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 .
The current application has the effective priority date of 02/12/2026.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-3 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 of U.S. Patent No. 11,504,038 B2 (Pat’038). Although the claims at issue are not identical, they are not patentably distinct from each other because Pat’038 discloses an invention that is analogous to and fully encompasses the invention of the current application, see overlapping limitations in the underlined limitation below. Noted differences includes: Pat’038 discloses a method that utilizes a wearable integrated sensor system for acquiring movement data; the current application discloses using a generic wearable system for motor data. These are known variants to a person of ordinary skill in the art.
Current application: 19/007,459
Pat’038
1. A method for detection of neurodegenerative disease comprising:
measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life, using a wearable body sensor system placed on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject; and
determining, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects;
and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information.
1. A method for detection of neurodegenerative disease comprising:
measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising an Integrated Clothing Sensing System, an Inertial Measurement Unit, an optical marker tracking system, acceleration sensors, and a Hydrocele Geodesic Sensor Net, and gathering movement data with the wearable body sensor system, wherein a measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity comprises:
gathering brain activity data comprising neuronal signals obtained using an implantable probe comprising a microelectrode array of carbon nanotube connections between electronic circuitry and in-vivo human neural tissue;
and determining, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects;
and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information.
2. A computer program product for detection of neurodegenerative disease, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
2. A computer program product for detection of neurodegenerative disease, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising at least two sensor systems selected from a group comprising an Integrated Clothing Sensing System, an Inertial Measurement Unit, an optical marker tracking system, acceleration sensors, and a Hydrocele Geodesic Sensor Net, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity comprises:
gathering brain activity data comprising neuronal signals obtained using an implantable probe comprising a microelectrode array of carbon nanotube connections between electronic circuitry and in-vivo human neural tissue;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises: analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
3. A system for detection of neurodegenerative disease, the system comprising:
at least one of a wearable body sensor system, apparatus for gathering everyday speech data, apparatus for neural oscillation detection, and an electro-encephalogram apparatus;
a processor;
memory accessible by the processor;
computer program instructions stored m the memory and executable by the processor to perform:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
3. A system for detection of neurodegenerative disease, the system comprising:
at least one of a wearable body sensor system, apparatus for gathering everyday speech data, apparatus for neural oscillation detection, and an electro-encephalogram apparatus;
a processor;
memory accessible by the processor;
computer program instructions stored m the memory and executable by the processor to perform:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising at least two sensor systems selected from a group comprising an Integrated Clothing Sensing System, an Inertial Measurement Unit, an optical marker tracking system, acceleration sensors, and a Hydrocele Geodesic Sensor Net, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity comprises:
gathering brain activity data comprising neuronal signals obtained using an implantable probe comprising a microelectrode array of carbon nanotube connections between electronic circuitry and in-vivo human neural tissue;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
Claims 1-3 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-3 of U.S. Patent No. 12,178,576 (Pat’576). Although the claims at issue are not identical, they are not patentably distinct from each other because Pat’576 discloses an invention that is analogous to and fully encompasses the invention of the current application, see overlapping limitations in the underlined limitation below. Noted differences includes: Pat’576 discloses a method that utilizes a wearable integrated sensor system for acquiring movement data; the current application discloses using a generic wearable system for motor data, and using a generic EEG scalp sensor for detecting brain data. These are known variants to a person of ordinary skill in the art.
Current application: 19/007,459
Pat’576
1. A method for detection of neurodegenerative disease comprising:
measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life, using a wearable body sensor system placed on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject; and
determining, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects;
and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information.
1. A method for detection of neurodegenerative disease comprising:
measuring functioning of a motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising at least one of an integrated clothing sensing system, an inertial measurement unit, an optical marker tracking system, and a hydrocele geodesic sensor net, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device;
and determining, at a computer system comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor, the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and measured motor and non-motor aspects;
and wherein analyzing the gathered brain activity comprises:
analyzing gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the gathered neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information.
2. A computer program product for detection of neurodegenerative disease, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises: analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
2. A computer program product for detection of neurodegenerative disease, the computer program product comprising a non-transitory computer readable storage having program instructions embodied therewith, the program instructions executable by a computer, to cause the computer to perform a method comprising:
receiving data measuring functioning of at least one of a motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising at least two sensor systems selected from a group comprising at least one of an integrated clothing sensing system, an inertial measurement unit, an optical marker tracking system, and a hydrocele geodesic sensor net, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the gathered neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
3. A system for detection of neurodegenerative disease, the system comprising:
at least one of a wearable body sensor system, apparatus for gathering everyday speech data, apparatus for neural oscillation detection, and an electro-encephalogram apparatus;
a processor;
memory accessible by the processor;
computer program instructions stored m the memory and executable by the processor to perform:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device, and wherein measuring brain activity of the subject comprises:
measuring electro-encephalogram(EEG) data using EEG sensors placed on the subject;
and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
3. A system for detection of neurodegenerative disease, the system comprising:
at least one of a wearable body sensor system, apparatus for gathering everyday speech data, apparatus for neural oscillation detection, and an electro-encephalogram apparatus;
a processor;
memory accessible by the processor;
computer program instructions stored m the memory and executable by the processor to perform:
receiving data measuring functioning of at least one of the motor system, cognitive function, and brain activity of a subject during everyday life, wherein measuring functioning of the motor system comprises:
placing a wearable body sensor system on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations, the wearable body sensor system comprising at least two sensor systems selected from a group comprising at least one of an integrated clothing sensing system, an inertial measurement unit, an optical marker tracking system, and a hydrocele geodesic sensor net, and gathering movement data with the wearable body sensor system, wherein measuring functioning of the cognitive function comprises:
gathering cognitive function data comprising everyday speech data gathered using an audio capture device; and determining the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject, wherein analyzing the gathered motor system data comprises:
analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis, and generating information indicating movement conditions, wherein analyzing the gathered cognitive function data comprises:
analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and measured motor and non-motor aspects, and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the gathered neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease;
and outputting information indicating severity of the neurodegenerative disease based on the generated information.
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.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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 limitations are:
“audio capture device” in claims 1-3
“wearable body sensor system” in claims 1-3
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 Objection
Claims 1-3 are objected to because of the following informalities:
Claim 1, lines 2-3 recites: “measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function…” The phraseology in this claim clause is awkward, as there is seemingly no relationship between ‘shoulder movement’ and ‘cognitive function.’ It is the Examiner’s understanding the “cognitive function” is separately detected.
Claims 2-3 are objected to under the same rationale as discussed to claim 1 above.
Appropriate correction is required.
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 1-3 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.
Regarding claim 1, the following terms lack proper antecedent basis: “the motor system” in line 2, “the presence of an abnormal condition” in line 15, and “the measured motor and non-motor aspects” in line 25, and “the gathered neural oscillation detection data” in line 27.
Further regarding claim 1, lines 2-3 recites: “measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function…” The phraseology in this claim clause is awkward and indefinite, as there is seemingly no relationship between ‘shoulder movement’ and ‘cognitive function.’ It is the Examiner’s understanding the “cognitive function” is separately detected.
Furthermore, the following limitation “including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life, using a wearable body sensor system placed on the subject….” in claim 1, lines 3-5 is indefinite for the following reasons: (1) the first “during” is ambiguous because it is unclear what is meant by “during combined physical motion and speech, and brain activity of a subject”; and (2) it is unclear what is meant by “combined physical motion and speech, and brain activity.” For purposes of examination, it is the Examiner’s understanding that physical motion, speech and brain activity are detected during everyday living situations.
Claim 1, line 25, “the measured motor and non-motor aspects” lacks proper antecedent basis, and it is also indefinite because the claim has not clarified “motor (aspect)” and “non-motor aspect.” It is the Examiner’s interpretation that: “motor (aspect)” refers to detected and/or analyzed motor (function), and “non-motor (aspect”) refers to detected and/or analyzed speech and brain activity data.
Claims 2 and 3 are analogous to claim 1, as such, claims 2 and 3 are rejected as indefinite under the same rationales. The Applicant is sincerely requested to amend through these claims similarly.
Claim Interpretation
Regarding claim 1, lines 5-8 the limitation “a wearable body sensor system…adapted to measure movements and object interaction of the subject….”; the ‘object interaction’ aspect in this claim clause is interpreted as a wearable body sensor system configured for detecting a user’s every day body movement (while the user interacts with objects). Note: The claim does not disclose camera, specific detectors or motion classification etc. for detecting and distinguishing between movements and interaction.
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 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Stivoric et al. US 2008/0275309 A1 (hereinafter “Stivoric”) in view of Simon et al. US 2016/0029946 A1 (hereinafter “Simon”). (Both references cited in parent applications 16/584654 IDS dated 5/25/2022).
Regarding claim 1, Stivoric discloses a method for detection of neurodegenerative disease (see Abstract: a computerized system and method adapted for long term and continuous monitoring of physiological parameters) comprising:
measuring functioning of the motor system, including measuring functioning of upper limb and shoulder movement, cognitive function, including measuring cognitive function during combined physical motion and speech, and brain activity of a subject during everyday life (see Abstract, and [0066-0067, 0070-0073], Table 1 and Table 2 discloses continuous monitoring a plethora of physiological parameters and contextual/environmental parameters of a subject during everyday life),
using a wearable body sensor system (wearable sensor device 10) placed on the subject, the wearable body sensor system adapted to measure movements and object interaction of the subject in everyday living situations ([0066] device 10 is adapted to be worn long term and continuously for detecting physiological data for normal day to day; also see [0093] and Table 1, accelerometer for detecting body movement and other movement related data; further see Table 2 regarding the list of movements and activities that are measured and distinguished),
wherein measuring functioning of the cognitive function comprises: gathering cognitive function data comprising everyday speech data ([0140] input speech) gathered using an audio capture device ([0140] microphone is a voice/audio capture device and has voice recognition), and
wherein measuring brain activity of the subject comprises: measuring electro-encephalogram (EEG) data using EEG sensors placed on the subject ([0067] and Table 2: EEG data); and
determining, at a computer system ([0078-0082] central monitoring unit 30) comprising a processor, memory accessible by the processor, and computer program instructions stored in the memory and executable by the processor ([0082] server computer and associated database which store and execute the Health Manager software; also see Figs.3-4), the presence of an abnormal condition based on the gathered at least one motor system data, cognitive function data, and brain activity data of the subject ([0108-0110] calculating activities of daily living health index; also see [0228] determining cumulative condition with regard to motor functions in Table 3 such as exercise, fatigue, likelihood of falling; see further description below),
wherein analyzing the gathered motor system data comprises: analyzing the gathered movement data to differentiate between movement conditions, and generating information indicating movement conditions (see [0230-0231], Table 3: “Instantaneous events” including falling, seizure, and also “Durative measurements” including distinguishing types of motion e.g. running, walking, biking, aerobic exercises, and positions of motion),
wherein analyzing the gathered cognitive function data comprises: analyzing the gathered speech data for motor and non-motor correlations related to severity of the neurodegenerative disease data, including determining inability to control voice tone based on jitter of the speech data and determining lack of normal voice modulation based on shimmer of the speech data, and generating information indicating severity of the neurodegenerative disease and the measured motor and non-motor aspects ([0228] and Table 3: cumulative condition include determining “Alzheimer’s” wherein the correlations of data are described in [0234-0237] based on EEG, speech and motion);
and wherein analyzing the gathered brain activity comprises:
analyzing the gathered neural oscillation detection data related to severity of the neurodegenerative disease, including determining spindles in the neural oscillation detection data based on a frequency spectrum of the neural oscillation detection data, and generating information indicating severity of the neurodegenerative disease ([0228] and Table 3: cumulative condition include determining “Alzheimer’s” wherein the correlations of data are described in [0234-0237] based on EEG, speech and motion);
and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information ([0228] cumulative condition include determining “Alzheimer’s”).
Regarding the analysis of motor system data, cognitive function data, and gather brain activity data, Stivoric does not disclose analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis; generating information indicating severity of the degenerative disease and the measured motor and non-motor aspects, and outputting from the computer system, information indicative severity of the neurodegenerative disease based on the generated information.
However, Simon another prior art reference in the analogous field of neurological condition analysis (see Abstract: Alzheimer’s analysis) discloses a system comprising a wearable EEG monitor for acquiring EEG time series ([0047, 0049, 0055] EEG headset), and accelerometer data from a patient ([0047, 0049]). The analysis system performs wavelet analysis on the acquired EEG and accelerometer data as discussed in [0048-0050, 0065-0077] so as to generate information indicating movement conditions ([0047] physical motion related to cognitive task). Simon further discloses analyzing the gathered movement data to differentiate between movement conditions using wavelet analysis ([0047, 0049] analyzing accelerometer similarly as EEG data); generating information indicating severity of the degenerative disease and the measured motor and non-motor aspects ([0036-0037] sensory and cognitive challenges; [0056] measuring cognitive function), and analyzing the gather EEG data related to the severity of neurodegenerative disease ([0047-0050] wavelet analysis of EEG); and outputting, from the computer system, information indicating severity of the neurodegenerative disease based on the generated information (Simon: [0055, 0065] determining Alzheimer’s severity ranging from mild to moderate).
It would have been obvious to one of ordinary skill in the art at the time of invention to modify Stivoric in view of Simon so as to include the wavelet analysis of EEG and accelerometer data to determine the severity of neurodegenerative disease; the motivation for doing so is because Stivoric discusses using linear and non-linear models/algorithms for the determination of cumulative conditions such as Alzheimer’s, ([0228, 0230-0234]), and Simon discloses that using wavelet analysis provides an advantage over non-linear analysis for processing EEG signals (Simon: [0048, 0066]), the advantage is estimating the power of transient signals without loss of frequency resolution, thus can clarify subtle information in the data (Simon: [0050]).
Regarding claim 2, it is rejected by Stivoric in view of Simon under the same analysis as discussed to claim 1 above, and both Stivoric and Simon discloses a computer product having software instructions stored on non-transitory memory and executable by a processor (Stivoric: see Abstract: a computerized system adapted for long term and continuous monitoring of physiological parameters; Simon: [0038]).
Regarding claim 3, it is rejected by Stivoric in view of Simon under the same analysis as to claim 1 above, and further both Stivoric and Simon discloses sensing device 10 adapted to be worn (Stivoric: [0067], Simon: [0055]), it comprises a plurality of sensors and a processor having computer program instructions (Stivoric: [0071], Simon: [0038]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHIRLEY X JIAN whose telephone number is (571)270-7374. The examiner can normally be reached M-F 8:00-4:00.
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/SHIRLEY X JIAN/Primary Examiner, Art Unit 3792
June 9, 2026
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