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 § 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 6 and 10 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites “providing the SCL information” in line 3 of the claim. There is insufficient basis for “the SCL” limitation in the claim. The lack of antecedent basis causes the meaning of the claim to be unclear. A lack of clarity arises because it is unclear as to which earlier recited element (if any) the limitations reference. For the purposes of examination, the limitations are interpreted as not referring to any earlier recited elements.
Claim 6 further recites “providing the SCL information with the highest weight of the set of measurable body conditions for classification”. A lack of clarity arises as it is unclear by what “the highest weight of the set” means. Is this a weighing factor utilized for classification or is it information that is outputted by a classification model with a higher weight . For the purposes of examination, it is interpreted that SCL information that is outputted by a classification model with a higher weight is what is being provided.
Claim 10 recites “considering the SCL information with the highest weight of the set of measurable body conditions for classification” in step “oa”. A lack of clarity arises as it is unclear by what “the highest weight of the set” means. Is this a weighing factor utilized for classification or is it information that is outputted by a classification model with a higher weight . For the purposes of examination, it is interpreted that SCL information that is outputted by a classification model with a higher weight is what is being considered.
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 1-13 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1-13 are all within at least one of the four categories.
The independent claim 1 recites:
determining a set of measurable body conditions from a group of body conditions of a human body, the body conditions being indicative of a menopausal state of a human body,
determining a set of training objects, the training objects being humans being capable of adopting a menopausal state and having a known state concerning their menopausal state as menopausal state information
measuring the measurable body conditions of the set of measurable body conditions of each of the training objects for a predetermined amount of time to provide measured body condition information for each of the training objects, and detecting occurrence of a symptom of a menopausal state,
preprocessing the measured body condition information to provide preprocessed body condition information,
providing a […] model adapted to classify a menopausal state,
inputting the preprocessed body condition information of a training object as training input information to the classification model,
inputting the detected occurrence of the symptom of a menopausal state as training classification information to the classification model,
adapting the classification model according to the training input information and training classification information.
The above claim limitations constitute an abstract idea that is part of the Mathematical Concepts and/or Mental Processes group identified in the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. See footnotes 14 and 15.
“A mathematical relationship is a relationship between variables or numbers. A mathematical relationship may be expressed in words ….” October 2019 Update: Subject Matter Eligibility, II. A. i. “[T]here are instances where a formula or equation is written in text format that should also be considered as falling within this grouping.” Id. at II. A. ii. “[A] claim does not have to recite the word “calculating” in order to be considered a mathematical calculation.” Id. at II. A. iii. See for example, SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161, 1163-65 (Fed. Cir. 2018) (performing a resampled statistical analysis to generate a resampled distribution).
The claimed steps of determining; measuring; preprocessing; providing; inputting; and adapting can be practically performed in the human mind using mental steps or basic critical thinking, which are types of activities that have been found by the courts to represent abstract ideas.
Examples of ineligible claims that recite mental processes include:
a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind, Electric Power Group, LLC v. Alstom, S.A.;
claims to “comparing BRCA sequences and determining the existence of alterations,” where the claims cover any way of comparing BRCA sequences such that the comparison steps can practically be performed in the human mind, University of Utah Research Foundation v. Ambry Genetics Corp.
a claim to collecting and comparing known information (claim 1), which are steps that can be practically performed in the human mind, Classen Immunotherapies, Inc. v. Biogen IDEC.
See p. 7-8 of October 2019 Update: Subject Matter Eligibility.
With respect to the pending claims, for example, an experienced physician can perform the claimed step of determining by mentally noting a set of measurable body condition, mentally noting a set of training objects and further mentally measure the measurable body conditions of each training object for a set period of time to detect a symptom of menopause. The physician can then process the measured body condition mentally and use that as a input to a model as a mathematical function input mentally. Further, the physician can further input the detected symptom of menopause mentally to the model as a mathematical function input mentally and then utilize that mathematical function mentally. Thus, the claims can be readily interpreted as being a mere application of a mental process on a computer.
Regarding the dependent claims 2-13, the dependent claims are directed to either 1) steps that are also abstract or 2) additional data output that is well-understood, routine and previously known to the industry. For example, dependent claims recite steps (e.g. measuring; transforming; providing; inputting; applying; adapting; smoothening; determining; preprocessing; classifying; and using) that can be performed in the mind. Although the dependent claims are further limiting, they do not recite significantly more than the abstract idea. A narrow abstract idea is still an abstract idea and an abstract idea with additional well-known equipment/functions is not significantly more than the abstract idea.
Regarding the dependent claims 5, the dependent claim further limits step c) into step ca) which recites “measuring electrodermal activity and providing measured electrodermal activity). The step of “measuring electrodermal activity” is not considered to be an abstract idea however “providing measured electrodermal activity” is. “Measuring electrodermal activity” does not integrate the abstract idea into practical application as noted below.
This judicial exception (abstract idea) in claims 1-20 is not integrated into a practical application because:
The abstract idea amounts to simply implementing the abstract idea on a computer. For example, the recitations regarding the generic computing components for determining; preprocessing; providing; inputting; adapting; transforming; providing; applying; smoothening; classifying; and using merely invoke a computer as a tool.
The data-gathering step (receiving; measuring) and the data-output step (transmitting) do not add a meaningful limitation to the method as they are insignificant extra-solution activity.
There is no improvement to a computer or other technology. “The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation that "improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process.” MPEP 2106.05(a) II. The claims recite a computer that is used as a tool for determining; measuring; preprocessing; providing; inputting; adapting; transforming; providing; applying; smoothening; classifying; and using.
The claims do not apply the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition. Rather, the abstract idea is utilized to determine a relationship among data to provide information regarding a menopausal state.
The claims do not apply the abstract idea to a particular machine. “Integral use of a machine to achieve performance of a method may provide significantly more, in contrast to where the machine is merely an object on which the method operates, which does not provide significantly more.” MPEP 2106.05(b). II. “Use of a machine that contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step or in a field-of-use limitation) would not provide significantly more.” MPEP 2106.05(b) III. The pending claims utilize a computer for determining; measuring; preprocessing; providing; inputting; adapting; transforming; providing; applying; smoothening; classifying; and using. The claims do not apply the obtained data to a particular machine. Rather, the data is merely output in an post-solution step.
The additional elements are identified as follows: a computer implemented classification model; a sensor unit with a sensing portion and an urging portion; a transmission unit; an evaluation unit .
Those in the relevant field of art would recognize the above-identified additional elements as being well-understood, routine, and conventional means for data-gathering and computing, as demonstrated by
Applicant' s Background in the specification which notes in [003] that prior art includes a variety of sensors that capture biophysical data signals which are then analyzed using intelligent computing methods like artificial neural networks and the like
the non-patent literature cited by applicant:
Tsiartas, Andreas, et al "A novel Hot-Flash classification algorithm via multi-sensor features integration " 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) IEEE, 2021.
Thus, the claimed additional elements “are so well-known that they do not need to be described in detail in a patent application to satisfy 35 U.S.C. § 112(a).” Berkheimer Memorandum, III. A. 3.
Furthermore, the court decisions discussed in MPEP § 2106.05(d)(lI) note the well-understood, routine and conventional nature of such additional elements as those claimed. See option III. A. 2. in the Berkheimer memorandum.
When considered in combination, the additional elements (i.e. the generic computer functions and conventional equipment/steps) do not amount to significantly more than the abstract idea. Looking at the claim limitations as a whole adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-2, 4-5 and 7 are rejected under 35 U.S.C. 103 as being unpatentable over Connor (US 20150320588 A1; cited by applicant) in view of Volosin (US 20190282178 A1; cited by applicant).
With respect to claim 1, Connor discloses a computer implemented method for providing a model for determining a menopausal state (see paragraph 0034-0036: system and method to predict when a person will have a hot flash; and see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0139: data processing can be done via various models and methods), comprising the steps:
a) determining a set of measurable body conditions from a group of body conditions of a human body (see paragraph 0131, wearable sensor can be an electromagnetic energy sensor such as a galvanic skin response sensor; see paragraph 0485: wearable sensor can be a skin conductance sensor or sweat sensor which is indicative of electrodermal activity; and see paragraph 0494: wearable sensor can be a thermal energy sensor such as a body temperature sensor), the body conditions being indicative of a menopausal state of a human body (see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0034: wearable sensors are used to predict when a person will have a hot flash; and see paragraph 0503: health related characteristics such as menopausal status and demographics of person can be incorporated into a multivariate statistical model to detect and/or predict the occurrence of a hot flash),
b) determining a set of training objects, the training objects being humans being capable of adopting a menopausal state and having a known state concerning their menopausal state as menopausal state information (see paragraph 0503: health related characteristics such as menopausal status and demographics of person such as gender and age can be incorporated into a multivariate statistical model to detect and/or predict the occurrence of a hot flash),
c) measuring the measurable body conditions of the set of measurable body conditions of each of the training objects for a predetermined amount of time to provide measured body condition information for each of the training objects, and detecting occurrence of a symptom of a menopausal state (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash),
d) preprocessing the measured body condition information to provide preprocessed body condition information (see paragraph 0480: data processing unit processes data from wearable sensor),
e) providing a computer implemented classification model adapted to classify a menopausal state (see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network),
Connor does not specifically disclose: f) inputting the preprocessed body condition information of a training object as training input information to the classification model; g) inputting the detected occurrence of the symptom of a menopausal state as training classification information to the classification model; and h) adapting the classification model according to the training input information and training classification information.
Volosin teaches inputting preprocessed body condition information of a training object as training information to a classification model (see paragraph 0255-0260: physical parameters relating to a patient condition which has preprocessed as medical history is given to train a predict analysis process which is an artificial neural network where the machine learning tool can be a tree decision model); inputting a detected occurrence of a symptom as training classification information to the classification model (see paragraph 0255-0260: physical parameters relating to a current patient condition is given to train a predict analysis process which is an artificial neural network where the machine learning tool can be a tree decision model); and adapting the classification model according to the training input information and training classification information (see paragraph 0266-0267: the machine learning classifier model is trained and retrained using patient metrics therefore is adapted).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Connor with the teachings of Volosin to have trained a classification model because it would have resulted in the predictable result of training an artificial neural network to produce a patient condition score (Volosin: see [0273]) to create a treatment regimen (Volosin: see [0064]).
With respect to claim 2, all limitations of claim 1 apply in which Connor further discloses wherein in step c), the measured body condition information is a time series of measured body conditions, respectively (see paragraph 0532: data from wearable sensors is a time series as it can be analyzed via time series analysis).
With respect to claim 4, all limitations of claim 1 apply in which Connor further discloses wherein in step a), the set of body condition information contains a subset of the set:
electrodermal activity (see paragraph 0131, wearable sensor can be an electromagnetic energy sensor such as a galvanic skin response sensor which measured electrodermal activity),
heart rate (see paragraph 0325: wearable sensor measures heart rate and person’s pulse), HRV (see paragraph 0325: wearable sensor measures heart rate and person’s pulse and/or other cardiac function which include heart rate variability (HRV))and Blood Pressure (see paragraph 0325: wearable sensor measures blood pressure), in particular photoplethysmographic information (see paragraph 0495: wearable sensor can be a light energy sensor such as a photoplethysmographic sensor) and ECG information (see paragraph 0487: wearable sensor can be an ECG sensor)
body temperature (see paragraph 0494: wearable sensor can be a thermal energy sensor such as a body temperature sensor),
body movement activity, in particular relocation or acceleration (see paragraph 0130: wearable sensor can be an accelerometer), or
body environment information, in particular ambient temperature, ambient humidity or ambient pressure (see paragraph 0494: wearable sensor can be a thermal energy sensor such as a humidity sensor).
With respect to claim 5, all limitations of claim 1 apply in which Connor further discloses
wherein step c) further comprises the step:
ca) measuring electrodermal activity and providing measured electrodermal activity information (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash), and
step d) further comprises the steps:
da) transforming the measured electrodermal activity information into SCL information and SCR information (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist where SCL is the conductivity information and SCR is the resistance information), and
db) providing the SCL information or the SCR Information as measured body condition information (see paragraph 0480: data processing unit processes data from wearable sensor including the SCL and SCR information), and
step f) comprises the step:
fa) inputting the SCL information or the SCR information as measured body condition information to the classification model (see paragraph 0532: data from wearable sensor including the SCL and SCR information can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network).
With respect to claim 7, all limitations of claim 1 apply in which Connor further discloses
wherein step d) further comprises at least one of the steps:
dc) applying a low pass filter, in particular a Butterworth filter, in particular a Butterworth filter with a cut off frequency of 0.5 Hz, to the measured body condition information,
dd) adapting a sampling rate of the measured body condition information to 1 Hz for the measurable body conditions of the set of measurable body conditions,
de) smoothen the measured body condition information by applying a sliding left windows of 60 seconds, in particular by determining a simple moving average by forming the unweighted mean of the previous 60 measured samples,
df) providing the measured body condition information with classification information such that the measured body condition information that occur within a time period of 180 seconds preceding and of 180 seconds succeeding a point in time of the detection of a symptom of a menopausal state are labeled as belonging to the symptom of the menopausal state, and that the measured body condition information that occur outside that time period are labeled as not belonging to a symptom of a menopausal state (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash where only if it meets the criteria is the data associated with a hot flash).
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Volosin as applied to claim 1, and further in view of Park (US 20190163949 A1).
With respect to claim 3, all limitations of claim 1 apply in which Connor further discloses wherein in step d) the computer implemented classification model is a decision tree model or a random forest model or an artificial neural network model (see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network).
Connor and Volosin do not specifically teach a recurrent neural network model or a convolutional neural network model, further in particular an echo state network model or a liquid state machine model.
Park teaches artificial neural networks that include a recurrent neural network model or a convolutional neural network model (see paragraph 0029: artificial neural networks include recurrent neural network and convolutional neural network), and further teaches an echo state network model (see paragraph 0029: artificial neural networks include echo state network).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified Connor and Volosin with the teachings of Park because it would have resulted in the predictable result of using an echo state network model and/or recurrent neural network models and/or convolutional neural network models because (1) artificial networks are used and Park teaches such artificial neural networks and/or (2) it is a simple substitution of one known element for another to obtain predictable results (Park: see [0029]).
Claims 6 and 8-13 are rejected under 35 U.S.C. 103 as being unpatentable over Connor in view of Volosin as applied to claim 1, and further in view of Wilde (US 20180160959 A1).
With respect to claim 6, all limitations of claim 1 apply in which Connor and Volosin do not specifically disclose wherein step h) further comprises the step: ha) providing the SCL information with the highest weight of the set of measurable body conditions for classification.
Wilde teaches physiological sensors that measure and track galvanic skin activity response, skin conductance and electrodermal activity response (see paragraph 0048) and further teaches using determining a weight vector for a sensor against that sensors ideal weight vector (see paragraph 0079) where sensor output with high contribution levels to an algorithm model is utilized (see paragraph 0080).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified which information is provided for classification as taught by Connor and Volosin with the teachings of Wilde because it would have resulted in the predictable result of using information that has high contribution levels ( in other words: weight) to an algorithmic model for classification (Wilder: see [0080]).
With respect to claim 8, Connor discloses a computer implemented system for determining a menopausal state (see paragraph 0034-0036: system and method to predict when a person will have a hot flash; and see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0139: data processing can be done via various models and methods), comprising:
a sensor unit (see paragraph 0480: wearable attachment member with a wearable sensor),
a transmission unit (see paragraph 0480: wireless data transmitter), and
an evaluation unit, containing a classification model provided according claim 1 (see paragraph 0480: data processing unit that processes data from wearable sensor; and see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network; see claim 1 above) ,
wherein the system is configured to perform the steps:
i) determining a diagnose object, the diagnose object being a human being capable of adopting a menopausal state and having an unknown state concerning their menopausal state as menopausal state information (see paragraph 0503: health related characteristics such as menopausal status and demographics of person such as gender and age can be incorporated into a multivariate statistical model to detect and/or predict the occurrence of a hot flash),
j) arranging at least one sensor at the body of the diagnose object at a sensor body location (see paragraph 0480-0481: wearable sensor is worn on wrist of person) ,
k) measuring, by means of the at least one sensor, a set of measurable body conditions from a group of body conditions of a human body (see paragraph 0131, wearable sensor can be an electromagnetic energy sensor such as a galvanic skin response sensor; see paragraph 0485: wearable sensor can be a skin conductance sensor or sweat sensor which is indicative of electrodermal activity; and see paragraph 0494: wearable sensor can be a thermal energy sensor such as a body temperature sensor; see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash), the body conditions being indicative of a menopausal state of a human body and being a subset of the set of measurable body conditions according to step a), as measured diagnose body condition information (see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0034: wearable sensors are used to predict when a person will have a hot flash; and see paragraph 0503: health related characteristics such as menopausal status and demographics of person can be incorporated into a multivariate statistical model to detect and/or predict the occurrence of a hot flash),
l) preprocessing the measured diagnose body condition information to provide preprocessed diagnose body condition information (see paragraph 0480: data processing unit processes data from wearable sensor),
m) inputting the preprocessed diagnose body condition information of the diagnose object as diagnose input information to the classification model (see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network),
n) classifying a menopausal state of the diagnose object as menopausal state information (see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0034: wearable sensors are used to predict when a person will have a hot flash; and see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network),
o) using the classified menopausal state information for providing a diagnose on the menopausal state of the diagnose object (see paragraph 0327: communication can be sent to healthcare provider based on wearable sensor data and analyzed data), wherein
the sensor unit is configured to measure a set of measurable body conditions from a group of body conditions of a human body (see paragraph 0480: wearable attachment member with a wearable sensor; see paragraph 0131, wearable sensor can be an electromagnetic energy sensor such as a galvanic skin response sensor; see paragraph 0485: wearable sensor can be a skin conductance sensor or sweat sensor which is indicative of electrodermal activity; and see paragraph 0494: wearable sensor can be a thermal energy sensor such as a body temperature sensor; see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash), the body conditions being indicative of a menopausal state of a human body and being a subset of the set of measurable body conditions (see paragraph 0007: hot flashes are symptoms of menopause; and see paragraph 0034: wearable sensors are used to predict when a person will have a hot flash; and see paragraph 0503: health related characteristics such as menopausal status and demographics of person can be incorporated into a multivariate statistical model to detect and/or predict the occurrence of a hot flash) and transmit measured body conditions as measured body condition information to the transmission unit (see paragraph 0480: wearable sensor transmits measured sensor data to wireless data transmitter and further from wireless data transmitter to wireless data receiver),
the transmission unit is configured to receive the body condition information from the sensor unit and to transmit the body condition information to the evaluation unit (see paragraph 0480: wearable sensor transmits measured sensor data to wireless data transmitter and further from wireless data transmitter to wireless data receiver where it then further transmits the data to the data processing unit), and
the sensor unit is configured to perform the step I) (see paragraph 0480: data processing unit processes data from wearable sensor) and the evaluation unit is configured to perform the steps n) and o) (see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network and communicate to healthcare provider).
With respect to claim 9, all limitations of claim 8 apply in which Connor further discloses wherein in step wherein in step I), the set of body condition information contains a subset of the set:
electrodermal activity (see paragraph 0131, wearable sensor can be an electromagnetic energy sensor such as a galvanic skin response sensor which measured electrodermal activity),
heart rate (see paragraph 0325: wearable sensor measures heart rate and person’s pulse), heart rate variability (see paragraph 0325: wearable sensor measures heart rate and person’s pulse and/or other cardiac function which include heart rate variability (HRV))and Blood Pressure (see paragraph 0325: wearable sensor measures blood pressure), in particular photoplethysmographic information (see paragraph 0495: wearable sensor can be a light energy sensor such as a photoplethysmographic sensor) and ECG information (see paragraph 0487: wearable sensor can be an ECG sensor)
body temperature (see paragraph 0494: wearable sensor can be a thermal energy sensor such as a body temperature sensor),
body movement activity, in particular relocation or acceleration (see paragraph 0130: wearable sensor can be an accelerometer), or
body environment information, in particular ambient temperature, ambient humidity or ambient pressure (see paragraph 0494: wearable sensor can be a thermal energy sensor such as a humidity sensor).
With respect to claim 10, all limitations of claim 8 apply in which Connor further discloses
wherein step I) further comprises the step:
la) measuring electrodermal activity and providing measured diagnose electrodermal activity information (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist; and see paragraph 0485: skin conductance is measured for a selected period of time such as <30seconds where an increase in skin conductance which is greater than a selected amount and occurs in less than a selected period of time is associated with a hot flash and/or helps predict a hot flash), and
step m) further comprises the steps:
ma) transforming the measured diagnose electrodermal activity information into SCL information and SCR information (see paragraph 0180: electromagnetic energy sensor can measure the conductivity, resistance and/or impedance of electrical energy flow through tissue of a person’s wrist where SCL is the conductivity information and SCR is the resistance information), and
mb) providing the SCL information or the SCR Information as measured diagnose body condition information (see paragraph 0480: data processing unit processes data from wearable sensor including the SCL and SCR information), and
step n) comprises the step:
na) inputting the SCL information or the SCR information as measured diagnose body condition information to the classification model (see paragraph 0532: data from wearable sensor including the SCL and SCR information can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network).
Connor and Volosin do not specifically disclose wherein step o) further comprises the step: oa) considering the SCL information with the highest weight of the set of measurable body conditions for classification
Wilde teaches physiological sensors that measure and track galvanic skin activity response, skin conductance and electrodermal activity response (see paragraph 0048) and further teaches using determining a weight vector for a sensor against that sensors ideal weight vector (see paragraph 0079) where sensor output with high contribution levels to an algorithm model is utilized (see paragraph 0080).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified which information is considered for classification as taught by Connor and Volosin with the teachings of Wilde because it would have resulted in the predictable result of using information that has high contribution levels (in other words: weight) to an algorithmic model for classification (Wilder: see [0080], [0085]).
With respect to claim 11, all limitations of claim 8 apply in which Connor further discloses
wherein step m) further comprises at least one of the steps:
mc) applying a low pass filter, in particular, a Butterworth filter, in particular a Butterworth filter with a cut-off frequency of 0.5 Hz, to the measured diagnose body condition information,
md) adapting a sampling rate of the measured diagnose body condition information to 1 Hz for the measurable body conditions of the set of measurable body conditions,
me) smoothen the measured diagnose body condition information by applying a sliding left window of 60 seconds, in particular by determining a simple moving average by forming the unweighted mean of the previous 60 measured samples,
mf) applying an additional filter to the measured diagnose body condition information based on measured diagnose body condition information, or
mg) providing a sensor unit at a sensor location, wherein the sensor location is arranged at a human body, in particular at a torso, in particular at the lower thorax, in particular below the sternum, either centrally or at the frontal left side of the thorax of the diagnose object (see paragraph 0399: wearable sensor such as a wearable thermal energy sensor can be positioned and worn on a person’s torso; and see paragraph 0494: wearable sensor can be a thermal energy sensor).
With respect to claim 12, all limitations of claim 8 apply in which Connor further discloses wherein the sensor unit (2), the transmission unit (3) or the evaluation unit (4) is configured to perform step m) (see paragraph 0532: data from wearable sensor can be analyzed via one or more statistical methods such as decision tree analysis, random forest analysis or artificial neural network using data processing unit).
With respect to claim 13, all limitations of claim 8 apply in which Connor further discloses wherein the sensor unit further comprises a sensing portion at a proximal side of the sensor unit, the sensing portion comprising a convex shape (see Fig. 83 and paragraph 0480-0485: wearable attachment member #8301 has a sensing portion being a wearable sensor #8302), and
an urging portion that is configured to urge the sensing portion towards a dermal surface at a sensor location, such that the sensing portion indents the dermal surface (see Fig. 83 and paragraph 0480-0485: wearable attachment member #8301 has a sensing portion being a wearable sensor #8302 which contacts person’s skin on the inside of the wearable attachment member #8301).
Conclusion
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/N.N.P./Examiner, Art Unit 3791
/MATTHEW KREMER/Primary Examiner, Art Unit 3791