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 .
DETAILED ACTION
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 4-11, 13, 15, and 18-21 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
With regards to Claims 1 and 13, the feature “wherein the sleeping monitoring device is used by at least two users” is not discloses in the Specification. While specification discloses a plurality of users, there is no specific “at least two user” requirement as argued (Applicant Arguments/Remarks, p.20). Paragraph [0118] refers to multiple users but not necessarily to “at least two users” as claimed and argued.
In that regard, the Examiner notes that Zhang, for example, discloses the feature similar to the Specification language (The devices may be associated with one or more users, with associated settings [0078]).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 4-11, 13, 15, and 18-21 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to Claims 1 and 13, the limitation “… dividing the respiration feature and the heartbeat feature, to obtain sub-stage-feature sets corresponding to the sleeping sub-stages” is indefinite as it is unclear what is the meaning of this limitation. The preceding limitation of “wherein the sleeping feature comprises at least: a respiration feature and a heartbeat feature” refers to only one feature but not both to then “divide” corresponding sets.
For the purpose of a compact prosecution, the Examiner treated this rejected limitation as “obtain(ing) sub-stage-feature sets corresponding to the sleeping sub-stages” without the “dividing” feature.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-11, 13, 15, and 18-21 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.
Specifically, representative Claim 1 recites:
“A method for processing sleeping data, wherein the method comprises:
collecting sleeping data of each user in advance to extract standard features as reference, and establishing and storing an incidence relation between user identity information and the standard features;
acquiring sleeping data collected by a sleeping monitoring device, wherein the sleeping monitoring device is used by at least two users;
extracting a sleeping feature in the sleeping data, comprising:
by using sleeping algorithms corresponding to sleeping sub-stages, acquiring sleeping datasets individually matching with the sleeping sub-stages in the sleeping data, and a sleeping-period time sequence of the sleeping data; and according to the sleeping-period time sequence and the sleeping datasets, acquiring the sleeping feature;
wherein the sleeping feature comprises at least: a respiration feature and a heartbeat feature;
inquiring an associated standard feature according to the user identity information;
according to the sleeping-period time sequence, dividing the respiration feature and the heartbeat feature, to obtain sub-stage-feature sets corresponding to the sleeping sub-stages;
comparing each of the sub-stage-feature sets with the associated standard feature, to obtain feature similarities corresponding to the sleeping sub-stages; and
by using weight values corresponding to the sleeping sub-stages, integrating the feature similarities, to obtain the comprehensive feature similarity; wherein the comprehensive feature similarity is used to reflect an overall similarity of features; and
on the condition that the comprehensive feature similarity satisfies a similarity requirement, attributing the sleeping feature to a target sleeping feature of the user, to determine an attributed user of the sleeping data.”
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements”.
Under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (process).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea).
In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, steps of “extracting a sleeping feature in the sleeping data, comprising: by using sleeping algorithms corresponding to sleeping sub-stages, acquiring sleeping datasets individually matching with the sleeping sub-stages in the sleeping data, and a sleeping-period time sequence of the sleeping data; and according to the sleeping-period time sequence and the sleeping datasets, acquiring the sleeping feature; wherein the sleeping feature comprises at least: a respiration feature and a heartbeat feature; according to the sleeping-period time sequence, dividing the respiration feature and the heartbeat feature, to obtain sub-stage-feature sets corresponding to the sleeping sub-stages; comparing each of the sub-stage-feature sets with the associated standard feature, to obtain feature similarities corresponding to the sleeping sub-stages; and by using weight values corresponding to the sleeping sub-stages, integrating the feature similarities, to obtain the comprehensive feature similarity; wherein the comprehensive feature similarity is used to reflect an overall similarity of features” are treated as belonging to the mathematical concepts grouping while the steps of “establishing … an incidence relation between user identity information and the standard features”, “inquiring an associated standard feature according to the user identity information”, and “on the condition that the comprehensive feature similarity satisfies a similarity requirement, attributing the sleeping feature to a target sleeping feature of the user, to determine an attributed user of the sleeping data” are treated as belonging to mental process grouping. These mental steps represent a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind. For example, the step of “establishing … an incidence relation between user identity information and the standard features” and “on the condition that the comprehensive feature similarity satisfies a similarity requirement, attributing the sleeping feature to a target sleeping feature of the user” in the context of this claim, encompasses a user manually observing/evaluating/matching a particular relation between user identity information and standard features (see instant application [0157], as published) while the step of attributing the sleeping feature to a target sleeping feature of the user making a selection corresponds to making a judgement by the user based on observed similarity value and a configured (known) corresponding threshold. The above two steps, under the BRI, alternatively/additionally is also treated as mathematical relationship step as indicated above (MPEP 2106.04.II: “construing the claims in accordance with their broadest reasonable interpretation”).
The step of “inquiring an associated standard feature according to the user identity information” represent a combination of mere data gathering (insignificant extra-solution activity) and a mental process step (“according to the user identity information”) that in the context of the claim, corresponds to data observation and manual selection of standard features corresponding to a particular user (“judgement” step).
Similar limitations comprise the abstract ideas of Claim 13.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
The above claims comprise the following additional elements:
In Claim 1: A method for processing sleeping data, wherein the method comprises: collecting sleeping data of each user in advance …; storing an incidence relation between user identity information and the standard features; acquiring sleeping data collected by a sleeping monitoring device, wherein the sleeping monitoring device is used by at least two users; inquiring an associated standard feature;
In Claim 13 : A computing and processing device, wherein the computing and processing device comprises: a memory storing a computer-readable code; and one or more processors, wherein when the computer-readable code is executed by the one or more processors, the computing and processing device implements the operations comprise: collecting sleeping data of each user in advance…, and … storing an incidence relation between user identity information and the standard features; acquiring sleeping data collected by a sleeping monitoring device, wherein the sleeping monitoring device is used by at least two users; inquiring an associated standard feature.
The additional elements in the preambles are recited in generality and represent insignificant extra-solution activity (field-of-use limitations) that is not meaningful to indicate a practical application.
The additional elements in the claims such as a memory storing a computer-readable code; and one or more processors, wherein when the computer-readable code is executed by the one or more processors, the computing and processing device implements the operations (Claim 13) and a step of storing an incidence relation between user identity information and the standard features (Claims 1 and 13) are examples of generic computer equipment (components) that are generally recited and, therefore, are not qualified as particular machines. The limitations that generically recite acquiring sleeping data collected by a sleeping monitoring device and inquiring an associated standard feature according to the user identity information (Claims 1 and 13) represent insignificant represent extra-solution activity of mere data gathering to the judicial exception. According to the October update on 2019 SME Guidance such steps are “performed in order to gather data for the mental analysis step, and is a necessary precursor for all uses of the recited exception. It is thus extra-solution activity, and does not integrate the judicial exception into a practical application”.
The limitation of “wherein the sleeping monitoring device is used by at least two users” represents insignificant extra-solution activity to the judicial exception directed to obtain the comprehensive feature similarity for multiple users.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
However, the above claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B analysis) because these additional elements/steps are well-understood and conventional in the relevant art based on the prior art of record.
The independent claims, therefore, are not patent eligible.
With regards to the dependent claims, claims 4-11, 15, and 18-21 provide additional features/steps which are part of an expanded abstract idea of the independent claims (additionally comprising abstract idea steps) and, therefore, these claims are not eligible without additional elements that reflect a practical application and qualified for significantly more for substantially similar reasons as discussed with regards to Claim 1.
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 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 1, 4, 6, 7, 13, 15, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Feng Zhang et al. (US 20200397365), hereinafter ‘Zhang’ in view of Hisham Alshaer (US 20140188006), hereinafter ‘Alshaer’, in further view of Redmond Shouldice et al. (CN 108474841), hereinafter ‘Shouldice’.
With regards to Claim 1, Zhang discloses
A method for processing sleeping data (Methods, apparatus and systems for wireless sleep monitoring, Abstract), wherein the method comprises:
collecting sleeping data of each user in advance to extract standard features as reference; establishing and storing relation between user identity information and the standard features (a system for monitoring a sleep motion of a user in a venue is described. The system comprises: at least one sensor in the venue, wherein the at least one sensor comprises a wireless non-contact sensor having no physical contact with the user; a processor communicatively coupled to the at least one sensor; a memory communicatively coupled to the processor; and a set of instructions stored in the memory. The set of instructions, when executed by the processor, causes the processor to perform: obtaining, based on the at least one sensor, a plurality of time series of sensing features (TSSF) associated with the sleep motion of the user in the venue, and monitoring the sleep motion of the user jointly based on the plurality of TSSF [0048]; Participants willing to collect PSG data are dressed with a number of contact sensors that record breathing and sleeping data. During sleep, these sensors and the disclosed system are simultaneously recording measurements. In total, one can have five nights of PSG data. The PSG data (mainly EEG) are then annotated with different sleep stages (mainly wake, REM, and NREM), according to the AASM specification [0274]; A database (e.g. in local server, hub device, cloud server, storage network) may be used to store the TSCI, characteristics, STI, signatures, patterns, behaviors, trends, parameters, analytics, output responses, identification information, user information [0071]; information of the user, location/speed/acceleration/direction/motion/gesture/gesture control/motion trace of the user, ID or identifier of the user, activity of the user, state of the user, sleeping/resting characteristics of the user, emotional state of the user, vital sign of the user [0178]);
acquiring sleeping data collected by a sleeping monitoring device (sleep monitoring system comprises: at least one sensor in a venue, Abstract; FIG. 4 illustrates an exemplary breathing signal based on real-world measurements, according to one embodiment of the present teaching [0057]; 1102, Fig.11), wherein the sleeping monitoring device is used by at least two users (The devices may be associated with one or more users [0078]),
extracting a sleeping feature in the sleeping data (FIG. 3 illustrates an exemplary scheme for breathing signal extraction and maximization [0056]);
performing similarity comparison to obtain a comprehensive feature similarity (A similarity score and/or component similarity score may be computed … based on a pair of temporally adjacent CI of a TSCI. The pair may come from the same sliding window or two different sliding windows. The similarity score may also be based on a pair of, temporally adjacent or not so adjacent, CI from two different TSCI. …. The characteristics and/or STI may be determined/computed based on the similarity score [0165]; Although it does not make much sense to compare the sleep score among different users, the trend or history of the sleep score for a particular user would reflect the changes of his/her sleep quality [0268]); and on the condition that the comprehensive feature similarity satisfies a similarity requirement, using the sleeping feature as a target sleeping feature of the user (FIG. 10 illustrates another exemplary algorithm design for sleep monitoring, according to one embodiment of the present teaching. As shown in FIG. 10, a CSI 1002 of a multipath channel can be used to generate power responses 1004. The multipath channel is impacted by a sleep motion, e.g. heartbeat or breathing, of a user whose sleep is to be monitored. The autocorrelation function (ACF) can be calculated at operation 1008 based on the power responses, according to methods discussed before. In this embodiment, based on the ACF, breathing signal is maximized at operation 1010, and breathing detection and estimation are performed at operation 1012 based on the maximization. This can generate an estimated breathing rate in real-time, which can be utilized for sleep monitoring. For example, at operation 1022, it can be detected, based on the estimated breathing rate of the user, that whether the user is asleep or awake, e.g. using a data-trained awake/asleep classifier 922 as shown in FIG. 9. [0271]; The PSG data (mainly EEG) are then annotated with different sleep stages (mainly wake, REM, and NREM), according to the AASM specification [0274], i.e. “the condition that the comprehensive feature similarity satisfies a similarity requirement”, emphasis added; Fig. 9, “Awake”, “REM”, etc.; [0291]); and attributing user of particular data based on sleeping features (the trend or history of the sleep score for a particular user would reflect the changes of his/her sleep quality [0268]; monitor the sleep motion of the user jointly by recognizing jointly the sleep stage as at least one of: REM, NREM, light sleep, deep sleep, sleep apnea, insomnia, hypersomnia, parasomnia, sleep disruption, nightmare, sleep walking, toss-and-turn, a sleep problem, a sleep condition, or a sleep behavior, based on the at least one detrended statistics [0305]).
Zhang also discloses extracting standard features as reference (computing a sleep analytics based on … periods of breathing problems, sleep quality score, daytime sleep, time periods of daytime sleep, total duration of daytime sleep, number of period of daytime sleep, average duration of period of daytime sleep, a trend, a daily trend, a weekly trend, a monthly trend, a yearly trend, a repeating trend, a summary, and a history [0297], i.e. “extracting standard feature as a reference”, emphasis added.
Zhang additionally discloses using sleeping algorithms corresponding to sleeping sub-stages (recognize sleep stages and assess the otherwise elusive sleep quality [0221]; Sleep Stage Recognition [0258]; FIG. 9 illustrates an exemplary algorithm design for sleep monitoring [0270]; different sleep stages (mainly wake, REM, and NREM), according to the AASM specification [0274]; Figs. 9-11); and according to the sleeping-period time sequence and the sleeping datasets, acquiring the sleeping feature (A database (e.g. in local server, hub device, cloud server, storage network) may be used to store the TSCI, characteristics, STI, signatures, patterns, behaviors, trends, parameters, analytics, output responses, identification information, user information, device information, channel information, venue (e.g. map, environmental model, network, proximity devices/networks) information, task information, class/category information, presentation (e.g. UI) information, and/or other information [0071]; recognizes different sleep stages, e.g., wake, REM (Rapid Eye Movement) and NREM (Non-REM), and accordingly assesses an individual's sleep quality [0046]; a percentage of time that the periodicity feature is non-zero, a percentage of time that the periodicity feature is in a default range, a percentage of time that a breathing rate is non-zero, a percentage of time that the breathing rate is in a default range, a percentage of time that a heartbeat is non-zero, and a percentage of time that the heartbeat is in a default range; and monitor the sleep motion of the user jointly by recognizing a sleep state jointly as either ASLEEP or AWAKE based on the motion statistics, and the periodicity statistics [0299]; also [0284]).
Zhang additionally discloses the sleeping feature comprises at least: a respiration feature and a heartbeat feature (As shown in FIG. 10, a CSI 1002 of a multipath channel can be used to generate power responses 1004. The multipath channel is impacted by a sleep motion, e.g. heartbeat or breathing, of a user whose sleep is to be monitored. The autocorrelation function (ACF) can be calculated at operation 1008 based on the power responses, according to methods discussed before. In this embodiment, based on the ACF, breathing signal is maximized at operation 1010, and breathing detection and estimation are performed at operation 1012 based on the maximization. This can generate an estimated breathing rate in real-time, which can be utilized for sleep monitoring. For example, at operation 1022, it can be detected, based on the estimated breathing rate of the user, that whether the user is asleep or awake, e.g. using a data-trained awake/asleep classifier 922 as shown in FIG. 9 [0271]); and the step of comparing each of the sub-stage-feature sets with the associated standard feature (Fig.9; [0167, 0174, 0210-0211, 0214, 0225, 0259, 0270, 0284].
Zhang also discloses that obtaining the comprehensive feature similarity comprises: according to the sleeping-period time sequence, dividing the respiration feature and the heartbeat feature, to obtain sub-stage-feature sets corresponding to the sleeping sub-stages (Fig. 9; [0284]).
Zhang also discloses using weight values corresponding to the sleeping sub-stages, integrating the feature similarities, to obtain the comprehensive feature similarity (The method of the sleep monitoring system of clause 11, further comprising: computing at least one detrended statistics based on the time series of detrended function of the second TSSF in a respective sliding time window, wherein the at least one detrended statistics comprises: a mean, weighted mean, variance, standard deviation, variation, derivative, slope, total variation, absolute variation, square variation, spread, dispersion, variability, deviation, absolute deviation, square deviation, total deviation, divergence, range, interquartile range, skewness, kurtosis, L-moment, coefficient of variation, quartile coefficient of dispersion, mean absolute difference, Gini coefficient, relative mean difference, median absolute deviation, average absolute deviation, distance standard deviation, coefficient of dispersion, entropy, variance-to-mean ratio, maximum-to-minimum ratio, variation measure, regularity measure, similarity measure, likelihood, probability distribution function, sample distribution, moment generating function, expected value, and expected function; and recognizing jointly the sleep stage as at least one of: REM, NREM, light sleep, deep sleep, sleep apnea, insomnia, hypersomnia, parasomnia, sleep disruption, nightmare, sleep walking, toss-and-turn, a sleep problem, a sleep condition, or a sleep behavior, based on the at least one detrended statistics [0289]).
However, Zhang does not specifically disclose obtaining feature similarities corresponding to the sleeping sub-stages.
Alshaer discloses obtaining feature similarities corresponding to the sleeping sub-stages by performing similarity comparison to a standard feature (the system can be configured to automatically assess the amplitude pattern at or around a given event in comparison with such previously identified patterns to automatically classify the event as indicative of CSA or OSA [0096]; [0177-0178]; the implementation phase 2204 of process 2200 may be applied to newly acquired breath sound data, which in the context of process 600, has already been processed to extract the EE of respective events of interest 2220. At step 2222, the RE and FE of each event is isolated and compared at step 2224 (e.g. via DTW) to output a similarity index to be associated with each event. The output similarity index(es) may then be compared at step 2226 with the classification criteria 2218 set therefor (e.g. either individually or as a group by way of a computed similarity index mean or distribution), the result of which comparison leading to an indication of possible OSA 2228 or CSA 2230 [0179]; In one embodiment, a comparative process may thus be implemented to automatically classify a pitch contour derived (e.g. via RAPT) from recorded breath sounds as indicative of a stable (normal) or collapsible (obstructive) airway, and thus usable in classifying a candidate's condition as CSA (or normal) vs. OSA. [0200]; compare aperiodic signatures identified in respect of a subject's breathing sound recording with preset signatures so to classify the newly acquired signatures as indicative of UA narrowing [0214]; the recorded breath sounds are first processed (e.g. via LPC) so to generate extractable features 3026 (e.g. one or more LPC coefficients) to be compared by classifier 3028 with the preset UA narrowing classification criteria 3020, in classifying the processed event as indicative of an open or narrowed airway [0240]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer to compare each of the sub-stage-feature sets with the associated standard feature, to obtain feature similarities corresponding to the sleeping sub-stages of a particular user in order to correctly classify sleep condition of that user (a comparative process may thus be implemented to automatically classify a pitch contour derived (e.g. via RAPT) from recorded breath sounds as indicative of a stable (normal) or collapsible (obstructive) airway, and thus usable in classifying a candidate's condition as CSA (or normal) vs. OSA, Alshaer [0200]).
Zhang does not specifically disclose individually matching with the sleeping sub-stages in the sleeping data, and a sleeping-period time sequence of the sleeping data.
Shouldice discloses individually matching with the sleeping sub-stages in the sleeping data, and a sleeping-period time sequence of the sleeping data (FIG. 20 shows a process for identifying two (or more) users in a sleep (or awake) environment thereby comparing sensor data to check that the detected user is an exemplary classification system of an intended user at the side of the bed, p.8; Although there is no single parameter can allow distinguishing the user (e.g., the average heart rate or average respiration rate in the static or specific sleep stage such as deep sleep), but more advanced system can combine a plurality of characteristics and thus preferred early integration mode, so as to send the characteristic group to the classifier. If the training data (mark data) is available, it can adopt supervised classification system, thereby providing a large training data set to the system to generate model parameters. Feedback from the first used data (day or night signal) meaning updating the user specific classifier, so that the biological characteristic " fingerprint " is increased by the user specific accuracy. This can be realized by the registration step, so as to store the sample obtaining template in the database. Subsequently, the matching step is performed to confirm the identity. in the absence of such detailed training data, p.18).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, and Shouldice to individually match with the sleeping sub-stages in the sleeping data, and a sleeping-period time sequence of the sleeping data to accurately identify/recognize sleep conditions because different substages may occur during different sleeping periods and/or substages of sleep (The algorithm can be based on the analysis of one or more data source classification system, and can utilize history data stored in the database, p.12; the Matching step is performed to confirm the identity, p.18, Shouldice).
Zhang does not specifically disclose an incidence relation between user identity information and the standard features and that on the condition that the comprehensive feature similarity satisfies a similarity requirement, attributing the sleeping feature to a target sleeping feature of the user, to determine an attributed user of the sleeping data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, and Shouldice to establish/store an incidence relation between user identity information and the standard features to properly relate user identity to standard features and use comprehensive feature similarity based on comparing the relevant standard features with corresponding sub-stage datasets in identifying/distinguish a particular (target) user among two users to accurately process user-sleeping data.
With regards to Claim 4, Zhang additionally discloses preprocessing data [0152] that includes denoising [0165], i.e. filtering “ineffective” data from the sleeping data.
Zhang additionally discloses the ineffective-data (requirement) comprises at least one of an ineffective-data-format requirement and an ineffective-data-valuing requirement (some components with larger noise or lower signal quality metric may have smaller or bigger weight [0171]; The quantity may be compared with a reference data [0174]; The second challenge is low breathing SNR. Breathing signals (i.e., the signals modulated on the received signals by breathing) are typically very weak and would easily fade out when propagating a long distance [0229]).
However, Zhang does not specifically disclose before the step of extracting the sleeping feature in the sleeping data, the method further comprises: filtering data.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, and Shouldice to filter (ineffective) data such as noise data before extracting useful signals to obtain/process to improve accuracy of sleep monitoring to maximize the breathing SNR, making accurate and instantaneous breathing estimation and further sleep staging possible [0230], Zhang.
With regards to Claim 6, Zhang additionally discloses receiving heartbeat messages periodically reported by the sleeping monitoring device [0132, 0141]; extracting a device state; on the condition that the device state is an operating state, and receiving the sleeping data that are sent by the sleeping monitoring device [0141, 0086, 0090, 0132, 0143, 0146, 0270, 0271].
However, Zhang does not specifically disclose extracting a device state in the heartbeat messages and sending a data acquiring request to the sleeping monitoring device.
Shouldice discloses sending a message to an external device, p.45.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, and Shouldice to extract a device state in the same heartbeat-related messages to reflect operational state of the device to be able to send a data acquiring request upon confirming the operation state to the sleeping monitoring device.
With regards to Claim 7, Zhang additionally discloses acquiring a current time from a time calibrating server, to perform clock synchronization with the sleeping monitoring device [0068, 0076, 0101, 0141].
However, Zhang is silent about time calibrating server.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, and Shouldice to use a time calibrating computer (server) to synchronize clocks between the device and sleep monitoring device (The multiple Type 1 devices/Type 2 devices may be synchronized and/or asynchronous, with same/different window width/size and/or time shift, same/different synchronized start time, synchronized end time, etc. [0076]: The time shift may be changed automatically (e.g. as controlled by processor/computer/server/hub device/cloud server) and/or adaptively (and/or dynamically [0158], Zhang).
With regards to Claims 13 and 15, Zhang in view of Alshaer, and Shouldice discloses the claim limitations as discussed above with regards to Claim 1.
In addition, Zhang discloses the computer program comprises a computer-readable code, and when the computer-readable code is executed in a computing and processing device, the computer-readable code causes the computing and processing device to implement the method for processing sleeping data [0308] and so does Alshaer [0022].
With regards to Claim 18, Zhang in view of Alshaer, and Shouldice discloses the claim limitations as discussed above with regards to Claims 13 and 4.
Claim 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Alshaer, Shouldice, and further in view of Yibing Wu (US 20160081575) hereinafter ‘Wu’.
With regards to Claim 5, Zhang in view of Alshaer, and Shouldice discloses the invention as discussed in Claim 2.
Zhang additionally discloses the sleeping feature comprises at least: a sleeping quality (monitor quality of sleep and any sleep apnea [0186]) as well as sleeping-period time sequence, sleeping datasets as discussed above, a sleeping duration [0178] and a sleeping efficiency/quality (Figs.9-10).
However, Zhang does not specifically disclose acquiring the sleeping feature comprises: according to the sleeping datasets and the sleeping-period time sequence, acquiring an apnea-hypopnea index, an awakening time quantity, a falling-asleep duration, a sleeping duration and a sleeping efficiency and integrating the apnea-hypopnea index, the awakening time quantity, the falling-asleep duration, the sleeping duration and the sleeping efficiency, to obtain the sleeping quality.
Wu discloses acquiring an apnea-hypopnea index (The Apnea-Hypopnea Index [0026]), an awakening time quantity [0133], a falling-asleep duration [0121, 0123], a sleeping duration [0092, 0096, 0118] and a sleeping efficiency [0044, 0131, 0134].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, Shouldice, and Wu to acquire a number of parameters related to sleep monitoring (Wu [0131]) and to integrate them to arrive at the sleep quality as discussed in Wu (determining sleep-related parameters or generating reports indicative of sleep quality [0069]).
With regards to Claim 19, Zhang in view of Alshaer, Shouldice, and Wu discloses the claim limitations as discussed above with regards to Claims 13 and 5.
Claim 8-11 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang in view of Alshaer, Shouldice, and Michael Wren (US 20220339380) hereinafter ‘Wren’.
With regards to Claim 8, Zhang in view of Alshaer, and Shouldice discloses the invention as discussed in Claim 1.
However, Zhang does not specifically disclose wherein after the step of using the sleeping feature as the target sleeping feature of the user, the method further comprises: extracting, from a sleeping-suggestion information base, a target sleeping-suggestion information that matches with the target sleeping feature and a user information, and according to the target sleeping feature, generating a sleeping view; and sending to a client a sleeping report that is formed by the sleeping view and the target sleeping-suggestion information, so that the client exhibits the sleeping report.
Wren discloses extracting, from a sleeping-suggestion information base, a target sleeping-suggestion information that matches with the target sleeping feature and a user information, and according to the target sleeping feature, generating a sleeping view; and sending to a client a sleeping report that is formed by the sleeping view and the target sleeping-suggestion information (Storing the generated report in the memory device 114 allows the system 100 to compare reports over the course of a plurality of sleep sessions to determine trends and provide recommendations and/or predictions, as described further herein [0090]; the generated report includes a sleep score or metric indicative of the quality of sleep. In such implementations, the generated report can also include a recommendation regarding an adjustment to one or more of the sleep habits described above to aid in improving the quality of the sleep score. For example, the recommendation can indicate to the user to modify the time that the user goes to bed and increase the duration of the sleep session to improve the sleep score. The report can further include a predicted quantitative improvement in the quality of the sleep score or metric corresponding to the user implementing the recommended adjustment to the one or more sleeping habits. For example, if the sleep score in the report is indicative of low quality sleep, the report can recommend increasing the sleep session duration (e.g., from 5 hours to 7 hours) and increasing the amount of time the user wear the mask 124 (e.g., from 50% of the sleep session at least 90% of the sleep session) and predict the quantitative improvement in the sleep score (e.g., an increase from a score of 50 to a score of 90). The quantitative prediction can be determined based on, for example, previously generated reports stored in the memory device 114 of the control system 110 [0094]; the report generated during step 405 can include a recommendation regarding an adjustment to one or more sleeping habits of the user 210 of the respiratory system 120 to aid in improving the quality of sleep metric for the bed partner 220. For example, the report can include a recommendation to increase or decrease in an average amount of time of use of the respiratory device 122 by the user 210 per sleep session [0101]; also [0096]; Sleeping view, 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 modify Zhang in view of Alshaer, Shouldice, and Wren to extract, from a sleeping-suggestion information base, a target sleeping-suggestion information that matches with the target sleeping feature and a user information, and according to the target sleeping feature, generate a sleeping view; and send to a client a sleeping report that is formed by the sleeping view and the target sleeping-suggestion information, so that the client improves sleep habits using the sleeping report.
With regards to Claim 9, Zhang in view of Alshaer, Shouldice, and Wren discloses the invention as discussed in Claim 8.
However, Zhang does not specifically disclose combining the sleeping view in a preset time period with the target sleeping-suggestion information, to obtain the sleeping report corresponding to the preset time period.
Wren discloses the sleeping view in a preset time period with the target sleeping-suggestion information as discussed in Claim 8.
Wren also discloses sleep periods [0089].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, Shouldice, and Wren to combine the sleeping view in a particular, preset time period with the target sleeping-suggestion information relevant to that period, to obtain the sleeping report corresponding to the preset time period being of interest of user as sleep parameters change during each state of sleep (Wren [0044]).
With regards to Claim 10, Zhang in view of Alshaer, Shouldice, and Wren discloses the invention as discussed in Claim 9.
However, Zhang does not specifically disclose wherein the step of combining the sleeping view in the preset time period with the target sleeping-suggestion information, to obtain the sleeping report corresponding to the preset time period comprises: according to an operation parameter of the sleeping monitoring device, generating an operation-indicator information; and combining the sleeping view in the preset time period, the target sleeping-suggestion information and the operation-indicator information, to obtain the sleeping report corresponding to the preset time period.
Wren discloses the step of combining the sleeping view in the preset time period with the target sleeping-suggestion information, to obtain the sleeping report corresponding to the preset time period as discussed in Claim 9.
Wren also discloses generating an operation-indicator information (The camera 150 outputs image data reproducible as one or more images (e.g., still images, video images, thermal images, or a combination thereof) that can be stored in the memory device 114. The image data from the camera 150 can be used by the control system 110 to determine one or more of the sleep-related parameters described herein. For example, the image data from the camera 150 can be used to identify a location of the user, to determine a time when the user 210 enters the bed 230 (FIG. 2), and to determine a time when the user 210 exits the bed 230 [0055]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, Shouldice, and Wren to generate an operation-indicator information; and combine the sleeping view in the preset time period, the target sleeping-suggestion information and the operation-indicator information, to obtain the sleeping report corresponding to the preset time period that would objectively inform about implementation of the recommendations/sleeping suggestions.
With regards to Claim 11, Zhang in view of Alshaer, Shouldice, and Wren discloses the invention as discussed in Claim 8.
In addition, Zhang in view of Alshaer, Shouldice, and Wren discloses the claimed limitations as discussed above with regards to Claims 9 and 10.
However, Zhang does not specifically disclose extracting, from the sleeping-suggestion information, the target sleeping-suggestion information that satisfies a user-configuration type, wherein the user-configuration type comprises at least one of an audio type, a video type and a text type.
Wren further discloses a user-configuration type, wherein the user-configuration type comprises at least one of an audio type, a video type and a text type (The display device 176 is generally used to display image(s) including still images, video images, or both. In some implementations, the display device 176 acts as a human-machine interface (HMI) that includes a graphic user interface (GUI) configured to display the image(s) and an input interface. The display device 176 can be an LED display, an OLED display, an LCD display, or the like. The input interface can be, for example, a touchscreen or touch-sensitive substrate, a mouse, a keyboard, or any sensor system configured to sense inputs made by a human user interacting with the external device 170 [0066]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Zhang in view of Alshaer, Shouldice, and Wren to extract, from the sleeping-suggestion information, the target sleeping-suggestion information that satisfies a user-configuration type, wherein the user-configuration type comprises at least one of an audio type, a video type and a text type available to the user via a graphical user interface as known in the art.
With regards to Claim 20, Zhang in view of Alshaer, Shouldice, and Wren discloses the claim limitations as discussed above with regards to Claims 13 and 6 and 8.
Response to Arguments
35 U.S.C. 101
Applicant's arguments filed 12/14/2025 have been fully considered but they are not persuasive.
The Applicant argues (p. 13): … the claims recite steps that cannot practically be performed in the human mind. For example, the steps of "collecting sleeping data of each user", "establishing and storing an incidence relation between user identity information and the standard features", "inquiring an associated standard feature according to the user identity information", "attributing the sleeping feature to a target sleeping feature of the user, to determine an attributed user of the sleeping data" cannot be practically applied in the human mind.
The Examiner respectfully disagrees and submits that "collecting sleeping data of each user" corresponds to insignificant extra-solution activity of mere data gathering. The steps "inquiring an associated standard feature according to the user identity information", "attributing the sleeping feature to a target sleeping feature of the user, to determine an attributed user of the sleeping data" represent mental processes as explained in the rejection.
The Applicant argues (p.14-15): Assuming, arguendo, that the Office nonetheless considers the claimed subject matter recites an abstract idea falling within one of the three above-discussed groupings of abstract ideas, Applicant respectfully submits that any such alleged abstract idea "is integrated into a practical application of that exception," rendering it patent eligible ... Applicant respectfully submits that a method for processing sleeping data, as recited in claim 1 and similarly recited in independent claim 13, by comparing the sleeping feature in the sleeping data collected by the sleeping monitoring device with the standard feature of the user, only when the comprehensive similarity of the comparison between them satisfies the similarity requirement, the sleeping feature is attributed to the user, and the attributed user of the sleeping data can be accurately determined without relying on the binding relation between the sleeping monitoring device and the user.
The Examiner submits that the Applicant opinion is incorrect as the listed above steps comprise abstract idea steps that are not used to demonstrate an integration into a practical application shown by meaningful additional elements.
The Applicant argues (p.17): The pending claim 1 recites a method for processing sleeping data recites elements such as "sleeping data of each user", "user identity information", "respiration feature", "heartbeat feature".
Applicant respectfully submit that these elements are not well-understood, routine, or conventional, e.g., are significantly more than any law of nature, natural phenomenon, or an abstract idea.
The Examiner submits the above argued claim elements are not representative of additional elements qualified for “significantly more”. It is unclear which additional elements or steps are as no specific arguments are presented. The Examiner evaluated additional elements in the rejection and concluded that note was qualified for “significantly more”.
With regards to Berkheimer memo, the Examiner submit that the prior art of record demonstrates that the additional elements are well-understood and conventional in the relevant art.
For example, Zhang, Alshaer, Shouldice, Wu, Wren, James Proud (US 20130281801), Melissa Susann (US 20160367184), Jantunen, and Anushiravani disclose claimed additional elements.
35 U.S.C. 103
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the new ground of rejection necessitated by the amendments.
In addition, Applicant's arguments filed 12/14/2025 have been fully considered but they are not persuasive.
The Applicant argues (p.21-22): It can be seen that, both Zhang and Alshaer are designed for "single user scenarios". Zhang only monitors the sleep status of a single user, while Alshaer only classifies breathing disorders of a single user. However, in the present application, the sleeping monitoring device is used by at least two users, and the sleeping feature is attributed to a target sleeping feature of the user, to determine an attributed user of the sleeping data. Neither Zhang nor Alshaer involves the technical problem of "data ownership when multiple users share one device" solved by the amended claim 1 of the present application.
The Examiner disagrees that Zhang considers “single user scenario”. As discussed in the rejection, multiple user scenario of a device is also disclosed [0074].
The Applicant argues (p.21-22):… the technical problem solved by Zhang is how to realize accurate monitoring of sleep status, stages and quality, and the technical problem solved by Alshaer is how to realize the automatic identification. characterization and diagnosis of OSA and CSA. Therefore, the technical problem of the amended claim 1 of the present application is different from that of Zhang and Alshaer.
Regarding the argued “different” technical problems in Zhang and Alshaer, the Examiner submits that under 35 U.S.C. 103(a) rejection, it is not necessary for the prior art to have the same or similar utility as in the claimed invention per MPEP 2144 IV (The court held “it is not necessary in order to establish a prima facie case of obviousness . . . that there be a suggestion or expectation from the prior art that the claimed [invention] will have the same or a similar utility as one newly discovered by applicant,” and concluded that here a prima facie case was established because “[t]he art provided the motivation to make the claimed compositions in the expectation that they would have similar properties.” 919 F.2d at 693, 16 USPQ2d at 1901 (emphasis in original)).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Joni Jorma Marius Jantunen (US 20190224443) discloses apparatus and methods associated with adjusting the sleep habits of one or more of a plurality of users including analysis module may determine the user's sleep phases during a sleep session based on received sleep data.
Ramin Anushiravani et al. (US 20200152330) discloses determining multiple conditions …such as sleep disorders, sleep stages (e.g., REM stages and Deep sleep) using the collected sensory and user information. A method comprises: generating, using one or more processors, profiles for a plurality of users, each profile including information collected from monitoring users over time; categorizing, using the one or more processors, a set of users into one or more predetermined categories based on the generated profiles.
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 extension fee 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 date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALEXANDER SATANOVSKY whose telephone number is (571)270-5819. The examiner can normally be reached on M-F: 9 am-5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached on (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ALEXANDER SATANOVSKY/
Primary Examiner, Art Unit 2863
Prosecution Insights