DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 29 January 2026 has been entered.
Status
This Office Action is in response to the communication filed on 29 January 2026. Claims 2-3 have been canceled, and claims 1 and 10 have been amended, and no new claims have been added. Therefore, claims 1 and 4-10 are pending and presented for examination.
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
A summary of the Examiner’s Response to Applicant’s amendment:
Applicant’s amendment does not overcome the rejection(s) under 35 USC § 101; therefore, the Examiner maintains the rejection(s) while updating phrasing in keeping with current examination guidelines.
Applicant’s amendment overcomes the rejection(s) under 35 USC §§ 102 and/or 103; therefore, the Examiner places new grounds of rejection.
Applicant’s arguments are found to be not persuasive; please see the Response to Arguments below.
Examiner’s Note
The Examiner notes claims 1 and 10 now indicate “the SUVR analyzer selects a portion of the partial regions according to a selection signal”. The Examiner has searched Applicant’s specification for an indication of what this signal is, where it comes from, or if it does anything other than indicate a selection. The only mentions related to a selection signal are that a region is selected “according to a selection signal (SS)” at Applicant ¶¶ 0041 and 0045. However, there is no indication where this signal comes from, or what it is beyond merely a selection. Therefore, this is being interpreted as being, or encompassing, a signal to select a region from a manual selection – e.g., a mouse, pointer, touch pad, etc. input from a user. This is to say that when a user selects a region, the SUVR for that region is calculated. Also, the selection signal may be of any other type or form such as an automated designation of a region.
The Examiner notes claims 1 and 10 now indicate “by analyzing genotypes of genomic variants”. The Examiner has searched Applicant’s specification for an indication of what this means, and notes that neither “genomic” nor “variant” nor any apparent derivation of those terms appears in the specification. So, apparently “genomic” indicates anything related to the genome or genetic information, and “variant” would indicate anything that may be related to any possible variation of that genome or genetic information – where “an apolipoprotein genotype (apolipoprotein epsilon 4 (APOE4) genetic information)” (at Applicant ¶ 0004) and/or an/the “apolipoprotein genotype status (apolipoprotein epsilon 4 (APOE4) genetic information)” (at Applicant ¶¶ 0032 and 0034) are encompassed since they appear to be the only indications related to this phrasing.
The Examiner notes that the final element of independent claims 1 and 10 indicates the amount, and predicting increases in, β-amyloid protein accumulation “aid in treating and preventing Alzheimer's disease by permitting early detection of the β-amyloid protein and predicted increases within the patient”. Since there is no indication in the description or claims of actually treating or preventing Alzheimer’s disease, this recitation is considered an intended or expected use or result (see, e.g., MPEP § 2103(I)(C)). As such, this phrase may be granted little if any patentable weight.
Affidavit Consideration
A declaration under 37 CFR 1.132 was submitted. The indications below are in consideration of that affidavit, per the reference paragraphs of the affidavit.
Points 1-5 indicate the affidavit is by the inventor, and that inventor’s qualifications. The Examiner notes that the affidavit may be granted less or little weight since it is from the inventor (see MPEP § 716.01(c)) and Ashland Oil, Inc. V. Delta Resins & Refractories, Inc., 776 F.2d 281, 294 (Fed. Cir. 1985) ("the opinion testimony of a party having a direct interest in the pending [proceeding] is less persuasive than opinion testimony by a disinterested party").
Point 6 discusses the Reitermann reference as prior art, but does not appear to indicate a positive or negative impact of the indications in “Reitermann at least at paragraph [0007]” (citing pt. 6).
Point 7 indicates Reitermann again, and an assertion as to what the claims recite, but does not appear to make a distinction between the art and the claims.
Points 8-9 allege that Reitermann does not disclose some or all of the current amendment or claim. The Examiner notes that whether this is the case, or not, is addressed at the current rejection. The Examiner also notes that per at least MPEP § 716.01(c), “While an opinion as to a legal conclusion is not entitled to any weight, the underlying basis for the opinion may be persuasive. In re Chilowsky, 306 F.2d 908, 134 USPQ 515 (CCPA 1962)”. Therefore, see the current rejections.
Point 10 asserts that “paragraph [0034] of Present Application” is represented in claims 1 and 10 and requires, in part, “analyzing rs429358 and rs7412 SNPs using PCR-based methods on genomic DNA extracted from EDTA-treated whole blood” (at pt. 10, pp. 2-3 of the Affidavit). However, with respect to this aspect, the claims merely require “identifying a presence of an apolipoprotein epsilon 4 allele by analyzing genotypes of genomic variants within the patient's genetic information” – there is no claim indication of the specifics argued. Therefore, this point of the affidavit appears to not correlate to the actual claim phrasing. Further, the Examiner notes that MPEP § 2111.01(II) indicates that it is improper to import claim limitations from the specification: "’Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment.’ Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004).”
Point 11 further indicates the SUVR calculation indications in the specification. However, as noted above, the specifics of the specification cannot be imported into the claims as limitations unless or until those specifics are recited in the claims themselves. The Examiner notes, however, that Point 11 is an explicit admission that the SUVR calculation, and how it is calculated and envisioned as calculated, are “widely recognized and utilized in the field”.
Point 12 also discusses specification descriptions of the SUVR analyzer. The Examiner notes as above that specification limitations cannot be, or should not be, read into the claims, but also that the indicate portion of the claims appears generally added by the current amendment. Therefore, the Examiner notes the current rejections as related to the current claim phrasing (and not the specification indications that are not required by the claim phrasing). The Examiner further notes that the Affidavit indicates “For the regional SUVR, instead of pre- selecting certain brain regions, SUVR values from all cortical areas defined by the standardized Desikan-Killiany atlas were included as input variables” (at Point 12); however, the Examiner searched the specification for any indication of any of “Desikan”, “Killiany”, or “atlas”, and finds no mention in the description. Therefore, this appears to be new subject matter and cannot appear to be persuasive in relation to the claims since not even described.
Point 13 indicates the selection signal “formed by the degree to which each sub-region's SUVR value contributes to the predicted output during the model's training process. Specifically, all global and regional SUVR values are provided as inputs to the predictive model, which then learns the relationship between these inputs and future amyloid seroconversion”. However, that is not what the claim indicates. At the claims, the SUVR analyzer is only indicated as “comparing regions of … PET[ ] test results of the patient” – there is no indication of training the SUVR analyzer, nor that the selection signal is based on training data. The claims indicate “the SUVR analyzer selects a portion of the partial regions according to a selection signal” and since the only indication of data for the SUVR analyzer is the test result data for the patient, it does not appear possible to interpret the claims as requiring the selection be based on training data. As phrased, the claims encompass merely including any and all partial regions – including to “regularly pre-select specific sub-regions according to predefined rules” that the Affidavit appears to imply is NOT what the claims are doing.
Point 14 asserts “the limitations of independent Claims 1 and 10 … effects a particular treatment or prophylaxis for patients with Alzheimer's Disease by identifying patients who are currently amyloid PET-negative but at high risk of future amyloid seroconversion”. However, MPEP § 2106.04(d)(2) indicates that “in order to qualify as a ‘treatment’ or ‘prophylaxis’ limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition.” Where the claims predict which patients may be at risk of developing or being diagnosed with Alzheimer’s Disease, the claim limitations (and the description) do not reflect or indicate any treatment for that or any other disease, nor do the claims indicate any prevention (i.e., prophylaxis) of Alzheimer’s or any other disease. Therefore, the claims do not qualify for a particular treatment or prophylaxis in Step 2A, Prong Two (where that analysis may be applied).
As such, based on each of the above reasons (and all of the above considered together), Applicant’s submitted Affidavit is not considered to be persuasive.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1 and 4-10 are 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.
Independent claims 1 and 10 each recite “identifying regions in order of accumulations in the amount of β-amyloid protein in each of the partial regions”. The Examiner has searched for the concept of identifying regions and whether those regions would be in any order of accumulation of β-amyloid protein and does not find it. The closest description the Examiner can find appears to be Applicant ¶¶ 0041-0045 describing the region selector; however, that portion of the specification indicates ordering the first, second, and/or third input information (i.e., age, gender classification, genotype status, or β-amyloid protein in a brain of the patient), and not rank ordering regions or partial regions.
Independent claims 1 and 10 each also recite “permitting early detection of the β-amyloid protein and predicted increases within the patient”. The Examiner has searched for the concept of early detection of β-amyloid protein and/or predicted increases of β-amyloid protein in the patient and does not find it. The closest description the Examiner can find appears to be Applicant ¶¶ 0014, 0036, and 0049 alleging early prediction of a potential patient that may develop Alzheimer’s disease. A prediction of a patient developing Alzheimer’s disease (whether early or not) is not the same as a detection, much less an early detection, of either β-amyloid protein or increases of that protein. Although there apparently is or may be correlation of β-amyloid protein presence and an Alzheimer’s diagnosis, there is no indication the Examiner has been able to find regarding how an early detection of the presence of β-amyloid protein would be performed. Although a person with the presence of β-amyloid protein and/or APOE4 may have some increase (through on-going and follow-up testing that can track a trend, and an increase may be presumed based on a trend), there is no description the Examiner has been able to find related to actual predicted increases.
Therefore, claims 1 and 10 lack proper written support for BOTH of the indicated concepts.
Claims 4-9 depend from claim 1, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 4-9 are also lacking written support.
Claims 1 and 4-10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Independent claims 1 and 10 each recite “an artificial intelligence (Al) model … to generate prediction results related to β-amyloid positive conversion status of the β-amyloid negative patient based on the amount of β-amyloid protein in one of all regions in the brain of the patient or partial regions in the brain” (citing claim 1, parallel phrasing at claim 10), but then, at the final element, “determining the amount of p-amyloid protein accumulation and predicting increases thereof in the brain of the patient aid in treating and preventing Alzheimer's disease by permitting early detection of the p-amyloid protein and predicted increases within the patient” (again, citing claim 1, claim 10 having parallel phrasing). It is noted that the AI model “generate[s] prediction results related to … conversion status”, i.e., something/anything related to a prediction of conversion status, and NOT a prediction of increases in β-amyloid protein accumulation.
It is further noted that Applicant’s specification indicates a goal or aspiration of “in the future, interest in a primary prevention aspect that may prevent the disease even before the deposition of beta (β)-amyloid protein is expected to increase and various studies related thereto are currently being conducted” (Applicant ¶ 0003 as submitted, 0004 as published). However, neither the specification or claims indicate any way of predicting an increase of the β-amyloid protein accumulation – the specification and claims merely detect or measure (according to the SUVR) existing amount or accumulation of β-amyloid protein. Whether or how that may be predicted to increase or decrease β-amyloid protein accumulation does not appear to be conveyed or described. The only related description says the (general) prediction results are done by an AI model, as at the claims. There is no indication of any type of AI model, or how it may work – it is just “trained” using the first, second, and third inputs (i.e., age and gender classification as first input, presence of an apolipoprotein epsilon 4 (“APOE4”) allele as second input, and SUVR as third input).
Further to the above, claims 1 and 10 also recite an SUVR analyzer “to determine an amount of β-amyloid protein in a brain of the patient”, where “the SUVR analyzer comprises: … to calculate a global SUVR from a global region … [and] to calculate a regional SUVR from each of partial regions … and wherein the SUVR analyzer selects a portion of the partial regions according to a selection signal generated by the processor, identifying regions in order of accumulations in the amount of β-amyloid protein in each of the partial regions that contribute to the generation of the prediction results by the Al model of the β-amyloid positive conversion status of the β-amyloid negative patient”. Therefore, the first part (at the 4th element of “a standardized uptake value ratio …”) refers to “a brain” that appears to reference all of the brain in general (i.e., a “global SUVR”?), whereas the second part that describes the activity of the SUVR analyzer (at the “wherein the SUVR analyzer … “) indicates that only a portion of the partial regions are selected (i.e., a “regional SUVR”?). As such, it is indefinite whether the calculated global SUVR is used or required, or if the regional SUVR is used or required, and what SUVR calculation is considered as the “generate[d] … SUVR as third information.
Based on the above, it is indefinite whether there is (or even could be) a prediction of an increase in β-amyloid protein accumulation.
And it is further indefinite as to which or what SUVR is used such that it is required to be calculated.
Further, it is noted that “wherein the SUVR analyzer …” indicates “identifying regions in order of accumulations” of APOE4; however, the order of regions does not appear used – it would appear that any region would “contribute to the generation of prediction results” regardless of the order they may be in.
Claims 4-9 depend from claim 1, but do not resolve the above issues and inherit the deficiencies of the parent claim(s); therefore claims 4-9 are also indefinite.
Independent claims 1 and 10 indicate “a standardized uptake value ratio (SUVR) analyzer configured to calculate an SUVR by comparing regions of amyloid positron emission tomography (PET) test results of the patient to determine an amount of β-amyloid protein in a brain of the patient and to generate the SUVR as third input information; … and wherein the SUVR analyzer comprises: a global value ratio provider configured to calculate a global SUVR from a global region of an amyloid PET video including all regions of the brain of the patient and the amount of β-amyloid protein in all regions and a regional value ratio provider configured to calculate a regional SUVR from each of partial regions of the amyloid PET video including the partial regions of the brain and the amount of β-amyloid protein in each of the partial regions; and wherein the SUVR analyzer selects a portion of the partial regions according to a selection signal generated by the processor, identifying regions in order of accumulations in the amount of β-amyloid protein in each of the partial regions that contribute to the generation of the prediction results by the Al model of the β-amyloid positive conversion status of the β-amyloid negative patient” (citing claim 1, parallel phrasing at claim 10). However, point 13 of the Affidavit submitted on 29 January 2026 indicates the SUVR is a predictive model that learns relationships based on training data and that it is the training data used to assess the contribution of each sub-region in relation to the predicted output. Therefore, it appears that the claims fail to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention – at least with respect to the SUVR analyzer and its operation.
Claims 4-9 depend from claim 1, but do not resolve the immediately above issues and inherit the deficiencies of the parent claim(s); therefore claims 4-9 are also indefinite for this reason also.
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 and 4-10 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Please see the following Subject Matter Eligibility (“SME”) analysis:
For analysis under SME Step 1, the claims herein are directed to a device (claims 1 and 4-9) and method (claim 10), a which would be classified under one of the listed statutory classifications (SME Step 1=Yes).
For analysis under revised SME Step 2A, Prong 1, independent claim 1 recites a beta (β)-amyloid positive conversion target prediction device comprising: a processor configured to include: a patient information analyzer configured to classify a patient among a plurality of patients by differentiating age and gender of a β-amyloid negative patient based on basic information of the patient and to generate the patient classification as first input information, wherein the basic information includes a β-amyloid negative status, an apolipoprotein genotype status, age, and gender of the patient; a genotype analyzer configured to determine the an apolipoprotein genotype status of the patient by identifying a presence of an apolipoprotein epsilon 4 allele by analyzing genotypes of genomic variants within the patient's genetic information and to generate the determined apolipoprotein genotype status as second input information; a standardized uptake value ratio (SUVR) analyzer configured to calculate an SUVR by comparing regions of amyloid positron emission tomography (PET) test results of the patient to determine an amount of β-amyloid protein in a brain of the patient and to generate the SUVR as third input information; and an artificial intelligence (Al) model, trained by the processor on the first input information, the second input information, and the third input information, configured to generate prediction results related to β-amyloid positive conversion status of the β-amyloid negative patient based on the amount of β-amyloid protein in one of all regions in the brain of the patient or partial regions in the brain; wherein the patient information analyzer comprises a patient classifier configured to classify the patient as one of a β-amyloid negative patient and a β-amyloid positive patient based on the basic information of the patient; and wherein the SUVR analyzer comprises: a global value ratio provider configured to calculate a global SUVR from a global region of an amyloid PET video including all regions of the brain of the patient and the amount of β-amyloid protein in all regions and a regional value ratio provider configured to calculate a regional SUVR from each of partial regions of the amyloid PET video including the partial regions of the brain and the amount of β-amyloid protein in each of the partial regions; and wherein the SUVR analyzer selects a portion of the partial regions according to a selection signal generated by the processor, identifying regions in order of accumulations in the amount of β-amyloid protein in each of the partial regions that contribute to the generation of the prediction results by the Al model of the β-amyloid positive conversion status of the β-amyloid negative patient; wherein determining the amount of β-amyloid protein accumulation and predicting increases thereof in the brain of the patient aid in treating and preventing Alzheimer's disease by permitting early detection of the β-amyloid protein and predicted increases within the patient.
Independent claim 10 is analyzed similarly to claim 1 above since directed to a method that performs the same or similar operations as at claim 1 above.
The dependent claims (claims 4-9) appear to be encompassed by the abstract idea of the independent claims since they merely indicate the third input information including the calculated global and regional values (claim 4), the SUVR analyzer comprising a region selector (claim 5), the claimed device further comprising a weight provider to weigh the pieces of information (claim 6) to provide a selection weight applied to the regional SUVR (claim 7), a result determiner to determine a negative patient has converted to positive based on being greater than a reference value (claim 8), and a display to display the results (claim 9).
The underlined portions of the claims are an indication of elements additional to the abstract idea (to be considered below).
The claim elements may be summarized as the idea of determining a patient is β-amyloid positive based on provided information; however, the Examiner notes that although this summary of the claims is provided, the analysis regarding subject matter eligibility considers the entirety of the claim elements, both individually and as a whole (or ordered combination). This idea is within at least the following grouping(s) of subject matter:
Mental processes (e.g., concepts performed in the human mind such as observation, evaluation, judgment, and/or opinion) – based at least on the classifying, generating a classification, determining a patient status, and generating predication results by evaluating the information and making judgments or opinion determinations based on “at least one” type of information;
Mathematical concepts (e.g., relationships, formulas, equations, and/or calculations) – based at least on the standardized uptake value ratio (SUVR) analyzer and calculating a global and regional SUVR, the AI modeling, and the weighting and calculating. The Examiner noting that the “artificial intelligence (AI) model” per the light of the specification, and the breadth of the claims apparently requires nothing beyond mere probability calculations (e.g., a person of X age and/or Y gender has a Z probability of converting from negative to positive status – especially since, including per dependent claim 6, the first input may have 100% weight and the second and third inputs may each have 0% weight for this example).
Therefore, the claims are found to be directed to an abstract idea.
For analysis under revised SME Step 2A, Prong 2, the above judicial exception is not integrated into a practical application because the additional elements do not impose a meaningful limit on the judicial exception when evaluated individually and as a combination. The additional elements are the claim to a processor and the selection signal being generated by the processor (at claims 1 and 10), and the use of a display (at claim 9). These additional elements do not reflect an improvement in the functioning of a computer or an improvement to other technology or technical field, effect a particular treatment or prophylaxis for a disease or medical condition (there is no medical disease or condition, much less a treatment or prophylaxis for one), implement the judicial exception with, or by using in conjunction with, a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing (there is no transformation/reduction of a physical article), and/or apply or use the judicial exception in some other meaningful way beyond generically linking use of the judicial exception to a particular technological environment.
The claims appear to merely apply the judicial exception, include instructions to implement an abstract idea on a computer, or merely use a computer as a tool to perform the abstract idea. The additional elements appear to merely add insignificant extra-solution activity to the judicial exception and/or generally link the use of the judicial exception to a particular technological environment or field of use.
For analysis under SME Step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements, as indicated above, are merely “[a]dding 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.” that MPEP § 2106.05(I)(A) indicates to be insignificant activity.
There is no indication the Examiner can find in the record regarding any specialized computer hardware or other “inventive” components, but rather, the claims merely indicate computer components which appear to be generic components and therefore do not satisfy an inventive concept that would constitute “significantly more” with respect to eligibility. The Examiner has found no information describing the computers, systems, or other technology that may be employed; therefore, the Examiner notes and finds that the components or technology cannot apparently be reasonably interpreted as anything but generic or general-purpose implementation.
The individual elements therefore do not appear to offer any significance beyond the application of the abstract idea itself, and there does not appear to be any additional benefit or significance indicated by the ordered combination, i.e., there does not appear to be any synergy or special import to the claim as a whole other than the application of the idea itself.
The dependent claims, as indicated above, appear encompassed by the abstract idea since they merely limit the idea itself; therefore the dependent claims do not add significantly more than the idea.
Therefore, SME Step 2B=No, any additional elements, whether taken individually or as an ordered whole in combination, do not amount to significantly more than the abstract idea, including analysis of the dependent claims.
Please see the Subject Matter Eligibility (SME) guidance and instruction materials at https://www.uspto.gov/patent/laws-and-regulations/examination-policy/subject-matter-eligibility, which includes the latest guidance, memoranda, and update(s) for further information.
NOTICE
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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1 and 4-10 are rejected under 35 U.S.C. 103 as being unpatentable over Reitermann et al. (U.S. Patent Application Publication No. 2024/0003918, hereinafter Reitermann) in view of Andreyev et al. (U.S. Patent Application Publication No. 2021/0298701, hereinafter Andreyev) .
Claim 1: Reitermann discloses a beta (β)-amyloid positive conversion target prediction device comprising:
a processor (see Reitermann at least at, e.g., ¶ 0009, “The step of generating an AD risk score is typically computer implemented. A computer implemented method can comprise executing, in a computer system having one or more processors coupled to a memory storing one or more computer readable instructions for execution by the one or more processors, the one or more computer readable instructions comprising instructions for generating an AD risk score for the subject from the dataset”; citation hereafter by number only) configured to include:
a patient information analyzer configured to classify a patient among a plurality of patients by differentiating age and gender of a β-amyloid negative patient based on basic information of the patient and to generate the patient classification as first input information, wherein the basic information includes a β-amyloid negative status, an apolipoprotein genotype status, age, and gender of the patient (0002, “Alzheimer’s disease (AD)”, 0008, “the dataset further includes one or more genetic risk markers of AD (e.g., APO E4) and/or one or more other factors such as age and gender (male or female)”, 0012, “An AD surrogate variable is a factor associated with AD risk, for example brain amyloid load, brain tau load, brain neurodegeneration, or clinical diagnosis of mild cognitive impairment (MCI) or AD. Thus, for example, when an AD surrogate value is brain amyloid load, the patient record data can include amyloid PET centiloid data; when an AD surrogate value is brain tau load, the patient record data can include Tau PET SUVR data; when an AD surrogate variable is brain neurodegeneration, the patient record data can include clinical dementia rating data; and when an AD surrogate value is clinical diagnosis of MCI or AD, the patient record data can include data relating to patient diagnosis of MCI or AD. By using the data for the protein markers as input variables and the AD surrogate variables as output variables, the training produces an AI-based algorithm that correlates levels of the protein markers to the AD surrogate variables. Additional input variables, for example, age, gender, education level, genetic risk markers for AD (e.g., APO E4, Clusterin (CLU), Sortilin-related receptor-1 (SORL1), ATP-binding cassette subfamily A member 7 (ABCA7)), age at tau scan, and combinations thereof can also be included”, 0025-0026 and Figs. 1B, 1C, 2B, 2C);
a genotype analyzer configured to determine the apolipoprotein genotype status of the patient by identifying a presence of an apolipoprotein epsilon 4 allele by analyzing genotypes of genomic variants within the patient's genetic information and to generate the determined apolipoprotein genotype status as second input information (0002, “A fourth gene, apolipoprotein E (“APOE or APO E”), is the strongest and most common genetic risk factor for AD, but does not necessarily cause it”, 0008, “the dataset further includes one or more genetic risk markers of AD (e.g., APO E4) and/or one or more other factors such as age and gender (male or female)”, 0155, “The Athena Diagnostics ADmark® ApoE Genotype Analysis and Interpretation (Symptomatic) and the LabCorp: APOE Alzheimer's Risk tests can be used to identify a subject's APOE4 allele”);
a standardized uptake value ratio (SUVR) analyzer configured to calculate an SUVR by comparing regions of amyloid positron emission tomography (PET) test results of the patient to determine an amount of β-amyloid protein in a brain of the patient and to generate the SUVR as third input information (0026, “PET SUVR in the mesial temporal region”, 0027, “PET SUVR in the temporal region”, 0190, “Other measures of brain amyloid load can also be used. For example, full brain amyloid standardized uptake value ratio (SUVR) or a volume of interest (VOI)-based amyloid standardized uptake value ratio (SUVR) or centiloid values”, 0193, “PET standardized uptake value ratio (SUVR) values …, for example a volume of interest (VOI) such as the mesial temporal region or temporal region)”); and
an artificial intelligence (Al) model trained by the processor on the first input information, the second input information, and the third input information configured to generate prediction results related to β-amyloid positive conversion status of the β-amyloid negative patient based on the amount of β-amyloid protein in one of all regions in the brain of the patient or partial regions in the brain (0009, “Artificial intelligence (AI) based algorithms for generating AD risk scores can be used. Exemplary AI-based algorithms include those based on logistic regression, light GBM, Random Forest, and CatBoost machine learning models that have been trained using a set of patient records”, 0026-0027, indicating values for the “mesial temporal (‘MT’) region” and “temporal (‘TJ’) region”);
wherein the patient information analyzer comprises a patient classifier configured to classify the patient as one of a β-amyloid negative patient and a β-amyloid positive patient based on the basic information of the patient (0010, “An AD risk score (or combination of AD risk scores) can be used to classify a subject into an AD risk category, for example a high, intermediate (sometimes referred to herein as a moderate or medium), or low risk category”, 0262 and Fig. 20); and
wherein the SUVR analyzer comprises: a global value ratio provider configured to calculate a global SUVR from a global region of an amyloid PET video including all regions of the brain of the patient and the amount of β-amyloid protein in all regions; and a regional value ratio provider configured to calculate a regional SUVR from each of partial regions of the amyloid PET video including the partial regions of the brain and the amount of β-amyloid protein in each of the partial regions (0026-0027, indicating values for the “mesial temporal (‘MT’) region” and “temporal (‘TJ’) region”, and 0079 indicating a total value); and
wherein the SUVR analyzer selects a portion of the partial regions according to a selection signal generated by the processor, (Fig. 20 indicating various regions, such as for the “MT” and “TJ” regions, where the regions must be identified – i.e., selected – in order to correlate the results to a particular region, which requires a selection signal of some sort).
Reitermann, however, does not appear to explicitly disclose identifying regions in order of accumulations in the amount of β-amyloid protein in each of the partial regions that contribute to the generation of the prediction results by the Al model of the β-amyloid positive conversion status of the β-amyloid negative patient; wherein determining the amount of β-amyloid protein accumulation and predicting increases thereof in the brain of the patient aid in treating and preventing Alzheimer's disease by permitting early detection of the β-amyloid protein and predicted increases within the patient. Where Reitermann discloses determining the amount of β-amyloid protein (SUVR data, counts and classifications at Reitermann at 0012, 0026, 0027, Figs. 3A-3L, 4A-4L, 0193, etc.) and the regions that contribute to the generation of (0026-0027, and Fig. 20, as above, indicating values for the “mesial temporal (‘MT’) region” and “temporal (‘TJ’) region”). Andreyev, though, teaches that “Beta amyloid (Aβ) deposits in brain tissue have been correlated with certain neurodegenerative diseases such as Alzheimer's disease” (Andreyev at 0017), and “In a PET embodiment coincidence detection is employed for spatial encoding of the counts” (Andreyev at 0026), where PET indicates positron emission tomography (Andreyev at 0002), and “The scan is repeated on an annual or clinician-prescribed basis, and the obtained singles count rates (and/or other count metric) for the current test are compared with the count rates for past tests to determine the trend for potential amyloid plaque accumulation. If the trend becomes positive (uptake increase in more recent tests), then the patient may be referred for further tests” (Andreyev at 0044). Since the trend analysis of Andreyev indicates predicting increases of accumulation, the Examiner understands and finds that to identify regions contributing to a change in conversion status and predict β-amyloid protein accumulation increases is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to establish a basis of further testing.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine or modify the information analysis of Reitermann with the trend analysis of Andreyev in order to identify regions contributing to a change in conversion status and predict β-amyloid protein accumulation increases so as to establish a basis of further testing.
The rationale for combining in this manner is that to identify regions contributing to a change in conversion status and predict β-amyloid protein accumulation increases is applying a known technique to a known device, method, or product ready for improvement to yield predictable results so as to establish a basis of further testing as explained above.
Claim 4: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 1, wherein the third input information includes the global SUVR and the regional SUVR (Reitermann at 0026-0027, indicating values for the “mesial temporal (‘MT’) region” and “temporal (‘TJ’) region”, and 0079 indicating a total value).
Claim 5: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 4, wherein the SUVR analyzer further comprises a region selector configured to select the portion of the partial regions, to generate the selection signal, and to provide the selection signal to the regional SUVR corresponding to the selected portion of the partial regions as a selection region (Reitermann at 0026-0027, indicating values for the “mesial temporal (‘MT’) region” and “temporal (‘TJ’) region”, and 0079 indicating a total value, Fig. 20).
Claim 6: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 5, further comprising: a weight provider configured to apply an input weight to each of the first input information, the second input information, and the third input information (Reitermann at 0060, “the levels of some (e.g., at least 2 or at least 3) of the protein markers are weighted equally and/or the levels of some (at least 2 or at least 3) of the protein markers are weighted differentially (e.g., a first peptide marker can be said to be weighted greater than a second peptide marker when the first peptide marker has a higher variable importance than the second peptide marker in a feature importance plot for an artificial intelligence-based algorithm, for example as show in FIGS. 13-18)”).
Claim 7: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 6, wherein the weight provider comprises a selection weight provider configured to apply a selection weight to the regional SUVR corresponding to the selection region (Reitermann at 0060, “the levels of some (e.g., at least 2 or at least 3) of the protein markers are weighted equally and/or the levels of some (at least 2 or at least 3) of the protein markers are weighted differentially (e.g., a first peptide marker can be said to be weighted greater than a second peptide marker when the first peptide marker has a higher variable importance than the second peptide marker in a feature importance plot for an artificial intelligence-based algorithm, for example as show in FIGS. 13-18)”).
Claim 8: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 7, further comprising: a result determiner configured to determine that the β-amyloid negative patient has a β-amyloid positive conversion probability when a value of the prediction results is greater than a preset positive conversion reference value (Reitermann at 0009, “The step of generating an AD risk score is typically computer implemented. A computer implemented method can comprise executing, in a computer system having one or more processors coupled to a memory storing one or more computer readable instructions for execution by the one or more processors, the one or more computer readable instructions comprising instructions for generating an AD risk score for the subject from the dataset. Artificial intelligence (AI) based algorithms for generating AD risk scores can be used. Exemplary AI-based algorithms include those based on logistic regression, light GBM, Random Forest, and CatBoost machine learning models that have been trained using a set of patient records”),.
Claim 9: Reitermann in view of Andreyev discloses the β-amyloid positive conversion target prediction device of claim 8, further comprising: a display configured to display the prediction results and the β-amyloid positive conversion probability of the β-amyloid negative patient (Reitermann at 0173, “the web-based service may transmit an AD risk score (and optionally information and/or recommendations based on the subject's risk score and other relevant information) to the medical practitioner's computer system or display an AD risk score (and optionally information and/or recommendations based on the subject's risk score and other relevant information) to the medical practitioner”).
Claim 10 is rejected on the same basis as claim 1 above since Reitermann discloses an operation method comprising the same or similar activities as indicated and recited to for claim 1.
Response to Arguments
Applicant's arguments filed 29 January 2026 have been fully considered but they are not persuasive.
Applicant first argues 101 rejections (Remarks at 7-15), discussing general guidance and repeating claim elements (Id. at 7-9), and then asserting that the claim elements “taken as a whole, are not well-understood, routine, or conventional activity and therefore is an improvement in the field of conventional medical field” (Id. at 9, emphasis at original) and as such, also “recite both a practical application of any alleged abstract ideas and an inventive concept” (Id. at 10, emphasis at original). However, the analysis for Steps 2A, parts 1 and 2 (as addressing whether the claims are directed to an abstract idea or not) does not consider well- understood, routine, or conventional activity. Applicant also asserts that the “Declaration Under 37 C.F.R. §1.132 of inventor Dr. Jun Pyo Kim [is] supporting its assertion that the claimed invention affects a particular treatment or prophylaxis for Alzheimer's disease” (Remarks at 9-10). However, as explained above in relation to the consideration given Point 14 of the Affidavit, MPEP § 2106.04(d)(2) indicates that “in order to qualify as a ‘treatment’ or ‘prophylaxis’ limitation for purposes of this consideration, the claim limitation in question must affirmatively recite an action that effects a particular treatment or prophylaxis for a disease or medical condition.” Where the claims predict which patients may be at risk of developing or being diagnosed with Alzheimer’s Disease, the claim limitations (and the description) do not reflect or indicate any treatment for that or any other disease, nor do the claims indicate any prevention (i.e., prophylaxis) of Alzheimer’s or any other disease. Therefore, the claims do not qualify for a particular treatment or prophylaxis in Step 2A, Prong Two (where that analysis may be applied).
Applicant then “asserts that the patient information analyzer, the genotype analyzer, and the SUVR analyzer components of the processor, and the Al model, are additional elements. Further, Applicant submits that limitations direct to a patient's basic information including age, gender, p-amyloid negative status and genetic information used by the patient information analyzer and genotype analyzer to generate an apolipoprotein genotype status, amyloid PET test results used by the SUVR analyzer to calculate and determine a global SUVR, a regional SUVR, and an amount of p-amyloid protein in all regions and in partial regions of the patient's brain, and the Al model that generates prediction results related to the p-amyloid positive conversion status of the p-amyloid negative patient based on the amount of p-amyloid protein in one of each of the regions in the brain of the patient or partial regions in the brain through analysis of the information provided and amyloid PET test results and regions of an amyloid PET video, are meaningful limitations recited in Claims 1 and 10, as now written” (Remarks at 10-11, emphasis at original). However, as Applicant indicates “the patient information analyzer, the genotype analyzer, and the SUVR analyzer [are merely] components of the processor” – they indicate the activities performed by the processor (analyzing information, genotype and SUVR), where those activities are considered to be part of the abstract idea and the processor itself as well as the software or programming that the processor runs is indicated as the additional element(s). The AI model, as far as the specification describes it (i.e., in the light of the specification) is merely indicated to require little or nothing more than relatively simple mathematics or estimations of probabilities, as indicated above. As such, the AI model is considered to be part of the abstract idea, although it may be considered to be in an additional grouping of the current examination guidelines indicating abstract ideas. The “limitations [of] basic information including age, gender, p-amyloid negative status and genetic information used by the patient information analyzer and genotype analyzer to generate an apolipoprotein genotype status, amyloid PET test results used by the SUVR analyzer to calculate and determine a global SUVR, a regional SUVR, and an amount of p-amyloid protein in all regions and in partial regions of the patient's brain” that Applicant argues as additional are merely the data that is being analyzed – at least Electric Power Group indicates that information is intangible, including when limited to particular content (which does not change its character as information), [and considered] as within the realm of abstract ideas (citing to Internet Patents, OIP Techs., Content Extraction, Digitech, and CyberSource Corp. (Electric Power Group, LLC v. Alstom SA, 830 F.3d 1350, 1353-54 (Fed. Cir. 2016). As such, most to all of the asserted information is actually properly considered as part of the abstract idea.
The arguments Applicant presents later at Remarks pp. 11-13 further allege that the data, as processed by the additional elements of the processor (running software to perform the analyses), when considered on their own or as a whole, “recite meaningful limitations that integrate the alleged abstract idea … into practical application (Id. at 11, similar at 13 in reference to being taken as a whole).
Applicant then argues Step 2B (Id. at 13-15), alleging that the same indication of data being analyzed “when considered as a whole and as an ordered combination, recite significantly more than simply generic components and or general-purpose implementation” (Id. at 14). However, for the same essential reasons as above – since the abstract idea itself cannot be considered significantly more than the abstract idea itself, the indicated elements are not considered significantly more.
Applicant then argues the prior art rejections (Remarks at 15-18), first again reciting claim 1 (Id. at 15-16) then mentioning Reitermann’s Abstract and ¶ 0007 (which were not recited at the rejection) as apparently all that Reitermann discloses (Id. at 16). Applicant then again alleges that “Because Reitermann fails to describe, teach, or suggest a p- amyloid positive conversion target prediction device and an operation method thereof as recited in Claims 1 and 10, as now presented, Reitermann does not describe, teach, or suggest each and every element of the claimed invention” (Id. at 17), but again or still does not appear to disclose which particular elements are not disclosed.
Applicant’s arguments appear to merely recite all, or most of, the claim elements and then assert that apparently NONE of that is taught by Reitermann. The Examiner and the analysis for, and at, the rejection is necessarily limited to what the claims say and the Examiner gets the impression that Applicant somehow has a narrow view of what the claims require and/or the impression that the claims require something more than what the claims actually say.
Therefore, the Examiner is not persuaded by Applicant’s argument(s).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Forman et al. (U.S. Patent Application Publication No. 2003/0139901, hereinafter Forman) indicates that machine learning is a branch of artificial intelligence: “Prediction technology (e.g., machine-learning (a branch of artificial intelligence technology), classification/categorization, and the like) is being used to create and maintain computer-based hierarchies” (Forman at 0010).
Al Bandar et al. (U.S. Patent Application Publication No. 2004/0181145, hereinafter Al Bandar) also indicates machine learning as a form or type of artificial intelligence: “The artificial intelligence used to analyse the channels may comprise an artificial Neural Network, genetic algorithm, decision tree, fuzzy logic, symbolic rules, machine learning and other forms of knowledge based systems. Pluralities and/or combinations of the above may be used” (Al Bandar at 0028).
Sternickel et al. (U.S. Patent Application Publication No. 2007/0167846, hereinafter Sternickel) further indicates machine learning as another name for, or reference to, artificial intelligence: “Three basic steps are always performed when applying artificial intelligence (machine learning) to measured data: 1. measurement of the data, 2. pre-processing of the measured data, 3. training of the adaptive classifier” (Sternickel at 0004).
Kim et al., Machine Learning to Predict Brain Amyloid Pathology in Pre-dementia Alzheimer's Disease Using QEEG Features and Genetic Algorithm Heuristic. Front Comput Neurosci. 2021 Nov 11;15:755499. DOI: 10.3389/fncom.2021.755499. PMID: 34867252; PMCID: PMC8632633. Downloaded 4 December 2024 from https://pmc.ncbi.nlm.nih.gov/articles/PMC8632633/, indicating that “The use of positron emission tomography (PET) as the initial or sole biomarker of β-amyloid (Aβ) brain pathology may inhibit Alzheimer’s disease (AD) drug development and clinical use due to cost, access, and tolerability. We developed a qEEG-ML algorithm to predict Aβ pathology among subjective cognitive decline (SCD) and mild cognitive impairment (MCI) patients, and validated it using Aβ PET. We compared QEEG data between patients with MCI and those with SCD with and without PET-confirmed beta-amyloid plaque.” (at Abstract).
Duara et al., Effect of age, ethnicity, sex, cognitive status and APOE genotype on amyloid load and the threshold for amyloid positivity, NeuroImage: Clinical, Vol. 22, 2019, 101800, ISSN 2213-1582, https://doi.org/10.1016/j.nicl.2019.101800, downloaded 4 December 2024 from (https://www.sciencedirect.com/science/article/pii/S2213158219301500), indicating that “The threshold for amyloid positivity by visual assessment on PET has been validated by comparison to amyloid load measured histopathologically and biochemically at post mortem. As such, it is now feasible to use qualitative visual assessment of amyloid positivity as an in-vivo gold standard to determine those factors which can modify the quantitative threshold for amyloid positivity. We calculated quantitative amyloid load, measured as Standardized Uptake Value Ratios (SUVRs) using [18-F]florbetaben PET scans, for 159 Hispanic and non-Hispanic participants, who had been classified clinically as Cognitively Normal (CN), Mild Cognitive Impairment (MCI) or Dementia (DEM). PET scans were visually rated as amyloid positive (A+) or negative (A-), and these judgments were used as the gold standard with which to determine (using ROC analyses) the SUVR threshold for amyloid positivity considering factors such as age, ethnicity (Hispanic versus non-Hispanic), gender, cognitive status, and apolipoprotein E ε4 carrier status. Visually rated scans were A+ for 11% of CN, 39.0% of MCI and 70% of DEM participants. The optimal SUVR threshold for A+ among all participants was 1.42 (sensitivity = 94%; specificity = 92.5%), but this quantitative threshold was higher among E4 carriers (SUVR = 1.52) than non-carriers (SUVR = 1.31). While mean SUVRs did not differ between Hispanic and non-Hispanic participants;, a statistically significant interaction term indicated that the effect of E4 carrier status on amyloid load was greater among non-Hispanics than Hispanics. Visual assessment, as the gold standard for A+, facilitates determination of the effects of various factors on quantitative thresholds for amyloid positivity. A continuous relationship was found between amyloid load and global cognitive scores, suggesting that any calculated threshold for the whole group, or a subgroup, is artefactual and that the lowest calculated threshold may be optimal for the purposes of early diagnosis and intervention.” (at Abstract).
Calculating SUV from PET images, Medical Connections, downloaded from https://web.archive.org/web/20211129075756/https://www.medicalconnections.co.uk/kb/Calculating-SUV-From-PET-Images via the Archive.org WayBack Machine, dated 29 November 2021, downloaded 21 April 2025, indicating equations used for calculating SUV and that “PET image pixels and the injected dose are decay corrected to the start of scan” (at pp. 1-2).
Jagust W., Is amyloid-β harmful to the brain? Insights from human imaging studies. Brain. 2016 Jan;139(Pt 1):23-30. doi: 10.1093/brain/awv326. Epub 2015 Nov 27. PMID: 26614753; PMCID: PMC4990654. Downloaded 21 April 2025 from https://pmc.ncbi.nlm.nih.gov/articles/PMC4990654/, indicating “The problem of detecting effects of amyloid-b on glucose metabolism is compounded by the fact that the apolipoprotein E4 allele, the major risk genetic risk for Alzheimer’s disease, is associated with both glucose hypometabolism and amyloid-b accumulation (Reiman et al., 2004; Morris et al., 2010)” (at 26).
Knopman et al., 18F-fluorodeoxyglucose positron emission tomography, aging, and apolipoprotein E genotype in cognitively normal persons. Neurobiol Aging 2014, Vol. 35, Iss. 9, Sept. 2014, Pp. 2096-2106, https://doi.org/10.1016/j.neurobiolaging.2014.03.006, downloaded from https://www.sciencedirect.com/science/article/pii/S0197458014002401 on 21 April 2025, indicating “Our objective was to examine associations between glucose metabolism, as measured by 18F-fluorodeoxyglucose positron emission tomography (FDG PET), and age and to evaluate the impact of carriage of an apolipoprotein E (APOE) ε4 allele on glucose metabolism and on the associations between glucose metabolism and age. We studied 806 cognitively normal (CN) and 70 amyloid-imaging-positive cognitively impaired participants (35 with mild cognitive impairment and 35 with Alzheimer’s disease [AD] dementia) from the Mayo Clinic Study of Aging, Mayo Alzheimer’s Disease Research Center and an ancillary study who had undergone structural MRI, FDG PET, and 11C-Pittsburgh compound B (PiB) PET. Using partial volume corrected and uncorrected FDG PET glucose uptake ratios, we evaluated associations of regional FDG ratios with age and carriage of an APOE ε4 allele in CN participants between the ages of 30 and 95 years, and compared those findings with the cognitively impaired participant” (at Abstract).
Nay et al. (U.S. Patent Application Publication No. 2007/0127796, hereinafter Nay) discusses image data analysis (Nay at 0003) and SUV determination (Nay at 0008-0009, 0038) where “a user may select a point and a box representing the focus region 150 may be automatically deposited on the reference image. An automatically deposited focus region (based on the selection of a point by a user) may be based on parameters configured previously by the user that specify the dimensions of the box” (Nay at 0033) and “a user may select a point and a focus region may be drawn automatically based at least in part on the point selected by the user. The focus region may be based at least in part on configurable parameters” (Nay at 0062).
Roussotte et al., Alzheimer's Disease Neuroimaging Initiative (ADNI). Apolipoprotein E epsilon 4 allele is associated with ventricular expansion rate and surface morphology in dementia and normal aging. Neurobiol Aging. 2014 Jun;35(6):1309-17. doi: 10.1016/j.neurobiolaging.2013.11.030. Epub 2013 Dec 5. PMID: 24411483; PMCID: PMC3961511. Downloaded 18 March 2026 from https://pmc.ncbi.nlm.nih.gov/articles/PMC3961511/, indicating, in part, that “The apolipoprotein E epsilon 4 allele (ApoE-ε4) is the strongest known genetic risk factor for late onset Alzheimer’s disease” (at Abstract).
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/SCOTT D GARTLAND/
Primary Examiner, AU3685