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 the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
The claims recite detecting and/or predicting one or more pregnancy complications based on wearable-derived temperature deviations from a pregnancy baseline -- “detecting an indication of one or more pregnancy complications … based at least in part on computing the deviation”.
However, the specification provides only a general statement that temperature deviations from a pregnancy baseline “may” indicate pregnancy complications, and at most provides a single illustrative example associated with hypertension during pregnancy. See, e.g., paragraphs [0015-0018, 0041-0044, 0098-0100, and 0135-0145]. The disclosure does not describe, for the full claimed scope, any specific correlation between:
a particular temperature deviation pattern and preeclampsia,
a particular temperature deviation pattern and gestational diabetes,
a particular temperature deviation pattern and eclampsia,
a particular temperature deviation pattern and infections, or
a particular temperature deviation pattern and the other recited complication types.
The specification also does not identify any distinguishing feature, threshold, statistical rule, model output, or other objective criterion showing how the claimed temperature deviation is mapped to each of the recited complication types. Instead, the disclosure is framed in functional, result-oriented terms, i.e., that the deviation “may indicate” one or more complications. Such generalized language does not reasonably convey possession of the broad genus of complication-specific diagnostic correlations now claimed.
Similarly, regarding claims 3-8, the specification discusses the metrics [e.g., PPG etc.] generally, but does not tie those features to a specific pregnancy complication with sufficient detail.
Accordingly, the present record does not show that the inventor was in possession of the claimed invention to the extent it purports to correlate wearable temperature deviations with the full set of recited pregnancy complications.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 12, 19-21 and associated dependent claims are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claims 1, 19, 20, the phrase “historical temperature data representative of average physiological values” is unclear: what physiological values are being averaged, whether the averages are user-specific or population-based, the relevant time window for averaging, and how the historical data is “representative” of the average values. The phrase “pregnancy baseline of temperature values for the user for at least a portion of the plurality of days” is ambiguous as to how the baseline is defined and over what temporal interval it applies as “at least” renders it open-ended.
Regarding claim 12, the phrase “identifying a false positive for detecting the indication of the one or more pregnancy complications” is unclear. It is not apparent whether the claim is referring to: the physiological measurement being false, the detection output being false, or the pregnancy-complication inference being false.
Regarding claim 21, the phrase “determining a minimum temperature and a maximum temperature of the time series … and identifying at least one slope … based on at least one of the minimum temperature or the maximum temperature” is unclear -- the claim does not explain whether the slope is: the slope between the minimum and maximum, a derivative of the time series, a regression slope, or some other measure. The phrase “the at least one slope comprises at least one of one or more positive slopes or one or more negative slopes” is circular and does not define a clear boundary for the claim scope.
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, 5, 7, 9-21 is/are rejected under 35 U.S.C. 103 as being unpatentable over “Kinnunen”, US Publication 20210007658, in view of “Kang”, CN 105249937, and “OPENSTAX”, “Allied Health Microbiology” Chapter 13.5 “Inflammation and Fever”.
With regards to claim 1, Kinnunen discloses a method comprising:
acquiring, via one or more temperature sensors of a wearable ring device configured to be worn by a user, physiological data associated with the user [0119+: wearable electronic device of the system is a ring configured to be suitably worn on a finger; temperature sensor for measuring the temperature of the user];
receiving, via a transceiver of a user device and from the wearable ring device, the physiological data associated with the user, the physiological data comprising at least temperature data [0121+: mobile communication device is wirelessly connected to the wearable electronic device by a wireless connection];
determining a time series of a plurality of temperature values taken over a plurality of days based at least in part on the temperature data [0016: measured over a plurality of days];
calculating, using historical temperature data representative of average physiological values, a baseline of temperature values for the user for at least a portion of the plurality of days [0153: comparing a value of the parameter to a long term average, for example over 5-30 days];
computing a deviation in the time series of the plurality of temperature values relative to the baseline of temperature values by identifying one or more temperature values of the plurality of temperature values that are lower or higher than the baseline [0155: elevated body temperature may decrease the value… lower body temperature may decrease the value];
detecting an indication of a health state of the user based at least in part on computing the deviation [0153-0155]; and
generating a message for display on a graphical user interface on the user device that indicates the indication [0173: alert may be a text or a voice message provided on the mobile communication device; [0263+: user feedback consists of a graph of numeric data… and a written instruction].
However, Kinnunen does not explicitly disclose that the user is pregnant, that the baseline is a pregnancy baseline, that the deviation specifically comprises identifying lower temperatures in a first portion and higher temperatures in a second portion after pregnancy onset, and detecting an indication of pregnancy complications.
Kang discloses monitoring temperature for a pregnant user to establish a pregnancy baseline and detecting pregnancy complications based on temperature deviations [S103 “based on the base model of basal body temperature… judges that early abortion, pregnant… high temperature sustained for more than 16 days is the symptom of pregnant… high temperature from the first 15 days for the first 34 days and 20 days after cooling, which generally is considered evidence of early abortion”]. While Kang explicitly teaches detecting a complication based on a lower temperature deviation (cooling), it does not explicitly detail detecting a subsequent higher temperature deviation as an indication of a complication.
OPENSTAX discloses the physiological mechanisms of systemic inflammatory responses and infections [systemic complications], teaching that such conditions exhibit a distinct biphasic thermal signature at the extremities. Specifically, OPENSTAX teaches that during the onset of a fever, there is an initial drop in peripheral temperature due to vasoconstriction [“During fever, the skin may appear pale due to vasoconstriction of the blood vessels in the skin, which is mediated by the hypothalamus to divert blood flow away from extremities, minimizing the loss of heat”], which is followed by an overall spike or elevation in body temperature [“A fever is an inflammatory response… resulting in an overall increase in body temperature”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kinnunen with the teachings of Kang and OPENSTAX. This modification would have been prompted in order to enhance the base device of Kinnunen with the well-known and applicable techniques Kang and OPENSTAX applied to comparable physiological monitoring methods. Specifically, adapting the wearable ring system of Kinnunen to monitor pregnancy complications using a pregnancy baseline, as taught by Kang, would enhance the device by providing continuous, automated pregnancy health tracking. Furthermore, because Kinnunen’s device is a ring worn on an extremity [e.g., finger], a person of ordinary skill in the art would have been motivated by the textbook physiological teachings of infection presented by OPENSTAX to configure the system’s processors to identify the specific sequence of a lower temperature deviation followed by a higher temperature deviation. An ordinary artisan would recognize that this “lower then higher” sequence at the extremities is the exact physiological signature of an infection (vasoconstriction diverting blood from the extremities, followed by a fever spike), which is a severe systemic pregnancy complication.
With regards to claim 4, Kinnunen discloses measuring the user’s heart rate and comparing it to a long-term average or baseline to detect physiological states [0153: comparing a value of the parameter to a long term average… of the user’s heart rate]. Kang teaches applying physiological baselines to a pregnant user. It would have been obvious to an ordinary artisan to combine these teachings to compare the measured heart rate against a pregnancy-specific heart rate baseline to detect complications, yielding the predictable result of utilizing cardiovascular data to supplement temperature data in health monitoring.
With regards to claim 5, Kinnunen discloses measuring heart-rate-variability and using it to determine the user’s physiological state [0118, 0124]. As analyzed in claim 4, it would have been obvious to an ordinary artisan to apply Kinnunen’s HRV tracking to the pregnancy baseline framework of Kang to detect complications.
With regards to claim 7, Kinnunen explicitly discloses measuring a respiration rate and using it to determine the user’s physiological state [0118], [0124]. It would have been obvious to an ordinary artisan to apply Kinnunen’s respiratory rate tracking to the pregnancy baseline framework of Kang to detect complications.
With regards to claim 9, Kinnunen discloses receiving user input and confirmation regarding their physiological state and symptoms, such as asking the user “How are you feeling”, “Are you feeling stressed”, or “if the person is injured or sick” and receiving answers to confirm the system’s physiological measurements [0175, 0187]. It would have been obvious to an ordinary artisan to adapt Kinnunen’s symptom confirmation prompts to ask about pregnancy-specific symptoms in the context of Kang’s pregnancy monitoring system.
With regards to claim 10, Kinnunen explicitly discloses measuring continuous body temperature during the night time [0167: body temperature of the user is measured by the wearable device… during the night time]. Kang similarly discloses continuous nighttime temperature sampling [S110 discussion].
With regards to claim 11, Kinnunen discloses estimating future health states and providing predictive warnings based on physiological deviations, such as warning a user “otherwise you will fall ill” [0176]. It would have been obvious to an ordinary artisan to apply this predictive estimation to the pregnancy complications taught by Kang and OPENSTAX.
With regards to claim 12, Kinnunen discloses identifying false positives in temperature data (e.g., when a user places their hand outside a blanket) and using other physiological measurements, such as heart rate or breathing rate, to verify the data and exclude the false readings [0167: exclude these times from analysis… use heart rate data or breathing rate data to fill in the excluded times].
With regards to claim 13, Kinnunen explicitly discloses transmitting alerts and feedback to the user’s mobile communication device [0122, 0173: alert may be a text or a voice message provided on the mobile communication device].
With regards to claim 14, Kinnunen discloses a GUI that allows the user to select and input symptom and feeling tags, such as “Feeling tired”, “Bad”, or “Very Bad” [0175, 0178]. It would have been obvious to an ordinary artisan to customize these GUI tags for pregnancy-specific symptoms when monitoring a pregnant user, as taught by Kang.
With regards to claim 15, Kinnunen explicitly discloses displaying written instruction messages and alerts on the user’s device based on the physiological indications [0263+: user feedback consists of a graph of numeric data… and a written instruction].
With regards to claim 16, Kinnunen discloses messages comprising adjusted sleep targets [0173: “need three more hours of sleep”], adjusted activity targets [0173: “Please do mild exercise”), recommendations [0269: “try doing some relaxing exercises”), and requests to input symptoms [0175].
With regards to claim 17, Kinnunen discloses inputting the physiological data into algorithms to classify the user’s state, such as classifying sleep into stages (deep, light, REM, wakefulness) based on movement and heart rate data [0132]. The Examiner notes that “machine learning classifier” is a broad term that encompasses the algorithmic classification models taught by Kinnunen.
With regards to claim 18, OPENSTAX explicitly discloses that the detected thermal signatures (fever, vasoconstriction) are indicative of infections [“certain bacterial or viral infections can result in the production of pyrogens… to elevate body temperature”].
With regards to claim 19, Kinnunen in view of Kang and OPENSTAX discloses a system, comprising: a plurality of sensors of a wearable ring device configured to be worn on a finger of a user; and one or more processors communicatively coupled with the plurality of sensors, wherein the one or more processors are configured to perform the method steps as analyzed above in claim 1. Kinnunen discloses the physical system architecture including the wearable ring device with sensors [0119+] and processors/controllers configured to analyze the data [0121+]. However, Kinnunen does not explicitly disclose configuring the processors to apply the specific pregnancy baselines and complication detection logic (lower then higher deviations). Kang and OPENSTAX disclose these missing limitations as detailed above in the analysis of claim 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kinnunen with the teachings of Kang and OPENSTAX as detailed above in the analysis of claim 1.
With regards to claim 20, Kinnunen in view of Kang and OPENSTAX discloses a non-transitory computer-readable medium storing code, the code comprising instructions executable by one or more processors to perform the method steps as analyzed above in claim 1. Kinnunen discloses memory storing data and algorithms executable by a microprocessor to perform physiological data analysis [0121: memory is used for storing… algorithms and other capabilities to perform such deep data analysis]. However, Kinnunen does not explicitly disclose instructions for applying the specific pregnancy baselines and complication detection logic. Kang and OPENSTAX disclose these missing limitations as detailed above in the analysis of claim 1. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of Kinnunen with the teachings of Kang and OPENSTAX as detailed above in the analysis of claim 1.
With regards to claim 21, KANG further teaches extracting temperature extrema and trend direction from a time series, including “average value of sequence elements, low point temperature, low temperature occurrence time point, generating period of the low temperature point, temperature point and the occurrence time point of high temperature, generation period of the high temperature point,” and determining that when high-temperature correlation exceeds low-temperature correlation, the first variation characteristic is positive and the second variation characteristic is negative, and vice versa [S102, S103: describing the sign index and that high-temperature correlation greater than low-temperature correlation yields a positive first variation characteristic and negative second variation characteristic, while the opposite relationship yields the inverse]. These teachings correspond to determining a minimum temperature and a maximum temperature of the time series and identifying at least one positive or negative slope or trend based on those extrema. It would have been obvious to an ordinary artisan to combine Kinnunen’s temperature tracking with Kang’s analysis of temperature rises and reductions (slopes) to accurately detect the onset of pregnancy complications.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnumen in view of Kang and OPENSTAX, and in further view of Sun (DIO: 10.3892/etm.2019.8405).
KINNUNEN does not explicitly disclose computing a photoplethysmography amplitude change of systolic and diastolic inflection points, or identifying a photoplethysmography reflection index relative to a pregnancy baseline.
SUN, also directed to noninvasive physiological monitoring, teaches that “PPG RI is derived from PPG amplitude changes of systolic and diastolic peak/inflection points in the PPG waveforms” and that “PPG RI = systolic peak amplitude/diastolic peak amplitude × 100,” or, where a diastolic peak is absent, “PPG RI = systolic peak amplitude/inflection point amplitude × 100.” [Abstract; Measurements of the PPG reflection index (PPG RI); Figure 1A-B]. SUN further teaches that PPG RI values were higher in the PIH and PE groups than in the normal pregnancy (NP) group, and that PPG RI had a significant correlation with the pregnancy-related markers PlGF and sEng. [Abstract; Results; Figure 3C; Figure 4C]. Thus, SUN teaches both the claimed PPG amplitude-change/reflection index computation and a pregnancy-related reference comparison using normal pregnancy as the baseline group.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the teachings of KINNUNEN with the teachings of SUN. This modification would have been prompted in order to enhance KINNUNEN’s wearable physiological monitoring system with SUN’s known noninvasive PPG-based index for assessing maternal vascular/inflammatory status in pregnancy -- computing a PPG reflection index from systolic/diastolic waveform features and determining that the PPG reflection index exceeds a pregnancy baseline, thereby improving identification of high-risk pregnancy conditions such as PIH and preeclampsia.
Claim(s) 5 and 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen in view of Kang and OPENSTAX, and in further view of Moors (DOI: 10.1016/j.preghy.2020.03.003).
Kinnunen discloses measuring heart-rate-variability and using it to determine the user’s physiological state [0118, 0124], but did not disclose explicitly low frequency heart rate variability data.
Firstly, Moors teaches heart rate variability in hypertensive pregnancy disorders. Moors discloses the method further comprising: determining that the heart rate variability data is less than a pregnancy baseline heart rate variability for the user for at least the portion of the plurality of days, wherein detecting the indication of the one or more pregnancy complications is based at least in part on determining that the heart rate variability data is less than the pregnancy baseline heart rate variability for the user (Moors pg 63, Section 4.1.1., [1-2] “A lower TP [total power heart rate variation] was found at 12, 24 and 31 weeks of gestation in women who would develop GH [gestational hypertension] later in pregnancy compared to normotensive pregnant controls… Comparing women with PE [preeclampsia] to normotensive pregnant women, TP [total power heart rate variation] showed no difference or a decrease.” Moors pg 64, Section 4.3.1, [2-3] “One study found a decreased HF [high frequency heart rate variation] at 28 weeks of gestation in women who would later develop GH [gestational hypertension] compared to normotensive controls… All three studies comparing HF(n.u.) between PE [preeclampsia] and normotensive controls found a decreased HF [n.u. high frequency normalized] in PE [preeclampsia]”).
Accordingly, Moors also teaches low frequency heart rate variability in hypertensive pregnancy disorders. Moors discloses the method further comprising: determining that the low frequency heart rate variability data exceeds a pregnancy baseline low frequency heart rate variability for the user for at least the portion of the plurality of days, wherein detecting the indication of the one or more pregnancy complications is based at least in part on determining that the low frequency heart rate variability data exceeds the pregnancy baseline low frequency heart rate variability for the user (Moors pg 63-4, Section 4.2.1., [1-3] In CH [chronic hypertension], LF [low frequency heart rate variability] was increased or comparable to normotensive controls… LF(n.u.) was increased in women who would develop GH [gestational hypertension] at 12, 24 and 31 weeks of gestation compared to normotensive controls… and an increased LF in PE [preeclampsia]).
Therefore, 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 the combination of Kinnunen/Kang/OPENSTAX to include how a decrease in heart rate variability data or an increase in low frequency heart rate variability data indicates pregnancy complications as disclosed in Moors because HPD (hypertensive pregnancy disorders) are a major cause of maternal and fetal mortality and morbidity worldwide (Moors, pg 56, col 2, [2]).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kinnunen in view of Kang and OPENSTAX, and in further view of Payne (DOI: 10.1016/S1701-2163(15)30358-3).
Kinnunen’s ring is disclosed as including “any number and types of sensors suitable for collecting data about the user” [0120] and applies its comparison methodology generically to physiological parameters [0153]. Although Kinnunen discloses utilizing a photon source and detector (optical sensor), Kinnunen did not disclose explicitly blood oxygen saturation.
Payne (DOI: 10.1016/S1701-2163(15)30358-3) teaches assessing blood oxygen saturation (SpO2) in a risk prediction model for adverse outcomes among pregnant women with a hypertensive disorder of pregnancy (HDP). Payne discloses the method further comprising: determining that the blood oxygen saturation data is less than a pregnancy baseline blood oxygen saturation for the user for at least a portion of the plurality of days, wherein detecting the indication of the one or more pregnancy complications is based at least in part on determining that the blood oxygen saturation data is less than the pregnancy baseline blood oxygen saturation for the user (Payne, pg 2, Results section, [1] “SpO2 of<93% was associated with a 30-fold increase in risk of adverse maternal outcome compared to women with SpO2 >97%”).
Therefore, 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 the combination of Kinnunen/Kang/OPENSTAX to include oxygen saturation data exceeding a baseline correlating with pregnancy complications as disclosed in Payne because SpO2 is a significant independent predictor of risk in pregnant women with a hypertensive disorder of pregnancy (Payne, pg 2, Conclusion [1]).
Response to Arguments
Applicant’s arguments filed 4/10/26 have been considered but are moot because the new ground of rejection does not rely on the combination of references applied in the prior rejection of record. Additionally, Applicant's arguments against Kang do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Applicant did not particular point out what constitutes “analogous” as Kang’s teaching of temperature phase comparisons in pregnancy would be considered analogous art.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/TSE CHEN/ Supervisory Patent Examiner, Art Unit 3791
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