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
The Amendment, filed on 7/1/2026, has been entered and acknowledged by the Examiner. Claims 1-20 are pending.
Response to Arguments
Applicant's arguments filed 7/1/2026 have been fully considered but they are not persuasive.
Issue: The applicant argues that claim 1 is generally directed to a method for acquiring one or more spectra for particles from a source material, and identifying a first set of spectra as an anomalous spectra for an unknown material. As filed, independent claim 1 recites a method comprising, inter alia, identifying a first set of spectra as an anomalous spectra for an unknown material, generating a plurality of synthetic spectra for the unknown material, and creating a synthetic entry with the plurality of synthetic spectra for the unknown material in a particle spectra library.
In rejecting independent claim 1, the Office Action relies on the combination of Cao and Lochner. Cao is a paper discussing ocean wave spectra retrieval using Synthetic Aperture Radar (SAR). The website sciencedirect.com defines an ocean wave spectrum as a mathematical or graphical representation that describes how the energy (or variance) of ocean waves is distributed across different wave frequencies and propagation directions. According to The Oceanography Society, an ocean wave spectrum measures how wave energy is distributed across different wave frequencies (or wavelengths) and directions. Lochner, however, is drawn to a computer skin care system, configured to find some marker in the skin which indicates that there can be a problem, (Lochner, col. 29, lines 6-7). Lochner refers to images of skin, and does not teach or suggest an anomalous spectra with respect to spectra for particles from an unknown material. Therefore, Lochner does not appear to disclose, or make obvious, acquiring spectra for particles. Hence, nothing in these references is seen to teach or suggest acquiring one or more spectra for particles from a source material, and identifying a first set of spectra as an anomalous spectra for an unknown material as recited in independent claim 1.
Response: The examiner respectfully disagrees and assert that the claimed invention refers to acquiring spectra for particles from an unknown material; however, the particles from an unknown materials, as defined in the specification, can be a non-exhaust list of things, e.g. particles of unknown organisms and/or species, e.g., environmental contaminants or interferents, pollens, emerging pathogens, bacteria, and/or one or more particle of known species, and/or species labels, metadata, e.g., locations at which the spectra were collected, occurrences of related events that associate certain spectra to a specific pathogen, etc. According to this list, ocean unknown detection as disclosed by Cao and skin anomalies as disclosed by Lochner. As such both references read on the claimed languages of “acquiring spectra for particles from an unknown material” unless it is specified what particles from an unknown material is being claimed. Therefore, Applicant’s argument is not persuasive.
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 1-20 are rejected under 35 U.S.C. 103(a) as being unpatentable over Cao (A Novel Method for Ocean Wave Spectra Retrieval Using Deep Learning From Sentinel-1 Wave Mode Data) in view of Lochner (US Pat. 12,324,680).
Regarding claim 1, Cao discloses a method for adaptive library building, the method comprising:
acquiring one or more spectra for particles from a source material using one or more environmental surveillance sensors via a computing device (p. 1, Synthetic aperture radar (SAR) can work day and night regardless of clouds and fog, making it a powerful tool for global wave observation. In addition, the high-resolution and 2-D imaging capabilities of SAR facilitate the acquirement of 2-D wave spectra information);
identifying a first set of spectra as an anomalous spectra for an unknown material based on a plurality of spectra for one or more known particles in a particle spectra library (p. 1, last para. identifying data using a relationship analysis);
generating a plurality of synthetic spectra for the unknown material using the first set of spectra (p. 1 last 2 para.);
creating a synthetic entry with the plurality of synthetic spectra for the unknown material in the particle spectra library (p. 1 creating a classification of spectra data);
acquiring a second set of spectra for the unknown material using the synthetic entry and spectra acquired from the one or more environmental surveillance sensors (p. 1, 2nd column);
validating the second set of spectra to be from a same source material as the first set of anomalous spectra (p. 2, 2nd para: validate based on comparison); and
replacing the synthetic entry with the second set of spectra for the unknown material in the particle spectra library (p. 2, sect. II).
While Cao discloses anomalies data, Cao does not explicitly disclose an anomalous spectra; however, in the same field of data analysis, Lochner discloses an anomalous spectra (col. 29, lines 22-30). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lochner into Cao to report back the abnormal information for further evaluation.
Regarding claim 2, Cao in view of Lochner discloses the method of claim 1, wherein the computing device further comprises at least one of a convolutional neural network (CNN) algorithm and a generative adversarial network (GAN) algorithm (p. 5, Pix2pix network is an end-to-end image translation model, which relies on the conditional generative adversarial network (CGAN) to achieve transformation between different domains).
Regarding claim 3, Cao in view of Lochner discloses the method of claim 2, wherein the CNN algorithm is configured to determine if the first set of spectra are the anomalous spectra for the unknown material based on the plurality of spectra in the particle spectra library (Cao, col. 29, lines 22-30).
Regarding claim 4, Cao in view of Lochner discloses the method of claim 2, wherein the CNN algorithm is configured to determine if the second set of spectra is from the same source material as the first set of anomalous spectra of the unknown material (Cao, col. 29, lines 22-30).
Regarding claim 5, Cao in view of Lochner discloses the method of claim 2, wherein the GAN algorithm is configured to generate the plurality of synthetic spectra for the unknown material using the anomalous spectra (Cao, col. 29, lines 22-30).
Regarding claim 6, Cao in view of Lochner discloses the method of claim 2, further comprising: computing a median spectrum of the first set of spectra (p. 9; Fig 12);
creating a plurality of noise patterns around the median spectrum via the GAN algorithm (p. 5); and
generating the plurality of synthetic spectra by combining the median spectrum with each of the plurality of noise patterns (p. 9).
Regarding claim 7, Cao in view of Lochner discloses the method of claim 2, wherein generating the plurality of synthetic spectra for the unknown material using the first set of spectra further comprises:
generating a first set of synthetic example spectra based on the first set of spectra using the GAN algorithm (p 5);
generating a second set of synthetic example spectra by mixing the first set of synthetic example spectra and the first set of spectra (p. 7; and
wherein the GAN algorithm further comprises a discriminator algorithm configured to: iteratively distinguish the second set of synthetic example spectra resulted at each iteration from an acquired spectra (p. 7, The first term of the adversarial loss function represents the probability that the discriminator distinguishes between fake and real, while the second term represents the probability that the discriminator fails to distinguish between fake and real);
produce an optimal set of realistic synthetic spectra using the second set of synthetic example spectra at each iteration using a loss function and/or a plurality of weights based on production at a previous iteration (p. 7); and
generate the plurality of synthetic spectra for the unknown material using the optimal set of realistic synthetic spectra if a pre-specified convergence is achieved (p. 7).
Regarding claim 8, Cao in view of Lochner discloses the method of claim 2, wherein: the GAN algorithm comprises a variational autoencoder (VAE) algorithm (p. 3, a variance function);
the VAE algorithm comprises at least an encoder, a variational generator, and a decoder (p. 3, a variance function; Lochner discloses an encoder function);
the encoder maps the first set of spectra to a latent space as latent distributions (p. 7);
the variational generator transforms the latent distributions to achieve convergence (p. 3, and p. 7); and the decoder converts the transformed latent distributions into the plurality of synthetic spectra (p. 3, and p. 7).
Regarding claim 9, Cao in view of Lochner discloses the method of claim 1, further comprising: reviewing the second set of spectra for the unknown material (p. 5, wave spectra can provide labels for the training of SAR2WV network); and assigning a label and metadata to the unknown material (p. 5).
Regarding claim 10, Cao discloses a method for adaptive library building, the method comprising:
acquiring one or more spectra for particles from one or more environmental surveillance sensors via a computing device (p. 1, Synthetic aperture radar (SAR) can work day and night regardless of clouds and fog, making it a powerful tool for global wave observation. In addition, the high-resolution and 2-D imaging capabilities of SAR facilitate the acquirement of 2-D wave spectra information);
identifying a plurality of spectra for an unknown material from an anomalous plurality of spectra based on a particle spectra library using a convolutional neural network (CNN) algorithm of the computing device (p. 1, last para. identifying data using a relationship analysis);
clustering similar spectra of the plurality of spectra for an unknown material into a first set of spectra (p. 4);
generating a plurality of synthetic spectra for the unknown material using the first set of spectra using a generative adversarial network (GAN) algorithm of the computing device (p. 1 last 2 para.);
appending the plurality of synthetic spectra for the unknown material as a synthetic entry to the particle spectra library (p. 1);
accumulating acquired spectra for the unknown material when the CNN algorithm identifies the acquired spectra to be the same as the plurality of synthetic spectra of the synthetic entry in the particle spectra library (p. 2, the accumulation of SAR data provides the foundation for the retrieval of ocean wave spectra using deep learning. At present, many researchers are exploring the use of deep learning methods to address and analyze nonlinear oceanic problems);
creating a second set of spectra from the accumulated spectra for the unknown material (p. 7);
validating the second set of spectra against the first set of anomalous spectra (p. 2, 2nd para: validate based on comparison); and
replacing the plurality of the synthetic spectra of the synthetic entry with the second set of spectra in the particle spectra library (p. 2, sect. II).
While Cao discloses anomalies data, Cao does not explicitly disclose an anomalous spectra; however, in the same field of data analysis, Lochner discloses an anomalous spectra (col. 29, lines 22-30). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Lochner into Cao to report back the abnormal information for further evaluation.
Regarding claims 11-18, see discussion of claims 1-5 and 7-9 respectively for the same reason of rejection.
Regarding claim 19, Cao in view of Lochner discloses the system of claim 11, wherein the one or more environmental surveillance sensors comprises a Raman spectroscopy device (Lochner discloses using spectrometry device).
Regarding claim 20, Cao in view of Lochner discloses the system of claim 11, wherein the one or more environmental surveillance sensors comprises a resource effective bioidentification system (REBS) or a next generation resource effective bioidentification system (REBS+) sensor (Lochner discloses similar to fingerprint patterns, skin pore or hair follicle patterns are a unique identifier of each person).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Upadhyay discloses a method for predicting a physiological indicator. US Pub. 2024/0206821.
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 TUANKHANH D PHAN whose telephone number is (571)270-3047. The examiner can normally be reached on Mon-Fri, 10:00am-18:00pm.
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, Boris Gorney can be reached on 571-270-5626. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 or 571-272-1000.
/TUANKHANH D PHAN/Examiner, Art Unit 2154
Prosecution Insights