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 .
DETAILED ACTION
In the response filed on 06 July 2026, the following has occurred; claims 1-3, 9, 11-13, 19 and 21 have been amended; claims 8, 10, 18 and 20 have been canceled; claims 22-24 are newly added.
Now claims 1-7, 9, 11-17, 19 and 21-24 are pending.
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 1-7, 9, 11-17, 19 and 21-24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 11 and 21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite methods and system for organizing and using a bioinformatics database. The limitations of:
Claim 1, which is representative of claim 11
making a bioinformatics database of age-related macular degeneration (AMD) data, […] comprising: obtaining live patient image data, the live patient image data including a first set of live patient fundus photos from one or more live patient eyes; obtaining eye bank image data, the eye bank image data including a second set of postmortem fundus photos captured from one or more postmortem eyes; processing the first set of live patient fundus photos and the second set of postmortem fundus photos, using one or more […] inferencing models, wherein the one or more […] inferencing models are configured to produce a grading or classification on the first set of live patient fundus photos and the second set of postmortem fundus photos that identifies a disease progression state of AMD in the respective photos in each data set; performing data analysis to correlate one or more characteristics of the live patient image data with one or more characteristics of the eye bank image data, using one or more […] correlation models, wherein the correlated characteristics include one or more common image data characteristics of each image data set, identified with the one or more […] correlation models, which occur in a same disease progression state of AMD as identified with the one or more […] inferencing models; and [… saving …] the common image data characteristics in a bioinformatics database.
Claim 21
using a bioinformatics database of age-related macular degeneration (AMD) data, […] comprising: [… obtaining …] one or more common image data characteristics from a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of postmortem fundus images from one or more postmortem eyes, wherein the common image data characteristics are produced using one or more […] correlation models, wherein the common image data characteristics occur in a same disease progression state of AMD in the live patient fundus images and the postmortem fundus images, and wherein the disease progression state of AMD is identified using one or more […] inferencing models that produce a grading or classification of the live patient fundus images and the postmortem fundus images; [… obtaining …] one or more tissue data characteristics from the bioinformatics database, wherein the tissue data characteristics are produced from one or more biological analysis techniques performed on tissue samples from the postmortem eyes, and wherein the bioinformatics database correlates the tissue data characteristics with the common image data characteristics that occur in same the disease progression state of AMD; and identifying a therapeutic target based on the tissue data characteristics, wherein identification of the therapeutic target is performed using one or more of: proteomic data analysis, transcriptomic data analysis, genomic data analysis, […], RNA or DNA methylation analysis, epigenetic modification analysis, posttranslational proteomic modifications, metabolomic biomarker identification, structural biological identification, or therapeutic targeting signaling analysis.
as drafted, is a system, which under its broadest reasonable interpretation, covers a method of organizing human activity (i.e., managing personal behavior including following rules or instructions) via human interaction with generic computer components. That is, by a human user interacting with one or more processor of a computing system, the claimed invention amounts to managing personal behavior or interaction between people, the Examiner notes as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”. For example, but for one or more processor of a computing system, the claim encompasses collection of various data sets, organization of data using the various data sets and storage of the various organized data for a human user to use in their treatment of a patient (i.e., human activity). If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior or interactions between people but for the recitation of generic computer components, then it falls within the “method of organizing human activity” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of one or more processor of a computing system, which implements the abstract idea. The one or more processor of a computing system is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Fig. 6, paragraphs [0054]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim recites the additional elements of “storing…”, “retrieving…”, “using one or more artificial intelligence (AI) inferencing models… using one or more AI correlation models” and “gene expression profiling, RNA or DNA sequencing…”. The “storing…” is recited at a high-level of generality (i.e., as a general means of storing data) and amounts to the mere storage of data, which is a form of extra-solution activity. The “retrieving…” steps are recited at a high-level of generality (i.e., as a general means of receiving/transmitting data) and amounts to the mere transmission and/or receipt of data, which is a form of extra-solution activity. The “using one or more artificial intelligence (AI) inferencing models… using one or more AI correlation models” is recited at a high-level of generality (i.e., training and using various off-the-shelf machine learning algorithm in a generic manner) and amounts to generally linking the abstract idea to a particular technological environment. The “gene expression profiling, RNA or DNA sequencing…” is recited at a high-level of generality (i.e., as a generic sequencing in a lab) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of one or more processor of a computing system to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “storing…”, “retrieving…”, “using one or more artificial intelligence (AI) inferencing models… using one or more AI correlation models” and “gene expression profiling, RNA or DNA sequencing…” were considered extra-solution activity and/or generally linking the abstract idea to particular technological environment. The “storing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.05(d)(II)(iv) “Storing and retrieving information in memory” is well-understood, routine, and conventional. The “retrieving…” has been re-evaluated under the "significantly more" analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in MPEP 2106.0S(d)(II)(i) "Receiving or transmitting data over a network" is well-understood, routine, and conventional. The “using one or more artificial intelligence (AI) inferencing models… using one or more AI correlation models” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Dighani (20220361752): see below but at least paragraph [0004]; Abraham (20230178245): paragraph [0007]; West (20200193004): paragraph [0098]; training and using of various machine learning model is well-understood, routine and conventional. The “gene expression profiling, RNA or DNA sequencing…” has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Ambati (20210348164): see below but at least paragraphs [0108], [0146]; Alter (20180122507): paragraph [0089], [0252]; Abraham (20230178245): paragraphs [0134]-[0135]; use of profiling or sequencing to identify targets is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 2-7, 9, 12-17, 19 and 22-24 are similarly rejected because either further define the abstract idea and/or do not further limit the claim to a practical application or provide as inventive concept such that the claims are subject matter eligible.
Claims 2-3 and 12-13 further describe obtaining tissue and live observation data for additional analysis and storage, however storage of data was already considered above and is incorporated herein.
Claims 4 and 14 further describe determination of relationships, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 5 and 15 recite the additional element of “generating a visual representation”, however this is recited at a high-level of generality (i.e., as a general displaying data in a user interface for a human user) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of “generating a visual representation” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Dighani (20220361752): see below but at least paragraph [0068]; Abraham (20230178245): paragraph [0089]; West (20200193004): Figure 11, paragraph [0055]; a user interface displaying data is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
Claims 6 and 16 recite additional elements of “gene expression profiling, RNA or DNA sequencing”, however these additional elements were already considered above and are incorporated herein.
Claims 7 and 17 recite the additional element of “a computing system”, however this is recited at a high-level of generality (i.e., a general-purpose computers/ computer components implementing generic computer functions; see Applicant’s Specification Fig. 6, paragraphs [0054]) such that it amounts no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “a computing system”, to perform the noted steps amounts to no more than mere instructions to apply the exception using generic hardware components. Mere instructions to apply an exception using generic hardware components cannot provide an inventive concept ("significantly more").
Claims 9 and 19 further describe the labels of images used, however does not recite any additional elements, therefore the claim cannot provide significantly more and/or a practical application.
Claims 22-24 recite the additional element of “administering a personalized treatment”, however this is recited at a high-level of generality (i.e., as a general means of administering an off-the-shelf drug to a patient) and amounts to merely linking of the abstract idea to particular technological environment. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claim is directed to an abstract idea.
Also, as discussed above with respect to integration of the abstract idea into a practical application, the additional element of “administering a personalized treatment” was considered generally linking the abstract idea to particular technological environment. This has been re-evaluated under the “significantly more” analysis and determined to amount to be well-understood, routine, and conventional elements/functions. As described in Ambati (20210348164): see below but at least paragraph [0007]; Jain (20210193323): paragraph [0044]; Abraham (20230178245): paragraph [0021]; administering a treatment to a patient is well-understood, routine, and conventional. Well-understood, routine, and conventional elements/functions cannot provide “significantly more.” As such the claim is not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claim(s) 1-3, 6, 8-13, 16 and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20220361752 (hereafter “Dirghani”), in view of U.S. Patent Pub. No. 20210348164 (hereafter “Ambati”).
Regarding (Currently Amended) claim 1, Dirghangi teaches a method for making a bioinformatics database of age-related macular degeneration (AMD) data (Dirghangi: paragraph [0004], “a system and method of using generalizable machine learning and artificial intelligence algorithms as an ophthalmic disease detection system… classify or detect various ophthalmic structures or eye diseases, including but not limited to glaucoma, diabetic retinopathy, retinopathy of prematurity, and age related macular degeneration”, paragraph [0109], “the results paired and entered into an associated bioinformatics database”), the method comprising:
obtaining live patient image data, the live patient image data including a first set of live patient fundus photos from one or more live patient eyes (Dirghangi: paragraph [0014], “The current invention also encourages the gold-standard examination technique of the retina—indirect ophthalmoscopy—and makes possible seamless wireless transmission of clinical photographs and videos from the clinical examination”, paragraph [0036], “generate fundus photographs… during the conventional workflow of the eye exam itself… acquire fundus photography/clinical documentation of the posterior segment examination of the eye in addition to the clinical examination of the eye”, paragraph [0065], “Upon capturing images, image export from the device onboard memory and file storage system may occur automatically”, paragraph [0098], “the hub will receive images and data/information from the device taught herein or other devices”. Also see, paragraph [0028]);
obtaining […] image data, the […] image data including a second set of […] fundus photos captured from one or more […] eyes (Dirghangi: paragraph [0012], “retrieval, and manipulation of on-device or off-device bioinformatics databases”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change, as well as comparison with reference databases of normal and abnormal clinical features, using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… enabling cross-comparison of images between patient examinations and between patients. Such cross-comparison can also be conducted, in one embodiment, by quick point-of-care cross-reference to a normative or pathologic database of fundus imagery (still or video) to enable immediate or close to immediate reference of patient pathology to an external image library for augmented examination enabling substantially enhanced clinical utility of the dilated fundus examination by the use of augmented examination technology”);
processing the first set of live patient fundus photos and the second set of […] fundus photos, using one or more artificial intelligence (AI) inferencing models, wherein the one or more AI inferencing models are configured to produce a grading or classification on the first set of live patient fundus photos and the second set of […] fundus photos that identifies a disease progression state of AMD in the respective photos in each data set (Dirghangi: paragraph [0004], “using generalizable machine learning and artificial intelligence algorithms as an ophthalmic disease detection system using an ophthalmoscope- or biomicroscope-based imaging system and machine learning-based tools to automatically perform quality control, image segmentation, and estimation of eye disease risk… The system could also use robust machine learning (“ML”) algorithms to classify or detect various ophthalmic structures or eye diseases, including but not limited to glaucoma, diabetic retinopathy, retinopathy of prematurity, and age related macular degeneration”, paragraphs [0109]-[0111], “the model and related algorithms would classify and sort large bioinformatics databases of clinical images… identify disease or risk level progression over time for a patient by the integrated analysis of a variety of data types and sources”. The Examiner notes both the obtained live patient data and reference image data is classified using a machine learning algorithm which teaches what is required of the claim under the broadest reasonable interpretation);
performing data analysis to correlate one or more characteristics of the live patient image data with one or more characteristics of the […] image data, using one or more AI correlation models, wherein the correlated characteristics include one or more common image data characteristics of each image data set, identified with the one or more AI correlation models, which occur in a same disease progression state of AMD as identified with the one or more AI inferencing models (Dirghangi: paragraph [0015], “feature recognition of the eye and ocular features of interest”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change… using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, paragraph [0105], “artifacts found in common… commonly-deployed and -used diagnostic examination instruments”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) one or more artificial intelligence (AI) inferencing models and (b) one or more AI correlation models, the machine learning/artificial intelligence models, of the system of Dirghangi teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the machine learning/artificial intelligence models as taught by Dirghangi separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04. The Examiner notes correlation is used with machine learning based feature extraction to determine common features and one of ordinary skill in the art would find prima facie obvious teaches what is required under the broadest reasonable interpretation); and
storing the common image data characteristics in a bioinformatics database (Dirghangi: paragraph [0024], “automatically generate a high-quality image library of the examination and of multiple serial clinical encounters over time for rapid cross-comparison and cross-reference of the images while partially or wholly automating much of the ordinarily labor-intensive process of manual association and filing of examination-specific elements or metadata such as the examining physician, patient, and date of service”, paragraph [0109], “The generalizable models and related algorithms would then be used to process a library of ophthalmic images of patients using slit lamp-based or indirect ophthalmoscope-based digital adapters, and labeling the artifact and pathology regions either algorithmically or by the user and the results paired and entered into an associated bioinformatics database”).
Dirghangi may not explicitly teach (underlined below for clarity):
obtaining eye bank image data, the eye bank image data including a second set of postmortem fundus photos captured from one or more postmortem eyes; processing the first set of live patient fundus photos and the second set of postmortem fundus photos, using one or more artificial intelligence (AI) inferencing models, wherein the one or more AI inferencing models are configured to produce a grading or classification on the first set of live patient fundus photos and the second set of postmortem fundus photos that identifies a disease progression state of AMD in the respective photos in each data set; performing data analysis to correlate one or more characteristics of the live patient image data with one or more characteristics of the eye bank image data, using one or more AI correlation models, wherein the correlated characteristics include one or more common image data characteristics of each image data set, identified with the one or more AI correlation models, which occur in a same disease progression state of AMD as identified with the one or more AI inferencing models;
Ambati teaches obtaining eye bank image data, the eye bank image data including a second set of postmortem fundus photos captured from one or more postmortem eyes; processing the first set of live patient fundus photos and the second set of postmortem fundus photos, using one or more artificial intelligence (AI) inferencing models, wherein the one or more AI inferencing models are configured to produce a grading or classification on the first set of live patient fundus photos and the second set of postmortem fundus photos that identifies a disease progression state of AMD in the respective photos in each data set; performing data analysis to correlate one or more characteristics of the live patient image data with one or more characteristics of the eye bank image data, using one or more AI correlation models, wherein the correlated characteristics include one or more common image data characteristics of each image data set, identified with the one or more AI correlation models, which occur in a same disease progression state of AMD as identified with the one or more AI inferencing models (Ambati: paragraph [0055], “the Alu cDNAs are single stranded, and their presence correlates with progression of age-related macular degeneration (AGE)”, paragraph [0097], “The study of deidentified tissue collected from deceased individuals and obtained from various eye banks… Donor eyes from patients with geographic atrophy (GA) or age-matched patients without age-related macular degeneration (AMD) were obtained from various eye banks. These diagnoses were confirmed through ophthalmic examination of dilated eyes before acquisition of the tissues or eyes or after examination of the eye globes post-mortem”);
One of ordinary skill in the art before the effective filing date would have found it obvious to include using an eye bank with post-mortem eye data as taught by Ambati within the use of a reference database for correlation of images as taught by Dirghangi with the motivation of “provide an increase in efficiency” (Ambati: paragraph [0059]).
Regarding (Currently Amended) claim 2, Dirghangi and Ambati teach the limitations of claim 1, and further teach obtaining tissue data, the tissue data including one or more tissue data characteristics produced from one or more biological analysis techniques performed on tissue samples from the postmortem eyes (Ambati: paragraph [0038], “a sample isolated from a subject (e.g., a biopsy, blood, serum, etc.) or from a cell or tissue from a subject (e.g., RNA and/or DNA and/or a protein or polypeptide isolated therefrom). Biological samples can be of any biological tissue or fluid or cells… a sample derived from a subject (i.e., a subject undergoing a diagnostic procedure and/or a treatment)”, paragraph [0110], “For human tissue, DNA and RNA were extracted using DNA and RNA Purification Kit (Epicentre); RNase A was added for DNA isolation, and DNase I was added for RNA isolation”);
performing additional data analysis to correlate the tissue data characteristics with the common image data characteristics which occur in same the disease progression state of AMD as identified with the one or more AI inferencing models (Dirghangi: paragraph [0015], “feature recognition of the eye and ocular features of interest”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change… using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, Ambati: paragraph [0049], “For some markers, expression or absence of expression is often in fact based on comparison with other cells which also express the marker”, paragraph [0055], “their presence correlates with progression of age-related macular degeneration (AGE)”); and
storing the tissue data characteristics in the bioinformatics database, wherein the tissue data characteristics are stratified by disease severity of AMD (Dirghangi: paragraph [0024], “automatically generate a high-quality image library of the examination and of multiple serial clinical encounters over time for rapid cross-comparison and cross-reference of the images while partially or wholly automating much of the ordinarily labor-intensive process of manual association and filing of examination-specific elements or metadata such as the examining physician, patient, and date of service”, paragraph [0109], “The generalizable models and related algorithms would then be used to process a library of ophthalmic images of patients using slit lamp-based or indirect ophthalmoscope-based digital adapters, and labeling the artifact and pathology regions either algorithmically or by the user and the results paired and entered into an associated bioinformatics database”; Ambati: paragraph [0093], “The selected dosage level will depend upon the activity of the therapeutic composition, the route of administration, combination with other drugs or treatments, the severity of the condition being treated, and the condition and prior medical history of the subject being treated… calculations of dose can consider subject height and weight, severity and stage of symptoms, and the presence of additional deleterious physical conditions”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 3, Dirghangi and Ambati teach the limitations of claim 2, and further teach obtaining live patient observation data, the live patient observation data including one or more attributes of respective human subjects corresponding to the live patient eyes (Dirghangi: paragraph [0005], “Recording clinical observations into the patient medical record”, paragraph [0021], “clinical metadata which can be scrubbed (manually or automatically/algorithmically) of protected health information and patient identifiers”, paragraphs [0027]-[0028], “electronically transmitting metadata and clinical imagery to a connected EMR/EHR (Electronic Medical Record/Electronic Health Record) system or a separate connected computing device or application linked to a patient's electronic chart… Videos, images, and clinical metadata, clinical data, user account information, and any other associated telemetry data pertinent to the operation of the device or system(s) used in the clinical data network may be encrypted by the device… generated clinical imagery and documentation, and maintain clinical data”, paragraph [0036], “acquire fundus photography/clinical documentation of the posterior segment examination of the eye”);
obtaining eye bank observation data, the eye bank observation data including attributes of respective human subjects corresponding to the postmortem eyes and measurements performed on the postmortem eyes (Ambati: paragraph [0055], “the Alu cDNAs are single stranded, and their presence correlates with progression of age-related macular degeneration (AGE)”, paragraph [0097], “The study of deidentified tissue collected from deceased individuals and obtained from various eye banks… Donor eyes from patients with geographic atrophy (GA) or age-matched patients without age-related macular degeneration (AMD) were obtained from various eye banks. These diagnoses were confirmed through ophthalmic examination of dilated eyes before acquisition of the tissues or eyes or after examination of the eye globes post-mortem”, paragraph [0159], “age, sex, ethnicity”);
performing additional data analysis to correlate clinical data characteristics from the live patient observation data with the eye bank observation data and with the common image data characteristics which occur in the same disease progression state of AMD as identified with the one or more AI inferencing models (Dirghangi: paragraph [0015], “feature recognition of the eye and ocular features of interest”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, paragraph [0105], “artifacts found in common… commonly-deployed and -used diagnostic examination instruments”. The Examiner notes correlation is used with feature extraction to determine common features which teaches what is required under the broadest reasonable interpretation); and
storing the clinical data characteristics in the bioinformatics database (Dirghangi: paragraph [0024], “automatically generate a high-quality image library of the examination and of multiple serial clinical encounters over time for rapid cross-comparison and cross-reference of the images while partially or wholly automating much of the ordinarily labor-intensive process of manual association and filing of examination-specific elements or metadata such as the examining physician, patient, and date of service”, paragraph [0109], “The generalizable models and related algorithms would then be used to process a library of ophthalmic images of patients using slit lamp-based or indirect ophthalmoscope-based digital adapters, and labeling the artifact and pathology regions either algorithmically or by the user and the results paired and entered into an associated bioinformatics database”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Original) claim 6, Dirghangi and Ambati teach the limitations of claim 2, and further teach using the bioinformatics database to identify a therapeutic target in the tissue data (Dirghangi: paragraph [0012], “navigation, retrieval, and manipulation of on-device or off-device bioinformatics databases, diagnostic testing data, and additional digital examination imagery”, paragraph [0111], “export generated qualitative and quantitative data to and from… bioinformatics database”; Ambati: paragraph [0063], “the gene target”, paragraph [0076], “target gene expression can be reduced by targeting deoxyribonucleotide sequences complementary to the regulatory region of the gene (i.e., the promoter and/or enhancers) to form triple helical structures that prevent transcription of the gene in target cells in the body”. The Examiner notes that “to identify a therapeutic target” is an intended use of the using the bioinformatic database that is not required to occur. This feature has been fully considered by the Examiner; however, the limitation does not provide patentable distinction over the cited prior art because it is an intended use or result of the use of the bioinformatic database);
wherein identification of the therapeutic target is performed using one or more of: proteomic data analysis, transcriptomic data analysis, genomic data analysis, gene expression profiling, RNA or DNA sequencing, RNA or DNA methylation analysis, epigenetic modification analysis, post-translational proteomic modifications, metabolomic biomarker identification, structural biological identification, or therapeutic targeting signaling analysis (Ambati: paragraph [0015], “Expression levels of genes”, paragraphs [0057]-[0058], “a single stranded or double-stranded RNA or DNA… The specific sequence utilized in design of the inhibitory nucleic acids is a contiguous sequence of nucleotides contained within the expressed gene message of the target”, paragraph [0111], “assess genomic DNA”, paragraph [0136], “Sequencing data have been deposited in the Gene Expression Omnibus (GEO) public functional genomics data repository”, paragraph [0252], “Affinity proteomics reveals”).
The motivation to combine is the same as in claim 1, incorporated herein.
Regarding (Currently Amended) claim 9, Dirghangi and Ambati teach the limitations of claim 1, and further teach wherein the first set of live patient fundus photos and the second set of postmortem fundus photos provide stereoscopic color fundus images, and wherein each image of the first set of live patient fundus photos and the second set of postmortem fundus photos depicts a disease progression state classified to a level of a multi-step grading system of AMD disease (Dirghangi: paragraph [0013], “tabletop stereoscopic red-free, disc photographs, automated visual field testing”, paragraph [0073], “algorithmic computer vision-based fundus photography image grading using computing applications and algorithms”, paragraph [0107], “high-quality color, red-free, or false-color digital fundus images”, paragraph [0111], “identify disease or risk level progression over time for a patient by the integrated analysis of a variety of data types and sourc”; Ambati: paragraph [0005], “an untreatable late stage of atrophic AMD”).
The motivation to combine is the same as in claim 1, incorporated herein.
REGARDING CLAIM(S) 11-13, 16 and 18-20
Claim(s) 11-13, 16 and 19 is/are analogous to Claim(s) 1-3, 6 and 9, thus Claim(s) 11-13, 16 and 19 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 1-3, 6 and 9.
Claim(s) 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20220361752 (hereafter “Dirghani”) and U.S. Patent Pub. No. 20210348164 (hereafter “Ambati”) as applied to claims 1 and 11 above, and further in view of U.S. Patent Pub. No. 20200193004 (hereafter “West”).
Regarding (Original) claim 4, Dirghangi and Ambati teach the limitations of claim 3, but may not explicitly teach wherein the bioinformatics database establishes relationships among one or more attributes associated with the common image data characteristics, the clinical data characteristics, and the tissue data characteristics.
West teaches wherein the bioinformatics database establishes relationships among one or more attributes associated with the common image data characteristics, the clinical data characteristics, and the tissue data characteristics (West: paragraph [0185], “data structures may be stored in computer-readable media in any suitable form… relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include determination of relationships as taught by West with the use of a bioinformatics database containing common image data characteristics, the clinical data characteristics, and the tissue data characteristics as taught by Dirghangi and Ambati with the motivation of providing “enhanced imaging functionality” (West: paragraph [0064]).
Regarding (Original) claim 5, Dirghangi, Ambati and West teach the limitations of claim 4, and further teach retrieving one or more of the common image data characteristics, the tissue data characteristics, or the clinical data characteristics from the bioinformatics database; and generating a visual representation of one or more of the common image data characteristics, the clinical data characteristics, or the tissue data characteristics (Dirghangi: paragraph [0012], “navigation, retrieval, and manipulation of on-device or off-device bioinformatics databases, diagnostic testing data, and additional digital examination imagery”, paragraph [0021], “reviewing, filing, and displaying clinical images and video. For example, this includes quick image review on a mobile device, simultaneous re-display”, paragraph [0027], “auto analysis can display its output by electronically transmitting metadata and clinical imagery”, paragraph [0111], “export generated qualitative and quantitative data to and from separate clinical decision support (CDS) computer software tools, image registration and montage software, image PACS systems, and bioinformatics database”. The Examiner notes data from the bioinformatics database is retrieved and organized into a user interface, which teaches what is required of the claim under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 4, incorporated herein.
REGARDING CLAIM(S) 14 and 15
Claim(s) 14 and 15 is/are analogous to Claim(s) 4 and 5, thus Claim(s) 14 and 15 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 4 and 5.
Claim(s) 7, 17 and 23-24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20220361752 (hereafter “Dirghani”) and U.S. Patent Pub. No. 20210348164 (hereafter “Ambati”) as applied to claims 1 and 11 above, and further in view of U.S. Patent Pub. No. 20210193323 (hereafter “Jain”).
Regarding (Original) claim 7, Dirghangi and Ambati teach the limitations of claim 1, and further teach […] use in therapeutic target identification, the therapeutic target identification to be performed by one or more hypothesis generation function or computational biology function implemented in a computing system (Ambati: paragraph [0015], “Expression levels of genes”, paragraphs [0057]-[0058], “a single stranded or double-stranded RNA or DNA… The specific sequence utilized in design of the inhibitory nucleic acids is a contiguous sequence of nucleotides contained within the expressed gene message of the target”, paragraph [0076], “target gene expression can be reduced by targeting deoxyribonucleotide sequences complementary to the regulatory region of the gene (i.e., the promoter and/or enhancers) to form triple helical structures that prevent transcription of the gene in target cells in the body”, paragraph [0111], “assess genomic DNA”, paragraph [0136], “Sequencing data have been deposited in the Gene Expression Omnibus (GEO) public functional genomics data repository”, paragraph [0252], “Affinity proteomics reveals”).
Dirghangi and Ambati may not explicitly teach (underlined below for clarity):
retrieving the common image data characteristics from the bioinformatics database; and providing the common image data characteristics for use in therapeutic target identification, the therapeutic target identification to be performed by one or more hypothesis generation function or computational biology function implemented in a computing system.
Jain teaches retrieving the common image data characteristics from the bioinformatics database; and providing the common image data characteristics for use in therapeutic target identification, the therapeutic target identification to be performed by one or more hypothesis generation function or computational biology function implemented in a computing system (Jain: paragraph [0009], “receiving a plurality of patient tissue image slides”, paragraph [0055], “using bioinformatics and statistical analysis”, paragraph [0063], “The system 200 analyzes this complex dataset and transfers it into structured and usable data for disease and patient response prediction, in one aspect, by determining morphological similarity and determining a more limited dataset of morphological sub-patterns within which these millions of cells and thousands of patches may be classified”, paragraph [0101], “Various clusters of morphologically similar features have been identified… determining potential drug or treatment targets and/or biomarkers indicating patient outcome or response”).
One of ordinary skill in the art before the effective filing date would have found it obvious to use common image data to determine a therapeutic target as taught by Jain with the bioinformatics taught by Dirghangi and Ambati with the motivation of “improve the accuracy of patient outcome prediction” (Jain: paragraph [0062]).
Regarding (New) claim 23, Dirghangi, Ambati and Jain teach limitations of claim 7, and further teach administering a personalized treatment to a patient based on the identified therapeutic target, wherein a particular disease progression state of AMD is identified in the patient from at least one fundus photo using the one or more AI inferencing models (Dirghangi: paragraph [0027], “using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, paragraphs [0109]-[0111], “the model and related algorithms would classify and sort large bioinformatics databases of clinical images… identify disease or risk level progression over time for a patient by the integrated analysis of a variety of data types and sources”; Ambati: paragraph [0007], “administering to a subject in need thereof a composition comprising an effective amount of an inhibitor of reverse transcriptase (RTase) activity. In some embodiments, the RTase activity is cytoplasmic RTase activity”, paragraph [0093], “A “treatment effective amount” or a “therapeutic amount” is an amount of a therapeutic composition sufficient to produce a measurable response (e.g., a biologically or clinically relevant response in a subject being treated).”. The Examiner notes an “effective amount” reads on at least a personalized treatment under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 7, incorporated herein.
REGARDING CLAIM(S) 17 and 24
Claim(s) 17 and 24 is/are analogous to Claim(s) 7 and 23, thus Claim(s) 17 and 24 is/are similarly analyzed and rejected in a manner consistent with the rejection of Claim(s) 7 and 23.
Claim(s) 21 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Pub. No. 20220361752 (hereafter “Dirghani”), in view of U.S. Patent Pub. No. 20210348164 (hereafter “Ambati”) and further in view of U.S. Patent Pub. No. 20210193323 (hereafter “Jain”).
Regarding (Currently Amended) claim 21, Dirghani teaches a method for using a bioinformatics database of age-related macular degeneration (AMD) data (Dirghangi: paragraph [0004], “a system and method of using generalizable machine learning and artificial intelligence algorithms as an ophthalmic disease detection system… classify or detect various ophthalmic structures or eye diseases, including but not limited to glaucoma, diabetic retinopathy, retinopathy of prematurity, and age related macular degeneration”, paragraph [0109], “the results paired and entered into an associated bioinformatics database”), the method comprising:
[… utilizing …] a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of […] fundus images from one or more […] eyes (Dirghangi: paragraph [0014], “The current invention also encourages the gold-standard examination technique of the retina—indirect ophthalmoscopy—and makes possible seamless wireless transmission of clinical photographs and videos from the clinical examination”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change… using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0036], “generate fundus photographs… during the conventional workflow of the eye exam itself… acquire fundus photography/clinical documentation of the posterior segment examination of the eye in addition to the clinical examination of the eye”, paragraph [0065], “Upon capturing images, image export from the device onboard memory and file storage system may occur automatically”, paragraph [0098], “the hub will receive images and data/information from the device taught herein or other devices”, paragraph [0109], “classify and sort large bioinformatics databases of clinical images”. Also see, paragraph [0028]),
wherein the common image data characteristics are produced using one or more AI correlation models, wherein the common image data characteristics occur in a same disease progression state of AMD in the live patient fundus images and the [… reference …] fundus images (Dirghangi: paragraph [0015], “feature recognition of the eye and ocular features of interest”, paragraph [0027], “Auto-analysis includes, but is not limited to, automatic algorithmic detection, flagging, and measurement of clinical features of interest, and comparison with prior detected features for evidence of clinical stability versus change… using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, paragraph [0105], “artifacts found in common… commonly-deployed and -used diagnostic examination instruments”), and
wherein the disease progression state of AMD is identified using one or more AI inferencing models that produce a grading or classification of the live patient fundus images and the [… reference …] fundus images (Dirghangi: paragraph [0004], “using generalizable machine learning and artificial intelligence algorithms as an ophthalmic disease detection system using an ophthalmoscope- or biomicroscope-based imaging system and machine learning-based tools to automatically perform quality control, image segmentation, and estimation of eye disease risk… The system could also use robust machine learning (“ML”) algorithms to classify or detect various ophthalmic structures or eye diseases, including but not limited to glaucoma, diabetic retinopathy, retinopathy of prematurity, and age related macular degeneration”, paragraphs [0109]-[0111], “the model and related algorithms would classify and sort large bioinformatics databases of clinical images… identify disease or risk level progression over time for a patient by the integrated analysis of a variety of data types and sources”. The Examiner notes that a claim may be rendered obvious where the limiting function is that of making a set of prior-known elements contiguous, i.e., bringing them together. However, the opposite is also true. In this case, the limiting function is that of splitting prior-known elements and or functionality into discrete elements: (a) one or more AI correlation models and (b) one or more AI inferencing models, the machine learning/artificial intelligence models, of the system of Dirghangi teach the functionality of the claimed elements respectively (see mapping above). As such, this claim would be obvious to one of ordinary skill in the art at the time of the invention to make the machine learning/artificial intelligence models as taught by Dirghangi separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04.); […].
Dirghangi may not explicitly teach (underlined below for clarity):
[… utilizing …] a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of images from one or more live patients with one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of postmortem fundus images from one or more postmortem eyes; wherein the common image data characteristics are produced using one or more AI correlation models, wherein the common image data characteristics occur in a same disease progression state of AMD in the live patient fundus images and the postmortem fundus images, and wherein the disease progression state of AMD is identified using one or more AI inferencing models that produce a grading or classification of the live patient fundus images and the postmortem fundus images;
retrieving one or more tissue data characteristics from the bioinformatics database, wherein the tissue data characteristics are produced from one or more biological analysis techniques performed on tissue samples from the postmortem eyes, and wherein the bioinformatics database correlates the tissue data characteristics with the common image data characteristics that occur in the same disease progression state of AMD; and
identifying a therapeutic target based on the tissue data characteristics,
wherein identification of the therapeutic target is performed using one or more of: proteomic data analysis, transcriptomic data analysis, genomic data analysis, gene expression profiling, RNA or DNA sequencing, RNA or DNA methylation analysis, epigenetic modification analysis, posttranslational proteomic modifications, metabolomic biomarker identification, structural biological identification, or therapeutic targeting signaling analysis
Ambati teaches [… utilizing …] a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of images from one or more live patients with one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of postmortem fundus images from one or more postmortem eyes; wherein the common image data characteristics are produced using one or more AI correlation models, wherein the common image data characteristics occur in a same disease progression state of AMD in the live patient fundus images and the postmortem fundus images, and wherein the disease progression state of AMD is identified using one or more AI inferencing models that produce a grading or classification of the live patient fundus images and the postmortem fundus images (Ambati: paragraph [0055], “the Alu cDNAs are single stranded, and their presence correlates with progression of age-related macular degeneration (AGE)”, paragraph [0097], “The study of deidentified tissue collected from deceased individuals and obtained from various eye banks… Donor eyes from patients with geographic atrophy (GA) or age-matched patients without age-related macular degeneration (AMD) were obtained from various eye banks. These diagnoses were confirmed through ophthalmic examination of dilated eyes before acquisition of the tissues or eyes or after examination of the eye globes post-mortem”);
retrieving one or more tissue data characteristics from the bioinformatics database, wherein the tissue data characteristics are produced from one or more biological analysis techniques performed on tissue samples from the postmortem eyes, and wherein the bioinformatics database correlates the tissue data characteristics with the common image data characteristics that occur in the same disease progression state of AMD (Ambati: paragraph [0022], “reference Alu sequence”, paragraph [0038], “Biological samples can be of any biological tissue or fluid or cells from any organism as well as cells cultured in vitro, such as cell lines and tissue culture cells. Frequently the sample will be a “clinical sample””, paragraph [0055], “the Alu cDNAs are single stranded, and their presence correlates with progression of age-related macular degeneration (AGE)”, paragraph [0097], “The study of deidentified tissue collected from deceased individuals and obtained from various eye banks”, paragraph [0108], “assessed with FastQC (Babraham Bioinformatics Group, Babraham Institute, Babraham, Cambridge, United Kingdom). The reads were then mapped to human reference chromosomes”); and
identifying a therapeutic target based on the tissue data characteristics (Ambati: paragraph [0063], “the gene target”, paragraph [0076], “target gene expression can be reduced by targeting deoxyribonucleotide sequences complementary to the regulatory region of the gene (i.e., the promoter and/or enhancers) to form triple helical structures that prevent transcription of the gene in target cells in the body”, paragraph [0165], “specific nucleic acid targets.”),
wherein identification of the therapeutic target is performed using one or more of: proteomic data analysis, transcriptomic data analysis, genomic data analysis, gene expression profiling, RNA or DNA sequencing, RNA or DNA methylation analysis, epigenetic modification analysis, posttranslational proteomic modifications, metabolomic biomarker identification, structural biological identification, or therapeutic targeting signaling analysis (Ambati: paragraph [0015], “Expression levels of genes”, paragraphs [0057]-[0058], “a single stranded or double-stranded RNA or DNA… The specific sequence utilized in design of the inhibitory nucleic acids is a contiguous sequence of nucleotides contained within the expressed gene message of the target”, paragraph [0111], “assess genomic DNA”, paragraph [0136], “Sequencing data have been deposited in the Gene Expression Omnibus (GEO) public functional genomics data repository”, paragraph [0252], “Affinity proteomics reveals”).
One of ordinary skill in the art before the effective filing date would have found it obvious to include using an eye bank with post-mortem eye data as taught by Ambati within the use of a reference database for correlation of images as taught by Dirghangi with the motivation of “provide an increase in efficiency” (Ambati: paragraph [0059]).
Dirghangi and Ambati may not explicitly teach (underlined below for clarity):
retrieving one or more common image data characteristics from a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of images from one or more live patients with one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of postmortem fundus images from one or more postmortem eyes;
Jain teaches retrieving one or more common image data characteristics from a bioinformatics database, wherein the bioinformatics database correlates one or more characteristics of images from one or more live patients with one or more characteristics of live patient fundus images from one or more live patients with one or more characteristics of postmortem fundus images from one or more postmortem eyes; (Jain: paragraph [0009], “receiving a plurality of patient tissue image slides”, paragraph [0055], “using bioinformatics and statistical analysis”, paragraph [0063], “The system 200 analyzes this complex dataset and transfers it into structured and usable data for disease and patient response prediction, in one aspect, by determining morphological similarity and determining a more limited dataset of morphological sub-patterns within which these millions of cells and thousands of patches may be classified”, paragraph [0101], “Various clusters of morphologically similar features have been identified… determining potential drug or treatment targets and/or biomarkers indicating patient outcome or response”);
One of ordinary skill in the art before the effective filing date would have found it obvious to use common image data to determine a therapeutic target as taught by Jain within the bioinformatics taught by Dirghangi and Ambati with the motivation of “improve the accuracy of patient outcome prediction” (Jain: paragraph [0062]).
Regarding (New) claim 22, Dirghangi, Ambati and Jain teach limitations of claim 21, and further teach administering a personalized treatment to a patient based on the identified therapeutic target, wherein a particular disease progression state of AMD is identified in the patient from at least one fundus photo using the one or more AI inferencing models (Dirghangi: paragraph [0027], “using a variety of techniques including, but not limited to, computer vision, deep learning, and artificial intelligence… auto-analysis will occur using connected software to correlate clinical images with an external library or set of algorithms determining attributes such as which eye is being examined, or flagging optic nerve and retinal periphery, noting abnormal features detected by the system, all of which may aid in clinical examination upon review of the image(s)”, paragraph [0070], “compare patient's examinations for progression versus stability of any clinical pathology found”, paragraphs [0109]-[0111], “the model and related algorithms would classify and sort large bioinformatics databases of clinical images… identify disease or risk level progression over time for a patient by the integrated analysis of a variety of data types and sources”; Ambati: paragraph [0007], “administering to a subject in need thereof a composition comprising an effective amount of an inhibitor of reverse transcriptase (RTase) activity. In some embodiments, the RTase activity is cytoplasmic RTase activity”, paragraph [0093], “A “treatment effective amount” or a “therapeutic amount” is an amount of a therapeutic composition sufficient to produce a measurable response (e.g., a biologically or clinically relevant response in a subject being treated).”. The Examiner notes an “effective amount” reads on at least a personalized treatment under the broadest reasonable interpretation).
The motivation to combine is the same as in claim 21, incorporated herein.
Response to Arguments
Applicant's arguments filed on 06 July 2026 have been fully considered but they are not persuasive. Applicant's arguments will be addressed below in the order in which they appear in the response filed on 06 July 2026.
Rejections under 35 U.S.C. § 101
Regarding the rejection of claims 1-21, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully traverses this construction of the claim and characterization of an abstract idea, and submits that this interpretation is rendered moot by the present amendments entered above. These amendments clarify how the underlying technology elements, and specifically one or more AI inferencing models and one or more AI correlation models, perform a specific type of data analysis using computer-executed operations. First, the claims are not directed to an abstract idea as alleged… The claims instead recite a specific technical method for constructing a disease-model database that correlates live patient fundus image data against postmortem eye bank tissue and image data… Second, even assuming arguendo that the claims are directed to the alleged abstract idea, Applicant respectfully submits that the claims integrate the alleged abstract idea into a practical application… The relevant technical field, as explained in paragraph [0002] of Applicant's specification, is not computer hardware but "medical data analysis, and particularly to data analysis performed in connection with image, clinical, biologic, and tissue data."… The use of the recited AI data processing accomplishes an improvement to a technical problem in life sciences which cannot be solved by mere human activity, mental effort, or a generic computer system… The claim provides specific evidence of a transition from abstract scientific principle to specific application… person of ordinary skill in the art would understand that the claims do not recite a generic treatment, but a particular treatment that is suited for the specific disease progression state of AMD.
The Examiner respectfully disagrees.
It is respectfully submitted, that the claims encompass collection and organization of data into a database and is human activity of a human user collecting and organizing data for the human activity of research into a medical condition, which as stated in 2106.04(a)(2), “certain activity between a person and a computer… may fall within the “certain methods of organizing human activity” grouping”.
The claimed additional elements do not recite a technical problem recited in Applicant’s specification, the Examiner first notes no portions of Applicant’s specification are argued for recitations of technical problems rooted in computer hardware technology, and instead Applicant argues paragraph [0002], for recitations of problems with medical data analysis, however medical data analysis is not a technical problem rooted in computer hardware technology, analysis of medical data is a human activity problem of organization of data for medical provider, using generic off-the-shelf machine learning models to generally link the abstract idea to a particular technological environment, and does not recite a technical solution to a technical problem recited in Applicant’s specification. Finally, the claims do not recite a particular treatment, administration of a personalized treatment may be an additional element but this is not a particular treatment, and instead is generally linking the abstract idea to a particular technological environment, as administration of a generic off-the-shelf treatment to address AMD is not particular, even if it is “personalized” it is not particular, as the claimed treatment is not particular the argument is not persuasive.
Rejections under 35 U.S.C. § 103
Regarding the rejection of claims 1-21, the Examiner has considered the Applicant’s arguments but does not find them persuasive. The Examiner has attempted to address all of the arguments presented by the Applicant; however, any arguments inadvertently not addressed are not persuasive for at least the following reasons:
Applicant argues:
Applicant respectfully submits that the present obviousness rejection cannot be maintained because there is no teaching, suggestion, or other rationale of obviousness of all claimed elements, which can be established or maintained from the cited combination of references… The claims as amended thus recite the use of at least two different AI models that perform different functions: inferencing operations "to produce a grading or classification", and correlation operations "to correlate one or more characteristics of the live patient image data with one or more characteristics of the eye bank image data."… Dirghangi's basic use of machine learning algorithms does not teach two separate models, as claimed… Dirghangi and the other cited references do not teach the two-model architecture as now recited in the claims, using one or more artificial intelligence (AI) inferencing models and using one or more AI correlation models… Neither Dirghangi nor Ambati, or a combination of the two suggests the claimed integration between the two AI model outputs, as claimed, and neither suggests any capability to identify that the common image data characteristics occur in a same disease progression state of AMD as identified with the one or more AI inferencing models as claimed.
The Examiner respectfully disagrees.
It is respectfully submitted, that the argued limitations are taught by the combination of Ambati within the teachings of Dirghangi, in particular, Dirghangi teaches a machine learning algorithm that uses live patient fundus image data and reference fundus image data to perform both classification of the fundus images to a level or stage of progression of age related macular degeneration (AMD) (see above but at least paragraphs [0004], [0014], [0027], [0109]-[0111]) and identifies common features in the various images (see above but at least [0027], [0105]), and although not explicitly claimed as two distinct AI models, paragraphs [0108]-[0109] describe various machine learning models, and it would be obvious to one of ordinary skill in the art at the time of the invention to make the machine learning/artificial intelligence models as taught by Dirghangi separable without undue experimentation or risk of unexpected results, see In re Dulberg, 289 F.2d 522, 523, 129 USPQ 348, 349 (CCPA 1961). MPEP 2144.04. Finally, although the reference images as taught by Dirghangi are not explicitly recited as postmortem fundus images from an eye bank, Ambati teaches an eye bank that has postmortem fundus eye images, one of ordinary skill in the art would find it prima facie obvious to use the eye bank containing postmortem images as taught by Ambati within the reference images from a reference image database for image analysis of AMD as taught by Dirghangi with the motivation of “provide an increase in efficiency” (Ambati: paragraph [0059]).
In addition, the Examiner respectfully notes that the cited reference was never applied as a reference under 35 U.S.C. 102 against the pending claims. As such, the Examiner respectfully submits that the issue at hand is not whether the applied prior art specifically teaches the claimed features, per se, but rather, whether or not the prior art, when taken in combination with the knowledge of average skill in the art, would put the artisan in possession of these features. Regarding this issue, it is well established that references are evaluated by what they suggest to one versed in the art, rather than by their specific disclosures, In re Bozek, 163 USPQ 545 (CCPA 1969). The issue of obviousness is not determined by what the references expressly state but by what they would reasonably suggest to one of ordinary skill in the art, as supported by decisions in In re DeLisle 406 Fed 1326, 160 USPQ 806; In re Kell, Terry and Davies 208 USPQ 871; and In re Fine, 837 F.2d 1071, 1074, 5 USPQ 2d 1596, 1598 (Fed. Cir. 1988) (citing In re Lalu, 747 F.2d 703, 705, 223 USPQ 1257, 1258 (Fed. Cir. 1988)). Further, it was determined in In re Lamberti et al, 192 USPQ 278 (CCPA) that:
(i) obviousness does not require absolute predictability;
(ii) non-preferred embodiments of prior art must also be considered; and
(iii) the question is not express teaching of references, but what they would suggest.
According to In re Jacoby, 135 USPQ 317 (CCPA 1962), the skilled artisan is presumed to know something more about the art than only what is disclosed in the applied references. In In re Bode, 193 USPQ 12 (CCPA 1977), every reference relies to some extent on knowledge of persons skilled in the art to complement that which is disclosed therein.
Conclusion
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.
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/A.E.L./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
