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 .
Claim Interpretation
Claims 8 and 10 recite the newly amended limitation of “if the determined sleep posture of the user is an undesirable sleep posture” which in an interpretation it may be construed as a conditional limitation where the limitations followed by the conditional limitations may not be given a full weight in light of the below decisions as for considering the other case scenario of “if the determined sleep posture of the user is” not being “an undesirable sleep posture”.
Claim 10 also recites similar “if” limitations.
In the recent Ex parte Gopalan decision, the PTAB addressed a claim where all of the features were recited in a conditional manner. A first step of “identifying … an outlier” was performed if “traffic is outside of a prediction interval.” A second step of “identifying” was performed “only when a count of outliers … is greater than or equal to two, and exceeds an anomaly threshold.” These were the only two elements of the independent claim. Thus, if the traffic is never outside Gopalan’s prediction interval, then the steps of the method are never performed.
However, the PTAB distinguished Schulhauser and noted that this construction “would render the entire claim meaningless.” Gopalan at p. 5. The Board went on to state, “Although each of these steps is conditional, they are integrated into one method or path and do not cause the claim to diverge into two methods or paths, as in Schulhauser. Thus, we conclude that the broadest reasonable interpretation of claim 1 requires the performance of both steps…” Id. at p. 6.”
Claim Objections
Claims 1, 9 and 11-14 are objected to because of the following informalities:
Claims 1, 9, and 11-14 recites the limitation of “and/or” which should rather be either “and” or “or” to prevent any possible ambiguity due to the interpretation.
Claim 14 appears to be written in an independent form, yet also refers back to the other independent claim 13. In an interpretation, claim 14 may be construed as an independent claim (yet the fee sheet shows ONLY three independent claims are paid for); and in another interpretation it may also be construed as a dependent claim. In order to prevent any foreseeable ambiguity, it is suggested to bring the entire claim 13 in to the claim 14 to have the claim construed as a proper independent claim; or, correct the dependency of the claim 14 (as shown in other depending claims e.g., claim 2, etc.) to have the claim construed as a proper dependent claim.
Appropriate correction is required.
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 1-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims 1 and 12-14 recites “determine a turnover event” and “determine the sleep posture”.
The limitation of “determine”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer controller components. That is, other than reciting “by a controller” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a controller” language, “determining a turnover event” in the context of this claim encompasses the user manually observing/calculating/determining the event. Similarly, the limitation of “determine the sleep posture”, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the “by a controller” language, “determining the sleep posture” in the context of this claim encompasses the user thinking that the sleep posture based on the turnover events. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a processor to perform the limitation of “determine a turnover event” and “determine the sleep posture”. The processor in both steps is recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of “determine a turnover event” and “determine the sleep posture” such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform “determine a turnover event” and “determine the sleep posture” steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
The depending claims also recite similar abstract ideas (e.g., determine the turnover event by applying a machine learning, determine a second likelihood value, etc.) without additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application.
Therefore, the claims are not patent eligible.
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.
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, 6-8 and 10-14 are rejected under 35 U.S.C. 103 as being unpatentable over Chen (as cited in the IDS, “Remote Recognition of In-Bed Postures Using a Thermopile Array Sensor with Machine Learning”, IEEE SENSORS JOURNAL, VOL. 21, NO. 9, MAY 1, 2021) in view of Liao et al (CN 112806962).
Regarding claim 1, Chen teaches a system for determining a sleep posture of a user during a sleep session, (“sleep quality monitoring. In-bed posture … evaluates sleep qualities but can also help improve the well-being of bedridden patients. … recognition of 5 in-bed postures using a wall-mounted thermopile array sensor” abst) wherein the system comprises:
two or more sensors from a plurality of sensors configured to measure a signal indicative of a motion of the user, said sensors comprising single pixel thermopile sensors and/or infrared sensors (“5 in-bed postures using a wall-mounted thermopile array sensor” abst); and
a controller configured to receive the signals from the two or more sensors indicative of a motion of the user (“identify 9 typical in-bed postures using advanced machine learning models. In particular, a preprocessing method and a synergistic feature extraction approach using integrated histogram of oriented gradient and principal component analysis” abst);
determine events based on the received signals, said predetermined plurality of reference events comprising one or more of facing up to facing right, facing up to facing left, facing down to facing right, facing down to facing left, facing left to facing up, facing left to facing down, facing right to facing down, and facing right to facing up (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section);
determine the sleep posture of the user from a predetermined plurality of reference sleep postures, said predetermined plurality of reference sleep postures comprising one or more of facing up, facing down, facing right lateral, and facing left lateral (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section).
Chen does not seem to point out the specifics of determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). The camera 1 and the thermopile array sensor 2 perform image calibration and matching in advance, so that the image pixels of the video image captured by the camera 1 and the infrared image collected by the thermopile array sensor 2 Pixels correspond one-to one (see example 1).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 2, Chen teaches all the claimed limitation except for the determination of the turnover event is based at least partially on a comparison of the first signal to the second signal.
Chen does not seem to point out the specifics of determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). Acquire the temperature corresponding to each pixel in the infrared visible image, and arrange the temperature values from low to high. count the number of pixels in the target area whose temperature is higher than the ambient temperature, and calculate all the pixels in the target area according to the statistical result. A first area of pixels with a temperature higher than the ambient temperature, and a first average temperature of all pixels with a temperature higher than the ambient temperature (see pg. 6).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with the determination of the turnover event is based at least partially on a comparison of the first signal to the second signal as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 3, Chen teaches all the claimed limitations including the applying a machine learning model on the received signals, wherein the machine learning model has been trained on the received signals indicative of a motion of the user (“recognition of 5 in-bed postures using a wall-mounted thermopile array sensor with the hand-crafted feature extraction-based machine learning. In this article, both ceiling-mounted and wall-mounted thermopile array sensing approaches are devised and compared to identify 9 typical in-bed postures using advanced machine learning models” abst).
Chen does not point out the specifics of turnover events.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). The camera 1 and the thermopile array sensor 2 perform image calibration and matching in advance, so that the image pixels of the video image captured by the camera 1 and the infrared image collected by the thermopile array sensor 2 Pixels correspond one-to one (see example 1).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 6, Chen teaches wherein the controller is configured to select a subset of the plurality of sensors based on the field of view of the plurality of sensors for monitoring the sleep posture of the user (“thermopile array sensing device consisting of a ceiling-mounted and a wall-mounted sensor node is developed for in-bed posture detection using advanced machine learning models” summary).
Regarding claim 7, Chen teaches wherein the controller is configured to determine a position from a set of predefined positions of the one or more sensors based on the Field-of-view of the one or more sensors relative to a position of the user, and wherein the controller is further configured to output the determined position of the one or more sensors to the user (“two sensor nodes are installed at two different locations for comparison. As shown in Fig. 5, one is placed on the top of the bed to simulate the ceiling-mounted condition, where the vertical distance between the sensor and the bed is 2.4m. Another sensor node attached to a tripod is placed next to the bed to simulate the wall-mounted condition. The vertical distance between the wall-mounted sensor node and the bed is 1.2m, and the sensor has an elevation angle of about −65◦. This setting makes the FOV of the sensors slightly larger than the bed surface area to ensure that the posture can be fully captured in the thermal image with the highest resolution.” Sensor Setup section).
Regarding claim 8, Chen teaches all the claimed limitation except for the output an alert signal after a predetermined period of time has elapsed after the detection of the undesirable sleep posture.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). The camera 1 and the thermopile array sensor 2 perform image calibration and matching in advance, so that the image pixels of the video image captured by the camera 1 and the infrared image collected by the thermopile array sensor 2 Pixels correspond one-to one… The electronic device 3 is used to determine the sleeping posture of the sleeper according to the video image; obtain the age information of the sleeper; and determine whether to send or not according to the infrared visible image, the age information and the sleeping posture of the sleeper Alert prompt (see example 1).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with output an alert signal after as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 10, Chen teaches wherein the controller is configured to:
receive sleep-related data from one or more further sensors from the plurality of sensors and determine based on the received sleep-related data if the sleep status of the user is an asleep sleep status (“evaluates sleep qualities”); and
determine the sleep posture of the user based on the condition that the sleep status of the user is an asleep status (“The edge directions can outline the shape of the user body postures, then their histogram values calculated Fig. 4. Feature extraction process. based on the gradient orientations can be used as effective features for posture identification” see Image Preprocessing section).
Regarding claim 11, Chen teaches wherein the controller is further configured to: receive self-annotating instances of sleep postures and/or turnover events by the user, and wherein the machine learning model is trained at least in part on the self-annotating sleep postures (“the detection accuracy based on hand-crafted feature selection using cross-user-validation” see Leave-One-Subject-Out Cross-Validation; “recognition of 5 in-bed postures using a wall-mounted thermopile array sensor with the hand-crafted feature extraction-based machine learning” abst).
Regarding claim 12, Chen teaches controller (a preprocessing method and a synergistic feature extraction approach) for determining a sleep posture of a user during a sleep session (“sleep quality monitoring. In-bed posture … evaluates sleep qualities but can also help improve the well-being of bedridden patients. … recognition of 5 in-bed postures using a wall-mounted thermopile array sensor” abst), the controller configured to:
receive signals from the two or more sensors indicative of a motion of the user, said sensors comprising single pixel thermopile sensors and/or infrared sensors (“5 in-bed postures using a wall-mounted thermopile array sensor” abst);
determine events based on the received signals, said predetermined plurality of reference events comprising one or more of facing up to facing right, facing up to facing left, facing down to facing right, facing down to facing left, facing left to facing up, facing left to facing down, facing right to facing down, and facing right to facing up (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section);
determine the sleep posture of the user from a predetermined plurality of reference sleep postures, said predetermined plurality of reference sleep postures comprising one or more of facing up, facing down, facing right lateral, and facing left lateral (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section).
Chen does not seem to point out the specifics of determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). The camera 1 and the thermopile array sensor 2 perform image calibration and matching in advance, so that the image pixels of the video image captured by the camera 1 and the infrared image collected by the thermopile array sensor 2 Pixels correspond one-to one (see example 1).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 13, Chen teaches method for determining a sleep posture of a user during a sleep session (“sleep quality monitoring. In-bed posture … evaluates sleep qualities but can also help improve the well-being of bedridden patients. … recognition of 5 in-bed postures using a wall-mounted thermopile array sensor” abst), the method comprising the steps of:
receive signals from the two or more sensors indicative of a motion of the user, said sensors comprising single pixel thermopile sensors and/or infrared sensors (“5 in-bed postures using a wall-mounted thermopile array sensor” abst);
determine events based on the received signals, said predetermined plurality of reference events comprising one or more of facing up to facing right, facing up to facing left, facing down to facing right, facing down to facing left, facing left to facing up, facing left to facing down, facing right to facing down, and facing right to facing up (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section);
determine the sleep posture of the user from a predetermined plurality of reference sleep postures, said predetermined plurality of reference sleep postures comprising one or more of facing up, facing down, facing right lateral, and facing left lateral (“evaluating the performance of classifying 9 postures, the dataset is also relabeled as 4 posture types: Front, Left, Right, and Back since they are the most commonly used posture category for in-bed posture detection. More specifically, ‘Front’ contains soldier and starfish, ‘Left’ contains left log, left fetus, and left yearner, ‘Right’ contains right log, right fetus, and yearner, and ‘Back’ only contains the freefall posture. This 4-posture dataset is down-sampled again to keep balanced data size within each posture” Leave-One-Subject-Out Cross-Validation section).
Chen does not seem to point out the specifics of determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events.
However, in the same field of endeavor, Liao teaches establishing a real-time sleep model according to the sleep posture information; the real-time sleep model is a three-dimensional model with space position relationship between the user and the sleep article; collecting the surface temperature information of the user through the infrared module based on the real-time sleep model (abst). Determine the number of times of turning over and rolling of the sleeper in one sleep period according to the video image. Generating a sleep record of the sleeper within a sleep period according to the sleeping posture and the number of times of turning over and rolling (SUMMARY OF THE INVENTION section). The camera 1 and the thermopile array sensor 2 perform image calibration and matching in advance, so that the image pixels of the video image captured by the camera 1 and the infrared image collected by the thermopile array sensor 2 Pixels correspond one-to one (see example 1).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determining a turnover event from a predetermined plurality of reference turnover and determine the sleep posture of the user from a predetermined plurality of reference sleep postures based on the turnover events as taught by Liao because it helps to safely, conveniently, accurately and timely find the child kicking the quilt, and give an alarm to remind parents, so that parents can not only sleep at ease but also prevent their children from getting cold and sick, which is what parents are looking forward to very much (of Liao).
Regarding claim 14, Chen teaches computer program product (a preprocessing method and a synergistic feature extraction approach) comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps of the method of claim 13 (“sleep quality monitoring. In-bed posture … evaluates sleep qualities but can also help improve the well-being of bedridden patients. … recognition of 5 in-bed postures using a wall-mounted thermopile array sensor” abst).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Chen in view of Liao as applied to claim 8 and 1 and further in view of Hui (CN 112315426).
Regarding claim 9, The combination noted above teaches all the claimed limitation except for determine a frequency of turnover events during a sleep session;
determine a sleep quality index based on the period of time elapsed in an undesirable sleep posture and/or the frequency of turnover events.
However, in the same field of endeavor, Hui teaches electronic monitoring technology, and in particular, relates to a turning-over monitoring device and a turning-over monitoring method… the turning-over detection module is used to monitor whether the ward has a turning-over action, and generates a turning-over signal when there is a turning-over action; the image information acquisition module is used to acquire the image information of the ward; the control module is used to judge whether the ward has turned over according to the turning-over signal and/or image information (abst). control module extracts an image of a turning time point to be stored as a sample… the change of the compression area exceeds a preset threshold value, considering the monitored person to have a turning action and sending a turning signal in time (see section example 2). The stored image sample information can be trained by using an SVM classifier when training the turn-over recognition model (See example 3). Recording the time points with the largest pressure area and the smallest area (see Step D0).
It would have been obvious to an ordinary skilled in the art before the invention was made to modify the method and/or device of the modified combination of reference(s) as outlined above with determine a frequency of turnover events during a sleep session, determine a sleep quality index based on the period of time elapsed in an undesirable sleep posture and/or the frequency of turnover events as taught by Hui because invention can automatically and accurately judge whether the ward has turned over ( abst of Hui).
Conclusion
Claims 4-5 are free from prior art.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guo et al (CN 108209871) teaches the sleep monitoring method comprises the following steps: obtaining video image and infrared video images according to the video images to determine a sleep posture of the sleeper, age information for obtaining the sleeper according to the infrared video image, age information and sleep posture of the sleeper according to the preset algorithm to determine whether to send the alarm prompt to the electronic device. by the method can safely and effectively monitoring the sleep.
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/SERKAN AKAR/ Primary Examiner, Art Unit 3797