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 .
Election/Restrictions
Applicant’s election without traverse of Claims 1-11 in the reply filed on 07/28/2026 is acknowledged.
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 07/07/2025 and 07/15/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the WSI data" in Line 7 and Line 9. There is insufficient antecedent basis for this limitation in the claim as it is unclear as to which WSI data is being referred to, the whole slide image data recited in Line 1 or the whole slide image data recited in Line 3, or if they are referring to the same whole slide image data. Examiner suggests amending “whole slide image (WSI) data” in Line 3 to “the whole slide image (WSI) data” and has interpreted the limitation as such.
Examiner notes that Claims 6, 7, and 11 also recite “the WSI data” and would have the same insufficient antecedent basis issue as disclosed above for claim 1. Examiner has interpreted the limitation in the same way as in claim 1.
Claims 2-11 depend on claim 1 and thus are also rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 6, 7, 10, and 11 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ocampo et al. (WO 2022/225995).
With regards to claim 1, Ocampo et al. discloses a method for generating representation data of whole slide image data, the method comprising:
(a) accessing whole slide image (WSI) data with a computer system (Para. 0142 lines 1-3, 0143 lines 1-2, 0162 lines 1-9, "whole slide images" "computer systems");
(b) accessing a machine learning model with the computer system, wherein the machine learning model comprises a generative model trained on training data to generate WSI embeddings from whole slide images (Para. 0165 lines 1-4, 0169 lines 1-7, 0170 lines 1-8, 0243 lines 1-5, 0244 lines 1-4, 0256 lines 1-8, 0257 lines 1-3 and 6-10, 0296 lines 1-10, "variational autoencoders");
(c) inputting the WSI data to the machine learning model, generating WSI representation data as an output, wherein the WSI representation data comprises at least one of WSI embeddings or classifications for the WSI data (Para. 0153 lines 1-10, 0157 lines 1-7, 0161 lines 1-5, "embeddings" "inferences"); and
(d) outputting the WSI representation data via the computer system (Para. 0163 lines 1-7, "output").
With regards to claim 2, Ocampo et al. discloses the method of claim 1, wherein the generative model comprises a variational autoencoder model (Para. 0256 lines 1-8, 0257 lines 1-3 and 6-10, “variational autoencoders”).
With regards to claim 6, Ocampo et al. discloses the method of claim 1, wherein the WSI representation data comprise compact representations of the WSI data (Para. 0256 lines 1-10, 0270 lines 1-2, 0271 lines 1-8 and 13-18, "dimensionality reduction" "compact").
With regards to claim 7, Ocampo et al. discloses the method of claim 6, wherein the compact representations of the WSI data comprise sparse and binary representation of the WSI data (Para. 0256 lines 1-10, 0270 lines 1-2, 0271 lines 1-8 and 13-18, 0295 lines 48-50, "compact").
With regards to claim 10, Ocampo et al. discloses the method of claim 1, wherein outputting the WSI representation data comprises displaying the WSI representation data to a user via the computer system (Para. 0163 lines 1-7, "output" "display").
With regards to claim 11, Ocampo et al. discloses the method of claim 1, wherein the WSI representation data comprise classifications for the WSI data and the generative model has been trained on the training data using at least a primary diagnosis classification loss (Para. 0165 lines 1-4, 0169 lines 1-7, 0170 lines 1-8, 0256 lines 1-8, 0257 lines 1-3 and 6-10, 0296 lines 1-10, "variational autoencoders" "loss").
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.
Claim(s) 3 and 4 are rejected under 35 U.S.C. 103 as being unpatentable over Ocampo et al. (WO 2022/225995) in view of Saldanha et al. (Data augmentation using Variational Autoencoders for improvement of respiratory disease classification).
With regards to claim 3, Ocampo et al. discloses the method of claim 2,
Ocampo et al. does not explicitly teach wherein the variational autoencoder model comprises a conditioned variational autoencoder model that is conditioned on a disease type.
However, Saldanha et al. discloses the concept of using a conditioned variational autoencoder model that is conditioned on a disease type in order to provide for more flexibility of the machine learning model (6.3.3 Conditional VAE: Para. 1 lines 1-7, "Conditional VAE" "one hot label" "control the class").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the concept of using a conditioned variational autoencoder model that is conditioned on a disease type as taught by Saldanha et al. into the method of Ocampo et al. The motivation for this would be to provide for more flexibility of the variational autoencoder model.
With regards to claim 4, the combination of Ocampo et al. and Saldanha et al. discloses the method of claim 3, wherein the disease type is represented by a one-hot encoded vector (Saldanha et al.: 6.3.3 Conditional VAE: Para. 1 lines 1-7, "one hot vector").
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Ocampo et al. (WO 2022/225995) in view of Keighley et al. (Digital pathology whole slide image compression with Vector Quantized Variational Autoencoders).
With regards to claim 5, Ocampo et al. discloses the method of claim 1.
Ocampo et al. does not explicitly teach wherein the generative model has been trained on the training data using at least one of a gradient sparsity loss or a gradient quantization loss.
However, Keighley et al. discloses the concept of using a vector quantized variational autoencoder which uses a gradient quantization loss (2.4 Algorithms: Loss function: Para. 1 lines 1-6, "loss function" "commitment loss") in order to abstract clinically relevant features for faster analysis of the data on the whole slide image (1.2 Related work: Proposal of the VAE: Para. 1 lines 7-11, "VQVAE2" "faster analysis").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the concept of using a vector quantized variational autoencoder which uses a gradient quantization loss as taught by Keighley et al. into the method of Ocampo et al. The motivation for this would be to abstract clinically relevant features for faster analysis of data on whole slide images.
Claim(s) 8 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ocampo et al. (WO 2022/225995) in view of Makarevych et al. (EP 4075325).
With regards to claim 8, Ocampo et al. discloses the method of claim 1, wherein the WSI representation data comprise an aggregated vector (Para. 0295 lines 41-48, "aggregate").
Ocampo et al. does not explicitly teach wherein the WSI representation data comprise a Fisher Vector.
However, Makarevych et al. discloses the concept of aggregating feature data of a whole slide image into one aggregated vector using a Fisher vector in order to allow for a more accurate and reliable system that also uses less computational power (Para. 0015 lines 1-10, 0058 lines 1-6, "Fisher vector" "accurate and reliable").
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to include the concept of aggregating feature data of a whole slide image into one aggregated vector using a Fisher vector as taught by Makarevych et al. into the method of Ocampo et al. The motivation for this would be to allow for a more accurate and reliable system that also uses less computational power.
With regards to claim 9, the combination of Ocampo et al. and Makarevych et al. discloses the method of claim 8, wherein the Fisher Vector is generated based on gradients of image patch embeddings from the WSI representation data (Makarevych et al.: Para. 0015 lines 1-10, 0058 lines 1-6, "patch" "Fisher vector").
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Applicants are directed to consider additional pertinent prior art included on the Notice of References Cited (PTOL 892) attached herewith.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CAROL W CHAN whose telephone number is (571)272-5766. The examiner can normally be reached 9:30-3:30 M-F.
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, Sumati Lefkowitz can be reached at (571) 272-3638. 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.
/CAROL W CHAN/Primary Examiner, Art Unit 2672
Prosecution Insights