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 .
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.
Response to Amendment
This is a response to Applicant’s amendment filed on 08 October 2025, wherein:
Claims 1-5, 8, 9, 13, 18, 21, and 22 are amended.
Claims 6, 7, 10-12, 14-17, 19, and 20 are original.
Claims 1-22 are pending.
Specification
The disclosure is objected to because of the following informalities:
The disclosure recites both “beta amyloid” and “beta-amyloid”. Uniformity is recommended.
Appropriate correction is required.
Claim Objections
Claims 1-22 are objected to because of the following informalities:
Claim 1 misspells “classifying” in line 8 of the claim.
Claims 1, 21, and 22 each include at least one amendment that does not conform with the guidance provided in MPEP 714. In particular, the addition of a single character is difficult to perceive. Hence the guidance “extra portions of text may be included before and after text being deleted, all in strike-through, followed by including and underlining the extra text with the desired change (e.g., number 14 as).” See MPEP 714(II)(C).
Claims 1, 21, and 22 recite “beta amyloid” while claim 15 recites “beta-amyloid”. Uniformity is recommended.
Claim 22 does not indent the first limitation as it does for all of the other limitations. This decreases clarity.
Dependent claims 2-20 inherit the deficiencies of their respective parent claims, and are thus objected to under the same rationale.
Appropriate correction is required.
The objection to claim 19 is maintained and incorporated by reference herein. It is reproduced below for Applicant’s convenience.
Claim 19 is objected to because of the following informalities:
There is an errant comma and a space preceding the period at the end of the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The text of those sections of Title 35, U.S. Code 112(b) not included in this action can be found in a prior Office action.
Claims 1-22 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.
Claims 1, 21, and 22 each recite the limitation "the one or more response" in line 5 of claim 1 and line 8 in each of claims 21 and 22. There is insufficient antecedent basis for this limitation in the claim. The preceding limitation recites “collecting multimodal data based on one or more responses to the battery of assessments from the patient”. Thus, due to the lack of antecedent basis, it is unclear what the one or more features sets are extracted from – 1) the one or more responses or 2) a response of the one or more responses. Dependent claims 2-20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claims 1, 21, and 22, each of these claims recites “a status of a PET beta amyloid biomarker”. Reciting an abbreviation without explicitly identifying what the abbreviation refers to leaves one of ordinary skill in the art to not be apprised of the metes and bounds of the patent protection sought. For the purposes of compact prosecution, “PET” is construed as positron emission tomography. Dependent claims 2-20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Claim 8 recites the limitation "the prediction" in line 2 of the claim. There is insufficient antecedent basis for this limitation in the claim.
Regarding claim 8, it is unclear what constitutes a “holistic state of the prediction” that is included in the one or more interventions. The claim recites that “determining the holistic state of the prediction comprises receiving additional data associated with the patient” and “weighing the prediction with the additional data”. However, the claims and the disclosure, itself, are silent regarding what the additional data are as well as silent regarding what weighing the prediction with the additional data entails. Thus, one of ordinary skill in the art would not be apprised of the metes and bounds of the patent protection sought.
The text of those sections of Title 35, U.S. Code 112(a) not included in this action can be found in a prior Office action.
Claims 1-22 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.
Regarding claims 1, 11-13, 15, 21, and 22, the disclosure fails to provide sufficient written description for “classifying, using the trained machine learning model, a status of a PET beta amyloid biomarker of the patient” in claim 1, “predicting, using the trained machine learning model, a status of a PET beta amyloid biomarker of the patient” in claims 21 and 22, wherein the trained machine learning model is a regression model” in claim 11, “wherein the regression model is a logistic regression classifier” in claim 12, “wherein the battery of assessments includes at least one of a digital clock and recall assessment and a graphomotor behavior assessment” in claim 13, and “wherein the biomarker comprises a beta-amyloid” in claim 15 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification merely recites similar language as the claim without any meaningful description. See, for example, at least Fig. 2 and para. 36, 37, 41, 43, 45, 48, and 50-52 of the specification which provide the most information yet recite that this function is performed in results-based language without any meaningful description for correlating assessment performance with biomarker status. Therefore, the disclosure at best merely recites that these limitations are performed in results-based language without providing the necessary description of the steps, calculations, or algorithms for performing the claimed functionality. Dependent claims 2-20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claims 1, 8, 17, 18, 21, and 22, the disclosure fails to provide sufficient written description for “determining one or more interventions based on the status, wherein the one or more interventions include values to the patient” in claim 1, “determining one or more interventions based on the prediction, wherein the one or more interventions include values to the patient” in claims 21 and 22, “wherein the one or more interventions include a holistic state of the prediction, and determining the holistic state of the prediction comprises: receiving additional data associated with the patient; and weighing the prediction with the additional data” in claim 8, “wherein the one or more interventions comprise a score for a plurality of categories and a recommendation for each category” in claim 17, and “wherein the recommendation includes a physical evaluation of the patient” in claim 18 to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification merely recites similar language as the claim without any meaningful description. See, for example, at least para. 36 48 of the specification which provide the most information yet recite that this function is performed in results-based language without any meaningful description for correlating assessment performance with biomarker status. Therefore, the disclosure at best merely recites that these limitations are performed in results-based language without providing the necessary description of the steps, calculations, or algorithms for performing the claimed functionality. Dependent claims 2-20 inherit the deficiencies of their respective parent claims, and are thus rejected under the same rationale.
Regarding claim 6, the disclosure fails to provide sufficient written description for “determining a health condition based on the status of the biomarker” to show one of ordinary skill in the art that Applicant had possession of the claimed invention. Claims may lack written description when the claims define the invention in functional language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. See MPEP 2161.01(I). In particular, the specification merely recites similar language as the claim without any meaningful description. See, for example, at least para. 9 of the specification which merely recites the same language as the claim without any further detail. Therefore, the disclosure at best merely recites that these limitations are performed in results-based language without providing the necessary description of the steps, calculations, or algorithms for performing the claimed functionality.
Regarding claim 8, the claim is additionally rejected because the originally filed disclosure is silent regarding the newly added language “determining the holistic state of the prediction comprises: receiving additional data associated with the patient; and weighing the prediction with the additional data”. In particular, the disclosure is silent regarding “determining the holistic state of the prediction” and thus also silent what “determining” comprises. Thus, this is new matter.
Claim Rejections - 35 USC § 101
The text of those sections of Title 35, U.S. Code 101 not included in this action can be found in a prior Office action.
Claims 1-22 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 including additional elements that are sufficient to amount to significantly more than the judicial exception itself.
Step 1
The instant claims are directed to methods which fall under at least one of the four statutory categories (STEP 1: YES).
Step 2A, Prong 1
Independent claim 1 recites:
A method for predicting a biomarker status, comprising:
administering a battery of assessments to a patient;
collecting multimodal data based on one or more responses to the battery of assessments from the patient;
extracting one or more feature sets from at least one response of the one or more responses;
providing the one or more feature sets to a trained machine learning model;
[classifying], using the trained machine learning model, a status of a PET beta amyloid biomarker of the patient;
providing the status into a recommendation engine;
determining one or more interventions based on the status, wherein the one or more interventions include values to the patient;
providing the one or more interventions as output.
Independent claim 21 recites:
A system for predicting amyloid biomarker status, the system comprising:
a computing node comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor of the computing node to cause the processor to perform a method comprising:
administering a battery of assessments to a patient;
collecting multimodal data based on one or more responses to the battery of assessments from the patient;
extracting one or more feature sets from at least one of the one or more responses;
providing the one or more feature sets to a trained machine learning model;
predicting, using the trained machine learning model, a status of a PET beta amyloid biomarker of the patient;
providing the prediction into a recommendation engine;
determining one or more interventions based on the prediction, wherein the one or more interventions include values to the patient;
providing the one or more interventions as output.
Independent claim 22 recites:
A computer program product for predicting amyloid biomarker status, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method comprising:
administering a battery of assessments to a patient;
collecting multimodal data based on one or more responses to the battery of assessments from the patient;
extracting one or more feature sets from at least one response of the one or more responses;
providing the one or more feature sets to a trained machine learning model;
predicting, using the trained machine learning model, a status of a PET beta amyloid biomarker of the patient;
providing the prediction into a recommendation engine;
determining one or more interventions based on the prediction, wherein the one or more interventions include values to the patient;
providing the one or more interventions as output.
All of the foregoing underlined elements identified above, both individually and as a whole, amount to the abstract idea grouping of a certain method of organizing human activity because it is managing personal behavior or interactions between people (including social activities, teaching, and following rules or instructions) by collecting information, analyzing the information, and outputting the results of the collection and analysis. This also amounts to the abstract idea grouping of mental processes as the claims, under their broadest reasonable interpretation, cover performance of the limitations in the mind with the aid of pen and paper (including observation, evaluation, judgment, opinion) but for the recitation of generic computer components. See MPEP 2106.04(a)(2)(III)(C) - A Claim That Requires a Computer May Still Recite a Mental Process.
The dependent claims amount to merely further defining the judicial exception.
Therefore, the claims recite a judicial exception. (STEP 2A, PRONG 1: YES).
Step 2A, Prong 2
This judicial exception is not integrated into a practical application because the independent and dependent claims do not include additional elements that are sufficient to integrate the exception into a practical application under the considerations set forth in MPEP 2106.04(d). The elements of the claims above that are not underlined constitute additional elements.
The following additional elements, both individually and as a whole, merely generally link the judicial exception to a particular technological environment or field of use: a trained machine learning model (claims 1, 21, and 22), a touchscreen (claim 14), a microphone (claim 14), a webcam (claim 14), a stylus (claim 14), a computing device (claim 16), a mobile computing device (claim 20), a system comprising a computing node comprising a computer readable storage medium (claim 21), a processor (claims 21 and 22), and a computer program product comprising a computer readable storage medium (claim 22). This is evidenced by the manner in which these elements are disclosed. See, for example, Fig. 4 which illustrate the elements as non-descript black boxes and stock icons, while at least para. 53-70 in the specification merely provide stock descriptions of generic computer hardware and software components in any generic arrangement. This also evidences that the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. It should be noted that because the courts have made it clear that the mere physicality or tangibility of an additional element or elements is not a relevant consideration in the eligibility analysis, the physical nature of the computing device and associated hardware does not affect this analysis. See MPEP 2106.05(I) for more information on this point, including explanations from judicial decisions including Alice Corp. Pty Ltd. v. CLS Bank Int’l, 573 US 208, 224-26 (2014). Thus, none of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Again, this is evidenced by the manner in which these elements are disclosed in the instant specification. In the event that “a trained machine learning model” (later claimed in claim 12 as “a logistic regression classifier”) and “a recommendation engine” are considered as additional elements, these element do not provide any specific rules with specific characteristics that improve the functionality of the computer system. This is evidenced by the manner in which these are disclosed. See, for example, Fig. 2 and para. 36, 37, 41, 43, 45, 48, and 50-52 which, at best, merely recite in results-based language that they are used. Thus, the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. Additionally, the claims do not apply or use a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition nor do they apply or use a judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. 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. Therefore, the claims are directed to the judicial exception. (STEP 2A, PRONG 2: NO).
Step 2B
The independent and dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under the considerations set forth in MPEP 2106.05. As identified in Step 2A, Prong 2, above, the claimed system and the process it performs does not require the use of a particular machine, nor does it result in the transformation of an article. The claims do not involve an improvement in a computer or other technology. Although claims recite computer components associated with performing at least some of the recited functions, these elements are recited at a high level of generality in a conventional arrangement for performing their basic computer functions (i.e., storing, processing, and outputting data). This is at least evidenced by the manner in which this is disclosed that indicates that Applicant believes the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 USC 112(a) as identified in Step 2A, Prong 2, above. This also evidences that the computer components are merely an attempt to link the abstract idea to a particular technological environment, but do not result in an improvement to the technology or computer functions employed. Thus, the focus of the claimed invention is on the analysis of the collected data, which is itself at best merely an improvement within the abstract idea. See pg. 2-3 in SAP America Inc. v. Investpic, LLC (890 F.3d 1016, 126 USPQ2d 1638 (Fed. Cir. 2018) which proffered “[w]e may assume that the techniques claimed are groundbreaking, innovative, or even brilliant, but that is not enough for eligibility. Nor is it enough for subject-matter eligibility that claimed techniques be novel and nonobvious in light of prior art, passing muster under 35 U.S.C. §§ 102 and 103. The claims here are ineligible because their innovation is an innovation in ineligible subject matter. Their subject is nothing but a series of mathematical calculations based on selected information and the presentation of the results of those calculations.” Furthermore, the steps are merely recited to be performed by, or using, the elements while the specification makes clear that the computerized system itself is ancillary to the claimed invention as identified above. This further evidences that the claims do not recite any specific rules with specific characteristics that improve the functionality of the computer system. In the event that “a trained machine learning model” (later claimed in claim 12 as “a logistic regression classifier”) and “a recommendation engine” are considered as additional elements, these element do not provide any specific rules with specific characteristics that improve the functionality of the computer system. This is evidenced by the manner in which these are disclosed. See, for example, Fig. 2 and para. 36, 37, 41, 43, 45, 48, and 50-52 which, at best, merely recite in results-based language that they are used. Thus, none of the hardware offer a meaningful limitation beyond generally linking the performance of the steps to a particular technological environment, that is, implementation via computers. Therefore, viewed as a whole, these additional claim elements do not provide any meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea of itself (STEP 2B: NO).
Thus, the claims are rejected under 35 USC 101 as being directed to non-statutory subject matter.
35 USC § 102, Public Use, On Sale, or Otherwise Publicly Available
Applicant and the Assignee of this application are required under 37 CFR 1.105 to provide the following information that the examiner has determined is reasonably necessary to the examination of this application.
An issue of public use, on sale activity, or other public availability has been raised in this application. In order for the examiner to properly consider patentability of the claimed invention under 35 U.S.C. 102(a)(1), additional information regarding this issue is required as follows:
The trademark for Core Cognitive Evaluation recites that it includes “downloadable software featuring a series of questions and tests, all for analyzing brain and mental health data, detecting and tracking brain and mental health disorders, and facilitating communication among patient and medical and research professionals; downloadable software and software applications for analyzing brain and mental health data, detecting and tracking brain and mental health disorders, and facilitating communication among patients, medical and research professionals”1. At least one Assignee publication2 indicate the Core Cognitive Evaluation as the disclosed invention. Thus, the disclosed invention, identified as Applicant’s Core Cognitive Evaluation, has been commercially available and in public use since 31 January 20221. Information is required regarding the contents of Core Cognitive Evaluation owned by Applicant created to provide the goods and services identified in the associated trademark that was in public use, on sale, or publicly available prior to 21 August 2023. See MPEP 2120.02.
Applicant is reminded that failure to fully reply to this requirement for information will result in a holding of abandonment.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code 103 not included in this action can be found in a prior Office action.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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.
Claims 1-22 are rejected under 35 U.S.C. 103 as being unpatentable over Pascual-Leone et al. (WO 2022/067189, hereinafter referred to as PL) as applied to claims 1 and 11, in view of Rentz et al.3 (hereinafter referred to as Rentz).
Regarding claims 1, 21, and 22, PL teaches a method for predicting a biomarker status (claim 1), a system for predicting amyloid biomarker status (claim 21), a computer program product for predicting amyloid biomarker status, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to perform a method (claim 21) (PL, Title, Systems and Methods for Machine-Learning-Assisted Cognitive Evaluation and Treatment) comprising:
administering a battery of assessments to a patient (PL, para. 77, “the following multimodal data may be collected concurrently form several patients performing a battery of tasks and/or assessments: 1. Eye tracking data; 2. Voice recording data; 3. Drawing assessment data; 4. EEG data; 5. fMRI taken on for subject during the assessments and then uploaded to the platform.”);
collecting multimodal data based on one or more responses to the battery of assessments from the patient (PL, para. 77, “the following multimodal data may be collected concurrently form several patients performing a battery of tasks and/or assessments: 1. Eye tracking data; 2. Voice recording data; 3. Drawing assessment data; 4. EEG data; 5. fMRI taken on for subject during the assessments and then uploaded to the platform.”);
extracting one or more feature sets from at least one response of the one or more responses (PL, para. 34, “first-order features of brain health may be extracted from these data… the second-order features may be latent variables extracted from an intermediate layer of the neural network.”);
providing the one or more feature sets to a trained machine learning model (PL, para. 34, “the first-order features and the raw health data may be input to a machine learning algorithm… the second-order features may be provided to other pre-trained machine learning algorithms”);
[classifying] (claims 21 and 22: predicting), using the trained machine learning model, a status of a PET amyloid biomarker of the patient (PL, para. 43, “brain biomarkers (e.g., amyloid, tau PET)”; para. 46, ”multimodal data may be input to the learning system (e.g., a neural network) used to determine a latent representation of a patient's health data for use in another learning system to determine a biomarker (e.g., a cognitive score) and/or health condition of the patient (e.g., cognitive disease).” Para. 88, “supervised machine learning methods to predict or detect biomarker values”, supervised machine learning methods perform classification tasks. Para. 93, “6. Evaluate the patient data model vector associated with the new subject using the one-class classification model to determine if the new subject is considered part of that class or not. If not, then the new subject is an outlier and deviates from normal neurological functioning.”);
providing the status (claims 21 and 22: prediction) into a recommendation engine (PL, para. 105, “5. The predicted biomarkers, health conditions, and potentially the features are fed into a recommendation engine…”);
determining one or more interventions based on the status (claims 21 and 22: prediction), wherein the one or more interventions include values to the patient (PL, para. 105, “…which considers the state of these features and recommends one or more interventions that it predicts will produce the most desired changes in those predictions and feature values”);
providing the one or more interventions as output (PL, para. 105, “…which considers the state of these features and recommends one or more interventions that it predicts will produce the most desired changes in those predictions and feature values” The act of recommending one or more interventions is providing the one or more interventions as output. This is further evidenced by “6. The clinician may perform the intervention recommended”.).
PL does not explicitly teach that the PET amyloid biomarker status is a PET beta amyloid biomarker status.
However, in a related art, Rentz teaches the PET amyloid biomarker status is a PET beta amyloid biomarker status (Rentz, pg. e1848, “This analysis will provide Class II evidence that the DCTclock results are associated with amyloid and tau burden among the clinically normal older adults. To explore this objective, those CN participants who had PET imaging were classified as Aβ+ vs Aβ− using 1.185 as the cutoff. A second ROC analysis was performed to evaluate the extent to which the DCTclock summary score, compared with the hand-scored clock and the PACC, could discriminate Aβ+ from Aβ− groups in CN participants only. A logistic regression was used to assess the ability of the DCTclock summary score, PACC, and age to predict Aβ status among CN participants.” Pg. e1852, “the DCTclock summary score was strongly associated with executive functions as well as amyloid deposition in regions found within the frontoparietal and default mode networks. However, it was the fine-grained features in spatial reasoning of the copy clock that were more strongly associated with increased amyloid and tau PET signal.”).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the PET amyloid biomarker status in PL is a PET beta amyloid biomarker status as illustrated by Rentz because “the DCTclock summary score was strongly associated with executive functions as well as amyloid deposition in regions found within the frontoparietal and default mode networks. However, it was the fine-grained features in spatial reasoning of the copy clock that were more strongly associated with increased amyloid and tau PET signal.” See Rentz at pg. e1852. Thus, it is merely using a known technique to improve similar methods in the same way.
Regarding claim 2, PL in view of Rentz teaches the method of claim 1, wherein the one or more feature sets is extracted using a first order measure (PL, para. 34, “first-order features of brain health may be extracted from these data.”).
Regarding claim 3, PL in view of Rentz teaches the method of claim 2, wherein the first order measure comprises a feature extracted from the response and organized by a modality associated with the feature (Fig. 12 examples this – Extracted Audio Features, Extracted EEG Features).
Regarding claim 4, PL in view of Rentz teaches the method of claim 1, wherein the one or more feature sets is extracted using a second order measure (PL, para. 34, “the second-order features may be latent variables extracted from an intermediate layer of the neural network.”).
Regarding claim 5, PL in view of Rentz teaches the method of claim 4, wherein the second order measure comprises extracting an embedded characteristics of the response and associating the characteristics with a modality (PL, Fig. 12 examples this – Voice Embedding, EEG Embedding, fMRI Embedding; para. 67, “the machine learning model may learn embedding for each time window of each modality, segment embeddings for each time window, and/or position embeddings for each time window.” Para. 68, “the embeddings learned in this process, and the hidden layers may be used as latent variables or second order features”).
Regarding claim 6, PL in view of Rentz teaches the method of claim 1, further comprising determining a health condition based on the status of the biomarker (PL, Fig. 1 and 3, Second-order measures include Existing brain constructs and Existing disease constructs; para. 86, “Using the patient data model states and variables as input to predicting disease conditions”).
Regarding claim 7, PL in view of Rentz teaches the method of claim 1.
PL does not explicitly teach wherein determining the one or more interventions comprises thresholding the biomarker status.
However, in a related art, Rentz teaches thresholding the biomarker status (Rentz, pg. e1848, “This analysis will provide Class II evidence that the DCTclock results are associated with amyloid and tau burden among the clinically normal older adults. To explore this objective, those CN participants who had PET imaging were classified as Aβ+ vs Aβ− using 1.185 as the cutoff. A second ROC analysis was performed to evaluate the extent to which the DCTclock summary score, compared with the hand-scored clock and the PACC, could discriminate Aβ+ from Aβ− groups in CN participants only. A logistic regression was used to assess the ability of the DCTclock summary score, PACC, and age to predict Aβ status among CN participants.”).
Thus, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for determining the one more interventions in PL to comprise thresholding the biomarker status because thresholding the biomarker status, as illustrated by Rentz, is merely providing a score that discriminates between binary determinations. In this case, the binary determinations are whether the biomarker status is Aβ+ or Aβ-. Thus, it is merely using a known technique to improve similar methods in the same way.
Regarding claim 8, PL in view of Rentz teaches the method of claim 1, wherein the one or more interventions include a holistic state of the prediction, and determining the holistic state of the prediction comprises:
receiving additional data associated with the patient (PL, para. 105, “2. Additional data from electronic health records, clinician feedback, and others is ingested and combined with multimodal assessment data”); and
weighing the prediction with the additional data (PL, para. 106, “values for the clusters may be calculated as the aggregation of the contributions of their constituent features to the prediction output (e.g., weighted by feature importance).”).
Regarding claim 9, PL in view of Rentz teaches the method of claim 1, wherein the trained machine learning model receives a subset of feature sets as training input (PL, para. 54, “time window subsets of the data may be provided during training of the network”).
Regarding claim 10, PL in view of Rentz teaches the method of claim 9, wherein the subset of feature sets includes a delayed recall score, a composite clock score, an average speed score, an oscillatory motion feature score, and a maximum speed score (PL, para. 59, “delayed recall”; para. 121, “The DCTclock test, is cleared to market and uses a digitizing ballpoint pen that, while drawing, also digitally records its position on the paper 75 times a second with a spatial resolution of two one-thousandths of an inch. DCTclock software detects and measures changes in pen position that cannot be seen by the naked eye, and because the data is time-stamped, the system captures the entire sequence of behaviors (e.g., every stroke, pause, or hesitation), rather than just the final result. This enables the capture and analysis of very subtle behaviors that have been found to correlate with changes in cognitive function. These measurements are all operationally defined in code (hence free of user bias) and carried out in real time.” DCTclock is what generates the claimed composite clock score, average speed score, oscillatory motion feature score, and maximum speed score as evidenced at least by para. 41 of the instant specification.).
Regarding claim 11, PL in view of Rentz teaches the method of claim 1, wherein the trained machine learning model is a regression model (PL, para. 55, “the activation function may be selected to train a regression model”; para. 88, “supervised machine learning methods to predict or detect biomarker values”).
Regarding claim 12, PL in view of Rentz teaches the method of claim 11.
PL does not explicitly teach wherein the regression model is a logistic regression classifier.
However, in a related art, Rentz teaches wherein the regression model is a logistic regression classifier. (Rentz, pg. e1848, “A logistic regression was used to assess the ability of the DCTclock summary score, PACC, and age to predict Aβ status among CN participants.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the regression model to be a logistic regression classifier as identified by Rentz because a logistic regression classifier is a widely used supervised machine learning algorithm for binary classification tasks. In this case, the binary classification task is predicting the biomarker status as Aβ+ or Aβ-. Thus, it is merely using a known technique to improve similar methods in the same way.
Regarding claim 13, PL in view of Rentz teaches the method of claim 1, wherein the battery of assessments includes at least one of a digital clock and recall assessment (PL, para. 40, “the system may begin by prompting a patient to complete two tasks: a clock drawing task and an item recall speech task) and a graphomotor behavior assessment (PL, para. 121, “DCTclock™”. DCTclock™ is a graphomotor behavior assessment).
Regarding claim 14, PL in view of Rentz teaches the method of claim 1, wherein the collecting the multimodal data comprises collecting data from at least a touchscreen, a microphone, a webcam, and/or a stylus (PL, para. 35, “a system may collect health data from tasks and/or assessments provided to the patient using suitable hardware (e.g., tablet, smartphone)”; para. 47, “inputs (i.e., time stamped X-axis and Y-axis coordinates on a touch screen) provided by a mobile device stylus while the patient performs a task or assessment on a mobile application, such as drawing a clock; eye tracking data while providing visual stimulus and requiring the patient to perform tasks that elicit their ability to perceive and respond to that stimulus; audio recording while providing audiovisual stimulus and requiring the patient to vocalize responses to that stimulus to elicit their ability to perceive and respond to such stimulus; video data recording of patients performing some task such as walking; accelerometer data recording of patients performing some task such as walking”; para. 128, “voice recordings captured and encrypted through a tablet, smartphone, or other voice-capturing device”).
Regarding claim 15, PL in view of Rentz teaches the method of claim 1, wherein the biomarker comprises an amyloid (PL, para. 88, “Amyloid protein”).
PL does not explicitly teach the amyloid being a beta-amyloid.
However, in a related art, Rentz teaches the amyloid being a beta-amyloid (Rentz, pg. 1847, Table 1, Abbreviations: Aβ = β-amyloid [beta-amyloid]; pg. e1848, “This analysis will provide Class II evidence that the DCTclock results are associated with amyloid and tau burden among the clinically normal older adults. To explore this objective, those CN participants who had PET imaging were classified as Aβ+ vs Aβ− using 1.185 as the cutoff.19 A second ROC analysis was performed to evaluate the extent to which the DCTclock summary score, compared with the hand-scored clock and the PACC, could discriminate Aβ+ from Aβ− groups in CN participants only. A logistic regression was used to assess the ability of the DCTclock summary score, PACC, and age to predict Aβ status among CN participants.”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention for the amyloid biomarker in PL to be a beta-amyloid because “Alzheimer disease (AD) is a continuum, with the pathophysiologic brain changes of β-amyloid (Aβ) plaques, neurofibrillary tau tangles, and subsequent neurodegeneration occurring 15–20 years before the clinical stage of AD dementia. Identifying individuals at risk for AD will be critical as prevention trials or potential treatments become available.” See Rentz at pg. e1845.
Regarding claim 16, PL in view of Rentz teaches the method of claim 1, wherein providing the one or more interventions as output comprises transmitting the one or more interventions to a computing device associated with a clinician (PL, at least Fig. 11 illustrates that the interventions are output to a computing device associated with a clinician).
Regarding claim 17, PL in view of Rentz teaches the method of claim 1, wherein the one or more interventions comprise a score for a plurality of categories and a recommendation for each category (PL, para. 42, “the formation of clusters can then be used to assign discrete classification scores to data, thus changing the second order features from multiple real valued components to single discrete classes. In various embodiments, future data may be processed similarly, either by rerunning classification on the transformed latent space representation, or by employing more static clustering methods like k-nearest neighbors (KNN) if time/processor constraints are present or if past clustering should not be changed. In various embodiments, by performing this kind of analysis on subcategories of metrics, higher order features of memory, executive function, fine and gross motor control, language processing, cognitive efficiency, spatial processing, information processing, psychological health, can be generated that may lend themselves to further combination or analysis.”).
Regarding claim 18, PL in view of Rentz teaches the method of claim 1, wherein the recommendation includes a physical evaluation of the patient (PL, para. 37, “after or during an initial collection of health data, the system may prompt further or additional collection based on an adaptive task administration. For example, for a given set of individual tasks captured and second-order features derived from them, the system may prompt the patient, physician, or patient care team to capture more individual tasks and repeat the process of generating the second-order features to generate updated second-order features.”).
Regarding claim 19, PL in view of Rentz teaches the method of claim 1, comprising receiving patient demographic and medical history from the patient, (PL, para. 33, “lifestyle/health history questionnaire.” Para. 44, “medical records”; para. 98, “electronic health records”).
Regarding claim 20, PL in view of Rentz teaches the method of claim 1, wherein the battery of assessments is conducted on a mobile computing device (PL, para. 46, “health assessments administered via mobile devices”).
Response to Arguments
Applicant's arguments with respect to the rejections of the claims under 35 USC 112(b) have been fully considered but they are not persuasive. In pg. 17-19, Applicant asserts that the claims have been amended to overcome the rejections.
Examiner is not persuaded. While some rejections have been obviated by the amendments and thus withdrawn, Applicant is directed to the remaining and new rejections above which have been updated to address the amendments to the claims.
Applicant's arguments with respect to the rejections of the claims under 35 USC 112(a) have been fully considered but they are not persuasive. Applicant disagrees with the Office’s identification that the specification fails to provide any meaningful description for correlating assessment performance with biomarker status and asserts that regression models are well understood to perform data classification and one of ordinary skill in the art would understand how to configure a regression model to receive[] and output useful data.
Examiner is not persuaded. As identified in MPEP 2161.01(I), claims may lack written description when the claims define the invention in function al language specifying a desired result but the specification does not sufficiently describe how the function is performed or the result is achieved. For software, this can occur when the algorithm or steps/procedure for performing the computer function are not explained at all or are not explained in sufficient detail (simply restating the function recited in the claim is not necessarily sufficient). In other words, the algorithm or steps/procedure taken to perform the function must be described with sufficient detail so that one of ordinary skill in the art would understand how the inventor intended the function to be performed. It is not enough that one skilled in the art could write a program to achieve the claimed function because the specification must explain how the inventor intends to achieve the claimed function to satisfy the written description requirement. (Bolded for emphasis). Thus, merely implying that a non-descript regression model performs the function is not enough. The disclosure is silent regarding how a regression model is configured to perform the claimed function, not merely receive and output useful data.
In pg. 10-14, Applicant also asserts that para. 38, 41, 43, 45-48, and 50-52 of the specification and Fig. 2 and 3 provide support for the limitations at issue in claims 1, 11-13, 15, 21, and 22.
Examiner is not persuaded. At least Fig. 2 and para. 41, 43, 45, 48, and 50-52 are explicitly identified in the rejection as insufficient and further identify that a logistic regression classifier is merely recited as a black box to perform the claimed function. Fig. 3 merely recites, in results-based language, that “a logistic regression classifier is trained using the five features as input, and a binary Aβ positivity status as output” as a single step. Thus, Fig. 3, in fact, does not describe the machine learning model. Similarly, para. 38 merely reciting a that “a patient may be directed to complete a battery of assessments, including a Digital Clock and Recall (DCR) assessment, among others” is not a description of the digital clock and recall assessment. It is merely reciting that it may be performed. Further regarding para. 41 of the specification, the absence of any meaningful description of “the specific combination of predictive features in the statistical model” is a clear identification for the necessity of the rejection.
Regarding claims 9 and 10, the amendments obviate the associated rejection. Thus, the rejection is withdrawn for the limitations at issue from these claims.
In pg. 14-15, with respect to “determining one or more interventions”, Applicant asserts that para. 35 and 41 of the specification are detailing the correlation between the administered tests and amyloid status.
Examiner is not persuaded. The citations from para. 35 and 41 amount to merely assertions of ability, not any detailing of the correlation between the administered tests and amyloid status. This is exemplified by the language “embodiments of the present disclosure consider multiple modalities to predict the presence of important ADRD biomarkers” in para. 35 and “Embodiments of the present disclosure can incorporate five metrics/features that correlate with Aβ± status” in para. 41 with merely reciting the names of the five metrics/features without any description whatsoever.
In pg. 15-16, regarding holistic state of the prediction in claim 8, Applicant asserts that para. 36 and Fig. 1 provide support and that the claim is amended to clarify how the holistic state is determined.
Examiner is not persuaded. Neither para. 36 nor Fig. 1 provide sufficient written description for the one or more interventions to include a holistic state of the prediction. For instance, the citation of merely “consider[ing] the holistic state of the prediction” is not a description, it’s merely a statement of consideration. There is no description of the function. It is further noted that there is a rejection under 35 USC 112(b) regarding the indefiniteness of the term that informs the rejection under 35 USC 112(a).
In pg. 16-17, Applicant asserts that para. 48-49 and Table 3 in the specification provide support.
Examiner is not persuaded. At least para. 48 is explicitly identified as insufficient. In particular, the citations and Table 3 merely recite what an output may be without any description of the function necessary to determining such outputs as incorporated from independent claim 1.
Applicant's arguments with respect to the rejection of the claims under 35 USC 101 have been fully considered but they are not persuasive. In pg. 19-20, Applicant asserts that the actual performance of managing interactions between people is not recited in the claims.
Examiner is not persuaded. The courts have repeatedly identified that collecting information, analyzing the information, and outputting the results of the collection and analysis is managing personal behavior or interactions between people and thus a certain method of organizing human activity. Furthermore, “predicting a biomarker status” is part of the output of the collection and analysis, and thus firmly rooted in the judicial exception.
In pg. 20-22, Applicant asserts that “administering a battery of assessments to a patient”, “providing the one or more feature sets to a trained machine learning model”, or “classifying, using the trained machine learning model, a status of a PET beta amyloid biomarker” do not amount to mental processes.
Examiner is not persuaded. As identified in the rejection, the processes of observation, evaluation, judgment, opinion are included under mental processes. Administering a battery of assessments to a patient clearly falls within these. Furthermore, MPEP 2106.04(a)(2)(III)(C) identifies that a claim that requires a computer may still recite a mental process. The mere use of a non-descript “trained machine learning model” is, at best, an additional element that merely acts to tie the judicial exception to implementation with a computer.
In pg. 22-23, Applicant asserts that the claims recite additional elements that illustrate integration into a practical application. Here, Applicant asserts that the claim does not recite abstract ideas is thus entirely reciting additional elements. Applicant then asserts that “collecting multimodal data based on one or more responses to the battery of assessments from the patients”, “extracting one or more feature sets”, and “a status of a PET beta amyloid biomarker of the patient” are additional elements.
Examiner is not persuaded. The rejection correctly identifies what language in the claims recite a judicial exception. This includes “collecting multimodal data based on one or more responses to the battery of assessments from the patients”, “extracting one or more feature sets”, and “a status of a PET beta amyloid biomarker of the patient”. It is further noted that “a status of a PET beta amyloid biomarker of the patient” is merely information that is output. The rejection, under Prong 2 of Step 2A and Prong 2B evaluate the identified additional elements, both individually and as a whole, to neither integrate the judicial exception into a practical application nor add significantly more.
Applicant's arguments with respect to 37 CFR 1.105 request regarding an issue of public use, on sale activity, or other public availability made under the claim rejections under 35 USC 102 have been fully considered but they are not persuasive. Applicant asserts that the summary of the present application is incorrect, that the components of CCE may furnish input to the prediction of functional dependence, and that the present application does not state that the claimed subject matter is equivalent to or is contained in the CCE and examples “[t]he present disclosure describes a proposed system and methods for predicting an individual’s PET Aβ± status using multimodal digital cognitive assessment technology that provides a quick, inexpensive, digital, noninvasive solution for predicting Aβ± obtained from the multimodal digital assessment technology.” Applicant also asserts that the present claims are directed towards a specific method related to determining functional impairment status.
Examiner is not persuaded. As identified in the 105 request the trademark for CCE recites that it includes "downloadable software featuring a series of questions and tests, all for analyzing brain and mental health data, detecting and tracking brain and mental health disorders, and facilitating communication among patient and medical and research professionals; downloadable software and software applications for analyzing brain and mental health data, detecting and tracking brain and mental health disorders, and facilitating communication among patients, medical and research professionals”. This directly coincides with Applicant’s example language – “system and methods for predicting an individual’s PET Aβ± status using multimodal digital cognitive assessment technology that provides a quick, inexpensive, digital, noninvasive solution for predicting Aβ± obtained from the multimodal digital assessment technology.” Further evidence is provided by the publication identified in the 105 request which recites “Linus Health’s Core Cognitive Evaluation™ (CCE™) is a digital assessment solution that combines objective assessment of cognitive function to detect early signs of cognitive impairment with an evaluation of brain health risk factors to assess the risk of developing dementia in the future. The CCE is composed of two components: The Digital Clock and Recall (DCR™) and the Life and Health Questionnaire (LHQ).” Thus publication also identifies that the CCE uses a “proprietary algorithm” as the evaluation of the results from the two components. Thus, Applicant’s assertions provide further evidence that the CCE is the disclosed invention.
Applicant's arguments with respect to the rejections of the claims under 35 USC 102 have been fully considered but they are not persuasive. Applicant asserts that PL does not address PET beta amyloid status.
Examiner is not persuaded. This is an argument towards newly added language in the claims. Applicant is directed to the rejections of the claims which have been updated to address the amendments.
Applicant also asserts that PL does not use the specific step of “classifying” any status of the patient.
Examiner is not persuaded. Again, this is an argument towards newly added language in the claims. Applicant is directed to the rejections of the claims which have been updated to address the amendments. It is noted that supervised machine learning methods, which is taught by PL, are classifiers.
Applicant then asserts that the dependent claims are allowable due to their dependencies.
Examiner respectfully disagrees. Appellant is directed to the rejections which identify that neither the independent claims nor the dependent claims are allowable.
Applicant's arguments with respect to the rejections of claims 7, 12, and 15 under 35 USC 103 have been fully considered but they are not persuasive. Applicant asserts that Rentz does not cure the failure of PL to disclose independent claim 1, from which these claims depend.
Examiner is not persuaded. This is merely a conclusory statement made without substantive support, and is not persuasive. Applicant is directed to the rejections of the claims which have been updated to address the amendments to the claims.
The rejections stand.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 DANIEL LANE whose telephone number is (303)297-4311. The examiner can normally be reached Monday - Friday 8:00 - 4:30 MT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xuan Thai can be reached at (571) 272-7147. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DANIEL LANE/Examiner, Art Unit 3715
/XUAN M THAI/Supervisory Patent Examiner, Art Unit 3715
1 Linus Health, Inc. (2023, May 2). Trademark for Core Cognitive Evaluation. Trademark Electronic Search System. Retrieved April 18, 2025, from https://tsdr.uspto.gov/#caseNumber=97522115&caseSearchType=US_APPLICATION&caseType=DEFAULT&searchType=statusSearch
2 Ciesla, M., Jannati, A., Gomes-Osman, J., & Pascual-Leone, A. (2022, June 17). Dementia risk estimation in the Linus Health Core Cognitive Evaluation. Linus Health. https://linushealth.com/dementia-risk-estimation-in-the-linus-health-core-cognitive-evaluation
3 Rentz, D. M., Papp, K. V., Mayblyum, D. V., Sanchez, J. S., Klein, H., Souillard-Mandar, W., Sperling, R. A., & Johnson, K. A. (2021). Association of Digital Clock drawing with pet amyloid and tau pathology in normal older adults. Neurology, 96(14), e1844–e1854. https://doi.org/10.1212/wnl.0000000000011697