DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Election/Restrictions
Claim 1 is withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Group, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 18 June 2026.
Claim Status
Claim 1 is withdrawn.
Claims 2-4 are pending.
Claims 2-4 are rejected.
Priority
This application is claims benefit of application no. 63/256,352, filed 10/15/2021. The instant application has the effective filing date of 15 October 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/14/2022 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement has been considered by the examiner.
The information disclosure statement filed 10/14/2022 fails to comply with 37 CFR 1.98(a)(2), which requires a legible copy of each cited foreign patent document; each non-patent literature publication or that portion which caused it to be listed; and all other information or that portion which caused it to be listed. It has been placed in the application file, but the information referred to therein has not been considered because: a copy has not been provided for Foreign Document no. 2015127065.
Drawings
The drawings, submitted on 10/14/2022, are accepted by the examiner.
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 2-4 are rejected under U.S.C 101 because the claimed invention is directed to abstract ideas and laws of nature without significantly more, as detailed in the analysis below.
Eligibility Step 1: Subject matter eligibility evaluation in accordance with MPEP § 2106:
Claims 2-4 are directed to a statutory category (system).
Therefore, in accordance with MPEP § 2106.03 all claims have patent eligible subject matter.
[Eligibility Step 1: YES]
Eligibility Step 2A: This step determines whether a claim is directed to a judicial exception in accordance with MPEP § 2106.
Eligibility Step 2A -- Prong One: Limitations are analyzed to determine if the claims recite any concepts that could equate to a judicial exception (i.e. abstract idea, law of nature, or natural phenomenon). Possible judicial exceptions are explored below.
Recitations of Judicial Exceptions:
Claim 2: A system for predicting a disease comprising: a database of biomarkers for an individual, including baseline entries of the biomarkers for the individual; and compare the biomarkers to a correlation table of biomarkers and diseases (mental process, natural correlation).
Claim 3: wherein the database includes entries for multiple individuals. (mental process, natural correlation)
Claim 4: an artificial intelligence and machine learning algorithm configured to monitor the database for similar disease biomarkers in more than two individuals. (mathematical concept, mental process, natural correlation)
Step 2A – Prong One Analysis:
Correlating, comparing, and making determinations of data equate to analysis techniques that require no more than the human mind to enact mental observations of data and pen/paper. Limitations that recite these processes fall under the mental process grouping of abstract ideas. Dependent claims that merely further limit the data being analyzed are similarly classified (claim 3).
Correlating biomarkers with disease represent natural phenomena/law of nature concepts as exemplified by Cleveland Clinic Foundation v. True Health Diagnostics, LLC, 859 F.3d 1352, 1361, 123 USPQ2d 1081, 1087 (Fed. Cir. 2017), which presents the correlation between the presence of myeloperoxidase (an immune profile biomarker) in a bodily sample and cardiovascular disease risk (MPEP 2106.04 b).
Artificial intelligence and machine learning algorithms equate to mathematical formulas and calculations that derive secondary data. Limitations that recite these techniques fall under the mathematical concepts grouping of abstract ideas.
Eligibility Step 2A – Prong Two: A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. If the claim contains no additional claim elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)).
Additional Elements within the claimed invention include:
Claim 2: a storage medium; and a processor connected to the storage medium, wherein the processor
Claim 4: a processer
Step 2A – Prong 2 Analysis:
These limitations represent generic computer components. When viewed separately or in the context of a whole claimed invention, they provide mere instructions to implement the abstract ideas per Alice Corp., 573 U.S. at 223, 110 USPQ2d at 1983. See also 573 U.S. at 224, 110 USPQ2d at 1984 and do not integrate the judicial exceptions into practical application.
[Eligibility Step 2A – Prong Two: NO]
Eligibility Step 2B: Claim elements are probed for inventive concept equating to significantly more than the judicial exception (MPEP 2106.04(II)).
Step 2B Analysis:
The limitations are further found to be well-understood routine and conventional, without a recited improvement to technology or usage of a particular machine per FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016) for accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer and TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48 for gathering and analyzing information using conventional techniques.
[Eligibility Step 2B: NO]
As such, claims 2-4 are directed to judicial exceptions without significantly more and are rejected under 35 U.S.C 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 2-4 are rejected under 35 U.S.C. 102(a)(1)(a)(2) as being anticipated by Lamrani et al. (2021/0007643).
Claim 2 is directed to a system for predicting a disease that includes a database of biomarkers for an individual with baseline biomarker entries; and comparing the biomarkers to a correlation table of biomarkers and diseases.
Lamrani et al. describes a system for collecting and utilizing health data [abstract].
Lamrani et al. teaches comparing the user's biomarker profile from earlier sessions of wearing that contact lens, to obtain a baseline profile that is specific to that user [0467]; the database's input can identify correlations between health conditions and specific levels of different types of biomarkers; and thus, the database can send information related to the diagnosis of a disease, a disease severity assessment, a risk stratification, a therapeutic decision or request, a recommendation to the user to be tested for a specific type of condition, or combinations thereof [0223] configured on a computer readable storage medium [0310].
Lamrani et al. further teaches the method can also include aggregating the user submitted health data with the archived user health data; analyzing the aggregated health data using a processor; and correlating the aggregated health data with a health condition indicator to predict a health condition of the user [0003].
Claim 3 is directed to the database including entries for multiple individuals.
Lamrani et al. teaches in some examples the database is built using thousands of users [0223].
Claim 4 is directed to the system further including an artificial intelligence and machine learning algorithm that monitors the database for similar disease biomarkers in more than two individuals.
Lamrani et al. teaches as data relating to a user's biomarker characteristics is collected, this data and data from a plurality of other users can contribute to the information stored within the database; and in some examples, patient data can be used as predictors in a statistical machine learning process [0223].
Lamrani et al. further teaches in some examples, the algorithms applied to the collected data can also include support vector machines, neural networks, decision trees, gaussian mixture models, hidden markov methods, and wavelet analysis [0311]; and modern data mining processes, machine learning, and artificial intelligence can also be used to process the data [0311].
Claims 2-4 are rejected under 35 U.S.C. 102(a)(1)(a)(2) as being anticipated by Spetzler et al. (2014/0141986).
Claim 2 is directed to a system for predicting a disease that includes a database of biomarkers for an individual with baseline biomarker entries; and comparing the biomarkers to a correlation table of biomarkers and diseases.
Spetzler et al. describes methods of detecting and using circulating biomarkers.
Spetzler et al. teaches methods of the invention can be used to characterize diseases and disorders that can be assessed via biomarkers [0183] by storing a reference value in a database and for the diagnosis, prognosis, theranosis, disease stratification, disease monitoring, treatment monitoring or prediction of non-responder/responder status of a disease or condition based on the level or amount of circulation biomarkers [0340], that may be based on samples assessed from the same subject so to provide individualized tracking [0331]; and used as a baseline for detection of one or more circulating biomarker populations in a test subject for a reference population [0332].
Spetzler et al. teaches FIG. 1 (a)-(g) represents a table which lists exemplary cancers by lineage, group comparisons of cells/tissue, and specific disease states and antigens specific to those cancers, group cell/tissue comparisons and specific disease states, where the antigen can be a biomarker [0038]; if the subject's biosignature correlates more closely with reference values indicative of cancer, a diagnosis of cancer may be made; and conversely, if the subject's biosignature correlates more closely with reference values indicative of a healthy state, the subject may be determined to not have the disease [0340].
Spetzler et al. further teaches an embodiment that uses pattern recognition methods; involves comparing biomarker expression profiles for various biomarkers or biosignature portfolios to ascribe diagnoses; and fixing the expression profiles of each of the biomarker comprising the biosignature portfolio in a medium such as a computer readable medium [1263].
Claim 3 is directed to the database including entries for multiple individuals.
Spetzler et al. teaches assessing and storing vesicles or other circulation biomarkers from reference subjects with and without the cancer in the database [0340].
Claim 4 is directed to the system further including an artificial intelligence and machine learning algorithm that monitors the database for similar disease biomarkers in more than two individuals.
Spetzler et al. teaches performing classification using supervised methods with a methodology including [0359]: choosing a learning algorithm such as artificial neural networks, decision trees, Bayes classifiers or support vector machines; and using the learning algorithm is to build the classifier [0363] for levels of circulation biomarkers of interest in reference subjects with and without a disease as the training and test sets; assessing circulating biomarker levels found in a sample from a test subject; and using the classifier to classify the subject as with or without the disease [0365].
Spetzler et al. further teaches unsupervised learning approaches can also be used with the invention [0366].
Conclusion
No claims are currently allowed.
Correspondence
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Milana Thompson whose telephone number is (571)272-8740. The examiner can normally be reached Monday - Friday, 9:00-6:00 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Karlheinz Skowronek can be reached at (571) 272-1113. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.K.T./Examiner, Art Unit 1687
/Karlheinz R. Skowronek/Supervisory Patent Examiner, Art Unit 1687
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