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 .
Response to Arguments
Applicant’s arguments, see page 8, filed 03/18/2026, with respect to the objection to Claim 4 have been fully considered and are persuasive. The objection to Claim 4 has been withdrawn.
Applicant’s arguments, see pages 8-10, filed 03/18/2026, with respect to the rejection(s) of independent Claims 1, 15, and 18 under 35 U.S.C. § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Angelucci, Hong, and Chowdary. All other rejections under 35 U.S.C. § 103 have been updated accordingly.
Applicant's arguments filed 03/18/2026 have been fully considered but they are not persuasive.
35 U.S.C. § 101
In short, the claims language as currently filed clearly recites an abstract idea except for one modifier—the word “passively”.
Step 2A, Prong One
The applicant has argued “First, several of the claim features cannot be performed mentally and therefore are not mental processes under Step 2A, Prong One of the Office's eligibility analysis. For instance, amended Claim 1 requires "passively determine[ing], using the selected activity-based technique and the accessed motion data, a breathing rate of the user." As the Specification explains in paragraph 16, a passive determination means that the "breathing rate is determined without that user's (or any other person's) conscious awareness or effort," therefore excluding any mental processes from the claims. This step of passively determining the breathing rate of a user must therefore be considered under Step 2A, Prong Two.”. However, merely modifying an abstract idea that, under broadest reasonable interpretation, can be done in the human mind to be performed without that user's conscious awareness or effort amounts to merely performing a mental process in a computer environment. See Symantec Corp., 838 F.3d at 1316-18, 120 USPQ2d at 1360 and MPEP 2106.04(a)(2)(III)(C). Similar to this case, with the exception of generic computer-implemented steps (i.e., the steps being performed by a computer without a person’s conscious awareness or effort), there is nothing in the claims themselves that foreclose them from being performed by a human, mentally or with pen and paper.
Step 2A, Prong Two/Step 2B
The applicant has also argued “the claimed technologies enable breathing-rate determinations to be passively made by a device rather than requiring active user determination and the resulting deficiencies of that approach. Indeed, as explained above, the claims here exclude manual breathing-rate determinations as properly interpreted in light of the Specification. And as described in the Specification, the claimed technology results in passive, device-based determinations of breathing rates that are more accurate than obtained by existing device-based techniques”. However, passive breathing-rate determinations do not take the claim language out of the realm of mental processes as it is merely performing mental steps on a generic computer, and making the determinations more accurate than previous methods amounts to an improvement in the abstract idea. It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. In other words, the improvement in technology cannot come from improvement in the abstract idea. See MPEP 2106.05(a)
The applicant also argues “the Office Action considers the identified additional elements only in isolation, Office Action at 5, without considering, for example, how the wearable device and corresponding motion data recited by the claims result in device-based breathing-rate determinations that are incompatible with the mental processes described by the Office Action, which mental processes are now are excluded by the language of the claims”. However, even when considering the claim as a whole, as currently filed it still clearly recites an abstract idea except for one modifier—the word “passively”. This is not enough to exclude all mental processes from the claim language, as only a singular word directed automating the mental process in a generic computer-implemented step (i.e., the step being performed by a computer without a person’s conscious awareness or effort) does not take the claim language out of the realm of mental processes.
The applicant has also argued the Office Action does not address the Specification, and states “the Interview Summary states that using passive techniques "does not alter or affect how the process steps of determining a breathing rate of the user [are] performed." However, that is incompatible with the Specification, which explains both that the claimed techniques differ from active techniques (which use, e.g., a microphone rather than motion data from a motion sensor) and that the claimed techniques are more accurate than existing passive techniques...If the Office continues to assert that providing a device with the ability to more accurately predict a person's breathing rate, as the claimed techniques do, are not a practical application over the manual, mental process suggested in the Office Action, then Applicant respectfully requests the Office to provide evidence that the claimed techniques are the same as (i.e., do not alter) the techniques used during active breathing-rate determinations.” The examiner’s position is that the claims as currently filed recite steps that, under broadest reasonable interpretation, are not precluded from the human mind save for one modifier—the word “passively”. This is not enough to significantly alter how the process steps of determining a breathing rate of the user are performed besides limiting it to a generic automated computer process. Although claims are read in light of the specification, the specification should not be imported directly into the claims.
Finally, the applicant has argued “the claimed techniques efficiently make passive, accurate breathing rate determinations, thereby preserving device battery life. For example, paragraph 53 of the Specification explains how the quality-based sensor activation improves device battery life while still permitting continuous passive breathing-rate determinations. Preserving battery life is both an improvement to device technology and a practical application of device capabilities... as explained in MPEP 2106.05.I, "the novelty of any element or steps in a process, or even of the process itself, is of no relevance in determining whether the subject matter of a claim falls within the § 101 categories of possibly patentable subject matter.”. However, MPEP 2106.05(I) makes clear that “ [A]n inventive concept can be found in the non-conventional and non-generic arrangement of known, conventional pieces.”. In contrast, activating a previously inactive sensor only when needed in order to save power and preserve battery life is a combination of steps in a process that is conventional and known (see, for example: US 20190183389 A1, [0048]; US 20180114133 A1 [0041]).
For these reasons, the rejection of the claims under 35 U.S.C. § 101 is maintained.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) as a whole, considering all claim elements both individually and in combination, do not amount to significantly more than an abstract idea. A streamlined analysis of claim 1 follows.
Regarding Claim 1, the claim recites one or more non-transitory computer readable storage media storing instructions. Thus, the claim is directed to an apparatus, which is one of the statutory categories of invention (Step 1).
The claim is then analyzed to determine whether it is directed to any judicial exception (Step 2A, Prong One). The following limitations set forth a judicial exception:
determine, from the motion data, an activity level of the user
select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user
determine, using the selected activity-based technique and the accessed motion data, a breathing rate of the user
determine a quality associated with the determined breathing rate
compare the determined quality associated with the determined breathing rate with a threshold quality
in response to a determination that the determined quality is not less than the threshold quality, then use the determined breathing rate as a final breathing-rate determination for the user
in response to a determination that the determined quality is less than the threshold quality, then… determine, based on data from the second sensor, a breathing rate for the user
These limitations describe a mathematical calculation and/or a mental process as the skilled artisan is capable of performing the recited limitations and making a mental assessment thereafter. Examiner also notes that nothing from the claims suggest that the limitations cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time. Examiner also notes that nothing from the claims suggests an undue level of complexity that the mathematical calculations and/or the mental process steps cannot be practically performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps.
For example:
Determining, from the motion data, an activity level of the user is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
A human is capable of manually/mentally selecting, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user, e.g. by manual user input or deciding mentally.
Determining, using the selected activity-based technique and the accessed motion data, a breathing rate of the user is a mathematical calculation that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Determining a quality associated with the determined breathing rate is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Comparing the determined quality associated with the determined breathing rate with a threshold quality is a mental process that can be performed by a human with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
A human is capable of manually/mentally using the determined breathing rate as a final breathing-rate determination for the user in response to a determination that the determined quality is not less than the threshold quality with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
A human is capable of manually/mentally determining, based on data from the second sensor, a breathing rate for the user in response to a determination that the determined quality is less than the threshold quality with the aid of a pen and paper, or using a generic computer as a tool to perform mathematical calculations and/or mental process steps in real time.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, integrates the identified judicial exception into a practical application (Step 2A, Prong Two).
The following limitations amount to insignificant extra-solution activity to the judicial exception, e.g. mere data gathering. See MPEP 2106.05(g).
access motion data obtained by a first sensor of a wearable device from motion of a user wearing the wearable device
activate an inactive second sensor of the wearable device for a period of time
The following limitations amount to a recitation of the words "apply it" (or an equivalent) and/or nothing more than mere instructions to implement the abstract idea on a generic computer. See MPEP 2106.05(f).
One or more non-transitory computer readable storage media storing instructions and coupled to one or more processors that are operable to execute the instructions to…
The following limitations amount to merely indicating a field of use or technological environment in which to apply a judicial exception and cannot integrate a judicial exception into a practical application. See MPEP 2106.05(h).
passively determine, using the selected activity-based technique and the accessed motion data, a breathing rate of the user
passively determine, based on data from the second sensor, a breathing rate for the user
Therefore, these additional limitations do not integrate the judicial exception into a practical application.
Next, the claim as a whole is analyzed to determine whether any element, or combination of elements, amounts to significantly more than the identified judicial exception (Step 2B):
The following limitations do not amount to significantly more than the abstract idea for substantially similar reasons applied in Step 2A, Prong Two.
access motion data obtained by a first sensor of a wearable device from motion of a user wearing the wearable device
activate a second sensor of the wearable device for a period of time
One or more non-transitory computer readable storage media storing instructions and coupled to one or more processors that are operable to execute the instructions to…
passively determine, using the selected activity-based technique and the accessed motion data, a breathing rate of the user
passively determine, based on data from the second sensor, a breathing rate for the user
The following limitations is/are considered to be well-understood, routine, and conventional (WURC).
The first sensor is considered to be well-understood, routine, and conventional based on a statement from the applicant's specification filed 05/18/2023 (“For example, the first sensor may be an IMU, and the wearable device may be a pair of earbuds”, [20]; inertial measurement units, under broadest reasonable interpretation, are commercially available products).
The second sensor is considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 05/18/2023 (“While the example above is described in the context of audio data, i.e., the second sensor is a microphone, this disclosure contemplates that a similar process may be used to analyze data from any other second sensor”, [43]).
The wearable device is/are considered to be well-understood, routine, and conventional based on statement from the applicant' s specification filed 05/18/2023 (“For example, the first sensor may be an IMU, and the wearable device may be a pair of earbuds”, [20]; earbuds, under broadest reasonable interpretation, are commercially available products).
The storage media and the processor(s) is/are considered to be well-understood, routine, and conventional based on statement from the applicant's specification filed 05/18/2023 (See all of [64]-[68]).
Independent Claims 15 and 18 are also not patent eligible for substantially similar reasons.
Dependent Claims 3-6, 8-12, 17, and 20 also fail to add subject matter qualifying as significantly more to the abstract independent claims as they merely further limit the abstract idea.
Dependent Claims 1, 7, 10, 13-14, 16, and 19 also fail to add subject qualifying as significantly more to the abstract independent claims as they recite limitations that do not integrate the claims into a practical application for substantially similar reasons as set forth above.
Dependent Claims 1, 7, 10, 13-14, 16, and 19 also fail to add subject matter integrating the judicial exception or qualifying as significantly more to the abstract independent claims as they do not recite significantly more than the identified abstract idea for substantially similar reasons as set forth above.
Therefore, Claims 1-20 are not patent eligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 12, 14-15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Angelucci et al (US 20250009251 A1, hereinafter Angelucci, with an effectively filed date of 11/18/2021) in view of Hong et al (US 20140278139 A1, hereinafter Hong) and Chowdhary et al (US 20180114133 A1, hereinafter Chowdhary).
Regarding Claim 1, Angelucci discloses instructions to:
access motion data (“The sensor data are composed of three accelerometer components, three gyroscopes components and three magnetometer components and they are sent to the microcontroller with a 40 Hz rate”, [0035]) obtained by a first sensor of a wearable device from motion of a user wearing the wearable device (“the methods of the invention will be illustrated referring mainly to the particular case in which three inertial sensors (hereinafter referred to also as “sensor units”) are installed on the person's body, as shown in FIG. 1”, [0022]);
determine, from the motion data, an activity level of the user (Fig. 2A-2C; “human activity recognition may be performed by processing untreated signals (“raw data”) generated by the inertial sensor used as a reference (FIG. 2a), for example installed on the lower back of a person, or by processing untreated signals generated by the reference inertial sensor and another inertial sensor installed either on the thorax or on the abdomen (FIG. 2b) of the person, or yet by processing untreated signals generated by the reference inertial sensor, the inertial sensor installed on the thorax and the inertial sensor installed on the abdomen (FIG. 2c) of the person”, [0032]);
select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user (“it is preliminarily determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]);
passively (“The present disclosure presents an advanced prototype suitable for continuous monitoring”, [0029]; as the computer-implemented method runs continuously, it is also run at least some of the time without direct human involvement) determine, using the selected activity-based technique and the accessed motion data (“Therefore, as shown in FIG. 8, it is possible to get accurate estimations of respiratory parameters by determining first whether the person is carrying out either a static activity or a dynamic activity, then by executing the algorithm of FIG. 9 over either the first principal component or the second principal component depending on whether a static activity or a dynamic activity is in progress”, [0082]), a breathing rate of the user (“The method includes the steps of determining whether a person's activity in progress is either a static activity or a dynamical activity; and using this information for enhancing the accuracy of estimations of respiratory rate in static and dynamic activities”, Abstract).
Angelucci discloses the claimed invention except for expressly disclosing one or more non-transitory computer readable storage media storing instructions and coupled to one or more processors that are operable to execute the instructions to:
select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user;
determine a quality associated with the determined breathing rate;
compare the determined quality associated with the determined breathing rate with a threshold quality; and
in response to a determination that the determined quality is not less than the threshold quality, then use the determined breathing rate as a final breathing-rate determination for the user; and
in response to a determination that the determined quality is less than the threshold quality, then:
activate an inactive second sensor of the wearable device for a period of time; and
passively determine, based on data from the second sensor, a breathing rate for the user.
However, Hong teaches one or more non-transitory computer readable storage media storing instructions (“Generally speaking, the techniques and functions outlined above may be implemented in a biometric monitoring device as machine-readable instruction sets, either as software stored in memory...”, [0184]); and
one or more processors coupled to the non-transitory computer readable storage media, the one or more processors being operable to execute the instructions to (“Such instruction sets may be provided to a processor or processors of a biometric monitoring device to cause the processor or processors to control other aspects of the biometric monitoring device to provide the functionality described above”, [0184]):
select, based on the determined activity level, an activity-based technique (“In some conditions, the BMD automatically analyses motion signal provided by a motion sensor, and automatically switches motion intensity modes, which deploy different data processing algorithms to process motion data”, [0095]) for estimating the breathing rate of the user (“The measured physiological metrics may include... respiration rate”, [0171]) from a plurality of available activity-based techniques (“the device can determine a mode of the device using the motion sensor signal strength”, [0096]), wherein each activity-based technique varies at least in a number of sensor axes of the first sensor (“In some embodiments, data from one axis, or two axes, or three axes of one or more motion sensor may be used to determine the motion intensity”, [0096]) used to estimate the breathing rate of the user ([0160], [0171]).
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 instructions of Angelucci to select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user as taught by Hong, because this allows for less data to be processed when signal accuracy is high (Hong, [0096]). It also would have been obvious to add the instructions of Angelucci to one or more non-transitory computer readable storage media storing instructions and add one or more processors coupled to the non-transitory computer readable storage media, the one or more processors being operable to execute the instructions to the invention of Angelucci, because all of the claimed elements were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious to one of ordinary skill in the art.
Chowdhary teaches a method comprising the steps of:
determine a quality (Step 206, Fig. 2; “The method of operation disclosed herein includes first data from a single sensor (one of 102, 103, 104, 106, 114, and 118) to compute the posterior probability of each of the context classes using windowed frames of data. Suitable logic can be used to determine the context class from these probabilities for each frame. The set of each of these probabilities is called a “posteriorgram”. The posteriorgram is used to determine a confidence measure regarding the classification result”, [0041]) associated with a determined activity classification (“The context of the electronic device 100 may be …a current method of locomotion of the user (i.e. running, walking, driving, bicycling, climbing stairs, riding a train, riding a bus)”, [0038]);
compare the determined quality associated with the determined activity classification with a threshold quality (Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold...”, [0041]); and
in response to a determination that the determined quality is not less than the threshold quality, then use the determined breathing rate as a final activity determination for the user (“No”, Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams”, [0041]); and
in response to a determination that the determined quality is less than the threshold quality (“Yes”, Step 208, Fig. 2), then:
activate an inactive second sensor of the wearable device for a period of time (Step 212, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams, otherwise another sensor (another of 102, 103, 104, 106, 114, and 118) is switched on in addition to, or instead of, the first sensor to determine the posterior probability of the context classes using multi-sensor data fusion”, [0041]); and
passively determine, based on data from the second sensor, an activity classification for the user (Step 214, Fig. 2; “Second probabilistic context is then generated at Block 214. Referring additionally to FIG. 3B, generation of the second probabilistic context at Block 206 is now described. Raw digital data is acquired from the second sensor at Block 257. This data is pre-processed and buffered into time windowed frames at Block 259. Sensor specific features are extracted from each frame at Block 261. The features include class-discriminating information contained in the data. These features are used with probabilistic classification to obtain the posterior probability output for each class...”, [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the method steps of Chowdhary to the software instructions of Angelucci (which are directed towards determining breathing rate), and place them in one or more non-transitory computer readable storage media storing instructions and coupled to one or more processors that are operable to execute the instructions, because this creates an efficient tradeoff between the desired accuracy of respiration rate detection and the energy consumed by the sensors to achieve the desired level of accuracy (See Chowdhary, [0041]).
Regarding Claim 3, modified Angelucci discloses the media of Claim 1, wherein the activity level is selected from a set of activity levels comprising a resting activity level and a moving activity level (“ it is…determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]).
Regarding Claim 12, modified Angelucci discloses the media of Claim 1. Modified Angelucci discloses the claimed invention except for expressly disclosing wherein a value of the threshold quality depends on one or more of:
a user preference;
a user-specific breathing-quality score associated with the first sensor; or
a user-specific breathing-quality score associated with second sensor.
However, Chowdhary teaches wherein a value of the threshold quality depends on one or more of (“a (fixed or adaptive) threshold”, [0041]):
a user preference (“The thresholds T1 and T2 described above are based on the degree of classification accuracy required”, [0045];
a user-specific breathing-quality score associated with the first sensor (As this limitation is claimed in the alternative, it does not need to be expressly taught by the reference); or
a user-specific breathing-quality score associated with second sensor (As this limitation is claimed in the alternative, it does not need to be expressly taught by the reference).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the media of Angelucci with the instructions of Chowdary, because all of the claimed elements were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious (adjusting a required accuracy threshold based on desired accuracy) were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious to one of ordinary skill in the art.
Regarding Claim 14, modified Angelucci discloses the media of Claim 1, further coupled to one or more processors that are operable to execute the instructions to periodically repeat the procedure of Claim 1 (“The present disclosure presents an advanced prototype suitable for continuous monitoring”, [0029]; digital devices that operate continuously also operate in very short periods).
Regarding Claim 15, Angelucci discloses a method (“A method of continuously monitoring the respiratory rate of a person is disclosed”, Abstract) comprising:
accessing motion data (“The sensor data are composed of three accelerometer components, three gyroscopes components and three magnetometer components and they are sent to the microcontroller with a 40 Hz rate”, [0035]) obtained by a first sensor of a wearable device from motion of a user wearing the wearable device (“the methods of the invention will be illustrated referring mainly to the particular case in which three inertial sensors (hereinafter referred to also as “sensor units”) are installed on the person's body, as shown in FIG. 1”, [0022]);
determining, from the motion data, an activity level of the user (Fig. 2A-2C; “human activity recognition may be performed by processing untreated signals (“raw data”) generated by the inertial sensor used as a reference (FIG. 2a), for example installed on the lower back of a person, or by processing untreated signals generated by the reference inertial sensor and another inertial sensor installed either on the thorax or on the abdomen (FIG. 2b) of the person, or yet by processing untreated signals generated by the reference inertial sensor, the inertial sensor installed on the thorax and the inertial sensor installed on the abdomen (FIG. 2c) of the person”, [0032]);
selecting, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user (“ it is preliminarily determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]);
passively (“The present disclosure presents an advanced prototype suitable for continuous monitoring”, [0029]; as the computer-implemented method runs continuously, it is also run at least some of the time without direct human involvement) determining, using the selected activity-based technique and the accessed motion data (“Therefore, as shown in FIG. 8, it is possible to get accurate estimations of respiratory parameters by determining first whether the person is carrying out either a static activity or a dynamic activity, then by executing the algorithm of FIG. 9 over either the first principal component or the second principal component depending on whether a static activity or a dynamic activity is in progress”, [0082]), a breathing rate of the user (“The method includes the steps of determining whether a person's activity in progress is either a static activity or a dynamical activity; and using this information for enhancing the accuracy of estimations of respiratory rate in static and dynamic activities”, Abstract);
Angelucci discloses the claimed invention except for expressly disclosing the method comprising:
selecting, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user;
determining a quality associated with the determined breathing rate;
comparing the determined quality associated with the determined breathing rate with a threshold quality; and
in response to a determination that the determined quality is not less than the threshold quality, then using the determined breathing rate as a final breathing-rate determination for the user; or
in response to a determination that the determined quality is less than the threshold quality, then: activating a second sensor of the wearable device for a period of time; and
passively determining, based on data from the second sensor, a breathing rate for the user.
However, Hong teaches selecting, based on the determined activity level, an activity-based technique (“In some conditions, the BMD automatically analyses motion signal provided by a motion sensor, and automatically switches motion intensity modes, which deploy different data processing algorithms to process motion data”, [0095]) for estimating the breathing rate of the user (“The measured physiological metrics may include... respiration rate”, [0171]) from a plurality of available activity-based techniques (“the device can determine a mode of the device using the motion sensor signal strength”, [0096]), wherein each activity-based technique varies at least in a number of sensor axes of the first sensor (“In some embodiments, data from one axis, or two axes, or three axes of one or more motion sensor may be used to determine the motion intensity”, [0096]) used to estimate the breathing rate of the user ([0160], [0171]).
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 method of Angelucci with the method steps of Hong, because this allows for less data to be processed when signal accuracy is high (Hong, [0096]).
Chowdhary teaches disclosing determining a quality (Step 206, Fig. 2; “The method of operation disclosed herein includes first data from a single sensor (one of 102, 103, 104, 106, 114, and 118) to compute the posterior probability of each of the context classes using windowed frames of data. Suitable logic can be used to determine the context class from these probabilities for each frame. The set of each of these probabilities is called a “posteriorgram”. The posteriorgram is used to determine a confidence measure regarding the classification result”, [0041]) associated with a determined activity classification (“The context of the electronic device 100 may be …a current method of locomotion of the user (i.e. running, walking, driving, bicycling, climbing stairs, riding a train, riding a bus)”, [0038]);
comparing the determined quality associated with the determined activity classification with a threshold quality (Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold...”, [0041]); and
in response to a determination that the determined quality is not less than the threshold quality, then using the determined breathing rate as a final activity determination for the user (“No”, Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams”, [0041]); or
in response to a determination that the determined quality is less than the threshold quality (“Yes”, Step 208, Fig. 2), then:
activating an inactive second sensor of the wearable device for a period of time (Step 212, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams, otherwise another sensor (another of 102, 103, 104, 106, 114, and 118) is switched on in addition to, or instead of, the first sensor to determine the posterior probability of the context classes using multi-sensor data fusion”, [0041]); and
passively determining, based on data from the second sensor, an activity classification for the user (Step 214, Fig. 2; “Second probabilistic context is then generated at Block 214. Referring additionally to FIG. 3B, generation of the second probabilistic context at Block 206 is now described. Raw digital data is acquired from the second sensor at Block 257. This data is pre-processed and buffered into time windowed frames at Block 259. Sensor specific features are extracted from each frame at Block 261. The features include class-discriminating information contained in the data. These features are used with probabilistic classification to obtain the posterior probability output for each class...”, [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the method steps of Chowdhary to the method steps of Angelucci which are directed towards determining breathing rate) because this creates an efficient tradeoff between the desired accuracy of respiration rate detection and the energy consumed by the sensors to achieve the desired level of accuracy (See Chowdhary, [0041]).
Regarding Claim 17, modified Angelucci discloses the method of Claim 15, wherein the activity level is selected from a set of activity levels comprising a resting activity level and a moving activity level (“ it is…determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]).
Regarding Claim 18, Angelucci discloses a system (See Fig. 1) comprising:
A wearable device (See Fig. 1); and
instructions to:
access motion data (“The sensor data are composed of three accelerometer components, three gyroscopes components and three magnetometer components and they are sent to the microcontroller with a 40 Hz rate”, [0035]) obtained by a first sensor of a wearable device from motion of a user wearing the wearable device (“the methods of the invention will be illustrated referring mainly to the particular case in which three inertial sensors (hereinafter referred to also as “sensor units”) are installed on the person's body, as shown in FIG. 1”, [0022]);
determine, from the motion data, an activity level of the user (Fig. 2A-2C; “human activity recognition may be performed by processing untreated signals (“raw data”) generated by the inertial sensor used as a reference (FIG. 2a), for example installed on the lower back of a person, or by processing untreated signals generated by the reference inertial sensor and another inertial sensor installed either on the thorax or on the abdomen (FIG. 2b) of the person, or yet by processing untreated signals generated by the reference inertial sensor, the inertial sensor installed on the thorax and the inertial sensor installed on the abdomen (FIG. 2c) of the person”, [0032]);
select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user (“it is preliminarily determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]);
passively (“The present disclosure presents an advanced prototype suitable for continuous monitoring”, [0029]; as the computer-implemented method runs continuously, it is also run at least some of the time without direct human involvement) determine, using the selected activity-based technique and the accessed motion data (“Therefore, as shown in FIG. 8, it is possible to get accurate estimations of respiratory parameters by determining first whether the person is carrying out either a static activity or a dynamic activity, then by executing the algorithm of FIG. 9 over either the first principal component or the second principal component depending on whether a static activity or a dynamic activity is in progress”, [0082]), a breathing rate of the user (“The method includes the steps of determining whether a person's activity in progress is either a static activity or a dynamical activity; and using this information for enhancing the accuracy of estimations of respiratory rate in static and dynamic activities”, Abstract).
Angelucci discloses the claimed invention except for expressly disclosing the system comprising:
one or more non-transitory computer readable storage media storing instructions; and
one or more processors coupled to the non-transitory computer readable storage media, the one or more processors being operable to execute the instructions to:
access motion data obtained by a first sensor of a wearable device from motion of a user wearing the wearable device;
select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user;
determine a quality associated with the determined breathing rate;
compare the determined quality associated with the determined breathing rate with a threshold quality; and
in response to a determination that the determined quality is not less than the threshold quality, then use the determined breathing rate as a final breathing-rate determination for the user; and
in response to a determination that the determined quality is less than the threshold quality, then: activate an inactive second sensor of the wearable device for a period of time; and
passively determine, based on data from the second sensor, a breathing rate for the user.
However, Hong teaches a system (See Fig. 2) comprising:
one or more non-transitory computer readable storage media storing instructions (“Generally speaking, the techniques and functions outlined above may be implemented in a biometric monitoring device as machine-readable instruction sets, either as software stored in memory...”, [0184]); and
one or more processors coupled to the non-transitory computer readable storage media, the one or more processors being operable to execute the instructions to (“Such instruction sets may be provided to a processor or processors of a biometric monitoring device to cause the processor or processors to control other aspects of the biometric monitoring device to provide the functionality described above”, [0184]):
access motion data obtained by a first sensor (“the input may be direct output from an accelerometer”, [0095]) of a wearable device (Element 200, Fig. 2) from motion of a user wearing the wearable device (“ each user's activities will be registered in motion signals that have different characteristics”, [0085]);
select, based on the determined activity level, an activity-based technique (“In some conditions, the BMD automatically analyses motion signal provided by a motion sensor, and automatically switches motion intensity modes, which deploy different data processing algorithms to process motion data”, [0095]) for estimating the breathing rate of the user (“The measured physiological metrics may include... respiration rate”, [0171]) from a plurality of available activity-based techniques (“the device can determine a mode of the device using the motion sensor signal strength”, [0096]), wherein each activity-based technique varies at least in a number of sensor axes of the first sensor (“In some embodiments, data from one axis, or two axes, or three axes of one or more motion sensor may be used to determine the motion intensity”, [0096]) used to estimate the breathing rate of the user ([0160], [0171]).
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 instructions of Angelucci to select, based on the determined activity level, an activity-based technique for estimating the breathing rate of the user from a plurality of available activity-based techniques, wherein each activity-based technique varies at least in a number of sensor axes of the first sensor used to estimate the breathing rate of the user as taught by Hong, because this allows for less data to be processed when signal accuracy is high (Hong, [0096]). It also would have been obvious to add the instructions of Angelucci to one or more non-transitory computer readable storage media storing instructions and add one or more processors coupled to the non-transitory computer readable storage media, the one or more processors being operable to execute the instructions to the invention of Angelucci, because all of the claimed elements were known in the prior art before the effective filing date of the claimed invention, and one with ordinary skill in the art could have combined all the claimed elements by known methods, and the result would have been obvious to one of ordinary skill in the art.
Chowdhary discloses a method comprising the steps of:
determine a quality (Step 206, Fig. 2; “The method of operation disclosed herein includes first data from a single sensor (one of 102, 103, 104, 106, 114, and 118) to compute the posterior probability of each of the context classes using windowed frames of data. Suitable logic can be used to determine the context class from these probabilities for each frame. The set of each of these probabilities is called a “posteriorgram”. The posteriorgram is used to determine a confidence measure regarding the classification result”, [0041]) associated with a determined activity classification (“The context of the electronic device 100 may be …a current method of locomotion of the user (i.e. running, walking, driving, bicycling, climbing stairs, riding a train, riding a bus)”, [0038]);
compare the determined quality associated with the determined activity classification with a threshold quality (Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold...”, [0041]); and
in response to a determination that the determined quality is not less than the threshold quality, then use the determined breathing rate as a final activity determination for the user (“No”, Step 208, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams”, [0041]); and
in response to a determination that the determined quality is less than the threshold quality (“Yes”, Step 208, Fig. 2), then:
activate an inactive second sensor of the wearable device for a period of time (Step 212, Fig. 2; “If the confidence measure is above a (fixed or adaptive) threshold, then the single sensor continues to be used to determine the posteriorgrams, otherwise another sensor (another of 102, 103, 104, 106, 114, and 118) is switched on in addition to, or instead of, the first sensor to determine the posterior probability of the context classes using multi-sensor data fusion”, [0041]); and
passively determine, based on data from the second sensor, an activity classification for the user (Step 214, Fig. 2; “Second probabilistic context is then generated at Block 214. Referring additionally to FIG. 3B, generation of the second probabilistic context at Block 206 is now described. Raw digital data is acquired from the second sensor at Block 257. This data is pre-processed and buffered into time windowed frames at Block 259. Sensor specific features are extracted from each frame at Block 261. The features include class-discriminating information contained in the data. These features are used with probabilistic classification to obtain the posterior probability output for each class...”, [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the method steps of Chowdhary to the software instructions of Angelucci (which are directed towards determining breathing rate), because this creates an efficient tradeoff between the desired accuracy of respiration rate detection and the energy consumed by the sensors to achieve the desired level of accuracy (See Chowdhary, [0041]).
Regarding Claim 20, modified Angelucci discloses the system of Claim 18, wherein the activity level is an activity level selected from a set of activity levels comprising a resting activity level and a moving activity level (“ it is…determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]).
Claims 2, 7, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Angelucci in view of Hong and Chowdhary, and further in view of Grace et al (US 20190239772 A1, hereinafter Grace).
Regarding Claim 2, modified Angelucci discloses the media of Claim 1. Modified Angelucci discloses the claimed invention except for expressly disclosing wherein the wearable device comprises a head-worn device. However, Grace, which also discloses a wearable device for sensing motion data (Abstract), teaches wherein the wearable device comprises a head-worn device (Element 200, Fig. 2; “FIG. 2 illustrates a cross section 200 of the earphone 100. The earphone 100 includes a microphone 106 and an accelerometer/gyroscope 108”, [0037]). 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 wearable device of Angelucci as taught by Grace, because this allows motion data and/or respiration rate to be determined discretely and comfortably (See [0002] of Grace).
Regarding Claim 7, modified Angelucci discloses the media of Claim 1. Modified Angelucci discloses the claimed invention except for expressly disclosing wherein the second sensor comprises a microphone. However, Grace teaches wherein the second sensor comprises a microphone (Element 106, Fig. 2). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify Angelucci such that the wearable device comprises a microphone, as taught by Grace, because this allows motion data and/or respiration rate to be determined discretely and comfortably (See [0002] of Grace).
Regarding Claim 16, modified Angelucci discloses the method of Claim 15. Modified Angelucci discloses the claimed invention except for expressly disclosing wherein the wearable device comprises a head-worn device. However, Grace, which also discloses a wearable device for sensing motion data (Abstract), teaches wherein the wearable device comprises a head-worn device (Element 200, Fig. 2; “FIG. 2 illustrates a cross section 200 of the earphone 100. The earphone 100 includes a microphone 106 and an accelerometer/gyroscope 108”, [0037]). 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 wearable device of Angelucci as taught by Grace, because this allows motion data and/or respiration rate to be determined discretely and comfortably (See [0002] of Grace).
Regarding Claim 19, modified Angelucci discloses the system of Claim 18. Modified Angelucci discloses the claimed invention except for expressly disclosing wherein the wearable device comprises a head-worn device. However, Grace, which also discloses a wearable device for sensing motion data (Abstract), teaches wherein the wearable device comprises a head-worn device (Element 200, Fig. 2; “FIG. 2 illustrates a cross section 200 of the earphone 100. The earphone 100 includes a microphone 106 and an accelerometer/gyroscope 108”, [0037]). 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 wearable device of Angelucci as taught by Grace, because this allows motion data and/or respiration rate to be determined discretely and comfortably (See [0002] of Grace).
Claims 4 and 6 are rejected under 35 U.S.C. 103 as being unpatentable over Angelucci in view of Hong and Chowdhary, and further in view of Rahman et al (US 20210386318 A1, hereinafter Rahman).
Regarding Claim 4, modified Angelucci discloses the media of Claim 3, further coupled to one or more processors that are operable to execute the instructions to determine, using a resting activity-based technique associated with the resting activity level, the breathing rate of the user (“it is possible to get accurate estimations of respiratory parameters by determining first whether the person is carrying out either a static activity or a dynamic activity, then by executing the algorithm of FIG. 9 over either the first principal component or the second principal component depending on whether a static activity or a dynamic activity is in progress”, [0082]). Modified Angelucci discloses the claimed invention except for expressly disclosing these instructions to be done by:
applying a sliding window to the accessed motion data;
selecting, from the accessed motion data, motion data corresponding to a particular sensor axis;
filtering the selected motion data corresponding to the particular sensor axis; and
estimating, based on the filtered motion data, a breathing rate for the user.
However, Rahman, which also comprises one or more processors that are operable to execute the instructions to (“a computer program product includes a computer readable storage medium having instructions stored thereon. The instructions are executable by a processor to initiate operations”, 2:20-23) determine the breathing rate of a user (“Another example biomarker is respiratory or breathing rate, which is the rate at which breathing occurs, typically measured as breaths (breathing cycles) per minute (BPM)”, 4:19-22), teaches the instructions to be done by:
applying a sliding window to the accessed motion data (“Using a sliding window of the same duration (e.g., one minute) based on the waveform generated by the second device, signal synchronizer 110 iteratively advances the window in smaller time increments (e.g., 100 milliseconds) relative to the waveform generated by the first device. Starting in the temporal vicinity of the start time of the first device's waveform, signal synchronizer 110 advance the sliding window until a best or optimal match is found, thereby aligning the waveforms”, [0060]);
selecting, from the accessed motion data (Step 402, Fig. 4), motion data corresponding to a particular sensor axis (Step 404, “At block 404, the system selects, using signal processing performed by a signal processor embedded in the portable device, a preferred axis of measurement from among the multiple axes”, [0077]);
filtering the selected motion data corresponding to the particular sensor axis (Step 406, Fig. 4; “The system at block 406 filters, with the signal processor, waveforms generated by selected signals selected from among the plurality of signals, the selected signals corresponding to signals generated in response to movements of the user's body measured along the preferred axis of measurement”, [0077]); and
estimating, based on the filtered motion data, a breathing rate for the user (Step 408, Fig. 4; “At block 408, the system extracts from the waveforms, using the signal processor, one or more biomarkers corresponding to the lung activity”, [0078]; “Extracted biomarkers can include respiration rate…”, [0043]). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the additional instructions of Rahman to the instructions of Angelucci such that only data from a particular axis is processed, because allows the processor to process only the data most relevant to the parameter of interest.
Regarding Claim 6, modified Angelucci discloses the media of Claim 3, further coupled to one or more processors that are operable to execute the instructions to determine, using a moving activity-based technique associated with the moving activity level, the breathing rate of the user (“ it is preliminarily determined which activity is in progress without calculating a respiratory rate of the person, then the filtering is carried out with a filter corresponding to the determined activity, for example with a filter having a threshold frequency fthreshm=1 Hz when the patient is performing a static activity, or with a threshold frequency fthreshm=0.75 Hz when the patient is cycling or walking, or with a threshold frequency fthreshm=1.4 Hz when the patient is running”, [0046]). Modified Angelucci discloses the claimed invention except for expressly disclosing determining this breathing rate by segmenting the accessed motion data into motion segments, each motion segment corresponding to a breathing cycle of the user. However, Rahman teaches determining a breathing rate (“Biomarker extractor 106 extracts biomarkers from the selected signals. Extracted biomarkers can include respiration rate…”, [0043]) by segmenting the accessed motion data into motion segments, each motion segment corresponding to a breathing cycle of the user (“Signal filter 104 determines the start and end of the user's respiratory cycle by identifying signal waveform valleys”, [0041]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the additional instructions of Rahman to the instructions of Angelucci such that breathing rate is determined from motion segments corresponding to breathing cycles, because allows the processor to process only the data most relevant to the parameter of interest.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Angelucci in view of Hong, Chowdhary and Rahman, and further in view of Chan et al (TW I674882 B, hereinafter Chan; a machine translation was relied upon for this rejection).
Regarding Claim 5, modified Angelucci discloses the media of Claim 4. Modified Angelucci discloses the claimed invention except for expressly disclosing the media further coupled to one or more processors that are operable to execute the instructions to:
generate a plurality of breathing-rate estimates from a plurality of different breathing-rate algorithms; and
estimate the breathing rate for the user by interpolating the plurality of breathing-rate estimates.
However, Chan teaches instructions to:
generate a plurality of breathing-rate estimates (“generate one or more breathing rates”, Abstract) from a plurality of different breathing-rate algorithms (“d) the decision module analyzes the signal quality of the pulse oximetry signal to determine the breathing rate and learning module generated by using different strategies based on changing values Learning the breathing rate in order to generate a breathing rate”, page 2 of attached machine translation); and
estimate the breathing rate for the user by interpolating the plurality of breathing-rate estimates (“For better or worse, or the consistency of the breathing rate, different strategies are used to fuse one or more breathing rates and learn the predicted rate to generate a breathing rate signal”, Abstract). It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to add the instructions of Chan to the media of modified Angelucci, because this makes the breathing rate calculation more accurate.
Examiner’s Note
The Examiner notes the following claims are not currently rejected under prior art:
8. The media of Claim 7, further coupled to one or more processors that are operable to execute the instructions to determine a quality of the breathing rate determined based on data from the microphone, wherein the quality is based at least on (1) a noise-to-breathing ratio associated with the data from the microphone, and (2) a number of breathing clusters identified in the data from the microphone.
9. The media of Claim 7, further coupled to one or more processors that are operable to execute the instructions to: classify data from the microphone using a set of classes comprising a breathing class and a transition class; cluster the classified data into a plurality of clusters; and determine the breathing rate of the used based on a number of clusters having the transition class label.
10. The media of Claim 1, further coupled to one or more processors that are operable to execute the instructions to:
determine whether any activity-based technique for estimating the breathing rate of the user corresponds to the determined activity level; and
when no activity-based technique for estimating the breathing rate of the user corresponds to the determined activity level, then activate the second sensor of the wearable device
11. The media of Claim 1, further coupled to one or more processors that are operable to execute the instructions to, in response to a determination that the determined quality is less than the threshold quality, determine whether to activate the second sensor based on one or more of:
an amount of elapsed time since the second sensor was last activated; or
an amount of elapsed time since the user's breathing rate was last validly determined.
13. The media of Claim 1, wherein the second sensor is one of a plurality of second sensors, wherein each of the plurality of second sensors is ranked based at least on that sensor's power consumption and detection accuracy.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
See Yuen et al (US 20170035327 A1; [0220]).
See Koroku (US 20190183389 A1), which also activating an inactive sensor in response to a determination that the determined quality is less than the threshold quality (Fig. 6).
See Katingari et al (US 11479459 B2), relevant to Claims 1, 15, and 18.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN EPHRAIM COOPER whose telephone number is (571)272-2860. The examiner can normally be reached Monday-Friday 7:30AM-5:30PM EST.
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/JONATHAN E. COOPER/Examiner, Art Unit 3791
/JACQUELINE CHENG/Supervisory Patent Examiner, Art Unit 3791
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