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, 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 11/25/2025, the following has occurred: claims 7, 12, and 15 have been amended; claims 1-6, 8-11, 13-14, and 16 remain unchanged; and claims 17-20 have been added.
Claims 1-20 are pending.
Effective Filing Date: 10/03/2023
Response to Arguments
Claim Objection:
Applicant amended claim 12 to overcome the previous claim objection. Examiner withdraws the previous claim objection.
35 U.S.C. 101 Rejections:
Applicant argues that the enumerated subgroupings for abstract idea categorization should not be expanded past those sub-groupings. Applicant further cited the examples from the MPEP. These examples are not limiting, thus new abstract ideas may be categorized under these sub-groupings. The present claims recite certain methods of organizing human activity in the form of risk score generation for a user.
Applicant further states that the claims are directed towards a practical application because the claims contain a computer implementation, a machine learning model implementation, and extensive details about the risk score and the features upon which its determination is based. Examiner however respectfully disagrees that the claims are integrated into a practical application as the computing and machine learning elements are added into the claims in an “apply it” manner.
Applicant further states that the present claims are similar to Examples 35 and 36 provided by the office. Examiner however respectfully disagrees. With respect to claims 2 and 3 of Example 35, the present claims do not recite additional elements which integrate with the abstract idea to form significantly more. The above explanation also applies to the present claims in view of Example 36.
Lastly, Applicant argues with respect to Example 42 and the Desjardins opinion. As stated above, the claims do not contain additional elements which provide significantly more than the abstract concept, therefore the arguments directed towards Example 42 are not persuasive. With respect to Desjardins, Examiner has not examined the claims at a high level of generality. The claims however do reflect an abstract idea using a general computer as supported by Applicant’s own disclosure in paragraph [0053]. The recitation of the machine learning is also at a high level and the general assessment for this element can also be supported by Applicant’s specification in paragraph [0033].
35 U.S.C. 102/103 Rejections:
Applicant argues that Glik et al. does not teach the claim features based on the priority date of the filing of the instant application being earlier than the filing date of the Glik et al. reference. Applicant also stated that the features which Examiner relies on are not taught in the PCT from which the US Glik app gains priority from. The following is the rejection of claim 1 with the citations from US Glik in italics and the supporting citation in PCT Glik:
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: paragraph [0015] where there is reception of laboratory test results data of the subject. 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: paragraphs [0151] and [0266] where there is reception of prescriptions and medications data. See: page 5, lines 11-22 where there is medication 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: paragraph [0142] where the EMR stores diagnoses data. Also see: paragraph [0138] where there is accessing of EMR data. This data is being received. 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)
--receiving, at the computing device, an age and gender associated with the candidate patient; (see: paragraphs [0036] and [0263] where there are collected parameters including a birth date/age and a gender. 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)
--preprocessing, by the computing device, the laboratory test results thereby categorizing features of the laboratory test results; (see: paragraph [0266] where there is preprocessing of laboratory results. See: page 9, lines 5-9 where there is transforming 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: paragraph [0008] where there is generation of a risk score using a trained model based on extracted features of collected data. This collected data includes the aforementioned data. 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) and
--outputting, by the computing device, the Alzheimer’s risk score (see: paragraph [0012] where there is outputting of a predicted risk score for a neurodegenerative disease. Also see: paragraph [0051] where Alzheimer’s can be the disease. 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).
As per claim 2, Glik et al. 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: paragraph [0269] where there is a blood test of a subject. See: page 1, lines 26-29 where there is blood test data).
As per claim 3, Glik et al. 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: paragraph [0138] where the system be connected to a database of a laboratory service. There is a pharmacy system here in the form of an EMR 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. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data is sourced from at least one of a healthcare system and an insurance system (see: paragraph [0138] where the system be connected to a database of a laboratory service. There is an insurance system connected here in the form of a database. 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. 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: paragraph [0035] where there is an average value, etc. of data being generated. See: page 9, lines 1-4 where there is an average value of the data).
As per claim 6, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the categorized features relate to at least one of alanine transaminase, estimated glomerular filtration rate, hemoglobin, and hematocrit (see: paragraph [0042] where there is categorization features related to hemoglobin. See: page 8 where there is categorization of blood test data features related to hemoglobin).
As per claim 7, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, cerebrovascular disease, chest pain, chronic kidney disease, diseases of the heart, mobility, hearing loss, hypertension, and hypokalemia (see: paragraph [0046] where there is a diagnosis of a heart disease. See: page 5, lines 11-22 where there is a diagnosis of a heart disease).
Claims 9-15 are similar to the claims above, therefore their citations are also taught above.
As can be seen above, PCT Glik does support the subject matter disclosed in the citations of the U.S. Glik application. Applicant argues more specifically with respect to claim 1 and states that the “receiving” and “generating” steps were not taught in either the U.S. Glik application nor the PCT Glik application. Examiner however respectfully disagrees as Glik refers to condition data being used for the diagnosis data.
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. Claims 1-8 and 17-18 are drawn to a method and claims 9-16 and 19-20 are drawn to a system, each of which is within the four statutory categories. Claims 1-20 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;
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;
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; and
7) outputting, by the computing device, the Alzheimer’s risk score.
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, 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, 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, and 7) outputting the Alzheimer’s risk score. 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.
Depending claims 2-8 and 10-20 include all of the limitations of claims 1 and 9, and therefore likewise incorporate the above described abstract idea. Depending claims 2-8 and 10-20 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. Thus, depending claims 2-8 and 10-20 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 and b) at least one machine learning model to perform the claimed steps.
The a) computing device and b) at least one machine learning model 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)).
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 and b) at least one machine learning model 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), a general linking to a particular technological field, or 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 method steps of organizing human activity. Specifically, MPEP 2106.05(f) recites that the following limitations are not significantly more:
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 and b) at least one machine learning model, 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.
Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 1-20 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-7 and 9-15 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by U.S. 2025/0037877 to Glik 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: paragraph [0015] where there is reception of laboratory test results data of the subject)
--receiving, at the computing device, prescription data indicative of medications taken by the candidate patient; (see: paragraphs [0151] and [0266] where there is reception of prescriptions and medications data)
--receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient; (see: paragraph [0142] where the EMR stores diagnoses data. Also see: paragraph [0138] where there is accessing of EMR data. This data is being received)
--receiving, at the computing device, an age and gender associated with the candidate patient; (see: paragraphs [0036] and [0263] where there are collected parameters including a birth date/age and a gender)
--preprocessing, by the computing device, the laboratory test results thereby categorizing features of the laboratory test results; (see: paragraph [0266] where there is preprocessing of laboratory 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: paragraph [0008] where there is generation of a risk score using a trained model based on extracted features of collected data. This collected data includes the aforementioned data) and
--outputting, by the computing device, the Alzheimer’s risk score (see: paragraph [0012] where there is outputting of a predicted risk score for a neurodegenerative disease. Also see: paragraph [0051] where Alzheimer’s can be the disease).
As per claim 2, Glik et al. 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: paragraph [0269] where there is a blood test of a subject).
As per claim 3, Glik et al. 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: paragraph [0138] where the system be connected to a database of a laboratory service. There is a pharmacy system here in the form of an EMR system).
As per claim 4, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data is sourced from at least one of a healthcare system and an insurance system (see: paragraph [0138] where the system be connected to a database of a laboratory service. There is an insurance system connected here in the form of a database).
As per claim 5, Glik et al. 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: paragraph [0035] where there is an average value, etc. of data being generated).
As per claim 6, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the categorized features relate to at least one of alanine transaminase, estimated glomerular filtration rate, hemoglobin, and hematocrit (see: paragraph [0042] where there is categorization features related to hemoglobin).
As per claim 7, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. further teaches wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, cerebrovascular disease, chest pain, chronic kidney disease, diseases of the heart, mobility, hearing loss, hypertension, and hypokalemia (see: paragraph [0046] where there is a diagnosis of a heart disease).
As per claim 9, Glik et al. 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) 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: paragraph [0015] where there is reception of laboratory test results data of the subject)
--receiving, at the computing device, prescription data indicative of medications taken by the candidate patient; (see: paragraphs [0151] and [0266] where there is reception of prescriptions and medications data)
--receiving, at the computing device, diagnosis data indicative of medical diagnoses associated with the candidate patient; (see: paragraph [0142] where the EMR stores diagnoses data. Also see: paragraph [0138] where there is accessing of EMR data. This data is being received)
--receiving, at the computing device, an age and gender associated with the candidate patient; (see: paragraphs [0036] and [0263] where there are collected parameters including a birth date/age and a gender)
--preprocessing, by the computing device, the laboratory test results thereby categorizing features of the laboratory test results; (see: paragraph [0266] where there is preprocessing of laboratory 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: paragraph [0008] where there is generation of a risk score using a trained model based on extracted features of collected data. This collected data includes the aforementioned data) and
--outputting, by the computing device, the Alzheimer’s risk score (see: paragraph [0012] where there is outputting of a predicted risk score for a neurodegenerative disease. Also see: paragraph [0051] where Alzheimer’s can be the disease).
As per claim 10, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. further teaches wherein the laboratory test results are indicative of at least one blood test of the candidate patient (see: paragraph [0269] where there is a blood test of a subject).
As per claim 11, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. further teaches wherein the prescription data is sourced from a pharmacy system (see: paragraph [0138] where the system be connected to a database of a laboratory service. There is a pharmacy system here in the form of an EMR system).
As per claim 12, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. further teaches wherein the diagnosis data is sourced from at least one of a healthcare system and an insurance system (see: paragraph [0138] where the system be connected to a database of a laboratory service. There is an insurance system connected here in the form of a database).
As per claim 13, Glik et al. teaches the system of claim 9, see discussion of claim 9. 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: paragraph [0035] where there is an average value, etc. of data being generated).
As per claim 14, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. further teaches wherein the categorized features relate to at least one of alanine transaminase, estimated glomerular filtration rate, hemoglobin, and hematocrit (see: paragraph [0042] where there is categorization features related to hemoglobin).
As per claim 15, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. further teaches wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, cerebrovascular disease, chest pain, chronic kidney disease, diseases of the heart, mobility, hearing loss, hypertension, and hypokalemia (see: paragraph [0046] where there is a diagnosis of a heart disease).
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 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0037877 to Glik et al. in view of U.S. 2022/0406440 to Wang et al.
As per claim 8, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. 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. 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, Glik 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, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. 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. 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, Glik 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).
Claims 17-20 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2025/0037877 to Glik et al. in view of U.S. 2018/0236235 to Hettrick et al.
As per claim 17, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. may not further, specifically teach wherein the categorized features relate to estimated glomerular filtration rate.
Hettrick et al. teaches:
--wherein the categorized features relate to estimated glomerular filtration rate (see: paragraphs [0063] and [0139] where there is such a categorized feature).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the categorized features relate 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 18, Glik et al. teaches the method of claim 1, see discussion of claim 1. Glik et al. may not further, specifically teach wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, hearing loss, and hypokalemia.
Hettrick et al. teaches:
--wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, hearing loss, and hypokalemia (see: paragraph [0027] where there is a disease of atherosclerosis and [0041] where there is such data and it can be indicative of developing dementia).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, 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.).
As per claim 19, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. may not further, specifically teach wherein the categorized features relate to estimated glomerular filtration rate.
Hettrick et al. teaches:
--wherein the categorized features relate to estimated glomerular filtration rate (see: paragraphs [0063] and [0139] where there is such a categorized feature).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the categorized features relate to estimated glomerular filtration rate as taught by Hettrick et al. in the system 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 20, Glik et al. teaches the system of claim 9, see discussion of claim 9. Glik et al. may not further, specifically teach wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, hearing loss, and hypokalemia.
Hettrick et al. teaches:
--wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, hearing loss, and hypokalemia (see: paragraph [0027] where there is a disease of atherosclerosis and [0041] where there is such data and it can be indicative of developing dementia).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein the diagnosis data includes medical diagnosis related to at least one of anemia, aphasia, atherosclerosis, chronic kidney disease, mobility, hearing loss, and hypokalemia as taught by Hettrick et al. in the system 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.).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Steven G.S. Sanghera whose telephone number is (571)272-6873. The examiner can normally be reached M-F 7:30-5:00 (alternating Fri).
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684