Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of claims
Claims 1-20 have been reviewed and addressed below.
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 a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1:
Claims 1-20 are drawn to processor, diagnostic system, non-transitory computer storage medium, which is/are statutory categories of invention (Step 1: YES).
Step 2A Prong One:
Independent claims 1, 11, 20 recite “receiving first medical training data”, “training a plurality of machine learning models on the received first medical training data to generate an ensemble of diagnostic models”, “receiving second medical training data”, “training second machine learning model on the received second medical training data to generate a knowledge based diagnostic model”, “receiving new patient data”, “determining whether the ensemble of diagnostic models indicates a risk of Addison’s disease for the new patient data”, “in case where the ensemble of diagnostic models indicates a risk for Addison’s disease for the new patient data, assessing the risk of Addison’s disease using the knowledge based diagnostic model”.
The recited limitations, as drafted, under their broadest reasonable interpretation, cover mental process since the steps can be performed manually using a pen and paper. Additionally the claims can also be interpreted as certain methods of organizing human activity under specifically managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claims recite an abstract idea (Step 2A Prong One: YES).
Step 2A Prong Two:
This judicial exception is not integrated into a practical application. The claims are abstract but for the inclusion of the additional elements including “processor”, “memory”, “non-transitory computer readable medium” which are additional elements that are recited at a high level of generality such that they amount to no more than mere instruction to apply the exception using generic computer components. See: MPEP 2106.05(f).
The additional elements are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed (e.g., the “processor” language is incidental to what it is “configured” to perform). Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
The claims does not recite the additional element which can be considered limitations directed to insignificant extra-solution activity that does amount to an inventive concept because the limitations do not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. See: MPEP 2106.05(g). (g).
The combination of these additional elements is no more than mere instructions to apply the exception using generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Hence, the additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Accordingly, the claims are directed to an abstract idea (Step 2A Prong Two: NO).
Step 2B:
The claims do 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, using the additional elements to perform the abstract idea amounts to no more than mere instructions to apply the exception using generic components. Mere instructions to apply an exception using a generic components cannot provide an inventive concept. See: MPEP 2106.05(f).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are not integrated into the claim because they are merely incidental or token additions to the claim that do not alter or affect how the process steps or functions in the abstract idea are performed. Therefore, the claimed additional elements do not add meaningful limitations to the indicated claims beyond a general linking to a technological environment. See: MPEP 2106.05(h).
Further, the claimed additional elements, identified above, are not sufficient to amount to significantly more than the judicial exception because they are generic components that are configured to perform well-understood, routine, and conventional activities previously known to the industry. See: MPEP 2106.05(d). Said additional elements are recited at a high level of generality and provide conventional functions that do not add meaningful limits to practicing the abstract idea. The originally filed specification supports this conclusion at Figure 1, and
paragraph 39 that “data processing device system 110 includes one or more data processing devices that implement or execute, in conjunction with other devices, such as one or more of those in the system 100, control programs associated with some of the various aspects of the disclosure. Each of the phrases “data processing device,” “data processor,” “processor,” and “computer” is intended to include any data processing device, such as a central processing unit (“CPU”), a circuit, a field programmable gate array (FPGA), a desktop computer, a laptop computer, a mainframe computer, a tablet computer, a personal digital assistant, a cellular phone, and any other device configured to process data, manage data, or handle data, whether implemented with electrical, magnetic, optical, biological components, or the like”.
The claims does not recite the additional element which can be considered limitations directed to insignificant extra-solution activity that does amount to an inventive concept because the limitations do not impose meaningful limits on the claim such that is it not nominally or tangentially related to the invention. See: MPEP 2106.05(g). (g).
Viewing the limitations as an ordered combination, the claims simply instruct the additional elements to implement the concept described above in the identification of abstract idea with routine, conventional activity specified at a high level of generality in a particular technological environment.
Hence, the claims as a whole, considering the additional elements individually and as an ordered combination, do not amount to significantly more than the abstract idea (Step 2B: NO).
Dependent claim(s) 2-9, 10-19, when analyzed as a whole, considering the additional elements individually and/or as an ordered combination, are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation(s) fail(s) to establish that the claim(s) is/are not directed to an abstract idea without significantly more. These claims fail to remedy the deficiencies of their parent claims above, and are therefore rejected for at least the same rationale as applied to their parent claims above, and incorporated herein.
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)(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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 1022a as being Anticipated by Hacohen (2021/0104294).
With respect to claim 1 Hacohen teaches a processor executed method for assessing the risk of Addison's disease, comprising:
receiving first medical training data; training a plurality of first machine learning models on the received first medical training data to generate an ensemble of diagnostic models (Hacohen paragraph 8 “predicting the candidate peptides that bind to the HLA molecule using a machine learning algorithm model trained using structural features extracted from one or more output models simulating occupancy of one or more binding peptides and one or more non-binding peptides on the HLA binding pocket in a crystal structure of the HLA allele or the crystal structure of a similar HLA allele”);
receiving second medical training data; training a second machine learning model on the received second medical training data to generate a knowledge based diagnostic model (Hacohen paragraph 8 “the structural features are identified by a second machine learning algorithm model that infers occupancy of the one or more candidate peptides on the HLA binding pocket, and wherein the second machine learning algorithm model is trained using one or more output models simulating occupancy of one or more binding peptides and one or more non-binding peptides on the HLA binding pocket in a crystal structure of the HLA allele or the crystal structure of a similar HLA allele”);
receiving new patient data; determining whether the ensemble of diagnostic models indicates a risk for Addison's disease for the new patient data (Hacohen paragraph 75 “present invention provides methods for predicting peptides capable of binding to HLA alleles. The embodiments take a set of candidate peptide sequences and identify one or more structural features indicative of occupancy of the candidate peptides on the binding pocket of HLA alleles. These structural features are input into a machine learning algorithm model that was trained using structural features extracted from one or more output models” and paragraph 120 “Examples of autoimmune diseases include but are not limited to acute disseminated encephalomyelitis (ADEM); Addison's disease”); and
in a case where the ensemble of diagnostic models indicates a risk for Addison's disease for the new patient data, assessing the risk of Addison's disease using the knowledge based diagnostic model (Hacohen paragraph 75 “present invention provides methods for predicting peptides capable of binding to HLA alleles. The embodiments take a set of candidate peptide sequences and identify one or more structural features indicative of occupancy of the candidate peptides on the binding pocket of HLA alleles. These structural features are input into a machine learning algorithm model that was trained using structural features extracted from one or more output models” and paragraph 120 “Examples of autoimmune diseases include but are not limited to acute disseminated encephalomyelitis (ADEM); Addison's disease”).
Claim 11 is rejected as above.
Claim 20 is rejected as above.
With respect to claim 2 Hacohen teaches the method according to claim 1, wherein the ensemble of diagnostic models includes one or more of a decision tree model, a random forest model, a neural network, a support vector machine, or a nearest neighbor classifier (Hacohen paragraph 92 “Examples of autoimmune diseases include but are not limited to acute disseminated encephalomyelitis (ADEM); Addison's disease”).
Claim 12 is rejected as above.
With respect to claim 3 Hacohen teaches the method according to The method according to wherein training the plurality of first machine learning models includes: selecting at least a first machine learning model and a second machine learning model for the ensemble of diagnostic models, wherein the second machine learning model is different from the first machine learning model; training the first machine learning model using a first subset of the received first medical training data to generate a first model of the ensemble of diagnostic models; and training the second machine learning model using a second subset of the received first medical training data to generate a second model of the ensemble of diagnostic models, wherein the second subset of the received first medical training data is different from the first subset of the received first medical training data, and wherein the risk for Addison's disease for the new patient data is determined based on outputs of the first machine learning model and the second machine learning model (Hacohen paragraph 8 “the present invention provides for a method of predicting peptides capable of binding to an HLA allele comprising: identifying structural features indicative of occupancy of one or more candidate peptides on the binding pocket of the HLA allele; and predicting the candidate peptides that bind to the HLA molecule using a machine learning algorithm model trained using structural features extracted from one or more output models simulating occupancy of one or more binding peptides and one or more non-binding peptides on the HLA binding pocket in a crystal structure of the HLA allele or the crystal structure of a similar HLA allele. In certain embodiments, the structural features are identified by: providing a crystal structure of the HLA allele or the crystal structure of a similar HLA allele; generating one or more output models simulating occupancy of the one or more candidate peptides on the HLA binding pocket in the crystal structure; and extracting structural features from the output models. In certain embodiments, the structural features are identified by a second machine learning algorithm model that infers occupancy of the one or more candidate peptides on the HLA binding pocket, and wherein the second machine learning algorithm model is trained using one or more output models simulating occupancy of one or more binding peptides and one or more non-binding peptides on the HLA binding pocket in a crystal structure of the HLA allele or the crystal structure of a similar HLA allele. In certain embodiments, the method further comprises: providing a crystal structure of the HLA allele or the crystal structure of a similar HLA allele; simulating occupancy of one or more binding peptides and one or more non-binding peptides on the HLA binding pocket in the crystal structure to generate one or more output models; extracting structural features from the output models; and training the machine learning algorithm model using the extracted features. In certain embodiments, at least 100 to 300 output models are generated for each peptide”).
Claim 13 is rejected as above.
With respect to claim 4 Hacohen teaches the method according to claim 3, wherein, it is determined that the ensemble of diagnostic models indicates a risk for Addison's disease for the new patient data in a case where the outputs of either the first machine learning model or the second machine learning indicate the risk for Addison's disease for the new patient data (Hacohen paragraph 75 “present invention provides methods for predicting peptides capable of binding to HLA alleles. The embodiments take a set of candidate peptide sequences and identify one or more structural features indicative of occupancy of the candidate peptides on the binding pocket of HLA alleles. These structural features are input into a machine learning algorithm model that was trained using structural features extracted from one or more output models” and paragraph 120 “Examples of autoimmune diseases include but are not limited to acute disseminated encephalomyelitis (ADEM); Addison's disease”).
Claim 14 is rejected as above.
With respect to claim 5 Hacohen teaches the method according to The method according to wherein training the second machine learning model includes: generating an expert model including a plurality of rules based on domain knowledge included in the received second medical training data; refining the plurality of rules in the expert model based on patient data included in the received second medical training data; and generating the knowledge based diagnostic model based on the refined plurality of rules (Hacohen paragraph 275 “concentration of active compound in the drug composition will depend on absorption, distribution, inactivation, and excretion rates of the drug as well as other factors known to those of skill in the art. It is to be noted that dosage values will also vary with the severity of the condition to be alleviated. It is to be further understood that for any particular subject, specific dosage regimens should be adjusted over time according to the individual need and the professional judgment of the person administering or supervising the administration of the compositions, and that the concentration ranges set forth herein are exemplary only and are not intended to limit the scope or practice of the claimed composition. The active ingredient may be administered at once, or may be divided into a number of smaller doses to be administered at varying intervals of time”).
Claim 15 is rejected as above.
With respect to claim 6 Hacohen teaches the method according to claim1,wherein the knowledge based diagnostic model includes one or more of a rules based model and a data driven machine learning model (Hacohen paragraph 93).
Claim 16 is rejected as above.
With respect to claim 7 Hacohen teaches the method according to The method according to wherein assessing the risk of Addison's disease using the knowledge based diagnostic model further includes: displaying, on a user interface, a series of prompts to receive further patient medical data; and assessing the risk of Addison's disease based the received further patient medical data in response to the series of prompts (Hacohen paragraph 50).
Claim 17 is rejected as above.
With respect to claim 8 Hacohen teaches the method according to claim 7, wherein each subsequent prompt of the series of prompts is dynamically selected using the knowledge based diagnostic model based on a response to a preceding prompt of the series of prompts (Hacohen paragraph 381).
Claim 18 is rejected as above.
With respect to claim 9 Hacohen teaches the method according to The method according to wherein the knowledge based diagnostic model is an interactive model, and wherein assessing the risk of Addison's disease includes interacting with a user using the knowledge based diagnostic model to provide guidance on diagnosis and treatment of Addison's disease (Hacohen paragraph 75).
Claim 19 is rejected as above.
With respect to claim 10 Hacohen teaches the method according to claim l, further comprising, in response to the assessed risk of Addison's disease being greater than a predetermined threshold, administering at least one of intravenous fluids, glucocorticoids, or mineralocorticoids (Hacohen paragraph 382 “another embodiment glucocorticoids are administered with or before the therapy”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to REGINALD R REYES whose telephone number is (571)270-5212. The examiner can normally be reached 8:00-4:30 M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Shahid R. Merchant can be reached at (571) 270-1360. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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REGINALD R. REYES
Primary Examiner
Art Unit 3684
/REGINALD R REYES/Primary Examiner, Art Unit 3684