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 .
Specification
The lengthy specification has not been checked to the extent necessary to determine the presence of all possible minor errors. Applicant’s cooperation is requested in correcting any errors of which applicant may become aware in the specification.
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.
Claim 16 is 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 16 recites the limitation "the notification" in lines 2 and 3. There is insufficient antecedent basis for this limitation in the claim.
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.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-20 is/are drawn to a method or device which is/are a statutory category of invention (Step 1: YES).
The claim limitations within claims 1 and 18 that set forth or describe the abstract idea is/are: “processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence; selecting a model from a plurality of models based on types of data inputs received as the plurality of time-series data inputs; applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response”. The reasons that the limitations is/are considered an abstract idea is/are the following: The limitations of, “processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence; selecting a model from a plurality of models based on types of data inputs received as the plurality of time-series data inputs; applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response” is a process that under its broadest reasonable interpretation covers performance of the limitation in the mind with the assistance of pen and paper but for the recitation of generic computer components. That is, other than reciting “using a wearable computing device” and “processor configured to perform a plurality of operations”, nothing in the claim element precludes the steps from practically being performed in the mind with the aid of pen and paper. For example but for the “using a wearable computing device” and “processor configured to perform a plurality of operations”, “processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence; selecting a model from a plurality of models based on types of data inputs received as the plurality of time-series data inputs; applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response” in the context of this claim encompasses the user performing mathematical techniques to filter the data, the user selecting an appropriate mathematical model based on the data and then applying that mathematical model to calculate an indicator. There is nothing to suggest an undue level of complexity in the steps of form/forming and set/setting. If a claim limitation, under its broadest reasonable interpretation covers a metal process, i.e. performance of the limitation in the mind, but for the recitation of generic computer components, then it falls with the “Mental Processes” grouping of abstract ideas. Accordingly the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to concepts that have been identified as abstract by the courts, such as: collecting information, analyzing it, and reporting certain results of the collection and analysis in Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016), selecting certain information, analyzing it using mathematical techniques, and reporting or displaying the results of the analysis in SAP America Inc. v. Investpic, LLC, 890 F.3d 1016, 126 USPQ2d 1638 (Fed Cir. 2018). Additionally, the limitations of, “processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence” and “applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response”, cover an abstract idea that is part of mathematical concepts. “A mathematical formula or equation will be considered as falling with the ‘mathematical concepts” grouping....”. October 2019 Update: Subject Matter Eligibility, “[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 Il. A. ii. See for example, Diamond v. Diehr, 450 U.S. 175, 177 n.2, 179 n.5, 191-92 (1981) or Parker v. Flook 437 U.S. 584, 585, 198 USPQ 193, 195 (1978) (calculating a number representing an alarm limit using a mathematical formula). The claimed steps of “processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence” and “applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response” recite a mathematical concept (i.e., mathematical formulas or equations, and mathematical calculations). The claimed steps recite performing a mathematical formula or equation to determine an indicator of a physiological response but for the recitation of generic computer components. The specification recites utilizing a Momentary Stress Algorithm for calculating the indicator, logistic regression model, a deep neural network or any other suitable machine learning model, see para. [0032] and [0070] of published application 2026/0060578. If a claim limitation, under its broadest reasonable interpretation covers a mathematical formula or equation but for the recitation of generic computer components, then it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly the claims recite an abstract idea. Although not drawn to the same subject matter, the claimed limitation(s) is/are similar to concepts that have been identified as abstract by the courts, such as: a formula for computing an alarm limit in Parker v. Flook 437 U.S. 584, 585, 198 USPQ 193, 195 (1978), the Arrhenius equation in Diamond v. Diehr, 450 U.S. 175, 177 n.2, 179 n.5, 191-92 (1981). Thus, the claim(s) are directed to a judicial exception and fall squarely within the realm of "abstract ideas," which is a patent-ineligible concept (Step 2A: Prong One YES).
Analyzing the claim as whole for a practical application, the claim does not include additional elements/steps that are sufficient to amount to significantly more than the judicial exception. The additionally recited element(s) appended to the abstract idea include: “receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user” (claims 1,18), “controlling a function of the wearable computing device when the indicator of the physiological response exceeds a threshold.” (claims 1 and 18), “an electronic display” (claim 18), and “at least one processor communicatively coupled to the plurality of biometric sensor electrodes, the at least one processor configured to perform a plurality of operations” (claim 18). The additional elements of "receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user” merely: add insignificant extra-solution activity, reciting “receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user”, is recited at a high level of generality (i.e. as a general means of gathering biometric data) and is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity). All uses of the recited judicial exception require the pre-solution activity of data gathering. As discussed above with respect to integration of abstract idea into a practical, the additional element of “an electronic display” (claim 18), and “at least one processor communicatively coupled to the plurality of biometric sensor electrodes, the at least one processor configured to perform a plurality of operations” (claim 18) to perform the plurality of operations amount to no more than mere instruction to apply the exception using generic computer components. The “an electronic display” (claim 18), and “at least one processor communicatively coupled to the plurality of biometric sensor electrodes, the at least one processor configured to perform a plurality of operations” (claim 18) are purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data and displaying to enable the abstract process. As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”, see MPEP 2106.05(f). Further, the additional elements of “controlling a function of the wearable computing device when the indicator of the physiological response exceeds a threshold.” relate the mental and/or mathematical processing steps of claims 1 and 18 to the field of use of control, however these elements are claimed in a broad and generic way such that they relate to controlling a generic "function" relating to the applied mental and/or mathematical processing steps of claims 1 and 18 of calculating an indicator and using it to perform some vague action of "controlling a function". Thus, limitations that amount to merely indicating a field of use or technological environment to apply the judicial exception (controlling a generic 'function' based on the output of the mental and/or mathematical processing steps), or which offer insignificant extra-solution activity to the judicial exception, cannot and do not integrate the judicial exception into a practical application. See MPEP 2106.06. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea (Step 2A, Prong Two, NO).
Claims 1 and 18 do not include additional elements, alone or in combination, that are sufficient to amount to significantly more than the judicial exception (i.e., an inventive concept) for the same reasons as described above. e.g., all the additional elements amount to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity), being purely general-purpose computer components recited as carrying out the general-purpose computer functions of processing data and displaying to enable the abstract process and amount to merely indicating a field of use or technological environment to apply the judicial exception, the additional elements do not amount to significantly more than the above-identified judicial exception(s). Similarly, when considered as an ordered combination, the additional components/steps of the claim(s) add nothing that is not already present when the steps are considered separately (Step 2B: NO). The claims are not patent eligible.
Claim(s) 2-17 and 19-20 depend directly or indirectly from claim(s) 1 and 18. Therefore, the dependent claims rely upon the same abstract idea as the independent claim(s), as set forth above. Additionally, the dependent claims do nothing more than further limiting the abstract idea while failing to qualify as "significantly more", and the specificity of an abstract idea does not make it any "less abstract" as it is still directed to concepts relating to organizing or analyzing information in a way that can be performed mentally or is analogous to human mental work subject matter. Therefore, the dependent claim(s) are also not patent eligible for the reasons discussed above. Claim(s) 3-13 and 19-20 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitation regarding calculating an indicator of physiological response, which can still nonetheless be considered mathematical concepts and/or mental processes, i.e. performed by a human using a pen and paper. Claim(s) 2 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitation regarding the “data gathering” in particular the type of data that is sensed which merely: add insignificant extra-solution activity, and is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to mere data gathering, which is a form of insignificant extra-solution activity (pre-solution activity). All uses of the recited judicial exception require the pre-solution activity of data gathering. Claim(s) 14-16 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitation regarding displaying the data and providing a notification which is merely nominally, insignificantly or tangentially related to the performance of the steps, i.e. amounts to insignificant application, which is a form of insignificant extra-solution activity (post-solution activity), see MPEP 2106.05(g). As such, this/these recitation(s) is/are nothing more than nominal recitation(s) of a computer covering an abstract concept. See Bancorp Servs. v. Sun Life Assurance Co., 687 F.3d 1266, 103 USPQ2d 1425 (Fed. Circ. 2012). See also Mayo Collaborative Services v. Prometheus Laboratories Inc., 101 USPQ2d 1961 (U.S. 2012), which establishes that a claim cannot simply state the abstract idea and add the words "apply it”. Claim(s) 17 fail(s) to provide significantly more, when considered as an ordered combination, as it/they merely provide further limitation regarding the processor which as discussed above amount to no more than mere instruction to apply the exception using generic computer components.
The instantly rejected claim(s) are therefore not drawn to eligible subject matter as they are directed to an abstract idea without significantly more. In the interest of advancing prosecution, the examiner suggests: providing evidence, for example, delineating how the abstract idea and/or additional elements appended to the abstract idea results in an improvement to the technology/technical field, which can show eligibility and/or adding a practical application of the claimed method outside of the computer (e.g. treating a patient). See MPEP § 716.01(c) for examples of providing evidence supported by an appropriate affidavit or declaration. For additional guidance, applicant is directed generally to MPEP § 2106.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-4,10-12 and 14-18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification to Chandra et al. (Chandra – cited by applicant) in view of US 2011/0245633 to Goldberg et al. (Goldberg).
In reference to at least claim 1
Chandra discloses a method of monitoring stress of a user using a wearable computing device, the method comprising: receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. pg. 221, section 3.1 “Equipment”: electrodermal activity, heart rate, skin temperature, blood volume pulse); processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence (e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”); selecting a model from a plurality of models based on types of data inputs received as the plurality of time-series data inputs (e.g. pg. 225, section 5; pg. 226, Table 2: four different models for respective different inputs are used) applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response (e.g. pg. 226, Table 2).
Chandra discloses that the use of commercially available smartwatch and wristband based devices is known (e.g. pg. 291, 1st para. “An alternative can be a commercially available smartwatch or wristband based device that can be worn all day without fatigue.”).However, Chandra does not explicitly disclose controlling a function of the wearable computing device when the indicator of the physiological response exceeds a threshold.
Goldberg, in the same field of endeavor, discloses a wearable computing device (e.g. wearable biosensor device 10) that includes sensors for monitoring EDA (e.g. “In a particular embodiment, the wearable biosensor devices of the invention at least include one or more sensors that measures electrodermal activity (EDA),”, para. [0057]) and sensor for monitoring at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. “The wearable biosensor devices can include one or more additional sensors for measuring a physiological response, in addition to the EDA sensors described above. For example, the wearable biosensor devices can further include a temperature sensor (e.g., a low-power temperature biosensor such as LM60 (National Semiconductor, Santa Clara, Calif.), a heart rate biosensor, an oxygen saturation biosensor, a blood pressure biosensor”, para. [0062]). The wearable biosensor device further includes at least one processor configured to perform a plurality of operations (e.g. microprocessor 3) that include receiving the plurality of time-series data inputs, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. “In a particular embodiment, the wearable biosensor devices of the invention at least include one or more sensors that measures electrodermal activity (EDA),”, para. [0057]; “The wearable biosensor devices can include one or more additional sensors for measuring a physiological response, in addition to the EDA sensors described above. For example, the wearable biosensor devices can further include a temperature sensor (e.g., a low-power temperature biosensor such as LM60 (National Semiconductor, Santa Clara, Calif.), a heart rate biosensor, an oxygen saturation biosensor, a blood pressure biosensor”, para. [0062]), applying a model to the processed plurality of time-series data inputs to calculate an indicator probability of a stress event of the user at a certain time by the user (e.g. “machine learning algorithms”, para. [0014], “The processor on-board the wearable biosensor device includes one or more algorithms for recognizing variations and patterns in the detected physiological parameters accumulated over time.”, para. [0024], “configured for deriving data indicative of a psychological disorder and/or psychological state (e.g., an anxiety disorder, post-traumatic stress disorder, obsessive-compulsive disorder, panic disorder, a phobic disorder, depression, bipolar disorder, a psychotic disorder,”, para. [0012], [0021]), , wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator probability of the stress event (e.g. “Preferably, the processor on-board the wearable biosensor device analyzes the physiological data detected by the one or more sensors in real-time using summary metrics and pattern recognition algorithms that become increasingly personalized to the wearer,”, para. [0070], “Use of a learning machine allows a classification scheme to adapt in response to data.”, para. [0072], [0071]; “In certain embodiments, the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file)”, para. [0084]); and controlling a function of the wearable computing device when the indicator of the stress event exceeds a threshold (e.g. “The wearable biosensor devices of the invention can include an on-board processor that can map patterns of the physiological and motion data to personalized signals or alerts indicative of a likely anxiety attack, panic attack, or other states that the wearer would like to know about, or used to alert other people or devices for assistance”, para. [0070], “The data and/or instructions generated by the on-board processor are can be translated into an alert or signal to the wearer via the LED display, to alert the wearer in real-time of a detected physiological and/or psychological state or condition”, para. [0085]). Goldberg further discloses that having the processor on board eliminates the need for an external server, such as used in other devices and systems described in the art, when comparing real-time data to the stored personalized profile of the wearer (e.g. “The wearable biosensor can operate in stand-alone mode or in conjunction with an electronic device (e.g., smart phone, personal digital assistant, laptop computer, tablet, e-reader, television, gaming device, etc.) or a remote server. In stand alone mode, the wearable biosensor device is capable of collecting data, processing data, running analytics and delivering therapeutic stimuli without the need of external system.”, par. [0080]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Chandra to include carrying out the operations of Chandra with the biosensor wearable device disclosed by Goldberg including controlling a function of the biosensor wearable device such as displaying an alert or notification when the indicator varies significantly from a normal/baseline physiological state, as taught by Goldberg, in order to notify the user of an impending symptomatic episode so a therapeutic intervention can be delivered to overcome the symptomatic episode (‘633, para. [0098]-[0099]).
In reference to at least claim 2
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein the plurality of time-series data inputs comprise the heart rate of the user, the skin temperature of the user, the heart rate variability of the user, and the cEDA data of the user (e.g. pg. 221, section 3.1 “Equipment”: electrodermal activity, heart rate, skin temperature, blood volume pulse).
In reference to at least claim 3
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein processing the plurality of time-series data inputs using the plurality of filtering techniques in sequence further comprise: filtering the cEDA data of the user using a high-pass filter, a low-pass filter, or a median filter (e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”).
In reference to at least claim 4
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein processing the plurality of time-series data inputs using the plurality of filtering techniques in sequence further comprise: updating a certain time frame cache with the plurality of data inputs (e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”, use of window size of 4 entries).
In reference to at least claim 10
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein processing the plurality of time-series data inputs using the plurality of filtering techniques further comprise: normalizing the plurality of time-series data inputs using one or more normalization factors (e.g. pg. 224, section 3.2 “Feature Extraction”: calculating the mean value for which a normalization is carried out).
In reference to at least claim 11
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein the selected model is a machine learning model (e.g. p. 219, 2nd para. “We train four machine learning classifiers on these feature sets and present our results).
In reference to at least claim 12
Chandra modified by Goldberg renders obvious a method according to claim 1. Chandra further discloses wherein processing the plurality of time-series data inputs using the plurality of filtering techniques further comprise: transforming each of the plurality of time-series data inputs into a single value (e.g. pg. 224, section 3.2 “Feature Extraction”: calculating mean, median, mode standard deviation, minimum, maximum and first derivative).
In reference to at least claim 14
Chandra modified by Goldberg renders obvious a method according to claim 1. Goldberg further discloses wherein controlling the function of the wearable computing device comprises at least one of controlling a display of the wearable computing device and providing the indicator of the physiological response at the certain time to the user via the display (e.g. “The wearable biosensor devices of the invention can include an on-board processor that can map patterns of the physiological and motion data to personalized signals or alerts indicative of a likely anxiety attack, panic attack, or other states that the wearer would like to know about, or used to alert other people or devices for assistance”, para. [0070], “The data and/or instructions generated by the on-board processor are can be translated into an alert or signal to the wearer via the LED display, to alert the wearer in real-time of a detected physiological and/or psychological state or condition”, para. [0085]).
In reference to at least claim 15
Chandra modified by Goldberg renders obvious a method according to claim 1. Goldberg further discloses sending a notification to the user via the display indicating at least one of an occurrence of the indicator of the physiological response exceeding a threshold (e.g. “The wearable biosensor devices of the invention can include an on-board processor that can map patterns of the physiological and motion data to personalized signals or alerts indicative of a likely anxiety attack, panic attack, or other states that the wearer would like to know about, or used to alert other people or devices for assistance”, para. [0070], “The data and/or instructions generated by the on-board processor are can be translated into an alert or signal to the wearer via the LED display, to alert the wearer in real-time of a detected physiological and/or psychological state or condition”, para. [0085]), a graphical representation of physiological responses over time, and a summary of physiological responses over time (e.g. “The aggregate data from use of the device is provided to clinicians and/or patients, in detail and in summary report forms, to indicate the symptoms of, to monitor and analyze treatment effects, to detect and diagnose among disorders or subtypes, and to isolate the underlying causes of one or more psychological disorders and/or states. This aggregate data can be displayed over any of the associated devices and using secure protocols to protect the wearer's privacy.”, para. [0037], “For example, the digital media library can contain a range of exercises, questionnaires, tests, summary reports, real-time data-driven graphics,”, para. [0086]).
In reference to at least claim 16
Chandra modified by Goldberg renders obvious a method according to claim 1. Goldberg further discloses wherein a response to the notification comprises at least one of mood logging, journaling, guided breathing, guided meditation, and recording participation in a prescribed stress-relieving activity (e.g. “a therapeutic stimulus (including but not limited to cognitive behavioral therapy, exposure therapy, and breathing techniques such as deep breathing exercises and meditative techniques, photographs, audio, video, and text) to the individual wearing the device in real-time.”, para. [0086], “For example, as shown in FIG. 3, a therapeutic message 15 may be displayed on the wearable biosensor, or on the electronic device instructing the user to "breathe deeply".”, para. [0092], “The wearer of the biosensor is alerted of an impending symptomatic episode and is delivered a targeted stimulus, such as a breathing technique, via a display on either the wearable biosensor device or an accompanying portable electronic device, to overcome the anxiety or panic attack.”, para. [0098]-[0099]).
In reference to at least claim 17
Chandra modified by Goldberg renders obvious a method according to claim 1. Goldberg further discloses wherein the processor is part of one of the wearable computing device or a separate mobile device (e.g. “a processor or microprocessor 3 for reading/analyzing the physiological data detected by the one or more sensors,”, para. [0050], [0094]; “The wearable biosensor can operate in stand-alone mode or in conjunction with an electronic device (e.g., smart phone, personal digital assistant, laptop computer, tablet, e-reader, television, gaming device, etc.) or a remote server.”, para. [0080]).
In reference to at least claim 18
Chandra discloses a wearable computing device (e.g. pg. 221, section 3.1: “Equipment”, E4 Empatica Wristband) comprising: a plurality of biometric sensor electrodes for sensing a plurality of time-series data inputs relating to biometrics of a user of the wearable computing device (e.g. pg. 221, section 3.1: “Equipment”, electrodermal activity, heart rate, skin temperature, blood volume pulse); and at least one processor communicatively coupled to the plurality of biometric sensor electrodes (e.g. pg. 221, section 3.1: “Equipment”, E4 Empatica Wristband inherently has an internal microcontroller/processor to handle data collection and operations of the device). Chandra further discloses a processor configured to perform a plurality of operations, the plurality of operations comprising: : receiving, via a processor communicatively coupled to the wearable computing device, a plurality of time-series data inputs from a plurality of biometric sensor electrodes of the wearable computing device, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. pg. 221, section 3.1 “Equipment”: electrodermal activity, heart rate, skin temperature, blood volume pulse); processing the plurality of time-series data inputs using a plurality of filtering techniques in sequence (e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”); selecting a model from a plurality of models based on types of data inputs received as the plurality of time-series data inputs (e.g. pg. 225, section 5; pg. 226, Table 2: four different models for respective different inputs are used) applying the selected model to the processed plurality of time-series data inputs to calculate an indicator of a physiological response of the user at a certain time, wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator of the physiological response (e.g. pg. 226, Table 2).
Chandra discloses that the use of commercially available smartwatch and wristband based devices is known (e.g. pg. 291, 1st para. “An alternative can be a commercially available smartwatch or wristband based device that can be worn all day without fatigue.”).However, Chandra does not explicitly disclose the wearable computing device including an electronic display, the at least one processor within the wearable device performing the plurality of operations and controlling a function of the wearable computing device when the indicator of the physiological response exceeds a threshold.
Goldberg, in the same field of endeavor, discloses a wearable computing device (e.g. wearable biosensor device 10) that includes a display (e.g. display 6), sensors for monitoring EDA (e.g. “In a particular embodiment, the wearable biosensor devices of the invention at least include one or more sensors that measures electrodermal activity (EDA),”, para. [0057]) and sensor for monitoring at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. “The wearable biosensor devices can include one or more additional sensors for measuring a physiological response, in addition to the EDA sensors described above. For example, the wearable biosensor devices can further include a temperature sensor (e.g., a low-power temperature biosensor such as LM60 (National Semiconductor, Santa Clara, Calif.), a heart rate biosensor, an oxygen saturation biosensor, a blood pressure biosensor”, para. [0062]). The wearable biosensor device further includes at least one processor configured to perform a plurality of operations (e.g. microprocessor 3) that include receiving the plurality of time-series data inputs, the plurality of time-series data comprising continuous electrodermal activity (cEDA) data of the user and at least one of heart rate data of the user, skin temperature data of the user, and heart rate variability (HRV) data of the user (e.g. “In a particular embodiment, the wearable biosensor devices of the invention at least include one or more sensors that measures electrodermal activity (EDA),”, para. [0057]; “The wearable biosensor devices can include one or more additional sensors for measuring a physiological response, in addition to the EDA sensors described above. For example, the wearable biosensor devices can further include a temperature sensor (e.g., a low-power temperature biosensor such as LM60 (National Semiconductor, Santa Clara, Calif.), a heart rate biosensor, an oxygen saturation biosensor, a blood pressure biosensor”, para. [0062]), applying a model to the processed plurality of time-series data inputs to calculate an indicator probability of a stress event of the user at a certain time by the user (e.g. “machine learning algorithms”, para. [0014], “The processor on-board the wearable biosensor device includes one or more algorithms for recognizing variations and patterns in the detected physiological parameters accumulated over time.”, para. [0024], “configured for deriving data indicative of a psychological disorder and/or psychological state (e.g., an anxiety disorder, post-traumatic stress disorder, obsessive-compulsive disorder, panic disorder, a phobic disorder, depression, bipolar disorder, a psychotic disorder,”, para. [0012], [0021]), , wherein the selected model is tailored to use all of the plurality of time-series data inputs in the calculation of the indicator probability of the stress event (e.g. “Preferably, the processor on-board the wearable biosensor device analyzes the physiological data detected by the one or more sensors in real-time using summary metrics and pattern recognition algorithms that become increasingly personalized to the wearer,”, para. [0070], “Use of a learning machine allows a classification scheme to adapt in response to data.”, para. [0072], [0071]; “In certain embodiments, the on-board processor further includes algorithms for mapping the detected physiological data to a psychological state based on the wearer's personalized profile associated with the device (on-board data file)”, para. [0084]); and controlling a function of the wearable computing device when the indicator of the stress event exceeds a threshold (e.g. “The wearable biosensor devices of the invention can include an on-board processor that can map patterns of the physiological and motion data to personalized signals or alerts indicative of a likely anxiety attack, panic attack, or other states that the wearer would like to know about, or used to alert other people or devices for assistance”, para. [0070], “The data and/or instructions generated by the on-board processor are can be translated into an alert or signal to the wearer via the LED display, to alert the wearer in real-time of a detected physiological and/or psychological state or condition”, para. [0085]). Goldberg further discloses that having the processor on board eliminates the need for an external server, such as used in other devices and systems described in the art, when comparing real-time data to the stored personalized profile of the wearer (e.g. “The wearable biosensor can operate in stand-alone mode or in conjunction with an electronic device (e.g., smart phone, personal digital assistant, laptop computer, tablet, e-reader, television, gaming device, etc.) or a remote server. In stand alone mode, the wearable biosensor device is capable of collecting data, processing data, running analytics and delivering therapeutic stimuli without the need of external system.”, par. [0080]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the device of Chandra to include carrying out the operations of Chandra with the biosensor wearable device that includes a microprocessor and display disclosed by Goldberg including controlling a function of the biosensor wearable device such as displaying an alert or notification when the indicator varies significantly from a normal/baseline physiological state, as taught by Goldberg, in order to notify the user of an impending symptomatic episode so a therapeutic intervention can be delivered to overcome the symptomatic episode (‘633, para. [0098]-[0099]).
Claim(s) 5-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification to Chandra et al. (Chandra – cited by applicant) in view of US 2011/0245633 to Goldberg et al. (Goldberg) as applied to claim 1 further in view of US 2012/0289788 to Jain et al. (Jain).
In reference to at least claim 5
Chandra modified by Goldberg renders obvious a method according to claim 1. However, Chandra modified by Goldberg does not explicitly disclose wherein processing the plurality of time-series data inputs using the plurality of filtering techniques in sequence further comprise: determining whether a time-series data input from the plurality of time-series data inputs indicate one of a plurality of modes of the wearable computing device and, if so, eliminating or modifying the time-series data input from the plurality of time-series data inputs.
Jain, in the same field of endeavor, discloses that sensors provide erroneous data when a physical connection between a sensor and the subject moves or physical movement of the subject such as exercise (e.g. “many sensors provide erroneous data when accelerated, for example, because the physical connection between the sensor and the subject moves, causing the sensor to generate an inaccurate or false reading. “, para. [0246]). Jain further discloses the use of an accelerometer to contextualize physiological sensor data and eliminate physiological data or erroneous sensor measurements cause by physical movement (e.g. “By contextualizing physiological sensor data with accelerometer data, analysis system 180 may be able to eliminate changes in physiological data or erroneous sensor measurements caused by physical movement and thereby more accurately measure and monitor stress in a person.”, para. [0246]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Chandra modified by Goldberg to include determining whether a time-series data input indicates one of a plurality of modes of the wearable computing device such as a physical connection between the subject and wearable device moving or physical movement of the subject which provides erroneous sensor measurements and eliminating associated time-series data from the plurality of time-series data inputs used for calculating the indicator, as taught by Jain, in order to accurately measure and monitor stress in a person by reducing inaccurate or false sensor measurement readings (‘788, para. [0246]).
In reference to at least claim 6
Chandra modified by Goldberg and Jain renders obvious a method according to claim 1. Jain further discloses wherein the plurality of modes of the wearable computing device comprise one of a sleep mode, an exercise mode, a do-not-disturb mode, or an off-wrist mode (e.g. “Furthermore, many sensors provide erroneous data when accelerated, for example, because the physical connection between the sensor and the subject moves, causing the sensor to generate an inaccurate or false reading. “, para. [0246], therefore the mode includes at least an indication of a “off-wrist mode” and “an exercise mode”).
In reference to at least claim 7
Chandra modified by Goldberg renders obvious a method according to claim 1. However, Chandra modified by Goldberg does not explicitly disclose wherein processing the plurality of time-series data inputs using the plurality of filtering techniques in sequence further comprise: filtering the plurality of time-series data inputs based on a plurality of confounders and eliminating or modifying time-series data inputs of the plurality of time-series data inputs that satisfy one or more of the plurality of confounders.
Jain, in the same field of endeavor, discloses that sensors provide erroneous data when a physical connection between a sensor and the subject moves or physical movement of the subject such as exercise (e.g. “many sensors provide erroneous data when accelerated, for example, because the physical connection between the sensor and the subject moves, causing the sensor to generate an inaccurate or false reading. “, para. [0246]). Jain further discloses the use of an accelerometer to contextualize physiological sensor data and eliminate physiological data or erroneous sensor measurements cause by physical movement (e.g. “By contextualizing physiological sensor data with accelerometer data, analysis system 180 may be able to eliminate changes in physiological data or erroneous sensor measurements caused by physical movement and thereby more accurately measure and monitor stress in a person.”, para. [0246]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Chandra modified by Goldberg to include filtering the plurality of time-series data inputs based on a plurality of confounders such as an accelerometer used to measure physical movement of the subject or the physical connection between the sensor and the subject moving which provides erroneous sensor measurements and eliminating associated time-series data that satisfy one or more of the plurality of confounders, as taught by Jain, in order to accurately measure and monitor stress in a person by reducing inaccurate or false sensor measurement readings (‘788, para. [0246]).
In reference to at least claim 8
Chandra modified by Goldberg and Jain renders obvious a method according to claim 1. Jain further discloses wherein the plurality of confounders comprise one of the cEDA data of the user increasing with increased motion, a percentage of the HRV data being above a certain threshold, a certain confidence of the heart rate data of the user, a motion classifier based on accelerometer values (e.g. “By contextualizing physiological sensor data with accelerometer data, analysis system 180 may be able to eliminate changes in physiological data or erroneous sensor measurements caused by physical movement and thereby more accurately measure and monitor stress in a person.”, para. [0246]), the wearable computing device being partially or fully submerged in liquid or exposed to the liquid, skin contact between the wearable computing device and the user being below a contact threshold (e.g. “Furthermore, many sensors provide erroneous data when accelerated, for example, because the physical connection between the sensor and the subject moves, causing the sensor to generate an inaccurate or false reading”, para. [0246]), or humidity being above a humidity threshold.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification to Chandra et al. (Chandra – cited by applicant) in view of US 2011/0245633 to Goldberg et al. (Goldberg) as applied to claim 1 further in view of US 2021/0233641 to Sasangohar et al. (Sasangohar).
In reference to at least claim 9
Chandra modified by Goldberg renders obvious a method according to claim 1. However, Chandra modified by Goldberg does not explicitly disclose wherein processing the plurality of time-series data inputs using the plurality of filtering techniques further comprise: imputing one or more data points into the plurality of time-series data inputs if a certain number of data points are missing from the plurality of time-series data inputs or dropping the plurality of time-series data inputs if the number of missing data points exceeds a threshold. It was well-known in the art before the effective filing date of the claimed invention to use Kalman filter imputation of missing value for filling gaps in data as evidence by Sasangohar (e.g. “”Kalman filter imputation of missing values (a technique well-known to those of ordinary skill in the art) may be used to fill in gaps in the data, e.g., up to a cutoff for the number of consecutive missing values (e.g., a maximum of five missing values) beyond which the data imputation is deemed unreliable”, para. [0021]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Chandra modified by Goldberg to include imputing one or more data points into the plurality of time-series data inputs if a certain number of data points are missing from the plurality of time-series data inputs or dropping the plurality of time-series data inputs if the number of missing data points exceeds a threshold as such technique was known in the art and would have yielded the predictable result of filling in missing gaps in the data.
Claim(s) 13 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification to Chandra et al. (Chandra – cited by applicant) in view of US 2011/0245633 to Goldberg et al. (Goldberg) as applied to claims 1 and 18 further in view of US 2017/0235917 to Bighamian et al. (Bighamian).
In reference to at least claims 13 and 20
Chandra modified by Goldberg renders obvious a method and device according to claims 1 and 18. However, Chandra modified by Goldberg does not explicitly disclose post-processing the indicator of the physiological response, wherein post- processing the indicator of the physiological response further comprises at least one ensuring that the physiological response comprises a duration above a certain threshold and grouping multiple physiological responses together if the multiple physiological responses occur within a certain time frame of each other. It was well-known in the art before the effective filing date of the claimed invention when monitoring the state and/or response of a subject to compare the identified the state and/or response to a duration threshold in order to allow the identification of brief or transient events that might not require direct intervention as evidence by Bighamian et al. (e.g. “In addition, a determination can be made whether at least one CV parameter defining the cardiovascular state of the subject exceeds one or more thresholds for a selected duration. This would allow the identification of brief or transient events which might not require direct intervention. In this manner, the number of actions or notifications for controlling the state of the subject may be minimized or restricted to those conditions when, statistically-speaking, specific CV parameters are likely to persist outside pre-determined or safe values for a certain amount of time, for instance.”, para. [0047]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method and device of Chandra modified by Goldberg to include post- processing the indicator of the physiological response by ensuring that the physiological response comprises a duration above a certain threshold since such technique was known in the art and would have yielded the predictable result of identifying brief or transient events that might not require direct intervention allowing action or notification to those conditions likely to persist outside pre-determined or safe values for a certain time (‘917, para. [0047]).
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Comparative Study of Physiological Signals from Empatica E4 Wristband for Stress Classification to Chandra et al. (Chandra – cited by applicant) in view of US 2011/0245633 to Goldberg et al. (Goldberg) as applied to claim 18 further in view of US 2012/0289788 to Jain et al. (Jain) and US 2021/0233641 to Sasangohar et al. (Sasangohar).
In reference to at least claim 19
Chandra modified by Goldberg renders obvious a device according to claim 18. Chandra further discloses filtering the cEDA data of the user using a high-pass filter, a low-pass filter, or a median filter (e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”); updating a certain time frame cache with the plurality of data inputs e.g. pg. 223, Fig. 2: Savitzky-Golay filter; Median Filter, pg. 224 section 3.2 “Filtering Noise”, use of window size of 4 entries); normalizing the plurality of time-series data inputs using one or more normalization factors, the one or more normalization factors comprising at least one of a mean, a median, a mode, or a standard deviation for a time scale suitable to each time-series data input (e.g. pg. 224, section 3.2 “Feature Extraction”: calculating mean, median, mode standard deviation, minimum, maximum and first derivative, calculating the mean value for which a normalization is carried out); and transforming each of the plurality of time-series data inputs into a single value (e.g. pg. 224, section 3.2 “Feature Extraction”: calculating mean, median, mode standard deviation, minimum, maximum and first derivative).
However, Chandra modified by Goldberg does not explicitly disclose wherein processing the plurality of time-series data inputs using the plurality of filtering techniques in sequence further comprise: determining whether a time-series data input from the plurality of time-series data inputs indicate one of a plurality of modes of the wearable computing device and, if so, eliminating or modifying the time-series data input from the plurality of time-series data inputs; filtering the plurality of time-series data inputs based on a plurality of confounders and eliminating or modifying time-series data inputs of the plurality of time-series data inputs that satisfy one or more of the plurality of confounders.
Jain, in the same field of endeavor, discloses that sensors provide erroneous data when a physical connection between a sensor and the subject moves or physical movement of the subject such as exercise (e.g. “many sensors provide erroneous data when accelerated, for example, because the physical connection between the sensor and the subject moves, causing the sensor to generate an inaccurate or false reading. “, para. [0246]). Jain further discloses the use of an accelerometer to contextualize physiological sensor data and eliminate physiological data or erroneous sensor measurements cause by physical movement (e.g. “By contextualizing physiological sensor data with accelerometer data, analysis system 180 may be able to eliminate changes in physiological data or erroneous sensor measurements caused by physical movement and thereby more accurately measure and monitor stress in a person.”, para. [0246]).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Chandra modified by Goldberg to include determining whether a time-series data input indicates one of a plurality of modes of the wearable computing device such as a physical connection between the subject and wearable device moving or physical movement of the subject which provides erroneous sensor measurements and eliminating associated time-series data from the plurality of time-series data inputs used for calculating the indicator, as taught by Jain, in order to accurately measure and monitor stress in a person by reducing inaccurate or false sensor measurement readings (‘788, para. [0246]). Further, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the method of Chandra modified by Goldberg to include filtering the plurality of time-series data inputs based on a plurality of confounders such as an accelerometer used to measure physical movement of the subject or the physical connection between the sensor and the subject moving which provides erroneous sensor measurements and eliminating associated time-series data that satisfy one or more of the plurality of confounders, as taught by Jain, in order to accurately measure and monitor stress in a person by reducing inaccurate or false sensor measurement readings (‘788, para. [0246]).
Furthermore, Chandra modified by Goldberg and Jain does not explicitly disclose wherein processing the plurality of time-series data inputs using the plurality of filtering techniques further comprise: imputing one or more data points into the plurality of time-series data inputs if a certain number of data points are missing from the plurality of time-series data inputs or dropping the plurality of time-series data inputs if the number of missing data points exceeds a threshold. It was well-known in the art before the effective filing date of the claimed invention to use Kalman filter imputation of missing value for filling gaps in data as evidence by Sasangohar (e.g. “”Kalman filter imputation of missing values (a technique well-known to those of ordinary skill in the art) may be used to fill in gaps in the data, e.g., up to a cutoff for the number of consecutive missing values (e.g., a maximum of five missing values) beyond which the data imputation is deemed unreliable”, para. [0021]). Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to further modify the operations of Chandra modified by Goldberg and Jain to include imputing one or more data points into the plurality of time-series data inputs if a certain number of data points are missing from the plurality of time-series data inputs or dropping the plurality of time-series data inputs if the number of missing data points exceeds a threshold as such technique was known in the art and would have yielded the predictable result of filling in missing gaps in the data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 2008/0214903 to Orbach which discloses physiological monitoring to includes using electro dermal activity. US 2022/0054080 to Liu et al. which discloses detection and response to arousal activations which discloses monitoring electrodermal activity.
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