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 .
Status of claims: claims 1-6, 10, 12-22 are pending below. Claims 7-9 and 11 are cancelled.
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 allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). 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, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 4/10/2026 has been entered.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/10/2026 was filed and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “feature extraction layers configured to”in claim 21.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 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.
Claims 1-6, 15-18 and 21-22 are rejected under 35 U.S.C. 103 as being unpatentable over MOLERO LEON et al (US 2023/0377747) in view of Colley et al (US 2021/0090694, IDS).
Claim 1:
MOLERO LEON et al (US 2023/0377747) teaches the following subject matter:
A method of, via a deep learning pipeline, generating a deep learning network configured to execute on one or more computers to use medical image data (0243 teach medical imaging data (e.g., including scans from or summaries of one or more: CTs, Mills, PET, or radiography)) to generate predictions of therapeutic responses (figure 8 block 840 and paragraph 0301 teaches prediction generated to treatment administration) to a new treatment in members of a cohort of interest, the cohort of interest comprising candidates for receiving the new treatment in a clinical trial (0002 teaches intelligent selection of treatment strategies (new treatment) for selection, schedule, dosage and activities for particular subject; 0109 teaches clinical administration of treatment), the method comprising:
Training (0011-0014 teaches training using training set of specific-subject data sets matter; 0048 teaches training with different data set), in succession, a plurality of respective deep learning networks from a first deep learning network to a last deep learning network using respective medical image datasets having respective degrees of relevance to the cohort of interest (0149 teaches database with subject matter that satisfy the constraints of patterns and common attributes (having respective degree or relevance to the cohort of interest), where database with central and other cloud-based that are based on user, physician and consults in paragraph 0157, and paragraph 0192 teaches first and second databases. Paragraph 0011 teaches use of one or more machine-learning using database as training set for subject matter (degree or relevance to the cohort of interest), where 0151 teaches machine-learning using deep learning neural network); and
transferring, in succession, learned parameters of one deep learning network of the plurality of respective deep learning networks to another deep learning network of the plurality of respective deep learning networks after training the one deep learning network with a one of the respective medical image datasets and before training the another deep learning network with another medical image dataset of the respective medical image datasets (0271 teaches transfer learning parameter, such as weight of the sub-network and layers of the subject specific AI model, to those of subject-specific AI specific model population (plurality of deep learning network); above teaches various dataset that are cloud-based, user based, physician based, consult based, common attributes based that are selected to be trained on).
MOLERO LEON et al teaches all the subject matter above but not the following:
wherein each deep learning network of the plurality of respective deep learning networks from a second deep learning network to the last deep learning network is trained using supervised learning and comprises a feature extraction network having feature extraction layers configured to generate one or more feature vectors, a classification network, and an attention network, and
for each of the second deep learning network to the last deep learning network, combining output of the feature extraction layers and output of the attention network to provide a combined output to the classification network, by multiplying each feature vector by a corresponding attention value for each feature vector obtained from data corresponding to a particular medical image; and generating a summarized feature vector summarizing the results of combining outputs corresponding to the particular medical image.
Colley et al (US 2021/0090694, IDS) teaches the following subject matter:
wherein each deep learning network of the plurality of respective deep learning networks from a second deep learning network to the last deep learning network is trained using supervised learning and comprises a feature extraction network having feature extraction layers configured to generate one or more feature vectors, a classification network, and an attention network (paragraph 1666 detail supervised machine learning for extracting feature for classification), and
for each of the second deep learning network to the last deep learning network, combining output of the feature extraction layers and output of the attention network to provide a combined output to the classification network, by multiplying each feature vector by a corresponding attention value for each feature vector obtained from data corresponding to a particular medical image; and generating a summarized feature vector summarizing the results of combining outputs corresponding to the particular medical image (paragraph 2561 and figure 325 and paragraph 2767 detail combined data for output from multiple predicting of the imaging features machine learning framework 14702 result in score the sample data).
MOLERO LEON et al and Colley et al are both in the field of image analysis, especially use of machine learning to generate prediction of response to new treatment such that combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify MOLERO LEON et al by Colley et al to normalized and corrected RNA seq data neural network layer(s) with imaging feature neural network layer(s) to produce an integrated neural network output that can be used with a prediction function to produce an immune infiltration score for sample data as disclosed by Colley et al in paragraph 2767.
Claim 2:
MOLERO LEON et al teach:
The method of claim 1 wherein the respective degrees of relevance to the cohort of interest increase from a first respective medical image dataset to a last respective medical image dataset used in training the respective deep learning networks (0149 teaches database with subject matter that satisfy the constraints of patterns and common attributes (having respective degree or relevance to the cohort of interest), where database with central and other cloud-based that are based on user, physician and consults in paragraph 0157).
Claim 3:
MOLERO LEON et al teach:
The method of claim 2 wherein the first medical image dataset is significantly larger than the last medical image dataset (0149 teaches database with subject matter that satisfy the constraints of patterns and common attributes (having respective degree or relevance to the cohort of interest), where database with central and other cloud-based that are based on user, physician and consults in paragraph 0157, and paragraph 0192 teaches first and second databases. Where 0052-0053 detail stores of communicating data with large population due to performing operation, care, alerting of provider. One ordinary skill in the art reading paragraph 0052-0053, 0149 and 0157 would come to the conclusion that databases are dynamic and every growing larger against each other.).
Claim 4:
MOLERO LEON et al teach:
The method of claim 1 wherein a first medical image dataset of the respective medical image datasets comprises unlabeled medical image data (0177 teaches subject record maybe label by user (unlabeled);0200 teaches training set may be label (unlabeled); 0289 teaches classifier trained with input data with not corresponding label (unlabeled)).
Claim 5:
MOLERO LEON et al teach:
The method of claim 4 wherein the first deep learning network comprises a feature extraction network (0308 teaches neural network for feature vector using classifier machine learning; 0316 teaches feature vector for machine-learning model) and a contrastive learning module (0307 teaches contrast encoding (contrastive learning)).
Claim 6:
MOLERO LEON et al teach:
The method of claim 5 wherein the first deep learning network further comprises a projection network configured to receive feature vectors from the feature extraction network and to provide feature vectors to the contrastive learning module (above teaches feature consideration in paragraph 0308 and 0316, and contrast in paragraph 0307, where together is part of prediction generation in paragraph 0301).
Claim 15:
MOLERO LEON et al teach:
The method of claim 1 wherein the respective medical image datasets comprise computerized tomography (CT) scan data (0243 teach medical imaging data (e.g., including scans from or summaries of one or more: CTs).
Claim 16:
MOLERO LEON et al teach:
The method of claim 15 further comprising pre-processing the respective medical image datasets to obtain pre-processed two-dimensional slices of the CT scan data wherein the pre-processed two dimensional slices include slices 38corresponding to one or more of axial views, coronal views, and sagittal views, wherein: a pre-processed two-dimensional slice of the pre-processed two dimensional slices corresponds to one of a full axial view, a full coronal view, or a full sagittal view; and the pre-processed two-dimensional slices collectively include at least some axial views, at least some sagittal views, and at least some coronal views (0243 teaches CTs images where CT includes all these computed slices and views, where computed tomography is computer generated and can generate all points of view).
Claim 17:
MOLERO LEON et al teach:
The method of claim 15 further comprising pre-processing the respective medical image datasets to obtain pre-processed two-dimensional slices of the CT scan data wherein the pre-processed two dimensional slices include slices corresponding to one or more of axial views, coronal views, and sagittal views wherein: a pre-processed two-dimensional slice of the pre-processed two dimensional slices corresponds to a tile portion of a full axial view, a full coronal view, or a full sagittal view; and for each full axial view, full coronal view, and full sagittal view the pre- processed two-dimensional slices collectively include a plurality of tile portions (0243 teaches CTs images where CT includes all these computed slices and views, where computed tomography is computer generated and can generate all points of view).
Claim 18:
MOLERO LEON et al teach:
The method of claim 15 further comprising pre-processing the respective medical image datasets to obtain pre-processed two-dimensional slices of the CT scan data wherein the pre-processed two dimensional slices include slices corresponding to one or more of axial views, coronal views, and sagittal views,wherein pre-processing comprises one or more of space adjustment, clipping, and normalizing (0243 teaches CTs images where CT includes all these computed slices and views; 0272 teaches pre-processing function may (for example) normalize, standardize, encode, categorize, filter and/or otherwise process data before feeding the data to the AI model(s)).
Claim 21:
MOLERO LEON et al (US 2023/0377747) teaches the following subject matter:
A computer system (figure 7 teaches a system) comprising:
a deep learning pipeline comprising one or more computers coupled to a non- transitory computer readable medium (paragraph 0028-0030 teaches use of non-transitory) storing instructions that are executable by one or more processors of the one or more computers for training, in succession, a plurality of respective deep learning networks using respective medical image datasets (0243 teach medical imaging data (e.g., including scans from or summaries of one or more: CTs, Mills, PET, or radiography)), each respective medical image dataset having a respective degree of relevance to a cohort of interest (0149 teaches database with subject matter that satisfy the constraints of patterns and common attributes (having respective degree or relevance to the cohort of interest), where database with central and other cloud-based that are based on user, physician and consults in paragraph 0157, and paragraph 0192 teaches first and second databases), wherein training comprises:
training a first deep learning network of the plurality of respective deep learning networks with one medical image dataset of the respective medical image datasets (0011-0014 teaches training using training set of specific-subject data sets matter; 0048 teaches training with different data set);
transferring a plurality of learned parameters of the first deep learning network to a second deep learning network of the plurality of 40respective deep learning networks (0271 teaches transfer learning parameter, such as weight of the sub-network and layers of the subject specific AI model, to those of subject-specific AI specific model population (plurality of deep learning network); above teaches various dataset that are cloud-based, user based, physician based, consult based, common attributes based that are selected to be trained on); and
training the second deep learning network with another medical image dataset of the respective medical image datasets (above teaches transfer to train subject-specific AI specific model population (plurality of deep learning network/second DNN)).
MOLERO LEON et al teaches all the subject matter above but not the following:
wherein each deep learning network of the plurality of respective deep learning networks from a second deep learning network to the last deep learning network is trained using supervised learning and comprises a feature extraction network having feature extraction layers configured to generate one or more feature vectors, a classification network, and an attention network, and
for each of the second deep learning network to the last deep learning network, combining output of the feature extraction layers and output of the attention network to provide a combined output to the classification network, by multiplying each feature vector by a corresponding attention value for each feature vector obtained from data corresponding to a particular medical image; and generating a summarized feature vector summarizing the results of combining outputs corresponding to the particular medical image.
Colley et al (US 2021/0090694, IDS) teaches the following subject matter:
wherein each deep learning network of the plurality of respective deep learning networks from a second deep learning network to the last deep learning network is trained using supervised learning and comprises a feature extraction network having feature extraction layers configured to generate one or more feature vectors, a classification network, and an attention network (paragraph 1666 detail supervised machine learning for extracting feature for classification), and
for each of the second deep learning network to the last deep learning network, combining output of the feature extraction layers and output of the attention network to provide a combined output to the classification network, by multiplying each feature vector by a corresponding attention value for each feature vector obtained from data corresponding to a particular medical image; and generating a summarized feature vector summarizing the results of combining outputs corresponding to the particular medical image (paragraph 2561 and figure 325 and paragraph 2767 detail combined data for output from multiple predicting of the imaging features machine learning framework 14702 result in score the sample data).
MOLERO LEON et al and Colley et al are both in the field of image analysis, especially use of machine learning to generate prediction of response to new treatment such that combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify MOLERO LEON et al by Colley et al to normalized and corrected RNA seq data neural network layer(s) with imaging feature neural network layer(s) to produce an integrated neural network output that can be used with a prediction function to produce an immune infiltration score for sample data as disclosed by Colley et al in paragraph 2767.
Claim 22:
MOLERO LEON et al teach:
The computer system of claim 21 wherein training further comprises, from a next deep learning network of the plurality of deep learning networks to a last deep learning network of the plurality of respective deep learning networks:
training, in succession, respective ones of the plurality of respective deep learning networks using respective medical image datasets having respective degrees of relevance to the cohort of interest (0011-0014 teaches training using training set of specific-subject data sets matter);
transferring, in succession, learned parameters of one deep learning network of the plurality of respective deep learning networks to another deep learning network of the plurality of respective deep learning networks after training the one deep learning network with a one of the respective medical image datasets and before training the another deep learning network with another medical image dataset of the respective medical image datasets (0271 teaches transfer learning parameter, such as weight of the sub-network and layers of the subject specific AI model, to those of subject-specific AI specific model population (plurality of deep learning network); above teaches various dataset that are cloud-based, user based, physician based, consult based, common attributes based that are selected to be trained on).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over MOLERO LEON et al (US 2023/0377747) in view of Colley et al (US 2021/0090694, IDS) as applied to claim 1 above, and further in view of Abrams et al (US 2022/0022800).
Claim 10:
MOLERO LEON et al and Colley et al teaches all the subject above, but the following is taught by Abrams et al:
The method of claim 8 wherein the attention network comprises one or more fully-connected layers configured to generate an attention value for each feature vector (0084 teaches attention learning for state, where state and mark for feature extraction).
MOLERO LEON et al and Colley et al and Abrams et al are in the field of image analysis especially using machine learning for transfer learning regarding prediction of treatment (Abrams et al teach in 0162 predict treatment in response to treatment using AI model) such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify MOLERO LEON et al and Colley et al by Abrams et al for the use of attention network such would improve DNN performance as disclosed by Abrams et al in paragraph 0162.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over MOLERO LEON et al (US 2023/0377747) in view of Colley et al (US 2021/0090694, IDS) as applied to claim 15 above, and further in view of Zhou et al (US 2021/0326653).
Claim 19:
MOLERO LEON et al and Colley et al teaches all the subject, pre-processing the respective medical image datasets to obtain pre-processed two-dimensional slices of the CT scan data wherein the pre-processed two dimensional slices include slices corresponding to one or more of axial views, coronal views, and sagittal views, above, but the following is taught by Zhou et al:
The method of claim 15 further comprising, wherein pre-processing comprises clipping and wherein clipping comprises setting upper and lower limits of a Hounsfield Unit (HU) intensity value (0087 teaches use of Hounsfield intensity clipping).
MOLERO LEON et al and Colley et al and Zhou et al are in the field of image analysis especially for CT image processing such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify MOLERO LEON et al and Colley et al by Zhou et al using Hounsfield Unit (HU) intensity of clipping directly to normalize as disclosed by Zhou et al, where normalizing help with viewing the dataset within a range for analysis.
Claim 20:
Zhou et al teach:
The method of claim 19 wherein, for CT scans having HU intensity values ranging from -3000 to +3000, clipping comprises applying a lower HU intensity value limit of -1000 and an upper HU intensity value limit of 400 (0287 teaches range of [-1000,1000]).
Allowable Subject Matter
Claims 12-14 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims. At the time of examination unable to find claim limitations, integrating elements, concept of claims 12 and 13.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
VODENCAREVIC et al (US 2021/0097439) teach METHOD AND SYSTEM FOR SCALABLE AND DECENTRALIZED INCREMENTAL MACHINE LEARNING WHICH PROTECTS DATA PRIVACY – 0221 teaches detecting lung cancer form medical image data or predicting treatment responses from combined image and laboratory data, updating to machine learning algorithms.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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