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 .
Response to Amendment
In light of the amendments, the previous claim objection has been overcome.
In light of the amendments, claim 21 is rejected under 35 U.S.C. 112(a).
In light of the amendments, the claims are rejected under 35 U.S.C. 101.
In light of the amendments, the claims are rejected under 35 U.S.C. 103.
Notice to Applicant
In the amendment dated 06/10/2026, the following has occurred: claims 1, 4, 6-7, 9, 12, 15, and 18 have been amended; claims 17 and 20 have been canceled; claims 2-3, 5, 8, 10-11, 13-14, 16, and 19 remain unchanged; and claim 21 has been added.
Claims 1-16, 18-19, and 21 are pending.
Effective Filing Date: 10/03/2023
Response to Arguments
35 U.S.C. 101 Rejections:
Applicant argues that the claims recite a providing step and the claims should now overcome the 101 rejections. Examiner however respectfully disagrees as the providing of a score is still done so in a broad manner and can further express sharing of information, while not actually affecting a patient in a particular treatment for a particular affliction manner. The limitation of “to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient” is merely intended use.
35 U.S.C. 102/103 Rejections:
Applicant argues that the Glik et al. reference does not teach the claimed invention. Examiner however respectfully disagrees and has provided a clearer mapping to prove this. Applicant states that Hettrick et al. does not teach the diagnosis data including the newly claimed features in the independent claims. Examiner however respectfully disagrees as Hettrick et al. does teach of data associated with chronic kidney disease. Applicant further argues that Hettrick et al. does not teach estimated glomerular filtration rate as a categorized feature at all, let alone a feature that is used to generate an Alzheimer’s risk score. The claim language however recites that the test results are related to estimated glomerular filtration rate, not the rate being a categorization feature.
Claim Rejections - 35 USC § 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 21 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 21 recites “at least an average value, a median value, a minimum value, and a maximum value for each of the at least one blood test result” however there is no support in the specification that all of these values are being determined for each of the blood test results leading Examiner to question whether Applicant had support of the claimed invention at the time of filing.
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 1-16, 18-19, and 21 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 1-8, 18, and 21 are drawn to a method and claims 9-16 and 19 are drawn to a system, each of which is within the four statutory categories. Claims 1-16, 18-19, and 21 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 1 recites a computer implemented method for determining a risk level of a candidate patient for developing Alzheimer’s disease, comprising:
1) receiving, at a) a computing device having one or more processors, laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient;
2) receiving, at the computing device, prescription data indicative of medications taken by the candidate patient;
3) receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient, wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, chronic kidney disease, mobility, hearing loss, and hypokalemia;
4) receiving, at the computing device, an age and gender associated with the candidate patient;
5) preprocessing, by the computing device, the laboratory test results thereby categorizing features of the laboratory test results related to estimated glomerular filtration rate;
6) generating, by the computing device, an Alzheimer’s risk score associated with the candidate patient utilizing b) at least one machine learning model based on the prescription data, diagnosis data, age, gender and categorized features;
7) outputting, by the computing device, the Alzheimer’s risk score; and
8) providing the Alzheimer’s risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient.
Claim 1 recites, in part, performing the steps of 1) receiving laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient, 2) receiving prescription data indicative of medications taken by the candidate patient, 3) receiving diagnosis data indicative of medical diagnoses associated with the candidate patient, wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, chronic kidney disease, mobility, hearing loss, and hypokalemia, 4) receiving an age and gender associated with the candidate patient, 5) preprocessing the laboratory test results thereby categorizing features of the laboratory test results related to estimated glomerular filtration rate, 6) generating an Alzheimer’s risk score associated with the candidate patient utilizing at least one model based on the prescription data, diagnosis data, age, gender and categorized features, 7) outputting the Alzheimer’s risk score, and 8) providing the Alzheimer’s risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes a way a person can generate a risk score using received data. Independent claim 9 recites similar limitations and is also directed to an abstract idea under the same analysis.
Claim 21 recites a computer implemented method for determining a risk level of a candidate patient for developing Alzheimer's disease prior to diagnosis of Alzheimer's disease or dementia in the candidate patient, comprising:
9) receiving, at a) a computing device having one or more processors, laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient and comprising at least one blood test result including at least one of an alanine transaminase value, an estimated glomerular filtration rate value, a hemoglobin value, and a hematocrit value;
10) receiving, at the computing device, prescription data indicative of medications taken by the candidate patient, the prescription data sourced from c) at least one of a pharmacy system and an electronic medical records system;
11) receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient, the diagnosis data including medical diagnosis related to at least two of anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia;
12) receiving, at the computing device, an age and gender associated with the candidate patient;
13) preprocessing, by the computing device, the laboratory test results by computing at least an average value, a median value, a minimum value, and a maximum value for each of the at least one blood test result, thereby generating categorized features;
13) selecting, by the computing device, b) a machine learning model from a plurality of machine learning models based on the age and gender of the candidate patient, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80, and wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data;
14) generating, by the computing device, an Alzheimer's risk score associated with the candidate patient by applying the selected machine learning model to the prescription data, diagnosis data, age, gender, and categorized features;
15) outputting, by the computing device, the Alzheimer's risk score for use in identifying the candidate patient as a candidate for further Alzheimer's disease screening prior to onset of cognitive decline symptoms; and
16) providing the Alzheimer's risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient.
Claim 21 recites, in part, performing the steps of 9) receiving laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient and comprising at least one blood test result including at least one of an alanine transaminase value, an estimated glomerular filtration rate value, a hemoglobin value, and a hematocrit value, 10) receiving prescription data indicative of medications taken by the candidate patient, the prescription data sourced, 11) receiving diagnosis data indicative of medical diagnoses associated with the candidate patient, the diagnosis data including medical diagnosis related to at least two of anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia, 12) receiving an age and gender associated with the candidate patient, 13) preprocessing the laboratory test results by computing at least an average value, a median value, a minimum value, and a maximum value for each of the at least one blood test result, thereby generating categorized features, 14) selecting a model from a plurality of models based on the age and gender of the candidate patient, 15) generating an Alzheimer's risk score associated with the candidate patient by applying the selected model to the prescription data, diagnosis data, age, gender, and categorized features, 16) outputting the Alzheimer's risk score for use in identifying the candidate patient as a candidate for further Alzheimer's disease screening prior to onset of cognitive decline symptoms, and 17) providing the Alzheimer's risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient. These steps correspond to Certain Methods of Organizing Human Activity, more particularly, managing personal behavior or relationships or interactions between people (including following rules or instructions). For example, the claim describes a way a person can generate and share a risk score for a patient using received data. Independent claim 9 recites similar limitations and is also directed to an abstract idea under the same analysis.
Claim 21 also recites, in part, performing the steps of 13) preprocessing the laboratory test results by computing at least an average value, a median value, a minimum value, and a maximum value for each of the at least one blood test result, thereby generating categorized features and 15) generating an Alzheimer's risk score associated with the candidate patient by applying the selected model to the prescription data, diagnosis data, age, gender, and categorized features. These steps correspond to Mathematical Concepts.
Going forward, the above abstract concepts will be considered as a singular abstract concept for further consideration.
Depending claims 2-8, 10-16, and 18-19 include all of the limitations of claims 1 and 9, and therefore likewise incorporate the above described abstract idea. Depending claims 5 and 13 add mathematical steps to the claims. Claims 2-4, 6-8, 10-12, 14-16, and 18-19 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Claims 3 and 11 add an additional element of “a pharmacy system”, claims 4 and 12 add “an insurance system”, and claims 8 and 16 add additional element details related to the machine learning models. These additional elements are further assessed below. Thus, depending claims 2-8, 10-16, and 18-19 are nonetheless directed towards fundamentally the same abstract idea as independent claims 1 and 9 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a computing device, b) at least one machine learning model, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80, and wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data, c) at least one of a pharmacy system and an electronic medical records system, and d) a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations (from claim 9) to perform the claimed steps.
The a) computing device, b) at least one machine learning model, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80, and wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data, and d) non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that it amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0033] where there is a generic description of a machine learning model and paragraph [0053] where there is a general-purpose computer for the computing device, see MPEP 2106.05(f)).
Additionally, the c) at least one of a pharmacy system and an electronic medical records system in these steps adds insignificant extra-solution activity to the abstract idea which amounts to to mere data gathering, see MPEP 2106.05(g).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. Looking at the limitations as an ordered combination adds nothing that is not already present 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. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
Accordingly, 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. 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, the additional elements of using a) a computing device, b) at least one machine learning model, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80, and wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data, c) at least one of a pharmacy system and an electronic medical records system, and d) a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations to perform the claimed steps amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity) and mere instructions to apply the exception using a generic computer component that does not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain mathematical concept steps and certain method steps of organizing human activity. Specifically, MPEP 2106.05(d) and MPEP 2106.05(f) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); and
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)).
The current invention generates a risk score utilizing a) a computing device, b) at least one machine learning model, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80, and wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data, and d) a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations, thus these computing devices are adding the words “apply it” with mere instructions to implement the abstract idea on a computer and using machine learning.
Additionally, the c) at least one of a pharmacy system and an electronic medical records system in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives medical data from a system, and transmits the data to another system over a network, for example the Internet.
Mere instructions to apply an exception using generic computer components or WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-16, 18-19, and 21 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
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.
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.
Claims 1-7, 9-15, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/209468 to Glik et al. in view of U.S. 2018/0236235 to Hettrick et al.
As per claim 1, Glik et al. teaches a computer implemented method for determining a risk level of a candidate patient for developing Alzheimer’s disease, comprising:
--receiving, at a computing device having one or more processors, laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient; (see: 810 of FIG. 8 and line 30 of page 1 where test results data is being received)
--receiving, at the computing device, prescription data indicative of medications taken by the candidate patient; (see: page 5, lines 11-22 where there is medication frequency/intake data being received for use in prediction)
--receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient; (see: page 7, lines 19-25 and FIG. 1 where there is an EMR and data is being received from the EMR by the system. Also see: page 22, claim 15 where there is medical condition used to make a prediction. Therefore the patient data contains a medical condition/diagnosis data here in the EMR)
--receiving, at the computing device, an age and gender associated with the candidate patient; (see: page 7, lines 19-25 and FIG. 1 where there is an EMR and data is being received from the EMR by the system. Also see: page 22, claim 14 where there is age and gender information used to make a prediction. Therefore the patient data contains an age and gender data here in the EMR)
--preprocessing, by the computing device, the laboratory test results thereby categorizing features of the laboratory test results; (see: page 9, lines 5-9 where there is transforming/preprocessing of the test results to categorize features of the results)
--generating, by the computing device, an Alzheimer’s risk score associated with the candidate patient utilizing at least one machine learning model based on the prescription data, diagnosis data, age, gender and categorized features; (see: page 22, claims 14 and 15 where there is generation of a risk score using a trained predictive model based on all of the aforementioned data. Also see: page 1, lines 26-29 where the risk can be for that of Alzheimer’s disease. A patient’s data is being used here to generate an Alzheimer’s risk score using a predictive model)
--outputting, by the computing device, the Alzheimer’s risk score; (see: page 23, claim 19 where there is an outputting of the predicted risk score. Also see: page 1, lines 26-29 where the risk can be for that of Alzheimer’s disease) and
--providing the Alzheimer’s risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient (see: page 13, line 32 to page 14, lines 4 where there is providing of a risk score for treatment of the patient. The limitation “to reverse buildup of amyloid plaque and slow cognitive decline” is merely intended use and provides little patentable weight).
Glik et al. may not further, specifically teach:
1) --wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, chronic kidney disease, mobility, hearing loss, and hypokalemia; and
2) laboratory test results related to estimated glomerular filtration rate.
Hettrick et al. teaches:
1) --wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, chronic kidney disease, mobility, hearing loss, and hypokalemia; (see: paragraph [0066] where there is diagnosis data of a chronic kidney disease) and
2) laboratory test results related to estimated glomerular filtration rate (see: paragraphs [0063] and [0139] where there is test result data related to an estimated filtration rate).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, chronic kidney disease, mobility, hearing loss, and hypokalemia and use 2) laboratory test results related to estimated glomerular filtration rate as taught by Hettrick et al. in the method as taught by Glik et al. with the motivation(s) of improving a mental condition risk score assessment (see: paragraph [0141] of Hettrick et al.).
As per claim 2, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the laboratory test results are indicative of at least one blood test of the candidate patient (see: page 1, lines 26-29 where there is blood test data).
As per claim 3, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the prescription data is sourced from a pharmacy system (see: page 7, lines 19-25 where there is a pharmacy EMR system connected to the system. The prescription data is being sourced from here).
As per claim 4, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data is sourced from an insurance system (see: page 7, lines 7-12 where there is a database here connected to the system, the system being the healthcare system and the database in the in insurance system).
As per claim 5, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the preprocessing generates at least one of an average, median, minimum and maximum value of the categorized features (see: page 9, lines 1-4 where there is an average value of the data).
As per claim 6, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the categorized features further relate to at least one of alanine transaminase, hemoglobin, and hematocrit (see: page 8 where there is categorization of blood test data features related to hemoglobin).
As per claim 7, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data includes medical diagnosis further related to diseases of the heart (see: page 5, lines 11-22 where there is a diagnosis of a heart disease).
As per claim 9, claim 9 is similar to claim 1 and is therefore rejected in a similar manner to that claim. Glik et al. further teaches a computing system, comprising:
--one or more processors; (see: paragraph [0072] where there is a processor) and
--a non-transitory computer-readable storage medium having a plurality of instructions stored thereon, which, when executed by the one or more processors, cause the one or more processors to perform operations (see: paragraph [0072] where there is such a medium).
As per claim 10, claim 10 is similar to claim 2 and is therefore rejected in a similar manner to claim 2.
As per claim 11, claim 11 is similar to claim 3 and is therefore rejected in a similar manner to claim 3.
As per claim 12, claim 12 is similar to claim 4 and is therefore rejected in a similar manner to claim 4.
As per claim 13, claim 13 is similar to claim 5 and is therefore rejected in a similar manner to claim 5.
As per claim 14, claim 14 is similar to claim 6 and is therefore rejected in a similar manner to claim 6.
As per claim 15, claim 15 is similar to claim 7 and is therefore rejected in a similar manner to claim 7.
As per claim 18, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. Hettrick et al. may not further, specifically teach wherein the diagnosis data further includes medical diagnosis related to atherosclerosis (see: paragraph [0046] where there are atherosclerosis parameters as data).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 1, and incorporated herein.
As per claim 19, claim 19 is similar to claim 9 and is therefore rejected in a similar manner to claim 9.
Claims 8, 16, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over W.O. 2024/209468 to Glik et al. in view of U.S. 2018/0236235 to Hettrick et al. as applied to claims 1 and 9, and further in view of U.S. 2022/0406440 to Wang et al.
As per claim 8, Glik et al. and Hettrick et al. in combination teaches the method of claim 1, see discussion of claim 1. The combination may not further, specifically teach wherein the at least one machine learning model includes a first model representative of females aged 50-64, a second model representative of females aged 65-80, a third model representative of females aged over 80, a fourth model representative of males aged 50-64, a fifth model representative of males aged 65-80; and a sixth model representative of males aged over 80.
Wang et al. teaches:
--wherein the at least one machine learning model includes a first model representative of females aged 50-64, a second model representative of females aged 65-80, a third model representative of females aged over 80, a fourth model representative of males aged 50-64, a fifth model representative of males aged 65-80; and a sixth model representative of males aged over 80 (see: Table 1 and paragraph [0108] where there are character models for young males, young females, middle-aged males, middle-aged females, elderly males, and elderly females. The exact age ranges of 50-64, 65-80, and 80+ are merely descriptive).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to substitute wherein the at least one machine learning model includes a first model representative of females aged 50-64, a second model representative of females aged 65-80, a third model representative of females aged over 80, a fourth model representative of males aged 50-64, a fifth model representative of males aged 65-80; and a sixth model representative of males aged over 80 as taught by Wang et al. for the model as disclosed by Glik et al. and Hettrick et al. in combination since each individual element and its function are shown in the prior art, with the difference being the substitution of the elements. In the present case, the combination of Glik et al. and Hettrick et al. already teaches of using a model(s) to make a risk score prediction thus one could switch the model for specific models based on certain age ranges and genders for individuals as predictable results would be obtained of using models to determine risk. Thus, one of ordinary skill in the art could have substituted the one known element for the other to produce a predictable result (MPEP 2143).
As per claim 16, claim 16 is similar to claim 8 and is therefore rejected in a similar manner.
As per claim 21, Glik et al. teaches a computer implemented method for determining a risk level of a candidate patient for developing Alzheimer's disease prior to diagnosis of Alzheimer's disease or dementia in the candidate patient, comprising:
--receiving, at a computing device having one or more processors, laboratory test results from a laboratory, the laboratory test results corresponding to the candidate patient (see: 810 of FIG. 8 and line 30 of page 1 where test results data is being received) and comprising at least one blood test result including at least one of an alanine transaminase value, an estimated glomerular filtration rate value, a hemoglobin value, and a hematocrit value; (see: page 1, lines 26-29 where there is blood test data. Also see: page 8 where there is categorization of blood test data features related to hemoglobin)
--receiving, at the computing device, prescription data indicative of medications taken by the candidate patient, (see: page 5, lines 11-22 where there is medication frequency/intake data being received for use in prediction) the prescription data sourced from at least one of a pharmacy system and an electronic medical records system; (see: page 7, lines 19-25 where there is a pharmacy EMR system connected to the system. The prescription data is being sourced from here)
--receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient, (see: page 7, lines 19-25 and FIG. 1 where there is an EMR and data is being received from the EMR by the system. Also see: page 22, claim 15 where there is medical condition used to make a prediction. Therefore the patient data contains a medical condition/diagnosis data here in the EMR) the diagnosis data including medical diagnosis related to at least anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia; (see: page 5, lines 11-22 where there is a diagnosis of a heart disease)
--receiving, at the computing device, an age and gender associated with the candidate patient; (see: page 7, lines 19-25 and FIG. 1 where there is an EMR and data is being received from the EMR by the system. Also see: page 22, claim 14 where there is age and gender information used to make a prediction. Therefore the patient data contains an age and gender data here in the EMR)
--preprocessing, by the computing device, the laboratory test results by computing at least an average value, a minimum value, and a maximum value for each of the at least one blood test result, thereby generating categorized features; (see: page 9, lines 5-9 where there is transforming/preprocessing of the test results to categorize features of the results. Also see: page 5, lines 29-34 where there are extracted blood test values which have been aggregated/transformed into an average, maximum, minimum value for these values)
--wherein each model is trained on data collected prior to an Alzheimer's disease diagnosis date of patients in the training data; (see: page 2, lines 1-7 where there is such a model training occurring based on past data)
--generating, by the computing device, an Alzheimer's risk score associated with the candidate patient by applying the selected machine learning model to the prescription data, diagnosis data, age, gender, and categorized features; (see: page 22, claims 14 and 15 where there is generation of a risk score using a trained predictive model based on all of the aforementioned data. Also see: page 1, lines 26-29 where the risk can be for that of Alzheimer’s disease. A patient’s data is being used here to generate an Alzheimer’s risk score using a predictive model)
--outputting, by the computing device, the Alzheimer's risk score for use in identifying the candidate patient as a candidate for further Alzheimer's disease screening prior to onset of cognitive decline symptoms; (see: page 23, claim 19 where there is an outputting of the predicted risk score. Also see: page 1, lines 26-29 where the risk can be for that of Alzheimer’s disease. The limitation “for use in….” is merely intended use and provides little patentable weight) and
--providing the Alzheimer's risk score as a diagnostic protocol for treatment of the candidate patient including medications to reverse buildup of amyloid plaque and slow cognitive decline of the candidate patient (see: page 13, line 32 to page 14, lines 4 where there is providing of a risk score for treatment of the patient. The limitation “to reverse buildup of amyloid plaque and slow cognitive decline” is merely intended use and provides little patentable weight).
Glik et al. may not specifically teach preprocessing, by the computing device, the laboratory test results by computing at least a median value. Glik et al. does however teach various scores for values such as an average value, standard deviation, and/or the like. Calculating a median value is well-known and would therefore fall within the “the like” category.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to compute a median value instead of standard deviation as disclosed by Glik et al. since there are a finite number of identified, predictable potential solutions to the recognized problem or need. In the present case, there is calculation of various statistics for the data and there are a finite amount of ways to represent aggregated data. Thus, one of ordinary skill in the art could have pursued the known potential solutions with reasonable expectation of success (MPEP 2143).
Glik et al. also may not further, specifically teach:
1) --the diagnosis data including medical diagnosis related to at least two of anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia; and
2) --selecting, by the computing device, a machine learning model from a plurality of machine learning models based on the age and gender of the candidate patient, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80.
Hettrick et al. teaches:
1) --the diagnosis data including medical diagnosis related to at least two of anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia (see: paragraph [0066] where there is diagnosis data of a chronic kidney disease. Glik et al. already teaches of data of diseases of the heart).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have 1) the diagnosis data include medical diagnosis related to at least two of anemia, aphasia, chest pain, chronic kidney disease, diseases of the heart, mobility impairment, hearing loss, and hypokalemia as taught by Hettrick et al. in the method as taught by Glik et al. with the motivation(s) of improving a mental condition risk score assessment (see: paragraph [0141] of Hettrick et al.).
Wang et al. teaches:
2) --selecting, by the computing device, a machine learning model from a plurality of machine learning models based on the age and gender of the candidate patient, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80 (see: Table 1 and paragraph [0108] where there are character models for young males, young females, middle-aged males, middle-aged females, elderly males, and elderly females. The exact age ranges of 50-64, 65-80, and 80+ are merely descriptive. Also see: [0029] where there is model selection. The models being trained models was already taught in the Glik et al. reference above).
Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include 2) selecting, by the computing device, a machine learning model from a plurality of machine learning models based on the age and gender of the candidate patient, wherein the plurality of machine learning models comprises a first model trained exclusively on data from females aged 50-64, a second model trained exclusively on data from females aged 65-80, a third model trained exclusively on data from females aged over 80, a fourth model trained exclusively on data from males aged 50-64, a fifth model trained exclusively on data from males aged 65-80, and a sixth model trained exclusively on data from males aged over 80 as taught by Wang et al. in the method of Glik et al. and Hettrick et al. in combination since the claimed invention is only a combination of these old and well known elements which would have performed the same function in combination as each did separately. In the present case Glik et al. and Hettrick et al. in combination teaches of using models and adding a selection step for the models would maintain the same functionality of the combination of Glik et al. and Hettrick et al., making the results predictable to one of ordinary skill in the art (MPEP 2143).
Conclusion
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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