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 .
Election/Restrictions
Applicant’s election without traverse of Group I, claims 1-12 in the reply filed on 8/18/2026 is acknowledged. Claims 13-24 are withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected invention, there being no allowable generic or linking claim. Election was made without traverse in the reply filed on 8/18/2026.
Claim Objections
Claim 6 is objected to because of the following informalities: Claim 6, line 1 should recite similar to – The system of claim 1, – in order to fix an inadvertent typographical error and match the system language recited within claim 1. Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “data collection module configured to store the collected biometric data” in claims 1-12, “a neural assessment module configured to extract a plurality of physiological signals from the stored biometric data..” in claims 1-12.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
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 1-12 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 7-12 recite “using a plurality of sensors configured to collect biometric data from the subject”, “using a data collection module configured to store the collected biometric data” and “using a neural assessment module configured to: extract a plurality of physiological signals from the stored biometric data…” but does not further clarify and/or specify what the “plurality of sensors”, “data collection module” and “neural assessment module” are used to do, clarification is required. For the purpose of examination, as best understood, any method that includes data collection and determining neurological risk scores using the data collected has been interpreted to read on the claim language.
Claim limitations “data collection module configured to store the collected biometric data” and “a neural assessment module configured to extract a plurality of physiological signals from the stored biometric data..” recited within claims 1-12 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The disclosure is devoid of any structure that performs the function in the claim. The disclosure simply recites the same language as the claims, see pg. 2, 2nd full paragraph and shows a generic box for the claimed modules, see Fig. 1, therefore the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
For the purpose of examination, as best understood, any structure including generic computer
components such as a processor which stores biometric data and extracts a plurality of
physiological signals from biometric data for determining neurological risk scores have been
interpreted to read on the claims.
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 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-12 is/are drawn a method and an apparatus which is/are a statutory category of invention (Step 1: YES).
The claim limitations within independent claims 1 and 7 that set forth or describe the abstract idea is/are: “extract a plurality of physiological signals from the stored biometric data; generate one or more neurological risk scores; and produce personalized health recommendations based on the risk scores.” The reasons that the limitations is/are considered an abstract idea is/are the following: The limitations of , “extract a plurality of physiological signals from the stored biometric data; generate one or more neurological risk scores; and produce personalized health recommendations based on the risk scores.” is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind with the aid of pen and paper but for the recitation of generic computer components. That is, other than reciting “using machine learning models trained on neurological data”, “data collection module configured to”, “processor..to receive data” and “neural assessment module configured to” nothing in the claim element precludes the steps from practically being performed in the mind with the aid of pen and paper. For example but for the recitation of “using machine learning models trained on neurological data”, “data collection module configured to”, “processor..to receive data” and “neural assessment module configured to”, “extract a plurality of physiological data”, “extract a plurality of physiological signals from the stored biometric data; generate one or more neurological risk scores; and produce personalized health recommendations based on the risk scores.” in the context of the claims encompasses the user, with the aid of pen and paper, determining physiological signals from the biometric data, calculating neurological risk scores and providing personalized health recommendations. Other than reciting the use of generic computer components and generic trained machine learning models, nothing in the elements of the claims precludes the steps from practically being performed in the mind with the aid of pen and paper. There is nothing to suggest an undue level of complexity in the recited steps. If a claim limitation, under its broadest reasonable interpretation covers a metal process, i.e. performance of the limitation in the mind, but for the recitation of generic computer components, then it falls with the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to concepts that have been identified as abstract by the courts, such as: collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016), selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis in SAP America Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed Cir. 2018). Thus, the claim(s) are directed to a judicial exception and fall squarely within the realm of "abstract ideas," which is a patent-ineligible concept (Step 2A: Prong One YES).
Analyzing the claim as whole for an inventive concept, the claim does not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception. The additionally recited element(s) appended to the abstract idea include: “a plurality of sensors configured to collect biometric data from the subject”, “at least one processor…”, “a data collection module configured to..”, “a neural assessment module configured to…” and “using machine learning models trained…”. The additional elements of “a plurality of sensors configured to collect biometric data from the subject”, merely: add insignificant extra-solution activity, reciting “a plurality of sensors configured to collect biometric data from the subject”, is recited at a high level of generality (i.e. as a general means of gathering biometric data) and is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity). All uses of the recited judicial exception require the pre- solution activity of data gathering. As discussed above with respect to integration of abstract idea into a practical, the additional element of “at least one processor…”, “a data collection module configured to..”, “a neural assessment module configured to…” and “using machine learning models trained…” amount to no more than mere instruction to apply the exception using generic computer components. The “at least one processor…”, “a data collection module configured to..”, “a neural assessment module configured to…” and “using machine learning models trained…” are purely general-purpose computer components recited as carrying out the general-purpose computer functions of storing and processing data to enable the abstract process. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”. Further, the addition of “using machine learning models trained…” does not preclude the claim from reciting an abstract idea, as the courts have indicated that mere automation of a manual process may not be sufficient to improve functionality, In re Credit Acceptance Corp. v. Westlake Services, 859 F.3d 1044, 1055, 123 USPQ2d 1100, 1108-09 (Fed. Cir. 2017), and automation of a task can still be performed by a person if their mental capability has been trained to do so. Examiner notes that the claimed invention is still directed to a judicial exception without significantly more, as they are still directed to a concept relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work. The use of “using machine learning models trained…” are nonspecific and amount to a drafting technique that places no meaningful limits on the claims. The inclusion of “using machine learning models trained…” is not sufficient to render a claim patent-eligible because not all transformations or machine implementations infuse an ineligible claim with an “inventive concept” - Solutran, Inc. v. Elavon, Inc., 931 F.3d 1161, 1169 (Fed. Cir. 2019) (citing DDR Holdings, LLC v. Hotels.com, L.P., 773 F.3d 1245, 1256 (Fed. Cir. 2014)). Although claims 1 and 7 are tied to machine learning models their ultimate focus is determining a personalized health recommendation based on risk scores that is directed towards an abstract idea and one that can be done by mental process. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong Two, NO).
Claims 1 and 7 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. e.g., all elements are directed to pre-solution activity, carried out using well-understood routine, conventional activities previously known to the industry and amount to elements that have been recognized as well- understood, routine and conventional activity in particular fields, e.g. receiving or transmitting data over a network, Symantec, see MPEP 2106.05(d)(II), MPEP 2106.05(g) or purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data to enable the abstract process (the specification recites that the system architecture can include multiple sensors that feeds into a computer system, see para. [0019] of published application US 2025/0152083), see MPEP 2106.05(f), the additional elements do not amount to significantly more than the above-identified judicial exception(s). Similarly, when considered as an ordered combination, the additional components/steps of the claim(s) add nothing that is not already present when the steps are considered separately (Step 2B: NO). The claims are not patent eligible.
Claim(s) 2-6 and 8-12 depend directly or indirectly from claim(s) 1 and 7. Therefore, the dependent claims rely upon the same abstract idea as the independent claim(s), as set forth above. Additionally, the dependent claims do nothing more than further limiting the abstract idea while failing to qualify as "significantly more", and the specificity of an abstract idea does not make it any "less abstract" as it is still directed to concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work subject matter. Therefore, the dependent claim(s) are also not patent eligible for the reasons discussed above. Claim(s) 2 and 8 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely add insignificant extra-solution activity that is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity). All uses of the recited judicial exception require the pre- solution activity of data gathering. Claim(s) 3-6 and 9-12 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitations regarding the abstract idea and/or the computer system which are purely general-purpose computer components recited as carrying out the general- purpose computer functions of processing data to enable the abstract process. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”.
The instantly rejected claim(s) are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. In the interest of advancing prosecution, the examiner suggests: providing evidence, for example, delineating how the abstract idea and/or additional elements appended to the abstract idea results in an improvement to the technology/technical field, which can show eligibility and/or adding a practical application of the claimed method outside of the computer (e.g. treating a patient). See MPEP § 716.01(c) for examples of providing evidence supported by an appropriate affidavit or declaration. For additional guidance, applicant is directed generally to MPEP §2106.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2 and 7-8 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by WO 2019/246032 to Rao et al. (herein Rao).
In reference to at least claims 1 and 7
Rao discloses a system/method for providing an assessment of a neurological condition of a subject (e.g. “The disclosed system and method provide an assessment of a neurological condition of a subject”, Para. [0016]), the system comprising: a plurality of sensors (one or more signals using a capture module communicatively coupled to one or more sensors, Para. [0027]) configured to collect biometric data from the subject (e.g. “”sensor signals 202 can include data from active and passive sensing devices with diagnostic and monitoring capabilities ... active sensors can provide signals that include, but are not limited to GPS location signals 204, biochemical signals 206, biomedical signals 208, such as respiratory rate, electrocardiography, electroencephalography, heart rate measurements, electrooculography, electromyography, or blood pressure measurement,” Para. [0062]); at least one processor (e.g. “a capture module in communication with the one or more processors, a plurality of signals associated with one or more neural parameters of the subject,” Para. [0030]) coupled to the sensors to receive biometric data (e.g. “sensor signals 202 can include data from active and passive sensing devices with diagnostic and monitoring capabilities ... active sensors can provide signals that include, but are not limited to GPS location signals 204, biochemical signals 206, biomedical signals 208, such as respiratory rate, electrocardiography, electroencephalography, heart rate measurements, electrooculography, electromyography, or blood pressure measurement,” Para. [0062]); a data collection module configured to store the collected biometric data (e.g. “one or more processors can adaptively extract, from a capture module in communication with the one or more processors, a plurality of signals associated with one or more neural parameters of the subject. In one implementations, the one or more neural parameters are selected from a plurality of neural parameters responsive to a prior clinical assessment of the neurological condition of the subject and based on the clinical data in the reference database, and the plurality of signals are stored to the computer-readable storage medium,” Para. [0016]); a neural assessment module configured to: extract a plurality of physiological signals from the stored biometric data (e.g. “use observed responses and clinical signals of the specific individual,” Para. [0010]; “the one or more processors dynamically generate, using a neural assessment module operatively coupled to the reference database, one or more neurological risk scores for the subject based on the plurality of signals by computing... the clinical data in the reference database,” Para. [0017]; “using the neural assessment module... the neurological condition of the individual with the one or more neurological risk scores, and determine whether the prior clinical assessment of the neurological condition is a false positive assessment and/or a false negative assessment,” [Para. 0019]); generate one or more neurological risk scores (e.g. “one or more pre-determined neurological risk scores associated with the set of recommendations, compare the one or more pre-determined neurological risk scores with the generated one or more neurological risk scores, generate a progress indicator for at least one of the one or more neurological risk scores based on the comparison, and adjust the set of recommendations based on the progress indicator for the at least one or more neurological risk scores,” Para. [0024]) using machine learning models trained on neurological data (e.g. “the system and method apply advanced machine learning techniques in order to identify sets of weights that contribute to the computations of the neurological risk scores,” Para. [0029]); and produce personalized health recommendations based on the risk scores (e.g. “automatically adjusting the selection of the one or more neural parameters based on the one or more neurological risk scores and the clinical data in the reference database, and providing a set of care-related recommendations based on at least one of the one or more neurological scores,” Para. [0032]; “recommendation module 106 can generate one or more alerts based on the obtained risk assessment scores to a health care professional. For example, such alerts can include recommendations for additional clinical testing, change in prescription medications or any other suitable recommendation,” Para. [0060]).
In reference to at least claims 2 and 8
Rao discloses wherein the plurality of sensors (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer, electrooculography, electromyography, augmented/ virtual reality sensors, and blood pressure measurement,” Para. [0027]) comprises at least three of: speech sensors (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer,” Para. [0027]; “capture the real-time brain function status within cognitive domains such as language,” Para. [0067]); blood sample sensors; wearable motion sensors (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer,” Para. [0027]; “tracking sensors that include ambient sensor technology, e-textile systems "wearable",” Para. [0062]); sleep monitoring sensors (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer,” Para. [0027]; “capture the real-time brain function status within cognitive domains such as language, memory, visuospatial, behavioral, executive, and motor. In addition, sleep screening,” Para. [0067]); heart rate sensors (e.g. “sensors are selected from the group consisting of... heart rate measurement,” Para. [0027]); stress monitoring sensors.
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) 3 and 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2019/246032 to Rao et al. (herein Rao) in view of the US 2022/0254461 to Vaughan (herein Vaughan).
In reference to at least claims 3 and 9
Rao discloses a system and method according to claims 1 and 7. Rao further discloses wherein the neural assessment module is further configured to: analyze speech recordings for linguistic biomarkers (e.g. “neurodegeneration, certain dementias such as semantic dementia variant of primary progressive aphasia, progressive non fluent variant of primary progressive aphasia, logopenic variant of primary progressive aphasia, behavioral variant of frontotemporal dementia, or posterior cortical atrophy, do not have memory as a predominant symptom, and hence individuals may score as independent in instrumental activities of daily living, although significant impairment in social, language, or visuospatial skills affects day to day function resulting in a diagnosis of dementia,” Para. [0066]; “capture the real- time brain function status within cognitive domains such as language, memory, visuospatial, behavioral, executive, and motor,” Para. [0067]); and compute a confidence index for the biomarkers (e.g. “one or more neurological risk scores... used by the one or more processors ... genetic and biomarker indicator, and/or mortality and/or morbidity indicator for the subject,” Para. [0026]; “determined confidence threshold may be automatically generated based on... the one or more risk scores and their trends as obtained from neural assessment module,” Para. [0081]).
However, Rao fails to explicitly disclose speech recordings for linguistic biomarkers comprising prosody, lexical complexity, and semantic coherence; and compute a confidence index for the linguistic biomarkers.
Vaughan is in the field of speech analysis (e.g. title; abstract) discloses analyzing speech recordings for linguistic biomarkers comprising prosody, lexical complexity, and semantic coherence (e.g. “estimated based on... biomarkers ... in order to determine an improved treatment plan... biomarkers can be used to determine when the patient may be at risk,” Para. [0012]; “recording speech patterns ... the active sources can include audio feed data source such as speech patterns, lexical/syntactic patterns ... size of vocabulary, correct/ incorrect use of pronouns, correct/incorrect inflection and conjugation, use of grammatical structures such as active/passive voice... sentence flow... higher order linguistic patterns ... coherence, comprehension, conversational engagement, and curiosity,” Para. [0063]; “subject's speech patterns ... prosody, lexical analysis,” Para. [0106]; “semantic content of each feature value is captured in the construction of the assessment model,” Para. [0118]); and compute a confidence index for the linguistic biomarkers (e.g. “estimated based on... biomarkers .. .in order to determine an improved treatment plan... biomarkers can be used to determine when the patient may be at risk,” Para. [0012]; “module can check whether the fitting of the data can generate a prediction of one or more specific disorders ... within a confidence interval exceeding a threshold value... within a 90% or higher confidence interval,” Para. [0132]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the biomarker data used to calculate a confidence interval of Rao to include linguistic biomarkers comprising prosody, lexical complexity, and semantic coherence, as taught by Vaughan, in order to provide a more comprehensive testing regimen aimed at generating an accurate initial diagnosis (‘461, Para. [0088]).
Claim(s) 4 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2019/246032 to Rao et al. (herein Rao) in view of the WO 2022/115705 to Reitermann (herein Reitermann).
In reference to at least claims 4 and 10
Rao discloses a system and method according to claims 1 and 7. Rao further discloses wherein the neural assessment module is further configured to: analyze samples for biomarkers (e.g. “system 100 identifies that a number of individuals have pre-determined Alzheimer's disease dementia based on the clinical signals and data... such as biomarkers”, Para. [0055]); and generate risk scores based on detected biomarker levels (e.g. “a computerized system and method that generates a health assessment and computes risk scores of neurological conditions ... system and method receive and process signals relating to the neurological condition of the subject, generate mathematical models based on the signals, compute risk assessment scores to predict, propose, and/or evaluate and recommend courses of treatment or care, Abstract; one or more processors can reserve one or more care-related resources based on the one or more neurological risk scores. Such scores can also be used by the one or more processors to generate ... biomarker indicator”, Para. [0026]).
However, Rao does not explicitly disclose analyzing blood samples for biomarkers comprising tau, p-tau, neurofilament-light, glial fibrillary acidic protein "GFAP", amyloid beta, and a-synuclein.
Reitermann, in the field of analyzing biomarkers to determine medical conditions (e.g. title; abstract), discloses analyzing blood samples for biomarkers (e.g. “methods and tests that measure biomarkers and collect clinical parameters from subjects, and computer-implemented processes for assessing a likelihood that a patient has or will develop... disease ... assigning... risk score, Abstract; methods typically comprise generating an AD risk score from a dataset associated with the subject that has quantitative data for at least 4 protein markers in one or more fluid samples from the subject e.g., blood samples such as plasma samples or serum samples, or cerebral spinal fluid”, Para. [0007]) comprising tau, p-tau, neurofilament-light, glial fibrillary acidic protein "GFAP" (e.g. “markers that can be used in the methods of the disclosure include tau peptide markers ... p-tau... amyloid peptide markers ... neurodegeneration markers ... neurofilament light... glial fibrillary acidic protein "GFAP"”, Para. [0008]), amyloid beta (e.g. “amyloid peptide markers that can be used in determining the risk score, [0075]; Beta-secretase cleavage of APP initially results in the production of an APP fragment that is further cleaved by gamma-secretase at residues 40-42 to generate two main forms of amyloid beta, Ab40 and Ab42. Amyloid beta (Ab) peptides”, Para. [0076]), and a-synuclein (e.g. “markers utilized in determining the risk score can further comprise one or more markers other than a tau peptide marker, an amyloid peptide marker, a neurodegeneration marker, a metabolic disease marker, and an inflammation marker ("other markers"), for example a frontotemporal lobe dementia "FTLD" marker (e.g., a-synuclein”, Para. [0112]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the biomarker data used to calculate a risk score in Rao to include blood samples for biomarkers that include tau, p-tau, neurofilament-light, glial fibrillary acidic protein (GFAP), amyloid beta, and a-synuclein to calculate a risk score, as taught by Reitermann, to enable accurate, reliable identification of subjects at risk for a disease, even before the display of symptoms (‘705, Paras. [0007]).
Claim(s) 5 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2019/246032 to Rao et al. (herein Rao) in view of the US 2022/0265178 to Tran (herein Tran).
In reference to at least claims 5 and 11
Rao discloses a system and method according to claims 1 and 7. Rao further discloses the neural assessment module (e.g. “the one or more processors dynamically generate, using a neural assessment module operatively coupled to the reference database, one or more neurological risk scores for the subject based on the plurality of signals by computing... the clinical data in the reference database,” Para. [0017]; “using the neural assessment module... the neurological condition of the individual with the one or more neurological risk scores, and determine whether the prior clinical assessment of the neurological condition is a false positive assessment and/or a false negative assessment,” Para. [0019]) is further configured to: analyze wearable sensor data comprising heart rate (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer,” Para. [0027]; “tracking sensors that include ambient sensor technology, e-textile systems "wearable",” Para. [0062]), blood pressure (e.g. “sensors can provide signals that include, but are not limited to... blood pressure measurement,” Para. [0061]), sleep patterns (e.g. “sensors are selected from the group consisting of: biochemical sensors, GPS location sensors, respiratory rate, electrocardiography, electroencephalography, gyroscope, heart rate measurement, accelerometer,” Para. [0027]; “capture the real-time brain function status within cognitive domains such as language, memory, visuospatial, behavioral, executive, and motor. In addition, sleep screening,” Para. [0067], movement analysis sensors are selected from the group consisting of...GPS location sensors ... gyroscope... accelerometer, Para. [0027]; tracking sensors that include ambient sensor technology, e-textile systems "wearable", Para. [0062]); and identify behavioral patterns indicative of neurological conditions (e.g. “neural assessment module 104 can generate rules for neurological conditions whereby matches with the provided signals and data from the individual can be made based on disease rules for core, supportive, and exclusion criteria. As discussed above, the rules can be stored in database 112 accessible by the neural assessment module 104 and can be generated for any assigned condition. For example, common stroke syndromes (ischemic and hemorrhagic) and neuromuscular syndromes (based on at nerve plexus, roots, division, cord, branch segments) are matched based on rules using reported symptoms and abilities with neuroanatomical principles in the central and peripheral nervous system,” Para. [0053]).
However, Rao fails to explicitly disclose analyzing sensor data comprising heart rate variability and gait analysis.
Tran, in the field of wearable sensors (e.g. title; abstract), discloses analyzing sensor data comprising heart rate variability (e.g. “track cardiovascular sensor data... heart rate variability (HRV) is a noninvasive measure of the autonomic nervous system,” Para. [0164]) and gait analysis ( e.g. “the gait can be sensed by providing a sensor on the floor and a sensor near the head and the variance in the two sensor positions are used to estimate gait characteristics,” Para. [0216]; “system can detect neurologic problems, as almost every neurologic condition will be associated in some way with an abnormality of gait, such as an inability to gait,” Para. [0358]; “monitoring person can instruct the monitored subject to perform a series of simple tasks which can be used for diagnosis of neurological abnormalities. These observations may yield early indicators of the onset of a disease,” Para. [0211]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the sensor data of Rao to include heart rate variability and gait, as taught by Tran, to provide an early diagnosis, which increases the success of treatment (‘136, Para. [0358]).
Claim(s) 6 and 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over WO 2019/246032 to Rao et al. (herein Rao) in view of the US 2023/0012186 to Siegel et al. (Siegel).
In reference to at least claims 6 and 12
Rao discloses a system and method according to claims 1 and 7. Rao further discloses machine learning models (e.g. “the one or more mathematical models may be generated using supervised, non-supervised or any other suitable method associated with machine learning,” Para. [0052]) comprise: a multi-layer neural network (e.g. “neural assessment module 104 may generate a mathematical model for identifying different subtypes of dementia using a multi-class neural network, support vector machine, convolutional neural networks or any other deep architecture using supervised learning”, para. [0052]) with specific architecture for processing data; and trained weights (e.g. “using a neural assessment module operatively coupled to the reference database, one or more neurological risk scores for the subject based on the plurality of signals by computing, at least in part, a set of weights for combining the plurality of signals, wherein the weights are computed based at least in part on the clinical data”, Para. [0017]) for feature extraction from each sensor type (e.g. “clinical data relating to the neurological condition. Further the method proceeds by adaptively extracting, from a capture module in communication with the one or more processors, a plurality of signals associated with one or more neural parameters of the subject,” Para. [0030]; “the one or more processors can reserve one or more care-related resources based on the one or more neurological risk scores. Such scores can also be used by the one or more processors to generate a brain tissue pathology, genetic and biomarker indicator "features extracted from weighted data", and/ or mortality and/or morbidity indicator,” Para. [0026]; “more processors configured to: adaptively extract, from a capture module in communication with the one or more processors, a plurality of signals associated with one or more neural parameters of the subject, wherein the one or more neural parameters are selected from a plurality of neural parameters responsive to a prior clinical assessment of the neurological condition of the subject and based on the clinical data”, Claim 1).
However, Rao fails to explicitly disclose multi-layer neural network with specific architecture for processing multi-modal data.
Siegel, in the field of neural networks using machine learning (e.g. title; abstract), discloses a multi-layer neural network (e.g. “an artificial neural network can be configured as a convolutional neural network ("CNN"), in which the network architecture includes one or more convolutional layers. For example, the two-stage neural network can include two distinct multi-layer convolutional neural network (CNN),” Para. [0173]) with specific architecture for processing multi-modal data (e.g. “biometrics scan represents a valid user, what condition the user is in, perhaps using multi-modal data such as heart rate or blood pressure prediction ... multi-modal data including... sensor outputs”, Para. [0192]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the neural network processing of sensor data of Rao to include a multi-layer neural network for processing multi-modal data, as taught by Siegel, to provide multi-layer neural networks and multi-modal data that help improve classification performance (‘186, Paras. [0041]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2023/0255564 to Pascual-Leone et al. which teaches system and methods for machine-learning-assisted cognitive evaluation and treatment.
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