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 .
Amendment Entered
In response to the amendment filed on July 31st, 2026, amended claims 31, 38-39, 41, and 44, and new claims 52-66 are entered. Claims 32-37, 40, 42, and 45-51 are canceled. Claims 31, 38-39, 41, 44, and 52-66 are currently under examination.
Response to Arguments
Applicant's remarks and amendments with respect to the rejections under 35 U.S.C. 112(b) have been fully considered. The rejections are withdrawn in view of the amendment.
Applicant's arguments, filed on July 31st, 2026, with respect to the rejections under 35 U.S.C. 101 have been fully considered but they are not persuasive. The rejections are maintained, and further clarified, in view of the amendment.
Applicant argues that the claim amendments integrate the abstract idea of predicting a delivery date into a practical application eligible for patent protection. Examiner respectfully disagrees. Although the Applicant has amended the claims, the newly added limitations still recite mental steps for calculating, identifying, and predicting.
The claims recite mental processes performed on a computer control system. The “Federal Circuit has explained, ‘[c]ourts have examined claims that required the use of a computer and still found that the underlying, patent-ineligible invention could be performed via pen and paper or in a person’s mind.’ Versata Dev. Group v. SAP Am., Inc., 793 F.3d 1306, 1335, 115 USPQ2d 1681, 1702 (Fed. Cir. 2015).” MPEP 2106.04(a)(2) III. There is no time limit recited for performing the steps. The claimed steps can be performed via pen and paper or in a person’s mind with no time limit. The computer is merely utilized as a tool to perform the mental steps.
Furthermore, as noted below, the claims fall under the mathematical concepts group and/or the mental processes group. “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).
Examiner would like to clarify that although the claims have been amended to “refer more concretely to particular modes of data acquisition”, the step of data acquisition is simply data gathering which is extra-solution activity and does not add significantly more to the abstract ideas identified, as it merely specifies the nature of the data which is exploited in the steps encompassing a mental process. See MPEP 2106.05(g). The same reasoning applies for the newly added limitation of “generating a notification”, which is simply data outputting, categorized as insignificant extra-solution activity and does not add significantly more to the abstract ideas identified. Additionally, the limitations related to the bio-signal measurement and delivery date prediction merely generally link to a particular technical environment. See MPEP 2106.05(h).
Applicant's arguments, filed on July 31st, 2026, with respect to the rejections under 35 U.S.C. 102 have been fully considered but they are not persuasive.
At Pgs. 7-8 of the Reply, Applicant argues that the Bilic reference does not anticipate the features of the independent claims. Examiner respectfully disagrees. The rejections are maintained, and further clarified, in view of the amendment.
Bilic clearly teaches wherein the history of the resting heart rate metric comprises multiple values over time and wherein each value of the multiple values represents a multi-day mean or a median of the resting heart rate metric over an interval within the history (The filtered set of heart rates might comprise an average, a cumulant and/or a statistical set of heart rates, such as a specific percentile of the heart rate distribution, which is for example calculated for each day, and/or for each week; Page 7 Lines 22-24);
identifying an inflection point in the history of the resting heart rate metric where the multiple values of the multi-day mean or median of the resting heart rate metric change from increasing over time until the inflection point and decreasing over time after the inflection point (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…after conception the heart rate increases to a local maximum around week 5-6 followed by a short period of a decrease. Starting from week 10, the heart rates in all groups increase approximately until week 30 after conception. Depending on the date of childbirth a period 50 of decreasing heart rates indicated by the boxed region follows to the maximum heart rate around week 30; Page 21 Lines 1-17);
calculating a predicted delivery date for the pregnancy (In Figure 2 box 200 relates to physiological parameters and other factors, including vascular activity and body movement of the pregnant person, which are used for determining and predicting the date of giving birth with the electronic system according to the invention, particularly by using the first and the second sensor system 101, 102; Page 18 Lines 21-25) at a predetermined number of days after the inflection point in the multiple values of the multi-day mean or median of the resting heart rate metric in the history of the resting heart rate metric for the user (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…a probability (confidence information) for each possible week of delivery can be given such that the pregnant person is able to put the determined date of childbirth in perspective. The electronic system and the method according to the invention allows for a novel, reliable and non-invasive way of predicting the date of childbirth; Page 21 Line 1 – Page 22 Line 14); and
generating a notification of the predicted delivery date (the predicted date of childbirth or a symbol being indicative of the date of childbirth or its approach is displayed to the pregnant person, e.g. on a mobile device such as a mobile phone; Page 14 Lines 12-14).
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 31, 38-39, 41, 44, and 52-66 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Each of Claims 31, 38-39, 41, 44, and 52-66 has been analyzed to determine whether it is directed to any judicial exceptions.
Step 1
Claims 31, 38-39, 41, 44, and 52-66 recite a series of steps or acts for predicting a delivery date for pregnancy. Thus, the claims are directed to a process, which is one of the statutory categories of invention.
Step 2A, Prong 1
Each of Claims 31, 38-39, 41, 44, and 52-66 recites at least one step or instruction for predicting a delivery date for pregnancy, which is grouped as a mental process under the 2019 PEG. The claims recite abstract ideas in the form of mental processes, as consistent with Mayo Collaborative Servs. v. Prometheus Labs., Inc., 566 U.S. 66 (2012). If a claim, under its broadest reasonable interpretation, covers performance in the mind but for the recitation of generic computer components, then it is still in the mental processes category unless the claim cannot practically be performed in the mind, see Intellectual Ventures I LLC v. Symantec Corp., 838 F.3d 1307, 1318 (Fed. Cir. 2016). Determining a predicted delivery date based on the calculation of heart rate data may be performed by a human.
The step of acquiring heart rate data from a wearable monitor is categorized as a data-gathering step, which is considered insignificant extra-solution activity. The steps of generating a notification is categorized as a data-outputting step, which is also considered insignificant, extra-solution activity. Accordingly, each of Claims 31, 38-39, 41, 44, and 52-66 recites an abstract idea.
Specifically, Claim 31 recites the abstract idea of: “calculating a history of a resting heart rate metric for the user during the pregnancy based on the heart rate data…identifying an inflection point in the history of the resting heart rate metric…and calculating a predicted delivery date for the pregnancy”, and Claim 57 recites the abstract idea of: “calculating a history of a heart rate variability metric for the user during the pregnancy based on the heart rate data…identifying an inflection point in the history of the heart rate variability metric…and calculating a predicted delivery date for the pregnancy”.
Furthermore, dependent Claims 31, 38-39, 41, 44, 52-56, and 58-66 merely include limitations that either further define the abstract idea (and thus don’t make the abstract idea any less abstract) or amount to no more than generally linking the use of the abstract idea to a particular technological environment or field of use because they’re merely incidental or token additions to the claims that do not alter or affect how the process steps are performed. Accordingly, each of the above-identified claims recites an abstract idea.
Step 2A, Prong 2
The above-identified abstract idea in each of independent Claims 31 and 57 (and their respective dependent Claims) is not integrated into a practical application under 2019 PEG because the additional elements, either alone or in combination, generally link the use of the above-identified abstract idea to a particular technological environment or field of use.
More specifically, the additional elements of: “wearable monitor” in independent Claims 31 and 57, “user device” in dependent Claims 44 and 61, “photoplethysmography monitor” in dependent Claims 56 and 66 are generically recited elements in the claims which do not improve the functioning of a computer, or any other technology or technical field. Nor do these above-identified additional elements serve to apply the above-identified abstract idea with, or by use of, a particular machine, effect a transformation or apply or use the above-identified abstract idea in some other meaningful way beyond generally linking the use thereof to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception. Furthermore, the above-identified additional elements do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. For at least these reasons, the abstract idea identified above in independent Claims 31 and 57 (and their respective dependent Claims) is not integrated into a practical application under 2019 PEG.
Moreover, the above-identified abstract idea is not integrated into a practical application under 2019 PEG because the claimed method merely implements the above-identified abstract idea (e.g., mental process) using rules (e.g., computer instructions) executed by a computer. In other words, these claims are merely directed to an abstract idea with additional generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. Additionally, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. That is, like Affinity Labs of Tex. v. DirecTV, LLC, the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution. Thus, for these additional reasons, the abstract idea identified above in independent Claims 31 and 57 (and their respective dependent Claims) is not integrated into a practical application under the 2019 PEG.
Accordingly, independent Claims 31 and 57 (and their respective dependent Claims) are each directed to an abstract idea under 2019 PEG.
Step 2B
None of Claims 31, 38-39, 41, 44, and 52-66 include additional elements that are sufficient to amount to significantly more than the abstract idea for at least the following reasons.
These claims require the additional elements of: “wearable monitor” in independent Claims 31 and 57, “user device” in dependent Claims 44 and 61, “photoplethysmography monitor” in dependent Claims 56 and 66. The above-identified additional elements are generically claimed components which enable the above-identified abstract idea(s) to be conducted by performing the basic functions of automating mental tasks and/or obtain data through data-gathering steps. The courts have recognized such computer functions as well understood, routine, and conventional functions when claimed in a merely generic manner (e.g., at a high level of generality) or as insignificant extra-solution activity. See, Versata Dev. Group, Inc. v. SAP Am., Inc. , 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); and OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93.
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 the Applicant’s specification (e.g. paragraphs [0103-113]) which discloses that the processor(s) comprise generic computer components that are configured to perform the generic computer functions (e.g. calculating and determining) that are well-understood, routine, and conventional activities previously known to the pertinent industry; the Applicant’s Background in the specification; and the non-patent literature of record in the application.
Accordingly, in light of Applicant’s specification, the term “processor” is reasonably construed as a generic computing device. Like SAP America vs Investpic, LLC (Federal Circuit 2018), it is clear, from the claims themselves and the specification, that these limitations require no improved computer resources, just already available computers, with their already available basic functions, to use as tools in executing the claimed process.
Furthermore, Applicant’s specification does not describe any special programming or algorithms required for the “processor”. This lack of disclosure is acceptable under 35 U.S.C. §112(a) since this hardware performs non-specialized functions known by those of ordinary skill in the computer arts. By omitting any specialized programming or algorithms, Applicant's specification essentially admits that this hardware is conventional and performs well understood, routine and conventional activities in the computer industry or arts. In other words, Applicant’s specification demonstrates the well-understood, routine, conventional nature of the above-identified additional elements because it describes these additional elements in a manner that indicates that the additional elements are sufficiently well-known that the specification does not need to describe the particulars of such additional elements to satisfy 35 U.S.C. § 112(a) (see Berkheimer memo from April 19, 2018, (III)(A)(1) on page 3). Adding hardware that performs “‘well understood, routine, conventional activit[ies]’ previously known to the industry” will not make claims patent-eligible (TLI Communications).
The recitation of the above-identified additional limitations in Claims 31, 38-39, 41, 44, and 52-66 amounts to mere instructions to implement the abstract idea on a computer. Simply using a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not provide significantly more. See Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016) (cellular telephone); and TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016) (computer server and telephone unit). Moreover, implementing an abstract idea on a generic computer, does not add significantly more, similar to how the recitation of the computer in the claim in Alice amounted to mere instructions to apply the abstract idea of intermediated settlement on a generic computer.
A claim that purports to improve computer capabilities or to improve an existing technology may provide significantly more. McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299, 1314-15, 120 USPQ2d 1091, 1101-02 (Fed. Cir. 2016); and Enfish, LLC v. Microsoft Corp., 822 F.3d 1327, 1335-36, 118 USPQ2d 1684, 1688-89 (Fed. Cir. 2016). However, a technical explanation as to how to implement the invention should be present in the specification for any assertion that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Here, Applicant’s specification does not include any discussion of how the claimed invention provides a technical improvement realized by these claims over the prior art or any explanation of a technical problem having an unconventional technical solution that is expressed in these claims. Instead, as in Affinity Labs of Tex. v. DirecTV, LLC 838 F.3d 1253, 1263-64, 120 USPQ2d 1201, 1207-08 (Fed. Cir. 2016), the specification fails to provide sufficient details regarding the manner in which the claimed invention accomplishes any technical improvement or solution.
For at least the above reasons, the method of Claims 31, 38-39, 41, 44, and 52-66 are directed to applying an abstract idea as identified above on a general purpose computer without (i) improving the performance of the computer itself, or (ii) providing a technical solution to a problem in a technical field. None of Claims 31, 38-39, 41, 44, and 52-66 provides meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that these claims amount to significantly more than the abstract idea itself.
Taking the additional elements individually and in combination, the additional elements do not provide significantly more. Specifically, when viewed individually, the above-identified additional elements in independent Claims 31 and 57 (and their respective dependent claims) do not add significantly more because they are simply an attempt to limit the abstract idea to a particular technological environment. That is, neither the general computer elements nor any other additional element adds meaningful limitations to the abstract idea because these additional elements represent insignificant extra-solution activity. When viewed as a combination, these above-identified additional elements simply implement the claimed functions with well-understood, routine and conventional activity specified at a high level of generality in a particular technological environment. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application. When viewed as whole, the above-identified additional elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Thus, Claims 31, 38-39, 41, 44, and 52-66 merely apply an abstract idea to a computer and do not (i) improve the performance of the computer itself (as in Bascom and Enfish), or (ii) provide a technical solution to a problem in a technical field (as in DDR).
Therefore, none of the Claims 31, 38-39, 41, 44, and 52-66 amounts to significantly more than the abstract idea itself. Accordingly, Claims 31, 38-39, 41, 44, and 52-66 are not patent eligible and rejected under 35 U.S.C. 101.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 31, 38-39, 41, 44, 55-61, and 64-66 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Bilic et al (WO2020229656A1; cited by Applicant).
Regarding Claim 31, Bilic discloses a method (system and method for precise determination of a date of childbirth with a wearable device; Abstract) comprising:
acquiring heart rate data from a wearable monitor (wearable device 1) worn by a user during a pregnancy of the user (Figure 1: shows a schematic illustration of an electronic system according to the invention for detecting the heart rate and further parameters related to the pregnancy of a person, the electronic system comprising a wearable device, in particular a wrist-worn bracelet, with an analysing module comprising a processor in the wearable device and/or in an external system; Page 15 Lines 10-14; As illustrated schematically in Figure 1, the wearable device 1 comprises several sensor systems 100, including the first sensor system 101 with optical sensors configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and breathing rate. For example, sensor system 101 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability as described in Simon Arberet et al., "Photoplethysmography-Based Ambulatory Heartbeat Monitoring Embedded into a Dedicated Bracelet", Computing in Cardiology 2013; 40:935-938, included herewith by reference in its entirety; Page 16 Lines 24-32), the heart rate data including a plurality of daily sleep intervals for the user (This allows the electronic system to detect phases of rest of the pregnant person, such that sleep phases can be detected. During sleep phases the heart rate can be detected and shows less inter-day variability, as the person is at rest; Page 9 Lines 27-29; the sleep phases of the pregnant person are determined by evaluating a heart rate variability and/or sensor signals indicative of a resting state pregnant person, such as acceleration. This embodiment allows distinguishing active phases and resting phases of the pregnant person such as to obtain more reliable measurements; Page 13 Line 27 – Page 14 Line 2; In step S5, the heart rate variability and the acceleration are used (by the processor 13 of the wearable device and/or by the processor(s) 30, 40 of the computer system 3 and/or the mobile communication device 4) to detect sleep phases with a resting pulse. Detecting sleep phases with resting pulse allows detecting the heart rate each night in the same state of activity of the pregnant person…the processor(s) 13, 30, 40 determine changes of the heart rate, i.e. changes in the duration of the interval between individual heart beats, respectively, that occur during the detected sleep phases with resting pulse; Page 20 Lines 12-27);
calculating a history of a resting heart rate metric for the user during the pregnancy based on the heart rate data (This allows the electronic system to detect phases of rest of the pregnant person, such that sleep phases can be detected. During sleep phases the heart rate can be detected and shows less inter-day variability, as the person is at rest; Page 9 Lines 27-29; the sleep phases of the pregnant person are determined by evaluating a heart rate variability and/or sensor signals indicative of a resting state pregnant person, such as acceleration. This embodiment allows distinguishing active phases and resting phases of the pregnant person such as to obtain more reliable measurements; Page 13 Line 27 – Page 14 Line 2; In step S5, the heart rate variability and the acceleration are used (by the processor 13 of the wearable device and/or by the processor(s) 30, 40 of the computer system 3 and/or the mobile communication device 4) to detect sleep phases with a resting pulse. Detecting sleep phases with resting pulse allows detecting the heart rate each night in the same state of activity of the pregnant person…the processor(s) 13, 30, 40 determine changes of the heart rate, i.e. changes in the duration of the interval between individual heart beats, respectively, that occur during the detected sleep phases with resting pulse; Page 20 Lines 12-27),
wherein the history of the resting heart rate metric comprises multiple values over time and wherein each value of the multiple values represents a multi-day mean or a median of the resting heart rate metric over an interval within the history (The filtered set of heart rates might comprise an average, a cumulant and/or a statistical set of heart rates, such as a specific percentile of the heart rate distribution, which is for example calculated for each day, and/or for each week; Page 7 Lines 22-24);
identifying an inflection point in the history of the resting heart rate metric where the multiple values of the multi-day mean or median of the resting heart rate metric change from increasing over time until the inflection point and decreasing over time after the inflection point (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…after conception the heart rate increases to a local maximum around week 5-6 followed by a short period of a decrease. Starting from week 10, the heart rates in all groups increase approximately until week 30 after conception. Depending on the date of childbirth a period 50 of decreasing heart rates indicated by the boxed region follows to the maximum heart rate around week 30; Page 21 Lines 1-17);
calculating a predicted delivery date for the pregnancy (In Figure 2 box 200 relates to physiological parameters and other factors, including vascular activity and body movement of the pregnant person, which are used for determining and predicting the date of giving birth with the electronic system according to the invention, particularly by using the first and the second sensor system 101, 102; Page 18 Lines 21-25) at a predetermined number of days after the inflection point in the multiple values of the multi-day mean or median of the resting heart rate metric in the history of the resting heart rate metric for the user (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…a probability (confidence information) for each possible week of delivery can be given such that the pregnant person is able to put the determined date of childbirth in perspective. The electronic system and the method according to the invention allows for a novel, reliable and non-invasive way of predicting the date of childbirth; Page 21 Line 1 – Page 22 Line 14); and
generating a notification of the predicted delivery date (the predicted date of childbirth or a symbol being indicative of the date of childbirth or its approach is displayed to the pregnant person, e.g. on a mobile device such as a mobile phone; Page 14 Lines 12-14).
Regarding Claim 38, Bilic discloses wherein the history of the resting heart rate metric includes a history of resting heart rate measurements for the user during the pregnancy (This allows the electronic system to detect phases of rest of the pregnant person, such that sleep phases can be detected. During sleep phases the heart rate can be detected and shows less inter-day variability, as the person is at rest; Page 9 Lines 27-29; the sleep phases of the pregnant person are determined by evaluating a heart rate variability and/or sensor signals indicative of a resting state pregnant person, such as acceleration. This embodiment allows distinguishing active phases and resting phases of the pregnant person such as to obtain more reliable measurements; Page 13 Line 27 – Page 14 Line 2; In step S5, the heart rate variability and the acceleration are used (by the processor 13 of the wearable device and/or by the processor(s) 30, 40 of the computer system 3 and/or the mobile communication device 4) to detect sleep phases with a resting pulse. Detecting sleep phases with resting pulse allows detecting the heart rate each night in the same state of activity of the pregnant person…the processor(s) 13, 30, 40 determine changes of the heart rate, i.e. changes in the duration of the interval between individual heart beats, respectively, that occur during the detected sleep phases with resting pulse; Page 20 Lines 12-27).
Regarding Claim 39, Bilic discloses wherein calculating the history of the resting heart rate metric includes calculating a weekly sequence of seven day moving medians for the resting heart rate metric based on the heart rate data (The filtered set of heart rates might comprise an average, a cumulant and/or a statistical set of heart rates, such as a specific percentile of the heart rate distribution, which is for example calculated for each day, and/or for each week; Page 7 Lines 22-24).
Regarding Claim 41, Bilic discloses wherein calculating the predicted delivery date includes identifying the inflection point in the resting heart rate metric from a first timewise decreasing value to a second timewise increasing value (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…after conception the heart rate increases to a local maximum around week 5-6 followed by a short period of a decrease. Starting from week 10, the heart rates in all groups increase approximately until week 30 after conception. Depending on the date of childbirth a period 50 of decreasing heart rates indicated by the boxed region follows to the maximum heart rate around week 30; Page 21 Lines 1-17).
Regarding Claim 44, Bilic discloses comprising transmitting the notification to a user device of the user identifying the predicted delivery date (the predicted date of childbirth or a symbol being indicative of the date of childbirth or its approach is displayed to the pregnant person, e.g. on a mobile device such as a mobile phone; Page 14 Lines 12-14).
Regarding Claim 55, Bilic discloses wherein acquiring heart rate data includes continuously acquiring heart rate data throughout the pregnancy (the electronic system is configured to determine the heart rate several times or continuously during 24h; Page 11 Lines 5-6).
Regarding Claim 56, Bilic discloses wherein the wearable monitor includes a photoplethysmography monitor (As illustrated schematically in Figure 1, the wearable device 1 comprises several sensor systems 100, including the first sensor system 101 with optical sensors configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and breathing rate. For example, sensor system 101 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability as described in Simon Arberet et al., "Photoplethysmography-Based Ambulatory Heartbeat Monitoring Embedded into a Dedicated Bracelet", Computing in Cardiology 2013; 40:935-938, included herewith by reference in its entirety; Page 16 Lines 24-32).
Regarding Claim 57, Bilic discloses a method (system and method for precise determination of a date of childbirth with a wearable device; Abstract) comprising:
acquiring heart rate data from a wearable monitor (wearable device 1) worn by a user during a pregnancy of the user (Figure 1: shows a schematic illustration of an electronic system according to the invention for detecting the heart rate and further parameters related to the pregnancy of a person, the electronic system comprising a wearable device, in particular a wrist-worn bracelet, with an analysing module comprising a processor in the wearable device and/or in an external system; Page 15 Lines 10-14; As illustrated schematically in Figure 1, the wearable device 1 comprises several sensor systems 100, including the first sensor system 101 with optical sensors configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and breathing rate. For example, sensor system 101 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability as described in Simon Arberet et al., "Photoplethysmography-Based Ambulatory Heartbeat Monitoring Embedded into a Dedicated Bracelet", Computing in Cardiology 2013; 40:935-938, included herewith by reference in its entirety; Page 16 Lines 24-32), the heart rate data including a plurality of daily sleep intervals for the user (This allows the electronic system to detect phases of rest of the pregnant person, such that sleep phases can be detected. During sleep phases the heart rate can be detected and shows less inter-day variability, as the person is at rest; Page 9 Lines 27-29; the sleep phases of the pregnant person are determined by evaluating a heart rate variability and/or sensor signals indicative of a resting state pregnant person, such as acceleration. This embodiment allows distinguishing active phases and resting phases of the pregnant person such as to obtain more reliable measurements; Page 13 Line 27 – Page 14 Line 2; In step S5, the heart rate variability and the acceleration are used (by the processor 13 of the wearable device and/or by the processor(s) 30, 40 of the computer system 3 and/or the mobile communication device 4) to detect sleep phases with a resting pulse. Detecting sleep phases with resting pulse allows detecting the heart rate each night in the same state of activity of the pregnant person…the processor(s) 13, 30, 40 determine changes of the heart rate, i.e. changes in the duration of the interval between individual heart beats, respectively, that occur during the detected sleep phases with resting pulse; Page 20 Lines 12-27);
calculating a history of a heart rate variability metric for the user during the pregnancy based on the heart rate data (the heart rate variability of the pregnant person is measured using the wearable device 1. Specifically, in the state of the device 1 being worn, e.g. on the wrist, the processor 13 of the wearable device 1 reads or receives from the first sensor system 101 the current heart rate variability of the pregnant person. The processor 13 stores the heart rate variability (value) in the data storage 12 together with a time stamp, including the current time and date…preferably, the measurements of the heart rate, the heart rate variability, and the acceleration of the pregnant person are performed concurrently. The measurements of the first and the second sensor system 101, 102 are performed periodically, for example the first sensor system 101 uses the optical sensors to measure the heart rate and heart rate variability every couple of milliseconds; Page 19 Lines 3-20), wherein the history of the heart rate variability metric comprises multiple values over time and wherein each value of the multiple values represents a multi-day mean or a median of the heart rate variability metric over an interval within the history (The filtered set of heart rates might comprise an average, a cumulant and/or a statistical set of heart rates, such as a specific percentile of the heart rate distribution, which is for example calculated for each day, and/or for each week; Page 7 Lines 22-24);
identifying an inflection point in the history of the heart rate variability metric where the multiple values change from decreasing over time until the inflection point and increasing over time after the inflection point (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…after conception the heart rate increases to a local maximum around week 5-6 followed by a short period of a decrease. Starting from week 10, the heart rates in all groups increase approximately until week 30 after conception. Depending on the date of childbirth a period 50 of decreasing heart rates indicated by the boxed region follows to the maximum heart rate around week 30; Page 21 Lines 1-17);
calculating a predicted delivery date for the pregnancy (In Figure 2 box 200 relates to physiological parameters and other factors, including vascular activity and body movement of the pregnant person, which are used for determining and predicting the date of giving birth with the electronic system according to the invention, particularly by using the first and the second sensor system 101, 102; Page 18 Lines 21-25) at a predetermined number of days after the inflection point in the multiple values of the multi-day mean or median of the heart rate variability metric in the history of the heart rate variability metric (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…a probability (confidence information) for each possible week of delivery can be given such that the pregnant person is able to put the determined date of childbirth in perspective. The electronic system and the method according to the invention allows for a novel, reliable and non-invasive way of predicting the date of childbirth; Page 21 Line 1 – Page 22 Line 14); and
generating a notification of the predicted delivery date (the predicted date of childbirth or a symbol being indicative of the date of childbirth or its approach is displayed to the pregnant person, e.g. on a mobile device such as a mobile phone; Page 14 Lines 12-14).
Regarding Claim 58, Bilic discloses wherein the history of the heart rate variability metric includes a history of heart rate variability measurements for the user during the pregnancy (the heart rate of the pregnant person wearing the wearable device 1 is measured using the wearable device 1. Specifically, in the state of the device 1 being worn, e.g. on the wrist, the processor 13 of the wearable device 1 reads or receives from the first sensor system 101 the current heart rate of the pregnant person. The processor 13 stores the heart rate (value) in the data storage 12 together with a time stamp, including the current time and date; Page 18 Line 30 – Page 19 Line 2).
Regarding Claim 59, Bilic discloses wherein calculating the history of the heart rate variability metric includes calculating a weekly sequence of seven-day moving medians for the heart rate variability metric based on the heart rate data (The filtered set of heart rates might comprise an average, a cumulant and/or a statistical set of heart rates, such as a specific percentile of the heart rate distribution, which is for example calculated for each day, and/or for each week; Page 7 Lines 22-24).
Regarding Claim 60, Bilic discloses wherein calculating the predicted delivery date includes identifying the inflection point in the heart rate variability metric from a first timewise increasing value to a second timewise decreasing value (As seen in Figure 3, determination of the date of delivery can be calculated as the date of onset of a decrease in heart rate in the third trimester plus 5.5 weeks +/- 0.5 weeks…As can been seen in all groups after conception the heart rate increases to a local maximum around week 5-6 followed by a short period of a decrease. Starting from week 10, the heart rates in all groups increase approximately until week 30 after conception. Depending on the date of childbirth a period 50 of decreasing heart rates indicated by the boxed region follows to the maximum heart rate around week 30; Page 21 Lines 1-17).
Regarding Claim 61, Bilic discloses transmitting the notification to a user device of the user identifying the predicted delivery date (the predicted date of childbirth or a symbol being indicative of the date of childbirth or its approach is displayed to the pregnant person, e.g. on a mobile device such as a mobile phone; Page 14 Lines 12-14).
Regarding Claim 64, Bilic discloses wherein acquiring heart rate data includes continuously acquiring heart rate data throughout the pregnancy (the electronic system is configured to determine the heart rate several times or continuously during 24h; Page 11 Lines 5-6).
Regarding Claim 65, Bilic discloses wherein acquiring heart rate data includes acquiring optical data indicative of cardiac activity for the user (As illustrated schematically in Figure 1, the wearable device 1 comprises several sensor systems 100, including the first sensor system 101 with optical sensors configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and breathing rate. For example, sensor system 101 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability as described in Simon Arberet et al., "Photoplethysmography-Based Ambulatory Heartbeat Monitoring Embedded into a Dedicated Bracelet", Computing in Cardiology 2013; 40:935-938, included herewith by reference in its entirety; Page 16 Lines 24-32).
Regarding Claim 66, Bilic discloses wherein the wearable monitor includes a photoplethysmography monitor (As illustrated schematically in Figure 1, the wearable device 1 comprises several sensor systems 100, including the first sensor system 101 with optical sensors configured to generate photoplethysmography (PPG) signals for measuring heart signals, heart rate, heart rate variability, perfusion, and breathing rate. For example, sensor system 101 comprises a PPG-based sensor system for measuring heart signals, heart rate and heart rate variability as described in Simon Arberet et al., "Photoplethysmography-Based Ambulatory Heartbeat Monitoring Embedded into a Dedicated Bracelet", Computing in Cardiology 2013; 40:935-938, included herewith by reference in its entirety; Page 16 Lines 24-32).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 52-54 and 62-63 are rejected under 35 U.S.C. 103 as being unpatentable over Bilic et al in view of Capodilupo et al (U.S. Publication No. 2019/0110755; previously cited).
Regarding Claim 52, Bilic fails to disclose identifying one or more periods of slow wave sleep for the user based on the heart rate data and calculating the resting heart rate metric based on a portion of the heart rate data acquired during the one or more periods of slow wave sleep.
In a similar technical field, Capodilupo teaches applied data quality metrics for physiological measurements (Abstract), comprising identifying one or more periods of slow wave sleep for the user based on the heart rate data and calculating the resting heart rate metric based on a portion of the heart rate data acquired during the one or more periods of slow wave sleep (As shown in step 618, the method 600 may include evaluating a quality of heart rate data using a data quality metric for a slow wave sleep period, e.g., the slow wave sleep period occurring most recently before the waking event. As noted above, the quality of heart rate measurements may vary over time for a variety of reasons. Thus the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method 600 may include using this quality data to select suitable values for calculating a recovery score. For example, the method 600 may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric; [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the teachings of Capodilupo into the invention of Bilic because the use of a heart rate variability measurement from the last phase of sleep provides an accurate and consistent basis for evaluating the user (Capodilupo [0120]).
Regarding Claim 53, Bilic fails to disclose wherein the resting heart rate metric includes a weighted sum of nighttime heart rate measurements.
In a similar technical field, Capodilupo teaches applied data quality metrics for physiological measurements (Abstract), wherein the resting heart rate metric includes a weighted sum of nighttime heart rate measurements (The recovery score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's heart rate variability (HRV)…the recovery score is a weighted combination of the user's heart rate variability (HRV), resting heart rate, sleep quality indicated by a sleep score, and recent strain (indicated, in one example, by the intensity score of the user)…by considering sleep and HRV alone or in combination; [0096]; the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method 600 may include using this quality data to select suitable values for calculating a recovery score. For example, the method 600 may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric; [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the teachings of Capodilupo into the invention of Bilic in order to ensure that the highest quality of heart rate data is used to provide an accurate and consistent basis for further calculations (Capodilupo [0120]).
Regarding Claim 54, Bilic fails to disclose wherein the resting heart rate metric is based on slow wave sleep measurements of cardiac activity.
In a similar technical field, Capodilupo teaches applied data quality metrics for physiological measurements (Abstract), wherein the resting heart rate metric is based on slow wave sleep measurements of cardiac activity (As shown in step 618, the method 600 may include evaluating a quality of heart rate data using a data quality metric for a slow wave sleep period, e.g., the slow wave sleep period occurring most recently before the waking event. As noted above, the quality of heart rate measurements may vary over time for a variety of reasons. Thus the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method 600 may include using this quality data to select suitable values for calculating a recovery score. For example, the method 600 may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric; [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the teachings of Capodilupo into the invention of Bilic because the use of a heart rate variability measurement from the slow wave sleep provides an accurate and consistent basis for evaluating the user (Capodilupo [0120]).
Regarding Claim 62, Bilic fails to disclose identifying one or more periods of slow wave sleep for the user based on the heart rate data and calculating the heart rate variability metric based on a portion of the heart rate data acquired during the one or more periods of slow wave sleep.
In a similar technical field, Capodilupo teaches applied data quality metrics for physiological measurements (Abstract), comprising identifying one or more periods of slow wave sleep for the user based on the heart rate data and calculating the heart rate variability metric based on a portion of the heart rate data acquired during the one or more periods of slow wave sleep (The recovery score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's heart rate variability (HRV)…the recovery score is a weighted combination of the user's heart rate variability (HRV), resting heart rate, sleep quality indicated by a sleep score, and recent strain (indicated, in one example, by the intensity score of the user)…by considering sleep and HRV alone or in combination; [0096]; As shown in step 612, the method 600 may include calculating a heart rate variability of the user at a moment in a last phase of sleep preceding the waking event based upon the heart rate data…an average heart rate variability or similar metric may be determined for any number of discrete measurements within a window around the time of interest; [0117-0118]; the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method 600 may include using this quality data to select suitable values for calculating a recovery score. For example, the method 600 may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric; [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the teachings of Capodilupo into the invention of Bilic in order to ensure that the highest quality of heart rate data is used to provide an accurate and consistent basis for further calculations (Capodilupo [0120]).
Regarding Claim 63, Bilic fails to disclose wherein the heart rate variability metric includes a weighted sum of nighttime heart rate measurements.
In a similar technical field, Capodilupo teaches applied data quality metrics for physiological measurements (Abstract), wherein the heart rate variability metric includes a weighted sum of nighttime heart rate measurements (The recovery score is customized and adapted for the unique physiological properties of the user and takes into account, for example, the user's heart rate variability (HRV)…the recovery score is a weighted combination of the user's heart rate variability (HRV), resting heart rate, sleep quality indicated by a sleep score, and recent strain (indicated, in one example, by the intensity score of the user)…by considering sleep and HRV alone or in combination; [0096]; the quality of heart rate data may be evaluated prior to selecting a particular moment or window of heart rate data for calculating heart rate variability, and the method 600 may include using this quality data to select suitable values for calculating a recovery score. For example, the method 600 may include calculating the heart rate variability for a window of predetermined duration within the slow wave sleep period having the highest quality of heart rate data according to the data quality metric; [0120]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to have incorporated the teachings of Capodilupo into the invention of Bilic because the use of a heart rate variability measurement from the slow wave sleep provides an accurate and consistent basis for evaluating the user (Capodilupo [0120]).
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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/CHANEL J YOON/Examiner, Art Unit 3791
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