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 .
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.
Claims 1 – 12, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Yancheva et al. (Using linguistic features longitudinally to predict clinical scores for
Alzheimer’s disease and related dementias, September 2015) in view of Hirst et al. (US PAP 2013/0297216).
As per claims 1, 19, and 20, Yancheva et al. teach a method/system for detecting longitudinal progression of Alzheimer's disease (AD) in a patient, comprising, by one or more computing devices:
receiving speech data comprising a patient's description of one or more previous or current experiences of the patient (“collected spontaneous speech data from 9 controls, 9 patients with AD, and 30 patients with frontotemporal lobar degeneration (FTLD) [9].”; page 134);
analyzing the speech data to quantify a plurality of speech variables, wherein the plurality of speech variables comprises a word-length variable and a use-of-particles variable (“Control subjects use longer utterances, more gerund + prepositional phrase constructions (VP! VBG PP, e.g., standing on the chair), more content words such as
noun phrases (NP) and verbs, and are more likely to talk about what they see through the window (info_window), which is in the background of the scene (e.g., it seems to be summer out).”; page 135; section 2.3);
determining a composite score based on a standardization of the quantified plurality of speech variables and a substantive weighting assigned to each of the quantified plurality of speech variables (“placing more weight on the content of what the speaker is saying as a way of discriminating the two classes…the longitudinal progression of MMSE scores and LSAS features,”; pages 134, 135);
detecting, based on the composite score, a predicted longitudinal change in the quantified plurality of speech variables (“Predicting MMSE score using LSAS features”; page 135, section 3.1); and
estimating, based on the predicted longitudinal change, a progression of AD for the patient (“the MMSE score is a measure of the progression of cognitive impairment
and is used to distinguish AD from CT”; page 135, section 2.3).
However, Yancheva et al. do not specifically teach the speech data was captured at a plurality of moments during a period of time.
Hirst et al. teach collecting two or more speech or text samples from a subject, and to determine a date for each sample and place the samples in a timeline based on the date for each sample (paragraph 12).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to capture speech during a period of time as taught by Hirst et al. in Yancheva et al., because that would help provide accurate, non-invasive early detection or diagnosis of cognitive deficit or mental illness (paragraph 30).
As per claim 2, Yancheva et al. in Hirst et al. further disclose receiving the speech data comprises receiving an audio file comprising an electronic recording of speech of the patient (Hirst et al., paragraphs 11, 12).
As per claim 3, Yancheva et al. in Hirst et al. further disclose the electronic recording of speech of the patient comprises an electronic recording of one or more verbal responses of the patient to a Clinical Dementia Rating (CDR) interview (“Clinical assessment of dementia…collecting two or more speech or text samples from a subject, and to determine a date for each sample and place the samples in a timeline based on the date for each sample”; Hirst et al., paragraphs 4, 12).
As per claim 4, Yancheva et al. in Hirst et al. further disclose the speech data was captured at an initial date and one or more dates selected from the group comprising: approximately 0.25, 0.5, 0.75, 1, 3, 6, 9, 12, 15, 18, 21, 24, 27, 30, 33, and 36 months from the initial date (“a date for each sample and place the samples in a timeline based on the date for each sample”; Hirst et al., paragraphs 12, 26, 46).
As per claim 5, Yancheva et al. in Hirst et al. further disclose the plurality of speech variables further comprises a word-frequency variable, a syntactic-depth variable, a use-of-nouns variable, or a use-of- pronouns variable (Yancheva et al. page 136, Table 1; Hirst et al. paragraph 89, 90, 101).
As per claim 6, Yancheva et al. in Hirst et al. further disclose the plurality of speech variables further comprises one or more Mel-frequency cepstral coefficient (MFCC) features (“including the standard Melfrequency cepstral coefficients (MFCCs), formant features”; Yancheva et al. pages 135, 136, Table 1).
As per claim 7, Yancheva et al. in Hirst et al. further disclose the one or more MFCC features comprise a mean of an 11th MFCC coefficient (MFCC mean 11), a variance of a first derivative of the 11th MFCC coefficient (MFCC var 25), or a variance of a first derivative of a 12th MFCC coefficient (MFCC var 26) [“including the standard Melfrequency cepstral coefficients (MFCCs), formant features…The first 42 MFCC parameters, along with their means, kurtosis and skewness, and the kurtosis and skewness of the mean of means.”; Yancheva et al. pages 135, 136, Table 1].
As per claim 8, Yancheva et al. in Hirst et al. further disclose determining the composite score comprises: standardizing the quantified plurality of speech variables; applying an equal weighting to each of the quantified plurality of speech variables; and combining the standardized and equally-weighted quantified plurality of speech variables to generate the composite score (“placing more weight on the
content of what the speaker is saying”; Yancheva et al., page 135).
As per claim 9, Yancheva et al. in Hirst et al. further disclose estimating, based on the predicted longitudinal change, the progression of AD comprises correlating the composite score with one or more clinical assessment metrics (Yancheva et al., pages 135, 136).
As per claim 10, Yancheva et al. in Hirst et al. further disclose the one or more clinical assessment metrics are selected from a group consisting of a Mini Mental State Examination (MMSE) score, a Clinical Dementia Rating (CDR) interview, a Clinical Dementia Rating-Sum of Boxes (CDR-SB) scale, a Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) subscale battery of tests, an Alzheimer's disease Cooperative Study Group-Activities of Daily Living Inventory (ADCS-ADL) scale, a Neuropsychiatric Inventory (NPI) scale, a Neuropsychiatric Inventory- Questionnaire (NPI-Q), a Caregiver Global Impression (CaGl) scale for Alzheimer's Disease, an Instrumental Activities of Daily Living (IADL) scale, an Amsterdam Activities of Daily Living Questionnaire (A-IADL-Q), and a Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) scale (“probable dementia is diagnosed
using the Mini Mental State Examination (MMSE)…Dementiabank”; Yancheva et al., page 134).
As per claim 11, Yancheva et al. in Hirst et al. further disclose determining, based on the estimated progression of AD, whether the patient is responsive to a treatment (“by summing the absolute gains and losses over a chronologically-ordered series transitions of text to text, the Sequential Vocabulary Gain-and-Loss Measure may indicate a person's enhancing, steady-state, or losing trend in vocabulary usage over time. AD patients typically use strategies to overcome memory loss over time.”; This Yancheva et al., page 135; Hirst et al., paragraph 97).
As per claim 12, Yancheva et al. in Hirst et al. further disclose analyzing the speech data to determine the quantified plurality of speech variables comprises analyzing the speech data utilizing one or more natural- language processing (NLP) machine-learning models (“A computational linguistics natural-language-processing system operable to apply a lemmatizer, parser, and syntactic pattern-matching rules may be utilized in such an implementation of the present invention.”; Hirst et al., paragraph 52).
Claims 13 – 18 are rejected under 35 U.S.C. 103 as being unpatentable over Yancheva et al. (Using linguistic features longitudinally to predict clinical scores for
Alzheimer’s disease and related dementias, September 2015) in view of Hirst et al. (US PAP 2013/0297216); and further in view Paul et al. (US PAP 2023/0183341).
As per claim 13, Yancheva et al. in Hirst et al. do not specifically teach in response to estimating the progression of AD, generating a recommendation for an adjustment of a treatment regimen for the patient
Paul et al. disclose monitoring the treatment of an individual being administered an anti-TREM2 antibody, comprising measuring tau burden in the brain of the individual, assessed by measuring the levels of tau in the brain of the individual, before and after the individual has received one or more doses of an anti-TREM2 antibody (paragraph 49).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective date of the claimed invention to adjust a treatment regimen for a patient as taught by Paul et al. in Yancheva et al. in Hirst et al., because that would help resulting in an improvement in the pathology and/or in one or more symptoms of dementia, frontotemporal dementia, Alzheimer's disease, Nasu-Hakola disease, cognitive deficit, memory loss, spinal cord injury, traumatic brain injury, a demyelination disorder, multiple sclerosis, Parkinson's disease, amyotrophic lateral sclerosis (ALS), Huntington's disease, adult-onset leukoencephalopathy with axonal spheroids and pigmented glia (ALSP), or a tauopathy disease (paragraph 136).
As per claim 14, Yancheva et al. in view of Hirst et al., and further in view of Paul et al. further disclose the treatment regimen comprises a therapeutic agent consisting of at least one compound selected from a group consisting of compounds against oxidative stress, anti-apoptotic compounds, metal chelators, inhibitors of DNA repair, 3- amino-1-propanesulfomic acid (3APS), 1,3-propanedisulfonate (1,3PDS), secretase activators, beta- and gamma-secretase inhibitors, tau proteins, anti-Tau antibodies, anti-Tau agents, gene therapies, neurotransmitters, beta-sheet breakers, anti-inflammatory molecules, an atypical antipsychotic, a cholinesterase inhibitor, other drugs, and nutritive supplements, a therapeutic agent selected from the group consisting of: a symptomatic medication, a neurological drug, a corticosteroid, an antibiotic, an antiviral agent, an anti-Tau antibody, a Tau inhibitor, an anti- amyloid-beta (anti-Aß) antibody, an beta-amyloid aggregation inhibitor, a therapeutic agent that binds to a target, an anti-BACE1 antibody, a BACE1 inhibitor, a cholinesterase inhibitor, an NMDA receptor antagonist, a monoamine depletory, an ergoloid mesylate, an anticholinergic antiparkinsonism agent, a dopaminergic antiparkinsonism agent, a tetrabenazine, an anti-inflammatory agent, a hormone, a vitamin, a dimebolin, a homotaurine, a serotonin receptor activity modulator, an interferon, and a glucocorticoid (Paul et la., paragraphs 21 – 31).
As per claim 15, Yancheva et al. in view of Hirst et al., and further in view of Paul et al. further disclose the symptomatic medication is selected from the group consisting of a cholinesterase inhibitor, galantamine, rivastigmine, donepezil, an N- methyl-D-aspartate receptor antagonist, memantine, and a food supplement (optionally wherein the food supplement is Souvenaid®) [Paul et al., paragraph 153].
As per claim 16, Yancheva et al. in view of Hirst et al., and further in view of Paul et al. further disclose the anti-Aß antibody is selected from the group consisting of bapineuzumab, solanezumab, aducanumab, gantenerumab, crenezumab, donanembab, and lecanemab (Paul et al., paragraphs 13 – 35).
As per claim 17, Yancheva et al. in view of Hirst et al., and further in view of Paul et al. further disclose the anti-Tau antibody is selected from the group consisting of an N-terminal binder, a mid-domain binder, and a fibrillar Tau binder (Paul et al., paragraphs 25, 31, 49,380).
As per claim 18, Yancheva et al. in view of Hirst et al., and further in view of Paul et al. further disclose the anti-Tau antibody is selected from the group consisting of semorinemab, BMS-986168, C2N-8E12, Gosuranemab, Tilavonemab, and Zagotenemab (“monitoring the treatment of an individual being administered an anti-TREM2 antibody, comprising measuring tau burden in the brain of the individual, assessed by measuring the levels of tau in the brain of the individual, before and after the individual has received one or more doses of an anti-TREM2 antibody. In some embodiments, the method of monitoring treatment provided herein also comprises a step of assessing the activity of the anti-TREM2 antibody in the individual based on the levels of tau in the brain of the individual.”; Paul et al., paragraphs 25, 31, 49).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Novikova et al. teach METHOD AND SYSTEM OF LONGITUDINAL DETECTION OF DEMENTIA THROUGH LEXICAL AND SYNTACTIC CHANGES IN WRITING. Rudzicz et al. teach method for cognitive training. Howard teaches detection of disease conditions and comorbidities. Quatieri et al. teach USING CORRELATION STRUCTURE OF SPEECH DYNAMICS TO DETECT NEUROLOGICAL CHANGES.
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/LEONARD SAINT-CYR/Primary Examiner, Art Unit 2658
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