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 Claim(s)
Claims 1-20 are examined.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: a patient intake module, a medical analysis module in claim 1 and a medical analysis module in claim 11, an intake module in claim 12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 (i.e. a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1
Claim(s) 1-10 recite(s) a method, which is within the four statutory categories (i.e. process). Claim(s) 11-20 recite(s) a system, which is within the four statutory categories (i.e. machine).
Step 2A | Prong One
Claim 1 recites
A method comprising:
(a) receiving, at an intake module, medical history information of a patient;
(b) extracting at least one patient-specific feature from the received medical history information, each feature corresponding to a medical history event, a medical diagnosis, a medical symptom, a medical procedure, a medication, a treatment, a response to treatment, an outcome, or a laboratory finding;
(c) correlating, via a medical analysis module, each feature to at least one of a plurality of variables in a predetermined knowledgebase, the predetermined knowledgebase including data indicative of an order of occurrence for the plurality of variables, the plurality of variables including a plurality of candidate output variables;
(d) creating, via the medical analysis module, a reduced knowledgebase from the predetermined knowledgebase based at least in part on the correlated variables;
(e) analyzing, via the medical analysis module, the reduced knowledgebase with respect to at least one of the plurality of candidate output variables; and
(f) identifying, via the medical analysis module, one or more of the candidate output variables based at least in part on the analyzing of the reduced knowledgebase,wherein the plurality of candidate output variables comprise different medical diagnoses or different responses to medical treatments.
The above limitations, under the broadest reasonable interpretation constitutes “organizing human activities” because creating a reduced knowdledgebase of medical record can be performed by human with pen and paper. Accordingly, the claim describes at least one abstract idea.
Furthermore, the abstract idea for claim 11 is identical as the abstract idea for claim 1, because the only difference between claim 1 and claim 11 is that claim 1 recites a method, whereas claim 11 recites a system.
Furthermore, the following depending claims further define the at least one abstract idea, and thus fail to make the abstract idea any less abstract.
Dependent claim 2-10, 12--20 recite specific limitation to create the health record, a reduced knowledgebase, as being part of the abstract idea, and thus part of certain methods of organizing human activity.
Step 2A | Prong Two
Claim 1 recites
A method comprising:
(a) receiving, at an intake module, medical history information of a patient;
(b) extracting at least one patient-specific feature from the received medical history information, each feature corresponding to a medical history event, a medical diagnosis, a medical symptom, a medical procedure, a medication, a treatment, a response to treatment, an outcome, or a laboratory finding;
(c) correlating, via a medical analysis module, each feature to at least one of a plurality of variables in a predetermined knowledgebase, the predetermined knowledgebase including data indicative of an order of occurrence for the plurality of variables, the plurality of variables including a plurality of candidate output variables;
(d) creating, via the medical analysis module, a reduced knowledgebase from the predetermined knowledgebase based at least in part on the correlated variables;
(e) analyzing, via the medical analysis module, the reduced knowledgebase with respect to at least one of the plurality of candidate output variables; and
(f) identifying, via the medical analysis module, one or more of the candidate output variables based at least in part on the analyzing of the reduced knowledgebase,wherein the plurality of candidate output variables comprise different medical diagnoses or different responses to medical treatments.
Furthermore, claims 1-20 are not integrated into a practical application because there is no application or use of the abstract idea in any meaningful way. In particular, the claims recite the additional elements, using a memory, a processing system, , etc…; for processing data. The specification’s written description describes these mere generic components. These components encompass any number of suitable computer architectures. And thus, these components are recited at a high-level of generality. The specification does not provide any specific information of marking a placement associated with the location information Furthermore, the use of the one or more processors to perform functions of one or more modules to create a reduced knowledgebase, to correlate a plurality of candidate variables, “add nothing when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology, and their collective functions merely provide conventional computer implementation.
(The Examiner further note that claim 1 recites an intake module, analysis module without a structure to perform the functions of plurality of modules.)
And thus, it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this 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 claims are directed to an abstract idea.
Step 2B
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect step 2A, Prong One, the additional elements are recited at a high level of generality, and the written description describes these elements such as the processing system, a memory are generic computer components, (Spec.; Paras 0030, 0050-0051). The Examiner submits that these limitations amount to merely using these computer devices as well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368, 125 USPQ2d 1649, 1654 (Fed. Cir. 2018).), and MPEP 2106.05(d)(I)(2). Further the use of generic computer components to perform abstract ideas does not provide a necessary inventive concept. See Alice, 573 U.S. at 223 (“mere recitation of a generic computer cannot transform a patient-ineligible abstract idea into a patent-eligible invention”). Thus, claims 1-20 have been held as patent ineligible under 35 USC 101.
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Bostic et al. (US20210202103A1 hereinafter Bostic) in view of Seward (US 20100094648A1).
With respect to claim 1, Bostic teaches a method comprising:
(a) receiving, at an intake module, medical history information of a patient (‘103; Para 0195: the data intake module 202 obtains data from one or more data sources (e.g., healthcare systems 130, pharmacy systems 140, testing lab systems 150 and/or insurance systems 160). The data intake module 202 may implement one or more public or private APIs (e.g., SOAP, RESTful APIs, and the like). The APIs may be passive (i.e., the data sources push data to the data intake module 202) and/or active);
(b) extracting at least one patient-specific feature from the received medical history information, each feature corresponding to a medical history event, a medical diagnosis, a medical symptom, a medical procedure, a medication, a treatment, a response to treatment, an outcome, or a laboratory finding (‘103; Paras 0077-0078: identifying a set of features based on the user profile; history of lab results, Para 0156 symptom and testing event; Para 0081: diagnosis; Para 0088: treatment; Para 0216: a machine learned model may be trained to recommend multiple types of tests. In these embodiments, the testing recommendation module 304 may input the set of features to the model, which outputs a confidence score for each type of potential test given the features (e.g., the features extracted from the proposed prescription and the patient information);
(c) correlating, via a medical analysis module, each feature to at least one of a plurality of variables in a predetermined knowledgebase, the predetermined knowledgebase including data indicative of an order of occurrence for the plurality of variables, the plurality of variables including a plurality of candidate output variables(‘103; Para 0034: Bostic further describes the patient health state inference is based at least in part on a set of patient test data comprising a machine learning module for analyzing a set of at least one of laboratory testing data including at least one corresponding outcome, a correlation module for correlating the outcome with signals from the patient test data, analyzing the testing data corresponding to a set of patients, and providing a listing of a set of patients most likely to have a specified pathology; Para 0035: the patient health state is compared to ideal disease state data, a measure of correspondence between the patent health state and the ideal disease state data is calculated. In embodiments, the ideal disease state data is based upon one or more clinical standards and/or optimal health outcomes. In embodiments, the patient health state is an organ-specific health condition metric. In embodiments, the patient health state is a weighted metric summarizing a plurality of organ-specific health condition metrics; Para 0295: Bostic further describes simulate a future health state of the patient based on a plurality of variables, such as by simulating a health state of a patient according to a combination of time frames, treatment plans, prescription drug schedules, and lifestyle changes )
Seward teaches
(d) creating, via the medical analysis module, a reduced knowledgebase from the predetermined knowledgebase based at least in part on the correlated variables (‘648; Abstract: Seward describes a knowledgebase comprising a plurality of feature-sets is created using complexity science. The knowledgebase is accessible through a network to a computer, a system, and a computer program code that receives and analyzes medical data to determine the existence of a disease state. The medical data is input and correlated to features within the knowledgebase to identify feature-sets, each feature-set indicating a particular medical condition. After one or more feature-sets have been selected, associative algorithms consider the magnitudes or values of the medical data and, with the values of features in the feature-sets, assess the risk burden of the medical condition associated with the feature-set. An output is generated that may include diagnoses of one or more medical conditions, the risk burden of the medical condition(s), possible treatment options and prevention techniques)
(e) analyzing, via the medical analysis module, the reduced knowledgebase with respect to at least one of the plurality of candidate output variables; and
(f) identifying, via the medical analysis module, one or more of the candidate output variables based at least in part on the analyzing of the reduced knowledgebase, wherein the plurality of candidate output variables comprise different medical diagnoses or different responses to medical treatments (‘648; Para 0019: Figs 3-5 illustrating medical data is analyzed to identify at-risk medical condition; Para 0038: Seward further describes create a new treatment assessment or reclassification and prevention or reduce misclassification thereby avoiding inappropriate treatment).
It would have been obvious to one of ordinary skill in the art before the effective filing date of claimed invention to modify simulation of current and future health states of Bostic with the technique of using expert knowledge for risk assessment and diagnosis as taught by Seward in order to identify the candidate output variables in a reduced knowledgebase.
Claim 11 is rejected as the same reason with claim 1.
With respect to claim 2, the combined art teaches the method of claim 1, further comprising, after (f): selecting, via the medical analysis module, a predetermined script for reporting the identified one or more candidate output variables; and transmitting, via a hallucination-free large language model of the intake module, the selected predetermined script (‘103; Para 0098-0099).
Claim 13 is rejected as the same reason with claim 2.
With respect to claim 3, the combined art teaches the method of claim 1, wherein: the analyzing of (e) comprises identifying at least one variable, which potentially has a direct effect for the at least one of the plurality of candidate output variables, that is missing from the reduced knowledgebase; and the method further comprises, prior to (f):requesting, via the intake module, further information from a user regarding the at least one variable missing from the reduced knowledgebase; receiving, via the intake module, the further information from the user; and updating, via the medical analysis module, the reduced knowledgebase based at least in part on the received further information (‘103; Para 0026: updating the digital twin of said patient based on the simulation of the future health state of said patient; updating the digital twin of said population of patients based on the simulation of the future health state of said population of patients; and presenting the healthcare research information determined to be relevant to at least one of said individual patient, said first population of patients, and said second population of patient).
Claim 14 is rejected as the same reason with claim 3.
With respect to claim 4, the combined art teaches the method of claim 1, wherein the intake module employs one or more large language models (‘103; Paras 0221-0222).
Claim 15 is rejected as the same reason with claim 4.
With respect to claim 5, the combined art teaches the method of claim 1, wherein: the receiving medical history information is via a conversation between the intake module and a user, and the medical analysis module manages the conversation to move between topics, ask clarifying questions, and/or ask for additional information from the user (‘648; Para 0272: one or more of a questionnaire, a risk score, and a report including feedback, the feedback including potential areas of improvement.).
Claim 16 is rejected as the same reason with claim 5.
With respect to claim 6, the combined art teaches the method of claim 1, wherein at least some of the plurality of variables in the predetermined knowledgebase correspond to structured codes from medical ontology (‘103; Para 0100).
With respect to claim 7, the combined art teaches the method of claim 1, wherein the creating of (d) comprises: calculating a direct effect for each correlated variable with respect to the at least one of the plurality of candidate output variables, and removing at least some variables from the predetermined knowledgebase that are not correlated to any extracted feature (‘103; Para 0035: the patient health state is compared to ideal disease state data, a measure of correspondence between the patent health state and the ideal disease state data is calculated).
Claim 17 is rejected as the same reason with claim 7.
With respect to claim 8, the combined art teaches the method of claim 7, wherein the reduced knowledgebase comprises a directed acyclical graph including the correlated variables and based on the calculated direct effects (‘103; Para 0159).
With respect to claim 9, the combined art teaches the method of claim 1, further comprising, prior to (a), building the predetermined knowledgebase by receiving a plurality of medical records including structured codes and timestamps ‘(103; Para 0163-0164).
Claim 18 is rejected as the same reason with claim 9.
With respect to claim 10, the combined art teaches the method of claim 9, wherein: a temporal order of occurrence of probabilities is calculated for each pair of variables based at least in part on the timestamps, a predetermined order of variables, or a percent of variation based on an alternative order of occurrence, a conditional probability is calculated for each variable; and directed arcs between related pairs of variables are based at least in part on the calculated conditional probabilities and the temporal order of occurrence. (‘103; Para 0160-0161)
Claim 19 is rejected as the same reason with claim 10.
With respect to claim 12, the combined art teaches the system of claim 11, wherein the one or more modules further comprise an intake module configured to receive medical history information from a user, each patient- specific feature being extracted from the received medical history information (103; Paras 0077-0078, 0081, 0088).
Claim 20 is rejected as the same reason with claim 12.
Conclusion
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/HIEP V NGUYEN/Primary Examiner, Art Unit 3686
