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
This action is in reply to the amendments filed on 06/30/2026.
Claims 1 and 13 were amended. Claims 6, 9 and 18 were cancelled.
Claims 1-5, 7-8, 10-17 and 19-20 are currently pending and have been examined.
Priority
Applicant’s claim to priority retrieved on March 18, 2025 is acknowledged.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the "right to exclude" granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Omum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321 (c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
Claims 1-5, 7-8, 10-17 and 19-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-18 of U.S. Patent No. 12,266,447.
Although the conflicting claims are not identical, they are not patentably distinct from each other because claims 1-5, 7-8, 10-17 and 19-20 of the instant pending application omit certain steps of claims 1-18 in patent 12,266,447 because it would have been obvious to omit certain steps with the motivation of providing a system and method for generating medical prediction related to biomarker from medical data.
Claim Rejections – 35 § 112(b)
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 1-5, 7-8, 10-17 and 19-20 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 “determining a region of interest in the first medical data”. It is unclear how a determination is made with regard to a region of interest in the first medical data. Is the determination being made by an algorithm or a formula? Claim 13 recites similar limitations. Claims 1 and 13 are therefore found to be indefinite, because the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-12 and 14-20, are rejected for at least the same reason.
Claim 1 recites “extracting one or more second features associated with the second medical data from region of the second medical data corresponding to the region of interest of the first medical data”. It is unclear how one or more second features associated with the second medical data from region of the second medical data corresponding to the region of interest of the first medical data is extracted. Is the extraction of one or more second features associated with the second medical data from region of the second medical data being performed using an algorithm or a formula being applied to the region of interest of the first medical data? How does the region of interest of the first medical data affect or constrain the step/action of extracting one or more second features associated with the second medical data from region of the second medical data? Claim 13 recites similar limitations. Claims 1 and 13 are therefore found to be indefinite, because the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-12 and 14-20, are rejected for at least the same reason.
Claim 1 recites “generating a medical prediction based on the one or more first features and the one or more second features”. It is unclear how a medical prediction based on the one or more first features and the one or more second features is generated. Is the medical prediction based on the one or more first features and the one or more second features being generated using an algorithm or a formula? Claim 13 recites similar limitations. Claims 1 and 13 are therefore found to be indefinite, because the resulting claim does not clearly set forth the metes and bounds of the patent protection desired. All dependent claims, namely claims 2-12 and 14-20, are rejected for at least the same reason.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-5, 7-8, 10-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-5, 7-8, 10-17 and 19-20: Step 1
Claims 1-5, 78 and 10-12 are drawn to a method performed by at least one processor for generating a medical prediction related to a biomarker from medical data, which is within the four statutory categories (i.e. process). Claims 13-17 and 19-20 are drawn to an information processing system, which is within the four statutory categories (i.e. machine).
Claims 1-5, 7-8, 10-17 and 19-20: Step 2A Prong One
Claim 1 recites obtaining first medical data and second medical data belonging to categories different from each other, determining a region of interest in the first medical data, extracting one or more first features associated with the first medical data based on the region of interest, extracting one or more second features associated with the second medical data from a region of the second medical data corresponding to the region of interest of the first medical data, generating a medical prediction based on the one or more first features and the one or more second features, and outputting the medical prediction, wherein the first medical data comprises radiographic image data and the second medical data comprises at least one of tissue image data or genomic data. Claim 13 recites similar limitations.
These limitations, as drafted, given the broadest reasonable interpretation, but for the recitation of generic computer components, encompass managing personal behavior by manually following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. But for the recitation of generic computer components, these limitations encompass a user obtaining first medical data and second medical data belonging to categories different from each other, determining a region of interest in the first medical data, extracting one or more first features associated with the first medical data based on the region of interest, extracting one or more second features associated with the second medical data from a region of the second medical data corresponding to the region of interest of the first medical data, generating a medical prediction based on the one or more first features and the one or more second features, and outputting the medical prediction. These steps could be carried out manually by a user following rules or instructions, which is a subgrouping of Certain Methods of Organizing Human Activity. Claim 13 recites similar limitations.
Claims 2-5, 7-8, 10-12, 14-17 and 19-20 incorporate the abstract idea identified above and recite additional limitations that expand on the abstract idea, but for the recitation of generic computer components. For example, but for the recitation of generic computer components, Claims 2 and 14 further define the region of interest. Claims 3, 4, 15 and 16 further define determining the region of interest. Claims 5 and 17 further define extracting the one or more first features. Claim 17 further defines extracting the one or more second features. Claim 7 further defines classifying the categories. Claims 8, 10, 11 and 19 further define generating the medical prediction. Claim 20 further defines the first medical data and the second medical data. Claim 12 further defines indicating on the medical data. Therefore, these claims are similarly drawn to Certain Methods of Organizing Human Activity.
Claims 1-5, 7-8, 10-17 and 19-20: Step 2A Prong Two
This judicial exception is not integrated into a practical application because the remaining elements amount to no more than general purpose computer components programmed to perform the abstract ideas along with insignificant, extra-solution data gathering activity, and adding limitations similar to adding the words “apply it” to the abstract idea. Claim 1 recites the additional elements that the method steps are performed by at least one processor. Claim 13 recites additional elements of an information processing system comprising a memory and a processor.
Claims 1-5, 7-8, 10-17 and 19-20, directly or indirectly, recite the following generic computer components: “method steps are performed by at least one processor,” and “An information processing system comprising a memory storing one or more instructions and a processor configured to execute the one or more instructions” which are similar to adding the words “apply it” to the abstract idea. The written description does not appear to further describe the computer hardware or the computer program, but rather repeats the recitation from the claim limitations themselves. The written description discloses that the recited computer components encompass generic components including “The “processor” should be interpreted broadly to encompass a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, and so forth. Under some circumstances, the “processor” may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field-programmable gate array (FPGA), and so on“ (see at least Paragraph [0043]) and “In addition, the “memory” should be interpreted broadly to encompass any electronic component capable of storing electronic information. The “memory” may refer to various types of processor-readable media such as random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, and so on” (see at least Paragraph [0043]). See MPEP 2106.05 (h). As set forth in the 2019 Eligibility Guidance, 84 Fed. Reg. at 55 “merely include[ing] instructions to implement an abstract idea on a computer” is an example of when an abstract idea has not been integrated into a practical application.
Claims 1-5, 7-8, 10-17 and 19-20: Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above with respect to integration into a practical application, the additional elements are recited at a high level of generality, and the written description indicates that these elements are generic computer components. Using generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patent-ineligible abstract idea into a patent-eligible invention.”). As explained above, the generic computer components and machine learning are at best the equivalent of merely adding the words “apply it” to the judicial exception.
Receiving and transmitting data over a network (i.e. receiving and communicating data or signals) has been recognized as well-understood, routine, and conventional activity of a general-purpose computer (see MPEP 2106.05(d) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014)).
Gathering and analyzing information using conventional techniques and displaying the result has also been found to be insufficient to show an improvement to technology, (see MPEP 2106.05(a) and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48).
Insignificant, extra solution, data gathering activity has been found to not amount to significantly more than an abstract idea (see MPEP 2106.05(g) and Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354-55, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016)). Therefore, the high-level recitation of an output of results also fails to include additional elements that are sufficient to amount to significantly more than the judicial exception.
Therefore, whether considered alone or in combination, the additional elements do not amount to significantly more than the abstract idea.
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 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 of this title, if the differences between the claimed invention and the prior art axe 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-5, 7-8, 10-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over WIPO Patent Application Publication WO 2019/191123 A1 to Theesfeld et al. in view of European Patent Application Publication EP 3 293 736 A1 to Zhou et al. and further in view of U.S. Patent Application Publication U.S. 2012/0290324 A1 to Ribbing et al.
Claim 1:
Theesfeld discloses the following limitations as shown below:
obtaining first medical data and second medical data belonging to categories different from each other (see at least Fig. 4, Ele. 401, obtain genetic sequence data of a biological sample; Paragraph 8, genetic data is obtained from a biological sample; Paragraph 166);
determining a region of interest in the first medical data (see at least Fig. 2, Ele. 205; Fig. 4, Ele. 403; Paragraph 8, The genetic data includes at least one gene sequence. The expression level of the at least gene is determined utilizing a computational framework. The computational framework utilizes the genetic data to determine epigenetic regulatory features spatially along a genetic sequence that includes the at least one gene sequence. The computational framework determines the expression level of the at least one gene based on the epigenetic regulatory features along the genetic sequence that includes the at least one gene sequence. A biochemical assay to assess the biological sample is performed based on the determination of the expression level of the at least one gene; Paragraph 145; Paragraph 166);
extracting one or more first features associated with the first medical data based on the region of interest (see at least Fig. 1, Ele. 103; Fig. 2, Ele. 205; Fig. 4, Ele. 405, 407; Fig. 5, Ele. 501; Paragraph 8, The computational framework utilizes the genetic data to determine epigenetic regulatory features spatially along a genetic sequence that includes the at least one gene sequence. The computational framework determines the expression level of the at least one gene based on the epigenetic regulatory features along the genetic sequence that includes the at least one gene sequence; Paragraph 15, determines the epigenetic regulatory features spatially along the genetic sequence by considering single nucleotide variants, insertions, and deletions within the genetic sequence; Paragraph 137; Paragraph 145; Paragraph 166; Paragraph 172);
extracting one or more second features associated with the second medical data (see at least Paragraphs 148-149, for each gene that is analyzed, epigenetic regulatory features are determined in relation to a sequence structure of the gene, such as (for example) the transcription start site (TSS), known promoter region, or similar [second features]. [ ... ] [For example,] epigenetic features are determined for 20kb upstream and downstream of the TSS of a gene more preferably (claim 7) including performing normalization of each of the one or more first features and the one or more second features; combining the normalized one or more first features and the normalized one or more second features to generate one or more third features; and generating the medical prediction for the patient based on the generated one or more third features; Paragraph 242, normalization of luminescence feature)
generating a medical prediction based on the one or more first features and the one or more second features (see at least Paragraphs 148-149, for each gene that is analyzed, epigenetic regulatory features are determined in relation to a sequence structure of the gene, such as (for example) the transcription start site (TSS), known promoter region, or similar [second features]. [ ... ] [For example,] epigenetic features are determined for 20kb upstream and downstream of the TSS of a gene more preferably (claim 7) including performing normalization of each of the one or more first features and the one or more second features; combining the normalized one or more first features and the normalized one or more second features to generate one or more third features; and generating the medical prediction for the patient based on the generated one or more third features; Paragraph 242, normalization of luminescence feature); and
outputting the medical prediction (see at least Fig. 6; Paragraphs 148-149, Paragraph 178, computer systems (601) may include an input/output interface (605) that can be utilized to communicate with a variety of devices, including but not limited to other computing systems, a projector, and/or other display devices),
Theesfeld may or may not disclose the following limitations, but Zhou as shown also discloses the following limitations:
medical data belonging to categories different from each other (see at least Paragraph 37, Where more than one frame is acquired, the different frames may represent different contrasts; Paragraph 38, Where more than one frame is acquired, the different frames may represent different types of measures (multi-modal or multi-parametric frames of data). By configuring the medical scanner, different types of measurements of the tissue may be performed. For example in magnetic resonance, both anatomical and functional measurements are performed. As another example in magnetic resonance, different anatomical or different functional measurements are performed; Paragraph 54, Other features may be added to the fully connected (FC) layers, such as non-imaging or clinical information ; Paragraph 60, Additional information may be used for extracting and/or classifying. For example, values of clinical measurements for the patient are used. The classifier is trained to classify based on the extracted values for the features in the frames of data as well as the additional measurements. Genetic data, blood-based diagnostics, family history, sex, weight, and/or other information are input as a feature for classification)
determining a region of interest in the first medical data (see at least Paragraph 6, claim 1, the scanning providing multiple frames of data representing a tissue region of interest in the patient; Paragraph 45, the data is extracted from the frame for further processing);
extracting one or more first features associated with the first medical data based on the region of interest (see at least Paragraph 6, claim 1, extracting, by a processor, values for features from the frames of data; Paragraph 46, a processor extracts values for features)
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld with Zhou with the motivation of providing the benefit of “… allowing earlier termination or alteration of the therapy …” as a result of “using machine-learnt classification, the number and time between measures after treatment may be reduced … for predicting the outcome of the treatment” (Zhou, see at least Paragraph 5).
Theesfeld may or may not disclose the following limitations, but Ribbing as shown also discloses the following limitations:
extracting one or more second features associated with the second medical data from a region of the second medical data corresponding to the region of interest of the first medical data (see at least Paragraph 27, Relevant features may be provided by medical images, such as distances between organs within a region of interest (e.g., the distance between rectum, bladder or prostate), or organ-specific dimensions such as intestinal wall thickness, prostate diameter, prostate volume, or so forth; Paragraph 35, In sum, the system of FIG. 1 extracts features from images and non-image medical data such as IVD data or mass spectral data, and creates datasets with definition of normal and disease slate multidimensional volumes. A feature set of a new patient is created and compared to site IVD and imaging features of tire database. We suggest to combine the IVD features with the orthogonal image features for each patient. In one embodiment, the results are co-displayed on a screen to assist diagnosis of the doctor; Paragraph 48, illustrative example is set forth using radiation therapy for treating prostate cancer as the illustrative medical condition. Here, image features may include: the distance between critical organs such as the rectum, bladder or prostate; organ-specific dimensions such as intestinal wall thickness, prostate diameter, prostate volume; or so forth. Relevant non-image features may be extracted from mass spectrometry measurements 20 or IVD tests 22. Image-based features that are expected to correlate with tumor response to the radiation therapy include, for example: lesion volume; lesion metabolism and proliferation as assessed by FDG-PET or other functional imaging; cellular integrity as assessed for instance by diffusion-weighted MR; or so forth. Many of these features are also expected to correlate with inflammation and radiation toxicity of healthy tissue in the critical organs. Predictive image-derived features of tumor response include: sizes/volumes; dose-volume histograms (DVHs); morphological features like textures; surface structure regularity or irregularity; and so forth. Molecular features of interest for prostate cancer therapy include extend of hypoxia as assessed for example by FMISO-PET. To determine such features, image processing algorithms can be applied, such as volume delineation techniques and frequency analysis. Dynamic imaging of contrast agents provides slopes and, in case of pharmacokinetic modelling, rate constants of tracer biodistributions);
wherein the first medical data comprises radiographic image data and the second medical data comprises at least one of tissue image data or genomic data (see at least Paragraph 22, acquire proteomic data or other mass spectrometric data or data derived from mass spectra; and an in vitro diagnostic facility 22 configured to acquire in vitro diagnostic (IVD) data such as proteomic or genomic molecular information from drawn blood or other (fluid) samples, and including histological data and so forth; Paragraph 27, Relevant features may be provided by medical images, such as distances between organs within a region of interest (e.g., the distance between rectum, bladder or prostate), or organ-specific dimensions such as intestinal wall thickness, prostate diameter, prostate volume, or so forth. Relevant features may also be provided by non-image medical data).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Zhou with Ribbing with the motivation of providing the benefit of “… facilitating joint data analysis in order to obtain information from synergistic combinations of image and non-image medical data” (Ribbing, see at least Paragraph 13).
Claim 13 recites substantially similar system limitations to those of method claim 1 and, as such, is rejected for similar reasons as given above.
Claim 2:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
wherein the region of interest includes all of a plurality of pixels associated with the first medical data (see at least Paragraph 28; Paragraph 38, anatomical measurements; Paragraphs 41-43, The tissue of interest is identified as a region around and/or including the tumor. For example, a box or other shape that includes the tumor is located; Paragraph 45, The tumor tissue with or without surrounding tissue is segmented. The data is extracted from the frame 35 for further processing. The pixel or voxel values for the region of interest are isolated; Paragraphs 49-50, A feature extraction module computes features from images to better capture essential subtleties related to cell density, vasculature, necrosis, and/or hemorrhage that are important to clinical diagnosis or prognosis of tissue. The values of the features are used for classification to provide outcomes).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Claim 14 recites substantially similar system limitations to those of method claim 2 and, as such, is rejected for similar reasons as given above.
Claim 3:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
wherein the determining the region of interest includes determining the region of interest to extract at least one of an anatomical feature, a geometric feature, or a histological feature from the first medical data (see at least Paragraph 28; Paragraph 38, anatomical measurements; Paragraphs 41-43, The tissue of interest is identified as a region around and/or including the tumor. For example, a box or other shape that includes the tumor is located; Paragraph 45, The tumor tissue with or without surrounding tissue is segmented. The data is extracted from the frame 35 for further processing. The pixel or voxel values for the region of interest are isolated; Paragraphs 49-50, A feature extraction module computes features from images to better capture essential subtleties related to cell density, vasculature, necrosis, and/or hemorrhage that are important to clinical diagnosis or prognosis of tissue. The values of the features are used for classification to provide outcomes).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Claim 15 recites substantially similar system limitations to those of method claim 3 and, as such, is rejected for similar reasons as given above.
Claim 4:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
includes determining the region of interest in the first medical data by using a feature extraction model that is trained to extract at least one of the anatomical feature, the geometric feature, or the histological feature from the first medical data (see at least Paragraph 42, The identification is performed by the user. … Alternatively, a processor automatically identifies the tissue region of interest without user selection; Paragraphs 51-52, a method is advantageously [provided], wherein extracting comprises extracting the values for the features with the features comprising deep-learnt features from deep learning, and wherein classifying comprises classifying by the machine-learnt classifier learnt with the deep learning).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Claim 16 recites substantially similar system limitations to those of method claim 4 and, as such, is rejected for similar reasons as given above.
Claim 5:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
detecting at least one of one or more target items and one or more factors included in the region of interest as the one or more first features (see at least Fig. 2, Ele. 205; Fig. 4, Ele. 403; Paragraph 8, The genetic data includes at least one gene sequence. The expression level of the at least gene is determined utilizing a computational framework. The computational framework utilizes the genetic data to determine epigenetic regulatory features spatially along a genetic sequence that includes the at least one gene sequence. The computational framework determines the expression level of the at least one gene based on the epigenetic regulatory features along the genetic sequence that includes the at least one gene sequence. A biochemical assay to assess the biological sample is performed based on the determination of the expression level of the at least one gene; Paragraph 145; Paragraph 166); and
Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
outputting information associated with at least one of the one or more target items and the one or more factors, wherein the one or more target items include at least one of cancer cells, immune cells, fibroblasts, lymphocytes, plasma cells, macrophage, endothelial cells, cancer areas, cancer stroma areas, tertiary lymphoid structure, normal region, necrosis, fat, blood vessel, high endothelial venule, lymphatic vessel, or nerve, and the one or more factors include at least one of mutations in DNA, gene expression values corresponding to RNA, epigenetic factors, expression values of proteomic bodies, ormicrobiome existing in a body (see at least Paragraph 6; claim 1, transmitting the therapy response; Paragraph 44, In response to the selection, the computer then returns the predicted success of treatment in an automated report. [ ... ] The treatment outcome or other classification is determined via a single-click on each image or frame of data without the need to perform any manual and/or automatic segmentation. A "single-click" or simple user input is provided for tumor diagnosis, treatment planning, and/or treatment response assessment; Paragraph 69, Information to enhance therapy monitoring, such as an intensity histogram, is output).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Claim 17 recites substantially similar system limitations to those of method claim 5 and, as such, is rejected for similar reasons as given above.
Claim 6:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
wherein the extracting the one or more second features includes extracting the one or more second features from a region of the second medical data corresponding to the region of interest of the first medical data (see at least Paragraph 37, Where more than one frame is acquired, the different frames may represent different contrasts; Paragraph 38, Where more than one frame is acquired, the different frames may represent different types of measures (multi-modal or multi-parametric frames of data). By configuring the medical scanner, different types of measurements of the tissue may be performed. For example in magnetic resonance, both anatomical and functional measurements are performed. As another example in magnetic resonance, different anatomical or different functional measurements are performed; Paragraph 54, Other features may be added to the fully connected (FC) layers, such as non-imaging or clinical information ; Paragraph 60, Additional information may be used for extracting and/or classifying. For example, values of clinical measurements for the patient are used. The classifier is trained to classify based on the extracted values for the features in the frames of data as well as the additional measurements. Genetic data, blood-based diagnostics, family history, sex, weight, and/or other information are input as a feature for classification).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Claim 18 recites substantially similar system limitations to those of method claim 6 and, as such, is rejected for similar reasons as given above.
Claim 7:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
wherein the categories are classified based on at least one of data type, associated disease, associated region, data generation time, or data generation method (see at least Paragraph 164, In a number of embodiments, gene expression levels and/or computational models are used in a number of downstream applications, including (but not limited to) clinical classification of biological tissue (e.g., clinical diagnostics), further molecular research into gene expression level including evolutionary, and site-directed).
Claim 19 recites substantially similar system limitations to those of method claim 7 and, as such, is rejected for similar reasons as given above.
Claim 8:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
performing normalization of each of the one or more first features and the one or more second features (see at least Paragraphs 148-149, for each gene that is analyzed, epigenetic regulatory features are determined in relation to a sequence structure of the gene, such as (for example) the transcription start site (TSS), known promoter region, or similar [second features]. [ ... ] [For example,] epigenetic features are determined for 20kb upstream and downstream of the TSS of a gene more preferably (claim 7) including performing normalization of each of the one or more first features and the one or more second features; combining the normalized one or more first features and the normalized one or more second features to generate one or more third features; and generating the medical prediction for the patient based on the generated one or more third features; Paragraph 242, normalization of luminescence feature);
combining the normalized one or more first features and the normalized one or more second features to generate one or more third features (see at least Paragraphs 148-149, for each gene that is analyzed, epigenetic regulatory features are determined in relation to a sequence structure of the gene, such as (for example) the transcription start site (TSS), known promoter region, or similar [second features]. [ ... ] [For example,] epigenetic features are determined for 20kb upstream and downstream of the TSS of a gene more preferably (claim 7) including performing normalization of each of the one or more first features and the one or more second features; combining the normalized one or more first features and the normalized one or more second features to generate one or more third features); and generating the medical prediction for the patient based on the generated one or more third features); and
generating the medical prediction based on the one or more third features (see at least Paragraphs 148-149, for each gene that is analyzed, epigenetic regulatory features are determined in relation to a sequence structure of the gene, such as (for example) the transcription start site (TSS), known promoter region, or similar [second features]. [ ... ] [For example,] epigenetic features are determined for 20kb upstream and downstream of the TSS of a gene more preferably (claim 7) including performing normalization of each of the one or more first features and the one or more second features; combining the normalized one or more first features and the normalized one or more second features to generate one or more third features).
Claim 20 recites substantially similar system limitations to those of method claim 8 and, as such, is rejected for similar reasons as given above.
Claim 9:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
wherein at least one of the first medical data and the second medical data includes at least one of medical image data related to medical imaging, tissue image data, genomic data, or biological data (see at least Paragraph 17; Paragraph 148; Paragraph 172, refer to genomic data, which is biological data).
Claim 10:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
wherein the generating the medical prediction includes generating a prediction result for at least one of a treatment method, a therapeutic drug, or a duration of treatment related to a patient's disease (see at least Paragraph 130; Paragraph 174, Resulting changes in expression can affect diagnostics and treatments; Paragraphs 188-190, Based on the gene expression data, an individual can be treated with various medications and therapeutic regimens; Paragraphs 201-204, altering treatments of individuals based on their variants that affect expression of genes involved with drug metabolism. (d) Based on metabolism results, administer an appropriate dose of the medication or administer an alternative medication. Claims 21, 40, 41: method to treat an individual, or to alter medication treatment for an individual).
Claim 11:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld further discloses the following limitations:
wherein the generating the medical prediction includes generating a prediction result for at least one of a therapeutic responsiveness of a patient or a survival rate of the patient for at least one of a specific treatment method or a specific therapeutic drug (see at least Paragraph 131, response to medications; Paragraph 203, (c) based on the effect of variants on gene expression levels, determine the ability of an individual to metabolize a medication. Claims 60-62: for an individual determined by the method of D2 to have a reduced/an increased ability to metabolize the therapeutic, lowering/increasing the dose of a therapeutic, or administering an alternative therapeutic; list of specific therapeutic drugs).
Claim 12:
The combination of Theesfeld/Zhou/Ribbing discloses the limitations as shown in the rejections above. Theesfeld may or may not disclose the following limitations, but Zhou as shown does:
indicating at least one of the region of interest, the one or more first features, the one or more second features, or the medical prediction on the medical data (see at least Paragraph 6; claim 1, transmitting the therapy response; Paragraph 44, In response to the selection, the computer then returns the predicted success of treatment in an automated report. [ ... ] The treatment outcome or other classification is determined via a single-click on each image or frame of data without the need to perform any manual and/or automatic segmentation. A "single-click" or simple user input is provided for tumor diagnosis, treatment planning, and/or treatment response assessment; Paragraph 69, Information to enhance therapy monitoring, such as an intensity histogram, is output).
At the time of the filing of the application it would have been obvious to one of ordinary skill in the art to combine the teaching of Theesfeld and Ribbing with Zhou with the motivation for at least the same reasons given for claim 1.
Response To Arguments
Applicant’s arguments from the response filed on 06/30/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed below in the order in which they appeared.
In the remarks, Applicant asserts that (1) Regarding 35 U.S.C. 112(b) rejection, the preamble in the independent claims indicate that the operations are being performed by at least one processor, which is similar to the parent application, and overcame similar rejection in the parent application; (2) Claim 1 has been amended to recite specific technical steps that cannot be practically performed in the human mind or by manually following rules. Specifically, claim 1 as amended recites "extracting one or more first features associated with the first medical data based on the region of interest" and "extracting one or more second features associated with the second medical data from a region of the second medical data corresponding to the region of interest of the first medical data," while indicating that "the first medical data comprises radiographic image data and the second medical data comprises at least one of tissue image data or genomic data". As such the rejections under 35 U.S.C. 101 should be withdrawn; and (3) Claim 1, as amended (with amended limitations “from a region of the second medical data corresponding to the region of interest of the first medical data” and “wherein the first medical data comprises radiographic image data and the second medical data comprises at least one of tissue image data or genomic data“), is patentable because the cited references, either alone or in combination thereof, do not teach or suggest all of the features of amended claim 1. Independent claim 13 is similarly amended, and is therefore patentable for similar reasons.
In response to applicant’s arguments (1) as listed above, the examiner respectfully disagrees. The 35 U.S.C. 112 (b) also rejected the claims for additional reasons such as for example, “It is unclear how a determination is made with regard to a region of interest in the first medical data. Is the determination being made by an algorithm or a formula?”, in addition to other additional reasons listed in the updated 35 U.S.C. 112(b) rejections above. As such, Applicant’s arguments have been considered but are not found to be persuasive.
In response to applicant’s arguments (2) as listed above, the examiner respectfully disagrees. The claim limitations as recited only broadly recites "extracting one or more first features associated with the first medical data based on the region of interest" and "extracting one or more second features associated with the second medical data from a region of the second medical data corresponding to the region of interest of the first medical data" while indicating that "the first medical data comprises radiographic image data and the second medical data comprises at least one of tissue image data or genomic data". Extracting one or more features can be broadly interpreted as getting certain data (feature) from other data (medical data). The claim limitations do not recite how the one or more (first or second) features can be extracted from the medical data. As such, extracting can be interpreted as a simple step such as getting data information such as for example, reading the title or patient name (or other relevant information) on the medical data image or reading the title or patient name (or other relevant information) on the genomic data. Examiner respectfully disagrees with Applicant’s assertion that these are considered “specific technical steps that cannot be practically performed in the human mind or by manually following rules”. Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. As such, Applicant’s arguments have been considered but are not found to be persuasive.
In response to applicant’s arguments (3) as listed above, the examiner respectfully disagrees. Applicant’s arguments pertain to newly amended limitations, and have been addressed in the rejections above. As such, Applicant’s arguments have been considered but are not found to be persuasive.
Conclusion
Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Joy Chng whose telephone number is 571.270.7897. The examiner can normally be reached on Monday-Thursday and every other Friday.
If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, JASON DUNHAM can be reached on 571.272.8109. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Joy Chng/
Primary Examiner, Art Unit 3686
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