DETAILED ACTION
Claims 1-20 are currently pending and have been examined.
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 .
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 1-20 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Criteria - Step 1:
Claims 1-13 are directed to a method (i.e., a process); Claims 14-20 are directed to a system (i.e., a machine). Accordingly, claims 1-20 are all within at least one of the four statutory categories.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong One:
Regarding Prong One of Step 2A, the claim limitations are to be analyzed to determine whether, under their broadest reasonable interpretation, they “recite” a judicial exception or in other words whether a judicial exception is “set forth” or “described” in the claims. MPEP 2106.04(II)(A)(1). An “abstract idea” judicial exception is subject matter that falls within at least one of the following groupings: a) certain methods of organizing human activity, b) mental processes, and/or c) mathematical concepts. MPEP 2106.04(a).
Representative independent claim 14 includes limitations that recite at least one abstract idea. Specifically, independent claim 14 recites:
14. A system comprising:
one or more data storage media configured to store specific computer-executable instructions; and
one or more computer hardware processors configured to communicate with the one or more data storage media, wherein the specific computer-executable instructions are configured to cause the one or more computer hardware processors to at least:
receive training data comprising (i) a plurality of blood test results, (ii) a first label, and (iii) a second label,
wherein each blood test result set, from the plurality of blood test results, comprises a plurality of features,
wherein each blood test result set, from the plurality of blood test results, comprises a metabolic panel test result and a complete blood count test result,
wherein each blood test result set, from the plurality of blood test results, is associated with a particular patient, and
wherein each patient is associated with either (i) the first label indicating an identification of a disease or a condition or (ii) the second label indicating absence of the disease or the condition;
train a machine learning model with the training data, wherein the machine learning model is configured to predict a likelihood that a patient has or will have the disease or the condition based at least in part on a new blood test result set for the patient;
receive a first blood test result set for a first patient;
extract a first plurality of features from the first blood test result set;
apply the machine learning model to the first plurality of features as first input, wherein the machine learning model outputs a score that reflects a first likelihood that the first patient has or will have the disease or the condition; and
output the score.
The Examiner submits that the foregoing underlined limitations constitute “methods of organizing human activity” because receiving patient data, predicting a medical condition based on the patient data, generating and sending a score based on the prediction are associated with managing personal behavior or relationships or interactions between people. For example, but for the system, this claim encompasses a person facilitating data access, receiving data, and outputting data in the manner described in the identified abstract idea. The Examiner notes that “method of organizing human activity” includes a person’s interaction with a computer – see MPEP 2106.04(a)(2)(II)(C). 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.
Accordingly, independent claim 14 and analogous independent claims 1 & 7 recite at least one abstract idea.
Furthermore, dependent claims 2-6, 8-13, & 15-20 further narrow the abstract idea described in the independent claims. Claims 2, 8 recites a disease or condition; Claim 3, 6-7, 9-10, 15, 18-19 recites generating feature data; Claims 4, 12-13, 16-17 recites clustering data and generating training data. These limitations only serve to further limit the abstract idea and hence, are directed towards fundamentally the same abstract idea as independent claim 14 and analogous independent claims 1 & 7, even when considered individually and as an ordered combination.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2A - Prong Two:
Regarding Prong Two of Step 2A of the Alice/Mayo test, it must be determined whether the claim as a whole integrates the abstract idea into a practical application. As noted at MPEP §2106.04(II)(A)(2), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.” MPEP §2106.05(I)(A).
In the present case, the additional limitations beyond the above-noted at least one abstract idea recited in the claim are as follows (where the bolded portions are the “additional limitations” while the underlined portions continue to represent the at least one “abstract idea”):
14. A system comprising:
one or more data storage media configured to store specific computer-executable instructions; and
one or more computer hardware processors configured to communicate with the one or more data storage media, wherein the specific computer-executable instructions are configured to cause the one or more computer hardware processors to at least:
receive training data comprising (i) a plurality of blood test results, (ii) a first label, and (iii) a second label,
wherein each blood test result set, from the plurality of blood test results, comprises a plurality of features,
wherein each blood test result set, from the plurality of blood test results, comprises a metabolic panel test result and a complete blood count test result,
wherein each blood test result set, from the plurality of blood test results, is associated with a particular patient, and
wherein each patient is associated with either (i) the first label indicating an identification of a disease or a condition or (ii) the second label indicating absence of the disease or the condition;
train a machine learning model with the training data, wherein the machine learning model is configured to predict a likelihood that a patient has or will have the disease or the condition based at least in part on a new blood test result set for the patient;
receive a first blood test result set for a first patient;
extract a first plurality of features from the first blood test result set;
apply the machine learning model to the first plurality of features as first input, wherein the machine learning model outputs a score that reflects a first likelihood that the first patient has or will have the disease or the condition; and
output the score.
For the following reasons, the Examiner submits that the above identified additional limitations do not integrate the above-noted at least one abstract idea into a practical application.
Regarding the additional limitations of the data storage media, processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).
Regarding the additional limitation of training and using a machine learning model to output a score, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
Thus, taken alone, the additional elements do not integrate the at least one abstract idea into a practical application.
Looking at the additional limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole with the abstract idea, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole does not integrate the abstract idea into a practical application of the abstract idea. MPEP §2106.05(I)(A) and §2106.04(II)(A)(2).
For these reasons, representative independent claim 14 and analogous independent claims 1 & 7 do not recite additional elements that integrate the judicial exception into a practical application.
Accordingly, the claims recite at least one abstract idea.
The remaining dependent claim limitations not addressed above fail to integrate the abstract idea into a practical application as set forth below:
Claims 5-6, 9-10, 18-19: This claim recites that the inputting additional data to a machine learning model which amounts to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
Claims 11, 20: These claims recite a type of machine learning model which amounts to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
Thus, taken alone, any additional elements do not integrate the at least one abstract idea into a practical application. Therefore, the claims are directed to at least one abstract idea.
Subject Matter Eligibility Criteria - Alice/Mayo Test: Step 2B:
Regarding Step 2B of the Alice/Mayo test, representative independent claim 14 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for reasons the same as those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application.
As discussed above, regarding the additional limitations of the data storage media, processor, the Examiner submits that these limitations amount to merely using computers as tools to perform the above-noted at least one abstract idea (see MPEP § 2106.05(f)).Regarding the additional limitation of training and using a machine learning model to output a score, the Examiner submits that these additional claim limitations amount to an attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result and is equivalent to the words “apply it”. See MPEP 2106.05(f)(1).
The dependent claims also do not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the dependent claims do not integrate the at least one abstract idea into a practical application.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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.
Claims 1-3, 7, & 14-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Tetreault (WO2022067426).
As per claim 1, Tetreault discloses a method comprising:
receive training data comprising (i) a plurality of blood test results, (ii) a first label, and (iii) a second label (para. 7, 38: classifier trained on various data including CBC test data and data assigned different labels),
wherein each blood test result set, from the plurality of blood test results, comprises a plurality of features (para. 71: features extracted from test result data),
wherein each blood test result set, from the plurality of blood test results, comprises a lipid panel test result, a metabolic panel test result, and a complete blood count test result (para. 13: data includes HDL, BMP, and CBC values),
wherein each blood test result set, from the plurality of blood test results, is associated with a particular patient (para. 13: patient data test results obtained), and
wherein each patient is associated with either (i) the first label indicating an identification of a disease or a condition or (ii) the second label indicating absence of the disease or the condition (para. 38, 74-75, 93: test result data is labeled with different types including abnormal/normal);
training a machine learning model with the training data, wherein the machine learning model is configured to predict a likelihood that a patient has or will have the disease or the condition based at least in part on a new blood test result set for the patient (para. 31-34, 38: trained machine learning models used to determine patient condition using new incoming test results);
receiving a first blood test result set for a first patient (para. 7-13: accessing results of the CBC test of a given patient);
extracting a first plurality of features from the first blood test result set (para. 7-13: various features extracted from CBC test results);
applying the machine learning model to the first plurality of features as first input, wherein the machine learning model outputs a score that reflects a first likelihood that the first patient has or will have the disease or the condition (Fig. 7; para. 8, 31-34, 38, 51-53: trained machine learning models used to determine patient condition using new incoming test results; likelihood determined reflecting possible medical condition); and
outputting the score (Fig.7; para. 53: augmented test report output including predicted values).
As per claim 2, Tetreault discloses the method of claim 1, wherein the disease or condition comprises at least one of: preterm labor, Parkinson's disease, juvenile rheumatoid arthritis, Alzheimer's disease, sleep apnea, osteoporosis with or without pathological fracture, breast cancer, endometriosis, seropositive rheumatoid arthritis, seronegative rheumatoid arthritis, shingles, pulmonary hypertension, colon cancer, Crohn's disease, systemic lupus erythematosus, celiac disease, colitis, epilepsy, basal cell carcinoma, prostate cancer, Type 1 diabetes, Type 2 diabetes, prediabetes, autoimmune disease, cardiovascular disease, respiratory disease, asthma, chronic obstructive pulmonary disease, infectious disease, metabolic disorder, or neurodegenerative condition (para. 7: diabetes).
As per claim 3, Tetreault discloses the method of claim 1, wherein a fast plasma glucose feature is absent from the first plurality of features (para. 55-56: machine learning model uses prior CBC test results and does not expressly teach include glucose test data).
As per claim 7, Tetreault discloses a method comprising:
receiving training data comprising (i) a plurality of blood test results, (ii) a first label, and (iii) a second label (para. 7, 38: classifier trained on various data including CBC test data and data assigned different labels),
wherein each blood test result set, from the plurality of blood test results, comprises a plurality of features (para. 71: features extracted from test result data),
wherein each blood test result set, from the plurality of blood test results, comprises a metabolic panel test result and a complete blood count test result (para. 13: data includes HDL, BMP, and CBC values),
wherein each blood test result set, from the plurality of blood test results, is associated with a particular patient (para. 13: patient data test results obtained), and
wherein each patient is associated with either (i) the first label indicating an identification of a disease or a condition or (ii) the second label indicating absence of the disease or the condition (para. 38, 74-75, 93: test result data is labeled with different types including abnormal/normal);
training a machine learning model with the training data, wherein the machine learning model is configured to predict a likelihood that a patient has or will have the disease or the condition based at least in part on a new blood test result set for the patient (para. 31-34, 38: trained machine learning models used to determine patient condition using new incoming test results);
receiving a first blood test result set for a first patient (para. 7-13: accessing results of the CBC test of a given patient);
extracting a first plurality of features from the first blood test result set (para. 7-13: various features extracted from CBC test results);
applying the machine learning model to the first plurality of features as first input, wherein the machine learning model outputs a score that reflects a first likelihood that the first patient has or will have the disease or the condition (Fig. 7; para. 8, 31-34, 38, 51-53: trained machine learning models used to determine patient condition using new incoming test results; likelihood determined reflecting possible medical condition); and
outputting the score (Fig.7; para. 53: augmented test report output including predicted values).
Claims 14-15 recite substantially similar limitations as those already addressed in claims 1-2, and, as such, are rejected for similar reasons as given above.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 4-5, 9, 11-13, 16-18, & 20 are rejected under 35 U.S.C. 103 as being unpatentable over Tetreault (WO2022067426) in view of Hodgson (US20200387810)
As per claim 4, Tetreault discloses the method of claim 1, but does not expressly teach further comprising:
clustering patient-related data that results in a plurality of data clusters; and
creating the training data based at least in part on a data cluster from the plurality of data clusters.
Hodgson however, teaches to clustering patient data into various clusters and using the clustered data to create a training data set (par. 113).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Hodgson with Tetreault based on the motivation of effectively process and evaluate large and complex data sets for various diseases (Hodgson – para. 2).
As per claim 5, Tetreault discloses the method of claim 1, but does not expressly teach wherein the training data further comprises genetic marker data, further comprising:
generating an additional feature for the patient corresponding to a genetic marker value, wherein the machine learning model receives the additional feature as second input.
Hodgson, however, teaches to extracting data including genomic data and using the data in a machine learning model (Fig. 10; para. 102).
The motivations to combine the above mentioned references are discussed in the rejection of claim 4, and incorporated herein.
Claims 9 & 18 recite substantially similar limitations as those already addressed in claim 5, and, as such, are rejected for similar reasons as given above.
As per claim 11, Tetreault discloses the method of claim 7, but does not expressly teach wherein the machine learning model comprises at least one of: a gradient-boosted tree, an ensemble model, a deep neural network, a transformer model, a reinforcement learning model, or a hybrid architecture combining a rule-based system and a machine learning system.
Hodgson, however, teaches to using a gradient-boosted tree algorithm for analyzing data (para. 6).
The motivations to combine the above mentioned references are discussed in the rejection of claim 4, and incorporated herein.
Claims 12 & 16 recite substantially similar limitations as those already addressed in claim 4, and, as such, are rejected for similar reasons as given above.
As per claim 13, Tetreault discloses the method of claim 12, but does not expressly teach wherein each data cluster of the plurality of data clusters corresponds to a particular disease or condition.
Hodgson however, teaches to clustering patient data into various clusters based on treatment or disease and using the clustered data to create a training data set (par. 113).
The motivations to combine the above mentioned references are discussed in the rejection of claim 4, and incorporated herein.
Claim 17 recites substantially similar limitations as those already addressed in claim 13, and, as such, is rejected for similar reasons as given above.
Claim 20 recites substantially similar limitations as those already addressed in claim 11, and, as such, is rejected for similar reasons as given above.
Claims 6, 10, & 19 are rejected under 35 U.S.C. 103 as being unpatentable over.
Tetreault (WO2022067426) in view of Neumann (US20200321119)
As per claim 6, Tetreault discloses the method of claim 1, but does not expressly teach wherein the training data further comprises ribonucleic acid sequence data, further comprising:
generating an additional feature for the patient corresponding to a ribonucleic acid sequence value, wherein the machine learning model receives the additional feature as second input.
Neumann, however, teaches to extracting data including RNA data and using the data in a machine learning model (para. 39, 77, 89).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Hodgson with Tetreault based on the motivation of providing automated analysis of data and correct transmission of said data (Neumann – para. 3).
Claims 10 & 19 recite substantially similar limitations as those already addressed in claim 6, and, as such, are rejected for similar reasons as given above.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over.
Tetreault (WO2022067426) in view of Brohman (US20210057039)
As per claim 8, Tetreault discloses the method of claim 7, but does not expressly teach wherein the disease or the condition comprises preterm labor.
Brohman, however, teaches to using machine learning analysis to predict risk of preterm labor (abstract).
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to combine the aforementioned features in Brohman with Tetreault based on the motivation of providing tools for determining whether a pregnant woman is at an increased risk for premature delivery, as well as tools for decreasing a pregnant subject's risk for premature delivery (Brohman – para. 5).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Adib (US20250166758) teaches to determining the risk of developing a known disease or condition or of identifying the presence of the known disease or condition in a subject includes obtaining subject data that includes results of blood tests. The blood tests include a basic metabolic panel (BMP) and a complete blood count (CBC) panel.
Sams (US20240420848) teaches to predicting health characteristic may include: obtaining patient medical information that includes protein data; a machine-learning model based on concordance with the characteristic, the patient medical information, a time-frame for prediction, or in the protein data.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jonathan K Ng whose telephone number is (571)270-7941. The examiner can normally be reached M-F 8 AM - 5 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anita Coupe can be reached at 571-270-7949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Jonathan Ng/ Primary Examiner, Art Unit 3619
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