MODEL”: “trained”-“machine learning models”, pg. 4, penult para, 1st S) relating to the (car-design) concept based on the first set (resulting in “a smaller set”, pg. 3, 2nd para, 2nd S) of images rated (fig. 3: “Aesthetic Rating”, pg. 17) via the (“SUV” (a vehicle), pg. 20, 1st S) user interface;
automatically obtainingnd to last S) set of images from the unlabeled dataset of images based on a selection (or likewise “we sample”3, pg. 7, 1st para, last S) of the second set of images by the machine-learned (or “trained”, pg. 4, penult para, 1st S: fig. 2: trained: “GENERATIVE MODEL (MACHINE)”: “PREDICTIVE MODEL (MACHINE)”) model (i.e., a “training” “encoder”, pg. 13, 4th para, 4th S: fig. 2: “ENCODER MODEL”: “trained”-“machine learning models”, pg. 4, penult para, 1st S) trained based on the first (training) set of images, according to an uncertainty score associated with the second set of images;
obtainingthe (“SUV” (a vehicle), pg. 20, 1st S) user interface for each image from the second (validation) set (represented in fig. 2 as validation “IMAGES”) of images to label each of the images from the second set of images (or said likewise “to predict ratings for any image generated by the model.”, pg. 8, 3rd S); and
retraining (via “retrain the blocks”, pg. 17, last para, 2nd S: fig. 3: blocks)st para, 1st S: fig. 2: trained: “GENERATIVE MODEL (MACHINE)”: “PREDICTIVE MODEL (MACHINE)”) model (i.e., a “training” “encoder”, pg. 13, 4th para, 4th S: fig. 2: “ENCODER MODEL”: “trained”-“machine learning models”, pg. 4, 1st para, 2nd S) relating to the concept based on the first (“training”, pg. 15, last para, penult S) set of images rated via the (“SUV” (a vehicle), pg. 20, 1st S) user interface 4 st S) st S) machine-learned (or “trained”, pg. 4, penult para, 1st S: fig. 2: trained: “GENERATIVE MODEL (MACHINE)”: “PREDICTIVE MODEL (MACHINE)”) model (i.e., a “training” “encoder”, pg. 13, 4th para, 4th S: fig. 2: “ENCODER MODEL”: “trained”-“machine learning models”, pg. 4, penult para, 1st S via:
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.
Burnap does not teach the difference of claim 1 of:
a (user)5 interface…6
the (user) interface…
the (user) interface…
according to an uncertainty score associated with (the second set of images)…
the (user) interface…
the (user) interface…
Bala teaches the difference of claim 1 in the context of a person spending a long time to recognize an image (similar to the speed-problem faced by applicants):
[0032] According to various features described herein with regard to FIGS. 2-4, when uploading a given image for personalization, locations or regions within the image are suggested that can be effectively personalized by the design tool, thus reducing the time and iterations spent in the design process (i.e., minimizing or eliminating manual identification of suitable images and/or image regions by a user). In another example, if the user wishes to select a single image for personalization from a large collection, the "suitability for personalization" (SFP) metric can be pre-calculated for all images in that collection, stored, and fed to or retrieved by a file browsing application, which presents to the user the images sorted or ranked by SFP. The user can then quickly select from the top candidates. In a third scenario, the user may upload an image for a general image processing/editing task (i.e. not necessarily for personalization). The personalization analysis and SFP metric are calculated in the background. Only when the metric exceeds a predetermined threshold does the processor make a suggestion that this image is a good candidate for personalization, and offers an option to initiate the personalization process. All of these scenarios minimize wasted time and effort, and offer a productive design experience.
(“The method further comprises presenting the candidate regions to the user via”) a (user)7 interface (“GUI” [0005] last S)…8
the (user) interface (“that can be employed to permit designers to rate a set of images” [0012]) …
(“At 106, the candidate locations are presented to the user via” [0031] 5th S) the (user) interface (fig. 1:106: “PRESENT CANDIDATE LOCATIONS TO USER VIA GUI”)…
according to an uncertainty score associated with (the second set of images)…
(“FIG. 5 illustrates a screenshot of” [0036] 2nd S) the (user) interface…
(“The designers can also provide rationale for their ratings and choices via” [0036] penult S) the (user) interface…
(“a number of images (e.g., 16, 20, etc.) are sequentially displayed on the left side of” [0036] last S) the (user) interface (fig. 5).
Since Burnap teaches a similar concept problem (i.e., a conceptual free-hand design-concept drawing lacking in perceptivity/understanding) to applicant’s, via Brunap page 4:
Aesthetic designers create hundreds to thousands of freehand sketches9 that are converted to 2D images (Box 2). For example, Dyson and General Motors generate several hundred sketches per new device or vehicle, while IKEA generates fewer sketches given its product line variety and turnover (Bouchard, Aoussat, and Duchamp 2006; Toffoletto 2013). The human design team next screens potential designs to a smaller set of testable design concepts in a process known as “downselecting” (Box 3). Consumers evaluate the remaining designs in theme clinics resulting in more screening. Successful designs are advanced downstream for further development, including engineering, manufacturing, and marketing communications (advertising, social media, websites, etc.). The process is highly iterative and asynchronous across both generation and testing; our augmentation applies to all iterations.
one of skill in the art of design can make Burnap’s be as Bala’s seeing in the change “a productive design experience”, Bala [0032] last S:
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Burnap of the combination of Burnap-Bala does not teach the difference of claim 1 of:
(automatically obtaining, by the computing system, a second set of images from the unlabeled dataset of images based on a selection of the second set of images by the machine learned model trained based on the first set of images)10, according11 to an uncertainty score associated with (the second set of images) …12
PARAMESWARAN teach the difference of claim 1 of:
(automatically obtaining, by the computing system, a second set of images from the unlabeled dataset of images based on a selection of the second set of images by the machine learned model trained based on the first set of images)13, according14 to an uncertainty score associated with (the second set of images) (or likewise “according to an uncertainty score…providing to an operator an opportunity to review images having an uncertainty score”, page 13: last txt blk: [00033] last S & [00035]) …15
Since Burnap teaches semi-supervised labeling (i.e., rating) with problems thereof, via page 25, penult para:
The second managerial issue we address is the relatively limited amount of rated data on product images and aesthetic ratings. This was due to the expense required to collect rated data as well as show many unique designs exist; our rated data on new-SUV/CUV design images were only 203 unique SUV/CUVs over five years of theme clinics. If we were to train the deep learning model on these images alone, the embeddings and the generative capability would be weak if not impossible. We
overcome this issue using semi-supervised learning to combine the expensive rated “small data,” with the inexpensive and significantly larger “big data” of unrated images. Combining small data and big data made it feasible to train a deep learning model that does well on the theme-clinic-based data found in firms.
one of skill in the art could or would have done is refer to others as the solution and thus make Burnap’s be as PARAMESWARAN’s seeing in the change “the images from the system production model can be supplied to the image set of the present invention such that any labeling errors can be corrected, resulting in a more accurate production model.”, PARAMESWARAN [00062] last S, via explicit, creative, routine, inferential Supreme court steps, A,B,C:
A) Create an ACTIVE LEARNING program based on PARAMESWARAN’s fig. 4:
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B) ACTIVE LEARNING program Inputs:
B1) Input Burnap’s labelled images of cats of fig. 3 into the program (at 405) as “LABELED/TRAINING DATA”
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B2) Input Burnap’s ENCODER MODEL or PREDICTIVE OR GENERATIVE MODEL into the program (at 410) as “TRAINING”;
C) Run Program; and
D) See what happens (I foresee “the images from the system production model can be supplied to the image set of the present invention such that any labeling errors can be corrected, resulting in a more accurate production model.” (i.e., more accurate Burnap’s ENCODER MODEL or PREDICTIVE OR GENERATIVE MODEL)).
Re claim 4. (Original), Burnap of the combination of Burnap-Bala-PARAMESWARN teaches The computer-implemented method of claim 1, further comprising providing a rating tool (or a rating-to-page or tool rate pages via “web…pages”1617,Burnap, pg. 20, 2nd & 3rd paragraphs: page 31, A4. Example Rating Page from Aesthetic Rating Survey used in Theme Clinic) to obtain the first rating and the second rating.
Re claim 5. (Previously Presented), Burnap of the combination of Burnap-Bala-PARAMESWARAN teaches The computer-implemented method of claim 4, wherein the (page) rating tool (or rating-to-page or tool rate pages via “web…pages”1819,Burnap, pg. 20, 2nd & 3rd paragraphs: page 31, A4. Example Rating Page from Aesthetic Rating Survey used in Theme Clinic) comprises (via the above combination, illustrated above) the user (file) interface (of the combination illustrated above) which displays (page 31, A4. Example Rating Page from Aesthetic Rating Survey used in Theme Clinic) each image from among the first set of images and the second set of images, and
the user (file) interface (of the combination illustrated above) includes (GUI) user interface elements which are selectable to indicate whether an image is a positive (Bala, fig. 5: “Very Good”) image (via Burnap, page 24: fig. 8: Generated Designs – High Appeal) corresponding to the (car-design) concept or a negative (appeal: Bala, fig. 5: “Very Poor”) image that does not correspond to the (car-design) concept.
Re claim 7 (Currently Amended), Burnap of the combination of Burnap-Bala-PARAMESWARAN teaches The computer-implemented method of claim 1, wherein
20 (or likewise (1) “the generative model…defines the general21 nature…with… binary values”, pg. 12, 2nd para 1st three Ss, & (2) “model…classifier”, pg. 10,penult S & page 11, 2nd para, last two Ss), and
the first rating (of 7,000) is a binary22 (encoder) rating indicating whether an image from the first (training) set of images is a positive image (to “create credible product aesthetic image”, Burnap, pg. 23, 7.2 Generative Capability From embedding to images, 1st S: page 24, fig. 5: “Generated Designs – High Appeal) corresponding to the (design) concept or a negative image (not to “create credible product aesthetic image”: page 24: fig. 5: “Generated Designs – Low Appeal”) that does not correspond to the (car-design) concept (via page 24: fig. 5:
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Re claim 14. (Previously Presented), Burnap of the combination (illustrated above) of Burnap,Bala teaches The computer-implemented method of claim 1, wherein training the machine-learned model relating to the (design) concept based on the first (training) set of images rated via the user interface comprises:
implementing one or more (three) pretrained models (in Burnap’s fig. 3: “until we reach the target image resolution of 256 x 256”, pg. 17, 5.2 Progressive Training, 1st para, 2nd S) to train a neural network (“augmented through adversarial training”, pg. 8, 2nd S) using image embeddings (“in the embedding space”, pg. 14, 4th para, 2nd S) provided by the one or more (three) pretrained models (via Burnap’s page 17, fig. 3:
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Re 21. (New), PARAMESWARAN of the combination of Burnap-Bala-PARAMESWARAN teaches The computer-implemented method of claim 1, wherein the uncertainty score is determined according to a selection method implemented (or likewise “ the operator is particularly invited to review the images above that threshold since those images have the highest uncertainty values…by a different selection process”, machine translation, page 49, zig-zag text-block and page 50, 1st text blk) to automatically obtain ( or likewise “A group of images having the greatest
uncertainty is then fed back to labeling step 120 for reconsideration by the operator”, machine translation, pg. 22, [00066] 2nd S, “where the operator can be either automated or human”, machine translation, page 13, last S) the second set of images, the selection method including23 24 sampling method (or likewise “the active learning function…as process 145…where a random sample of the image data, for example from 200A of Figure 2, is labeled by an operator at 400,substantially as shown at 210 in Figure 2.”, page 34 [00091] 1st S & page 35 [00092]).
Claim 15 is rejected like claim 1:
15. (Currently Amended), Burnap of the combination (illustrated above) of Burnap,Bala teaches A computing system, comprising:
one or more processors (comprised by machine learning25); and
one or more non-transitory computer-readable media (comprised by machine learning) that store instructions that, when executed by the one or more processors, cause the computing system to perform operations, the operations comprising:
receiving an input (via said arrows of fig. 1) via a user interface relating to a (car-design) concept;
automatically obtaining (via said automated, fed device) a first (training) set of images from an unlabeled dataset of images based on the input;
obtaining a first (aesthetic) rating via the (web-page) user interface for each image from the first (training) set of images to label each of the images from the first set of images;
training a machine-learned model (until reaching an image size of 256 x 256) relating to the (car-design) concept based on the first (training) set of images (aesthetically) rated via the user interface;
automatically obtaining a second (validation) set of images from the unlabeled dataset of images based on a selection of the second set of images by the machine-learned model trained based on the first (training) set of images, according to an uncertainty score associated with the second set of images;
obtaining a second rating (via fig. 3: “Predictive Model”: “Aesthetic Rating”: “2.3”) via the user interface for each image from the second (validation) set of images to label each of the images from the second set of images ; and
retraining the machine-learned model (or “blocks”, pg. 17, 2nd S) relating to the concept based on the first set of images rated via the user interface and the second set of images rated via the user interface to obtain an updated machine-learned model (“10 times as much”, pg. 18, penult S).
Re claim 16. (Original), Burnap of the combination (illustrated above) of Burnap,Bala teaches The computing system of claim 15, wherein the input comprises a plurality of text phrases (i.e., “product attributes”, pg. 2, penult para, 2nd S), the plurality of text phrases including at least one positive textual description (to create an appealing car image) relating to the concept and at least one negative textual description (to create a not appealing car image) relating to the (car-design) concept.
Claim 17 is rejected like claim 7:
Re 17. (Previously Presented), Burnap of the combination of Burnap,Bala- teaches The computing system of claim 15,
wherein 262728 29 3031
the first rating is a binary rating indicating whether an image from the first set of images is a positive image corresponding to the concept
Claim 20 is rejected like claims 1 and 15:
Re 20. (Currently Amended), Burnap of the combination (illustrated above) of Burnap,Bala teaches One or more non-transitory computer-readable media (comprised by machine learning) that collectively store instructions that, when executed by one or more processors, cause the one or more processors to perform operations, the operations comprising:
receiving an (arrow) input via a user interface relating to a (car-design) concept;
automatically obtaining a first set of images (via said automated, fed device) from an unlabeled dataset of images based on the input;
obtaining a first rating (of 7,000) via the user interface for each image from the first (training) set of images to label each of the images from the first set of images;
training a machine-learned (encoder) model relating to the (car-design) concept based on the first (training) set of images rated (on a scale of 1 to 5) via the user interface;
automatically obtaining a second (validation) set of images from the unlabeled dataset of images based on a selection of the second set of images by the machine-learned (encoder) model trained based on the first (training) set of images, according to an uncertainty score associated with the second set of images ;
obtaining a second rating (of said 7,000) via the user interface for each image from the second (validation) set of images to label each of the images from the second set of images; and
retraining (i.e., updating) the machine-learned (encoder) model (10 times less than the generator model) relating to the (car-design) concept based on the first (training) set of images rated via the user interface .
Claim(s) 2,3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Timoshenko (Identifying Customer Needs from User-Generated Content):
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Re 2. (Original), Burnap of the combination of Burnap,Bala teaches The computer-implemented method of claim 1, wherein the input comprises a plurality of text phrases.
Burnap of the combination (illustrated above) of Burnap,Bala does not teach “a plurality of text phrases”. Timoshenko teaches “a plurality of phrases” or “a small set of sentences”, pg. 7, 4. Methodology, 1st bullet.
Since Burnap of the combination (illustrated above) of Burnap,Bala cites to Timoshenko, via Burnap, page 5, last para:
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, one of skill in the art of attributes32 can make Burnap’s of the combination (illustrated above) of Burnap,Bala be as Timoshenko’s predictably recognizing the change being a “satisfied” “customer”, Timoshenko, page 3, 2nd para:
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Re claim 3. (Original) , Burnap of the combination (illustrated above) of Burnap,Bala, Timoshenko teaches The computer-implemented method of claim 2, wherein the plurality of text phrases (or attributes33 or sentences) includes at least one positive textual description (to “create credible product aesthetic image”, Burnap, pg. 23, 7.2 Generative Capability From embedding to images, 1st S: page 24, fig. 5: “Generated Designs – High Appeal) relating to the (design) concept and at least one negative textual description (or attribute not to “create credible product aesthetic image”: page 24: fig. 5: “Generated Designs – Low Appeal”) relating to the concept.
Claim(s) 7 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of VAHDAT et al. (US 2022/0101144 A1):
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Re claim 7 (Currently Amended), Burnap of the combination of Burnap-Bala- teaches, under a narrow subset of claim scope, The computer-implemented method of claim 1, wherein
34 nature…with… binary values”, pg. 12, 2nd para 1st three Ss, & (2) “model…classifier”, pg. 10,penult S & page 11, 2nd para, last two Ss), and
the first rating (of 7,000) is a binary35 (encoder) rating indicating whether an image from the first (training) set of images is a positive image (to “create credible product aesthetic image”, Burnap, pg. 23, 7.2 Generative Capability From embedding to images, 1st S: page 24, fig. 5: “Generated Designs – High Appeal) corresponding to the (design) concept or a negative image (not to “create credible product aesthetic image”: page 24: fig. 5: “Generated Designs – Low Appeal”) that does not correspond to the (car-design) concept (via page 24: fig. 5:
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Burnap of the combination of Burnap-Bala- does not teach, under “a narrow subset of claim scope”36, the difference of claim 7 of:
binary (classifier model)37.
VAHDAT teach the difference of claim 7 of:
binary (classifier model)38 (or likewise “After training of the VAE is complete, a separate machine learning model called a “classifier”…212 includes a binary classifier” , [0031] 2nd S & [0048] 2nd S).
Since Burnap suggests looking to others or elsewhere for a known “classifier” via pages 30 last S to page 11, 2nd para, last two Ss:
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one of skill in the art could or would have done is refer to others for the known classifier and thus make Burnap’s be as VAHDAT’s seeing in the change good, via VAHDAT [0011]:
[0011] At least one technical advantage of the disclosed techniques relative to the prior art is that the disclosed techniques produce generative output that looks more realistic and similar to the data in a training dataset compared to what is typically produced using conventional variational autoencoders. Another technical advantage is that, with the disclosed techniques, a complex distribution of latent variables produced by an encoder from a training dataset can be approximated by a machine learning model that is trained and executed in a more computationally efficient manner relative to prior art techniques. These technical advantages provide one or more technological improvements over prior art approaches.
via explicit, creative, routine, inferential, Supreme court steps A,B,C:
A) create a three-model training program based on Burnap’s teaching of training-loss-function via page 19: section 5.4: Gradient Backpropagation Using Local Reparameterization:
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B) create a generative-model program based on VAHDAT’s figures 6,7:
B1) at step 602 create code that calls Burnap’s three model training program:
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C) run VAHDAT’s generative-model program of figures 6 and 7:
C1) at step fig. 6:602 minimize the loss-function of Burnap’s three model training program;
C1.1) use the minimized loss-function results for the next binary-classifier training step 604;
D) continue with the rest of VAHDAT’s generative-model program of figures 6,7;
D) see what happens (I foresee:
[0011] At least one technical advantage of the disclosed techniques relative to the prior art is that the disclosed techniques produce generative output that looks more realistic and similar to the data in a training dataset compared to what is typically produced using conventional variational autoencoders. Another technical advantage is that, with the disclosed techniques, a complex distribution of latent variables produced by an encoder from a training dataset can be approximated by a machine learning model that is trained and executed in a more computationally efficient manner relative to prior art techniques. These technical advantages provide one or more technological improvements over prior art approaches.)
Claim 17 is rejected like claim 7:
Re 17. (Previously Presented), Burnap of the combination of Burnap,Bala-PARAMESWARAN teaches The computing system of claim 15,
wherein the machine-learned model is a binary classifier model, and the first rating is a binary rating indicating whether an image from the first set of images is a positive image corresponding to the concept or a negative image that does not correspond to the concept.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Kim et al. (Improving Cross-Modal Retrieval with Set of Diverse Embeddings):
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Re 8. (Original), Burnap of the combination (illustrated above) of Burnap,Bala teaches The computer-implemented method of claim 1, wherein obtaining the first set of images from the unlabeled dataset of images based on the input comprises:
co-embedding (pg. 7, fig. 2: “IMAGE EMBEDDING”) the unlabeled dataset of images and the input into a same (“embedding”, pg. 14, 2nd para, last S) space, and
performing a nearest-neighbor (“image…not…too far from the prior”, pg. 9, penult para, 5th S) search to retrieve the first (training) set of images from among the unlabeled (training) dataset of images to obtain (via said automated, fed device) images which are nearest to each text (attribute) embedding.
Burnap of the combination (illustrated above) of Burnap,Bala does not teach “performing a nearest-neighbor search to retrieve…nearest to”.
Kim teaches “performing a nearest-neighbor search to retrieve…nearest to” :
Page 5, 3.4 Training and Inference:
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Since Burnap of the combination (illustrated above) of Burnap,Bala teaches text embedding for finding what a customer wants, page 5, last para:
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, one of skill in the art of embedding can make Burnap’s of the combination (illustrated above) of Burnap,Bala be as Kim’s:
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predictably recognizing the change accurately searching car products for a customer39, Kim, page 8:
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Claim(s) 9,10,11,12,13 and 18,19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Bala et al. (US 2013/0182946 A1) further in view of PARAMESWARAN et al. (WO 2023/215253 A1) with SEARCH machine translation as applied in claims 1,4,5,7,14,21 and 15,16,17 and 20 further in view of Piety et al. (US 5,637,781):
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Re claim 9. (Currently Amended), Burnap of the combination (illustrated above) of Burnap,Bala teaches The computer-implemented method of claim 1, further comprising:
automatically obtaining (via said automated, fed device), by the computing system, a third set (via “more screening”, pg. 3, 2nd para, 3rd S: fig. 3: “CONSUMER EVALUATION (HUMAN)” resulting in an even smaller consumer test-set, comprised by a 3rd function40 in page 16, Table 1) of images from the unlabeled (fig. 2: “UNLABLED IMAGES”) dataset (represented in fig. 3 as “GENERATIVE MODEL (MACHINE)”: trained) of images based on the (“competing”, pg. 18, last para, 1st S) updated machine-learned (encoder) model trained based on the first (“training”, pg. 15, last para, penult S) set (comprised by a 1st function in page 16, Table 1) of images and the second (“validation”, pg. 15, last para, 2nd to last S) set (comprised by a 2nd function in page 16, Table 1) of images;
obtaining, by the computing system, a third rating (via said 7,000 consumer ratings) by a plurality of (“eliminated” pg. 22, 1st S) users via a further (“SUV” (a vehicle), pg. 20, 1st S) user interface for each image from the third set (via “more screening”, pg. 3, 2nd para, 3rd S: fig. 3: “CONSUMER EVALUATION (HUMAN)” resulting in an even smaller consumer test-set, comprised by a 3rd function41 in page 16, Table 1) of images to label each of the images from the third set of images ; and
retraining, by the computing system, the (“often”42, pg. 18, last para, 2nd S) updated machine-learned (encoder) model (“until we reach the target image resolution of 256 x 256”, pg. 17, last para, 2nd S) relating to the (car-design) concept based on the first (“training”, pg. 15, last para, 2nd to last S) set (comprised by a 1st function in page 16, Table 1) of images rated (fig. 2: “images w/ ratings”) via the (“SUV” (a vehicle), pg. 20, 1st S) user interface (via the combination (illustrated above) of Burnap,Bala), the second (“validation”, pg. 15, last para, 2nd to last S) set (comprised by a 2nd function in page 16, Table 1) of images rated (fig. 2: “images w/ ratings”) via the (remaining- “SUV” (a vehicle), pg. 20, 1st S) user interface (via the combination (illustrated above) of Burnap,Bala), and the third set (via “more screening”, pg. 3, 2nd para, 3rd S: fig. 3: “CONSUMER EVALUATION (HUMAN)” resulting in an even smaller consumer “testing set”, pg. 15, last para, 2nd to last S, comprised by a 3rd function43 in page 16, Table 1 comprised by a 3rd function44 in page 16, Table 1) of images rated (fig. 2: “images w/ ratings”) via the further user interface, to obtain a further updated (often) machine-learned (encoder) model.
Burnap of the combination (illustrated above) of Burnap,Bala does not teach the difference45 of claim 9 of:
via a further (user) interface…
the further (user) interface.
Piety teaches the difference of claim 9:
via a further (user) interface (via “by46 additional47 user interfaces”, c. 13,ll. 57-61: fig. 4:294,296: mouse & keyboard) …
the further (user) interface (via “by additional user interfaces”, c. 13,ll. 57-61: fig. 4:294,296: mouse & keyboard):
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Since Burnap of the combination (illustrated above) of Burnap,Bala teaches a user, one of skill in the art of users can make Burnap’s of the combination (illustrated above) of Burnap,Bala be as Pierty’s seeing in the change “ergonomic48 features”, Piety, c. 13, ll. 57-61, designed to be comfortable, safe, and efficient to use, especially in or as a work environment:
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Re 10. (Original), Burnap of the combination (illustrated above) of Burnap,Bala, Piety teaches The computer-implemented method of claim 9, wherein a number (or “all” “7,308 rated images”, pg. 20, 4th full para, 1st S) of49 the third set (via “more screening”, pg. 3, 2nd para, 3rd S: fig. 3: “CONSUMER EVALUATION (HUMAN)” resulting in an even smaller consumer test-set, comprised by a 3rd function50 in page 16, Table 1) of images is greater (understood given all 7,308 rated images:100%) than a number (or “50%”, pg. 20, 4th para, last S of said all 7,308 rated images) of the first (training) set of images and greater (understood given all 7,308 rated images:100%) than a number (or “25%”, pg. 20, 4th para, last S of said all 7,308 rated images) of the second (validation) set of images.
Re 11. (Previously Presented), Burnap of the combination (illustrated above) of Burnap,Bala,Piety teaches The computer-implemented method of claim 10, wherein retraining (via “retrain the blocks”, pg. 17, last para, 2nd S: fig. 3: blocks), by the computing system, the updated51 (“often”52, pg. 18, last para, 2nd S, via fig. 2: “NEW IMAGES”) machine-learned model relating to the concept comprises weighting a rating (via a “rating-scale”, pg. 20, 1st full para, 2nd S, resulting in “aggregated”53 “Ratings”, Burnap, pg. 20, 4th full para, 1st S) obtained via the user (GUI) interface higher (“to be rated high”, Burnap, page 24, 1st full para, 1st S: fig. 5: “Generated Designs – Low Appeal” via “most unappealing to most appealing”, Burnap, pg. 20, 1st full para, 2nd S) than (“low rated”, Burnap, page 24, 1st full para, 1st S: fig. 5: “Generated Designs – Low Appeal”) ratings obtained via the further (mouse/keyboard) user interface.
Re 12. (Currently Amended), Burnap of the combination (illustrated above) of Burnap,Bala,Piety teaches The computer-implemented method of claim 9, further comprising providing a first rating tool (via “machine learning tools”, Burnap, pg. 2, 3rd para, 1st S) to obtain the first rating and the second rating via the (remaining/ non-eliminated) user interface (via the combination (illustrated above) of Burnap,Bala) and providing a second rating tool (or “controllable tool”, Burnap, pg. 2, 4th para, 3rd S) to obtain the third rating (via “rated ten sequential pages”, pg. 20, 2nd full para, 1st S) via the further (high quality user) user interface (via the combination (illustrated above) of Burnap,Bala,Piety).
Re 13. (Previously Presented), Burnap of the combination (illustrated above) of Burnap,Bala,Piety teaches The computer-implemented method of claim 12, wherein
the first (machine learning) rating tool comprises the user (“web…page”54, pg. 20, 2nd and 3rd Ss:GUI) interface (via the combination (illustrated above) of Burnap,Bala) which is configured to display each image from among the first set of images and the second set of images, the (GUI) user interface (via the combination (illustrated above) of Burnap,Bala) including user interface elements which are selectable to indicate whether an image from among the first set of (7000) images and the second set of (50% of saif 7000) images is a positive image (pg. 24: fig. 5: “Generated Designs – High Appeal) corresponding to the concept or a negative image that does not correspond to the concept, and
the second (control) rating tool comprises the further user interface (or a 2nd mouse, keyboard web page via the combination (illustrated above) of Burnap,Bala,Piety) which is configured to display each image from among the third set of images, the further (GUI, mouse, keyboard) user interface (via the combination (illustrated above) of Burnap,Bala,Piety) including information providing an explanation (Burnap: pg. 31: “RATE FROM 1 (VERY UNAPPEALING) TO 5 (VERY APPEALING)”) relating to the (car-design) concept for the plurality of (“eliminated”, Burnap: pg. 22, 1st S) users and user interface elements which are selectable to indicate whether an image from among the third set (via “more screening”, Burnap: pg. 3, 2nd para, 3rd S: fig. 3: “CONSUMER EVALUATION (HUMAN)” resulting in an even smaller consumer test-set, comprised by a 3rd function55 in page 16, Table 1) of images is a positive (appealing) image corresponding to the concept or a negative (appealing) image that does not correspond to the concept.
Claim 18 is rejected like claim 9:
Re 18. (Currently Amended), Burnap of the combination (illustrated above) of Burnap,Bala,Piety teaches The computing system of claim 15, further comprising:
automatically obtaining, by the computing system, a third set of images from the unlabeled dataset of images based on the updated machine-learned model trained based on the first set of images and the second set of images;
obtaining, by the computing system, a third rating by a plurality of users via a further user interface for each image from the third set of images to label each of the images from the third set of images; and
retraining, by the computing system, the updated classifier model relating to the concept based on the first set of images rated via the user interface, the second set of images rated via the user interface, and the third set of images rated by the plurality of users via a further user interface , to obtain a further updated machine-learned model.
Claim 19 is rejected like claims 10 and 11:
Re 19. (Previously Presented), Burnap of the combination (illustrated above) of Burnap,Bala,Piety teaches The computing system of claim 18, wherein
a number (said 7,000) of the third set of images is greater than a number (or “50%”, pg. 20, 5th param 2nd S, of said 7,000) of the first set of images and greater than a number (or “25%” of said 7000) of the second set of images, and
retraining (often) the updated (via inputting new data) machine-learned model relating to the (design) concept comprises weighting56 (via a scale) a rating (resulting in “aggregate”57 “Ratings”, pg. 20, 5th para, 1st S) obtained via the (visual-GUI) user interface higher (or very appealing) than (lower-appealing) ratings obtained via thest S) user interface.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action.
Citation
Relevance
DEMIRKAYA et al. (WO 2024/218175 A1) with SEARCH machine translation: this reference is applicable to the above 35 USC 103 rejection of claim 21.
DEMIRKAYA teaches “uncertainty sampling may comprise… confidence sampling…margin sampling …or entropy sampling” via [0059] last two Ss and fig. 3: “Scoring function based on uncertainty”:
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“There are several methods for uncertainty sampling, and any of those methods may be utilized as described or otherwise envisioned herein. For example, uncertainty sampling may comprise least confidence sampling, smallest/minimum margin sampling, ratio of confidence sampling, or entropy sampling, among other possible methods.”
as the closest to the claimed “the selection method including at least one of a margin sampling method…a least confidence sampling method, or an entropy sampling method” of claim 21.
Aliamiri et al. (US 2021/0142068 A1): this reference is applicable to the above 35 USC 103 rejection of claim 21.
Aliamiri teaches “uncertainty scores” and “uncertainty” is “ ‘margin sampling’ ” via fig. 6:650: “uncertainty score” and [0069]:
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[0069] At 650, the computing system 130, using uncertainty measurement module 550, computes uncertainty scores for each frame based on the sets bounding boxes, and associated probabilities, for each of the K-classes. In some embodiments, a pixel-wise approach is used to compare the two highest confidence detections for each pixel of the segmentation map. In some embodiments, uncertainty scores are computed on only the selected sets of bounding boxes, which are aggregated together (one box proposal will have only one associate class probability) Techniques for computing uncertainty metrics are described in, for example, Brust, Clemens-Alexander, Christoph Kading, and Joachim Denzler. “Active learning for deep object detection.” arXiv preprint arXiv:1809.09875 (2018), the entire disclosure of which is incorporated by reference herein. For example, “1-vs-2” or “margin sampling” may be used as a metric for the two highest scoring classes c.sub.1 and c.sub.2:”
as the closest to the claimed “the selection method including at least one of a margin sampling method…a least confidence sampling method, or an entropy sampling method” of claim 21.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM 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, Henok Shiferaw can be reached at 571-272-4637. 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.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
1The strike thru text in claim 1 “does not limit the scope of a claim” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024]
As a general matter, the grammar and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language ( “ , by a computing system, “) that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
The following types of claim language may raise a question as to its limiting effect (this list is not exhaustive [I am adding to the list non-restrictive comma phrases: “, by a computing system, “] ):
• preamble (MPEP § 2111.02);
• clauses such as "adapted to," adapted for," "wherein," and "whereby" (MPEP § 2111.04, subsection I);
• contingent limitations (MPEP § 2111.04, subsection II);
• printed matter (MPEP § 2111.05); and
• functional language associated with a claim term (MPEP § 2181).
2 machine learning: a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it, where artificial intelligence is defined: Computers, Digital Technology. the capacity of a computer, robot, programmed device, or software application to perform operations and tasks analogous to learning and decision making in humans, such as speech recognition or question answering. AI, A.I., wherein robot is defined: any machine or mechanical device that operates automatically with humanlike skill. (Dictionary.com)
3 sample: to take a sample or samples of; test or judge by a sample, wherein take is defined: to pick from a number; select. (Dictionary.com)
4 and: (used to connect alternatives). (Dictionary.com)
5 (italics) represent claim limitations already taught
6 ellipses (…) represent claim limitations already taught
7 (italics) represent claim limitations already taught
8 ellipses (…) represent claim limitations already taught
9 sketches: a rough design, plan, or draft, as of a book, wherein rough is defined: crude, unwrought, nonprocessed, or unprepared, wherein crude is defined: lacking in intellectual subtlety, perceptivity, etc.; rudimentary; undeveloped, wherein perceptivity is defined: having or showing keenness of insight, understanding, or intuition. (Dictionary.com)
10 (italics) represent claim limitations already taught
11 “according” is a participle participating in the action of “automatically obtaining”
12 ellipses (…) represent claim limitations already taught
13 (italics) represent claim limitations already taught
14 “according” is a participle participating in the action of “automatically obtaining”
15 ellipses (…) represent claim limitations already taught
16 web: Digital Technology., Sometimes Web World Wide Web (preceded by the, except when used before a noun), where World Wide Web is defined: Usually the World Wide Web (except when used before a noun) a system of extensively interlinked hypertext documents: a branch of the internet. WWW, wherein documents is defined: Digital Technology., a computer data file, especially one with formatted text, where file is defined: Computers., a collection of related data or program records stored on some input/output or auxiliary storage medium, wherein record is defined: Computers., a group of related fields, or a single field, treated as a unit and comprising part of a file or data set, for purposes of input, processing, output, or storage by a computer, wherein field is defined: An interface element in a graphical user interface that accepts the input of text. (Dictionary.com)
17 page: a screenful of information from a website, teletext service, etc, displayed on a television monitor or visual display unit, wherein monitor is defined: A device that accepts video signals from a computer and displays information on a screen. Monitors generally employ cathode-ray tubes or flat-panel displays to project the image, wherein device is defined: a machine or tool used for a specific task (Dictionary.com)
18 web: Digital Technology., Sometimes Web World Wide Web (preceded by the, except when used before a noun), where World Wide Web is defined: Usually the World Wide Web (except when used before a noun) a system of extensively interlinked hypertext documents: a branch of the internet. WWW, wherein documents is defined: Digital Technology., a computer data file, especially one with formatted text, where file is defined: Computers., a collection of related data or program records stored on some input/output or auxiliary storage medium, wherein record is defined: Computers., a group of related fields, or a single field, treated as a unit and comprising part of a file or data set, for purposes of input, processing, output, or storage by a computer, wherein field is defined: An interface element in a graphical user interface that accepts the input of text. (Dictionary.com)
19 page: a screenful of information from a website, teletext service, etc, displayed on a television monitor or visual display unit, wherein monitor is defined: A device that accepts video signals from a computer and displays information on a screen. Monitors generally employ cathode-ray tubes or flat-panel displays to project the image, wherein device is defined: a machine or tool used for a specific task (Dictionary.com)
20 see corresponding footnote in claim 17
21 general: of or relating to all persons or things belonging to a group or category. (Dictionary.com)
22 binary: consisting of, indicating, or involving two. (Dictionary.com)
23 include: to contain as a subordinate element; involve as a factor: (Dictionary.com)
24 The crossed text “does not limit the scope of a claim” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar (coordinate adjectives: e.g., “entropy sampling”) and ordinary meaning of terms (“or”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language [(coordinate adjectives: e.g., “entropy sampling”) & (“or”)] that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
25 machine learning: a branch of artificial intelligence in which a computer generates rules underlying or based on raw data that has been fed into it
26 “binary” is initial interpreted as a cumulative adjective: binary-classifier outputting only 1 or 0: need to review applicant’s disclosure (see paragraph [0109] reproduced below) on what is meant by “binary”.
27 Regarding “binary” in view of applicant’s disclosure:
[0109] FIG. 4 depicts a flow chart diagram of an example method to perform according to example embodiments of the disclosure. Although FIG. 4 depicts operations performed in a particular order for purposes of illustration and discussion, the methods of the disclosure are not limited to the particularly illustrated order or arrangement. The various operations of the method 4000 can be omitted, rearranged, combined, and/or adapted in various ways without deviating from the scope of the disclosure.
28 binary: Computers. of, relating to, or written in binary code; programmed or encoded using only the digits 0 and 1. (Dictionary.com): according to applicant’s disclosure [0109] binary operations can be omitted without deviating from the scope of claim 17: thus “binary” is a coordinate adjective.
29 The crossed text (in claim 7 and 17) is not “a limitation in a claim… where the clause gave "meaning and purpose to the manipulative steps” via MPEP 2111.04 "Adapted to," "Adapted for," "Wherein," "Whereby," and Contingent Clauses [R-10.2019]
I. "ADAPTED TO," "ADAPTED FOR," "WHEREIN," and "WHEREBY"
Claim scope is not limited by claim language that suggests or makes optional but does not require steps to be performed, or by claim language that does not limit a claim to a particular structure. However, examples of claim language, although not exhaustive, that may raise a question as to the limiting effect of the language in a claim are:
(A) "adapted to" or "adapted for" clauses;
(B) "wherein" clauses; and
(C) "whereby" clauses.
The determination of whether each of these clauses is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002) (finding that a "wherein" clause limited a process claim where the clause gave "meaning and purpose to the manipulative steps").
30 and: (used to connect alternatives). (Dictionary.com)
31 MPEP 2143.03, 3rd para: As a general matter, the grammar and ordinary meaning of terms (“and”) as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language limits the claim scope. Language (“and”) that suggests or makes a feature or step optional but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives, the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
32 attribute: Grammar., a word or phrase that is syntactically subordinate to another and serves to limit, identify, particularize, describe, or supplement the meaning of the form with which it is in construction. In the red house, red is an attribute of house. (Dictionary.com)
33 attribute: Grammar., a word or phrase that is syntactically subordinate to another and serves to limit, identify, particularize, describe, or supplement the meaning of the form with which it is in construction. In the red house, red is an attribute of house. (Dictionary.com)
34 general: of or relating to all persons or things belonging to a group or category. (Dictionary.com)
35 binary: consisting of, indicating, or involving two. (Dictionary.com)
36 MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 2nd para:
Examiners must consider all claim limitations when determining patentability of an invention over the prior art. In re Gulack, 703 F.2d 1381, 1385, 217 USPQ 401, 403-04 (Fed. Cir. 1983). The subject matter of a properly construed claim is defined by the terms that limit the scope of the claim when given their broadest reasonable interpretation. In Axonics, Inc. v. Medtronic, Inc., 73 F.4th 950, 958-59, 2023 USPQ2d 795 (Fed. Cir. 2023), the court found the claims were improperly narrowed based on a preferred embodiment to sacral anatomy or sacral neuromodulation, whereas the patent claims made no reference to sacral anatomy or sacral neuromodulation. Thus, the relevant prior art was improperly limited to a narrow subset of claim scope. See also MPEP § 2111 et seq. It is the subject matter of the properly construed claim that must be examined. The determination of whether particular language is a limitation in a claim depends on the specific facts of the case. See, e.g., Griffin v. Bertina, 285 F.3d 1029, 1034, 62 USPQ2d 1431 (Fed. Cir. 2002).
37 (italics) represent claim limitations already taught
38 (italics) represent claim limitations already taught
39MPEP 2143 I. F. Known Work in One Field of Endeavor May Prompt Variations of It for Use in Either the Same Field or a Different One Based on Design Incentives or Other Market Forces if the Variations Are Predictable to One of Ordinary Skill in the Art
To reject a claim based on this rationale, Office personnel must resolve the Graham factual inquiries (as shown above). Then, Office personnel must articulate the following:
(1) a finding that the scope and content of the prior art [Burnap et al. (Design and Evaluation of Product Aesthetics: A Human-Machine Hybrid Approach) in view of Kim et al. (Improving Cross-Modal Retrieval with Set of Diverse Embeddings)], whether in the same field of endeavor as that of the applicant’s invention or a different field of endeavor, included a similar or analogous device ((embedding) method, or product);
(2) a finding that there were design incentives or market forces (in Burnap) which would have prompted adaptation of the known device ([embedding] method, or product);
(3) a finding that the differences (as shown above) between the claimed invention (claim 8) and the prior art were encompassed in known variations or in a principle known in the prior art (of Kim’s fig. 2);
(4) a finding that one of ordinary skill in the art, in view of the identified (car-) design incentives or other market forces, could have implemented the claimed variation (Kin’s fig. 2) of the prior art, and the claimed variation would have been predictable (via accurate searching of consumer products) to one of ordinary skill in the art; and
(5) whatever additional findings based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness.
The rationale to support a conclusion that the claimed invention would have been obvious is that design incentives or other market forces could have prompted one of ordinary skill in the art to vary the prior art in a predictable manner to result in the claimed invention. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
40 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
41 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
42 often: many times (Dictionary.com)
43 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
44 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
45 THE CLAIMED INVENTION AS A WHOLE: Regarding the claimed--a further… interface—(applicant’s figure 6):
The multi-faceted (concept-user interaction-speed) problem faced by applicants is discussed in the rejection of claim 1.
The solution to this (concept-user interaction-speed) problem is in applicant’s disclosure:
[0125] FIG. 6 depicts an example graphical user interface screen for crowd raters (e.g., the plurality of users) to label images, according to example embodiments of the disclosure. Different from the graphical user interface of FIG. 3B, in FIG. 6 the graphical user interface 6100 may include additional information to provide the plurality of users with information relating to the concept as understood and defined by the user, to better align the interpretation of the concept of the user and the interpretations of the concept of the plurality of users. For example, the concept may be identified to the plurality of users. In FIG. 6, the plurality of users are given a description 6200 that identifies the concept as an “astronaut” and asks the plurality of users to indicate whether an image 6400 (from the third set of images) is indicative of the concept (e.g., by selecting one of the options 6500). The computing system may also be configured to provide a concept description 6300. In FIG. 6 the concept description 6300 describes the user’s interpretation of the concept for reference by the plurality of users in rating the image 6400. The computing system may also be configured to provide positive examples 6600 and negative examples 6700 relating to the concept. In FIG. 6 the graphical user interface 6100 includes images of positive examples 6600 with corresponding explanations of why the image corresponded to the concept and images of negative examples 6700 with corresponding explanations of why the image did not correspond to the concept.
I don’t see in claim 9 applicant’s solution’s [0125]’s “ Different from the graphical user interface of FIG. 3B, in FIG. 6 the graphical user interface 6100 may include additional information…For example, the concept may be identified to the plurality of users”.
This absence of applicant’s recognition/identification/interpretation solution in claim 9 is an indication of obviousness.
46 by: via; through (Dictionary.com: BRITISH)
47 additional: added; more; supplementary, wherein more is defined: additional or further (Dictionary.com)
48 ergonomic: designed to be comfortable, safe, and efficient to use, especially in or as a work environment. (Dictionary.com)
49 of: (used to indicate inclusion in a number [7,308:100%], class, or whole). (Dictionary.com)
50 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
51 update: Computers. to incorporate new or more accurate information in (a database, program, procedure, etc.). (Dictionary.com)
52 often: many times (Dictionary.com)
53 aggregate: collect into one sum, mass, or body, wherein mass is defined: Physics., the quantity of matter as determined from its weight or from Newton's second law of motion. M, wherein matter is defined: importance or significance. (Dictionary.com)
54 Web page: a single, usually hypertext document on the World Wide Web that can incorporate text, graphics, sounds, etc., wherein document is defined: Digital Technology., a computer data file, especially one with formatted text, where file is defined: Computers., a collection of related data or program records stored on some input/output or auxiliary storage medium, wherein record is defined: Computers., a group of related fields, or a single field, treated as a unit and comprising part of a file or data set, for purposes of input, processing, output, or storage by a computer, wherein field is defined: An interface element in a graphical user interface that accepts the input of text. (Dictionary.com)
55 function: A relationship between two sets that matches each member of the first set with a unique member of the second set. (Dictionary.com)
56 weighting: Statistics., to give a statistical weight to, wherein weight is defined: importance, moment, consequence, or effective influence. (Dictionary.com)
57 aggregate: collect into one sum, mass, or body, wherein mass is defined: Physics., the quantity of matter as determined from its weight or from Newton's second law of motion. M, wherein matter is defined: importance or significance. (Dictionary.com)
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