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 .
Information Disclosure Statement
Information Disclosure Statements (IDS)s submitted on 08/05/2024 and 10/09/2025 have been entered and fully considered by the examiner.
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 1-9 and 12-19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1 of the subject matter eligibility test (see MPEP 2106.03).
Claim 1 is directed to an “a system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claim 9 is directed to a “a system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Claim 15 is directed to “a system” which describes one of the four statutory categories of patentable subject matter, i.e., a machine.
Step 2A of the subject matter eligibility test (see MPEP 2106.04).
Prong One:
Claims 1, 9 and 15 recite (“sets forth” or “describes”) the abstract idea of a mental process, substantially as follows: extract breathing signals from the reflected wireless signals; automatically form feature maps for the breathing signa ls, each feature auto map representing a correlation of the breathing signals at each voxel in a plurality of voxels that represent three-dimensional locations within the room, each feature map representing a respective time interval of the time period, the time period subdivided into time intervals; automatically statistically compare the features maps with one or more location templates, each location template including a respective correlation of collected breathing signals for a respective known person at each voxel; and automatically identify the breathing signals for a first person and the breathing signals for a second person based, at least in part, on statistical correlations between each location template and each feature map..
In claims 1, 9 and 15, the above recited steps can be practically performed in the human mind, with the aid of a pen and paper or with a generic computer, in a computer environment, or merely using the generic computer as a tool to perform the steps. If a person were to visually examine, i.e., perform an observation of the signals received or with the aid of a pen and paper or with a general processor, the respiratory signals could be identified and a feature map generated and compared with a location template from memory or on the paper and identification of a person correlated with the signals could be achieved based on that information. There is nothing recited in the claim to suggest an undue level of complexity in how the signal is extracted or how the feature maps/ location templates are formed. Therefore, a person would be able to perform the steps mentally or with a generic computer.
Prong Two: Claims 1, 9 and 15 do not include additional elements that integrate the mental process into a practical application.
This judicial exception is not integrated into a practical application. In particular, the claims recites (1) additional steps of transmitting and receiving wireless signals over a time period;
The steps in (1) represent merely data gathering or pre-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality with conventionally used tools (see below Step IIB for further details).
As a whole, the additional elements merely serve to gather and feed information to the abstract idea while generically implementing it on conventionally used tools. There is no practical application because the abstract idea is not applied, relied on, or used in a meaningful way. No improvement to the technology is evident, and the estimated bio-information is not outputted in any way such that a practical benefit is realized. Therefore, the additional elements, alone or in combination, do not integrate the abstract idea into a practical application.
Step 2B of the subject matter eligibility test (see MPEP 2106.05).
Claims 1, 9 and 15 do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above, the claims recite additional steps of transmitting and receiving wireless signals represent mere data gathering, data outputting or pre/post/extra-solution activities that are necessary for use of the recited judicial exception and are recited at a high level of generality.
Mere insignificant conventional extra-solution activity cannot provide an inventive concept. The claims hence are not patent eligible.
Dependent Claims
The following dependent claims merely further define the abstract idea and are, therefore, directed to an abstract idea for similar reasons:
Defining the location template (claims 2, 6)
Defining the statistical model used (claim 3-5, 7-8, 10-12, 19-20)
Defining the location template details (claims 13-14, 16-18)
Taken alone and in combination, the additional elements do not integrate the judicial exception into a practical application at least because the abstract idea is not applied, relied on, or used in a meaningful way. They also do not add anything significantly more than the abstract idea. Their collective functions merely provide computer/electronic implementation and processing, and no additional elements beyond those of the abstract idea. Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements individually. There is no indication that the combination of elements improves the functioning of a computer, output device, improves technology other than the technical field of the claimed invention, etc. Therefore, the claims are rejected as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1, 2, and 4-8 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kleven (WO 2022101390) hereinafter “Kleven”.
Regarding claim 1, Kleven discloses a system for wireless detection of breathing from multiple people [see abstract of Kleven and [0135]], comprising:
one or more transmitting antennas configured [see [0037]-[0039] of Kleven disclosing several antennas] to produce transmitted wireless signals over a time period; [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp]
one or more receiving antennas configured to receive reflected wireless signals over the time period, [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp] the reflected wireless signals being reflected from objects and/or people in a room; [see [0023] of Kleven] and
a computer in electrical communication with the one or more receiving antennas to receive the reflected wireless signals,[see [0036]-[0038]; the monitoring device comprises a computer system connected to the phased array] the computer including a processor and non-transitory computer memory in electrical communication with the processor [processor 23/24; see [00102]; it is inherent that a computer would have a processor and a memory] the non-transitory computer memory storing computer-readable instructions that, when executed by the processor [see [0023]-[0024] of Kleven], cause the processor to:
automatically extract breathing signals from the reflected wireless signals; [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array]
automatically form feature maps for the breathing signals, [3D point cloud data map; see FIG. 3] each feature map representing a correlation of the breathing signals at each voxel in a plurality of voxels that represent three-dimensional locations within the room, each feature map representing a respective time interval of the time period, the time period subdivided into time intervals; [see [0042] and FIG. 3; each point represents a breathing signal at different voxels in a 3D space corresponding to different frames at different time-stamps (i.e. during a time interval)]
automatically statistically compare the features maps with one or more location templates, [see [0066] and [0104]; a statistical comparison between the points on the 3D map (i.e. the feature map) and the minimum bounding box is done to statistically determine which points belong to a single bounding box (i.e. location template)] each location template including a respective correlation of collected breathing signals for a respective known person at each voxel; and [see [0066] and [0083]-[0084] of Kleven]
automatically identify the breathing signals for a first person and the breathing signals for a second person based, at least in part, on statistical correlations between each location template and each feature map. [see [0136] and FIG. 23 showing distinguishing between two people and determining breathing signals belonging to each individual]
Regarding claim 2, Kleven further discloses that the one or more location templates comprise: a first location template that includes a first correlation of the collected breathing signals for the first person at each voxel, and a second location template that includes a second correlation of the collected breathing signals for a second person at each voxel, [see FIG. 23; the first cluster marked by a rectangle correspond to the first persons breathing signal and the second cluster marked by the second rectangle is the second person’s breathing signal; see [0135] of Kleven] and the computer-readable instructions further cause the processor to:
automatically assign, for each feature map having a higher statistical correlation with the first location template compared to with the second location template, the breathing signals associated with a respective feature map to the first person; and automatically assign, for each feature map having a lower statistical correlation with the first location template compared to with the second location template, the breathing signals associated with the respective feature map to the second person. [see [0066] and [0136] and FIG. 23; the data points (i.e. signals) that are associated with a first/ second bounding box (i.e. first/second location template) are associated with the first person]
Regarding claim 4, Kleven further discloses that the computer-readable instructions further cause the processor to: automatically calculate a respective average breathing rate of the breathing signals associated with each feature map; [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data] and automatically statistically compare the respective average breathing rate to a respective average location-template breathing rate for each location template. [see [0066] and [0104]; a statistical comparison between the points on the 3D map (i.e. the feature map) and the minimum bounding box is done to statistically determine which points belong to a single bounding box (i.e. location template)]
Regarding claim 5, Kleven further discloses that the statistical correlations include: a first statistical comparison of the respective average breathing rate for each feature map with a first average location-template breathing rate of the first location template, [see [0027] of Kleven disclosing that a respiration rate is extracted] and a second statistical comparison of the respective average breathing rate for each feature map with a second average location-template breathing rate of the second location template. [ see [0066]; the data points (i.e. respiratory signals) that are statistically associated with a bounding box (i.e. a location template) are associated each other]
Regarding claim 6, Kleven further discloses that the one or more location templates comprise a first location template that includes a first correlation of the collected breathing signals for the first person at each voxel, [see [0066] and [0083]-[0084] of Kleven]
and the computer-readable instructions further cause the processor to: automatically assign, for each feature map having a respective statistical correlation with the first location template higher than a threshold value, the breathing signals associated with a respective feature map to the first person; [see [0066] and [0104]; a statistical comparison between the points on the 3D map (i.e. the feature map) and the minimum bounding box is done to statistically determine which points belong to a single bounding box (i.e. location template)] and automatically assign, for each feature map having the respective statistical correlation with the first location template lower than or equal to the threshold value, the breathing signals associated with the respective feature map to the second person. [see [0066] and [0104]; a statistical comparison between the points on the 3D map (i.e. the feature map) and the minimum bounding box is done to statistically determine which points belong to a single bounding box (i.e. location template)]
Regarding claim 7, Kleven further discloses that the computer-readable instructions further cause the processor to[see [0023]-[0024] of Kleven],: automatically group at least some of the feature maps into clusters, each cluster representing a group of correlated breathing signals; [see [0062]-[0063] disclosing that grouping of the points can be done using various clustering algorithms]
and automatically identify the breathing signals for the first person and the breathing signals for a second person based, at least in part, on statistical correlations between each location template and the feature maps in each cluster. [see [0059]; step 5; the cluster/group closest to a core point (data related to a person as disclosed in step 2) are assigned to that core point associated with the person)]
Regarding claim 8, Kleven further discloses that the time period is a second time period, the one or more transmitting antennas [see [0037]-[0039] of Kleven disclosing several antennas] are configured to produce first transmitted wireless signals over a first time period, the first time period before the second time period to produce transmitted wireless signals over a time period; [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp] the one or more receiving antennas are configured to receive first reflected wireless signals over the first time period, [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp] t the first reflected wireless signals being reflected from the objects and/or the people in the room, [see [0023] of Kleven] and
and the computer-readable instructions further cause the processor [see [0023]-[0024] of Kleven] to:
automatically extract the collected breathing signals, with the computer, from the first reflected wireless signals; [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array]
automatically form first feature maps for the first breathing signals, [3D point cloud data map; see FIG. 3] each first feature map representing a first correlation of the first breathing signals at each voxel, each first feature map representing a respective first time interval of the first time period, the first time period subdivided into first time intervals; ; [see [0042] and FIG. 3; each point represents a breathing signal at different voxels in a 3D space corresponding to different frames at different time-stamps (i.e. during a time interval)]
automatically group at least some of the first feature maps into one or more clusters, each cluster representing a group of correlated first breathing signals; automatically form the one or more location templates using the one or more clusters; and [see [0062]-[0063] disclosing that groupin of the points can be done using various clustering algorithms]
label each location template with the respective known person. [see [0059]-[0062]; each group identified belongs to a different person]
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Kleven (WO 2022101390) hereinafter “Kleven” in view of Visvikis et al. (US Publication No. 2022/0378320) hereinafter “Visvikis”.
Regarding claim 3, Kleven discloses all the limitations of claim 1 [see rejection of claim 1]
Kleven does not disclose that the statistical correlations include Pearson correlations.
Visvikis, directed towards statistical analysis of vital signs using radar [see abstract of Visvikis] further discloses that the statistical correlations include Pearson correlations. [see [0173] of Pearson]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the design of Kleven such that the statistical correlations include Pearson correlations according to the teachings of Pearson since doing so would have been a simple substitution of one type of statistical analysis with another equivalent one and would have been obvious to try by an ordinarily skilled in the art (KSR Rationale B)
Claims 9-10 and 13-15, 19-20, are rejected under 35 U.S.C. 103 as being unpatentable over Kleven (WO 2022101390) hereinafter “Kleven” in view of Wang et al. (U.S. Publication No. 2020/0302187) hereinafter “Wang”.
Regarding claim 9, Kleven discloses a system for wireless detection of breathing from multiple people [see abstract of Keven and [0135]], comprising:
one or more transmitting antennas configured [see [0037]-[0039] of Kleven disclosing several antennas] to produce transmitted wireless signals over a time period; [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp]
one or more receiving antennas configured to receive reflected wireless signals over the time period, [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp] the reflected wireless signals being reflected from objects and/or people in a room; [see [0023] of Kleven] and
a computer in electrical communication with the one or more receiving antennas to receive the reflected wireless signals,[see [0036]-[0038]; the monitoring device comprises a computer system connected to the phased array] the computer including a processor and non-transitory computer memory in electrical communication with the processor [processor 23/24; see [00102]; it is inherent that a computer would have a processor and a memory] the non-transitory computer memory storing computer-readable instructions that, when executed by the processor [see [0023]-[0024] of Kleven], cause the processor to:
a. automatically extract breathing signals from the reflected wireless signals; [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array]
b. automatically form feature maps for the breathing signals, [3D point cloud data map; see FIG. 3] each feature map representing a correlation of the breathing signals at each voxel in a plurality of voxels that represent three-dimensional locations within the room, each feature map representing a respective time interval of the time period, the time period subdivided into time intervals; [see [0042] and FIG. 3; each point represents a breathing signal at different voxels in a 3D space corresponding to different frames at different time-stamps (i.e. during a time interval)]
c. automatically group at least some of the feature maps into clusters, [see [0062]-[0063] disclosing that groupin of the points can be done using various clustering algorithms]
d. automatically identify a plurality of anchor clusters, from the daily clusters, having respective groups of the correlated breathing signals that mutually overlap in time; [see [0066]-[0067]; there are overlapping geometries (i.e. clusters) that overlap across two frames (overlap in time) making up a minimum bounding box (i.e. anchor clusters)]
e. automatically form a plurality of meta clusters, each meta cluster including a respective anchor cluster, wherein the daily clusters not including the anchor clusters comprise non-anchor daily clusters [see [0067]; step b. any group that does not overlap with the minimum bounding box (i.e anchor cluster are the equivalent of meta clusters]
f. automatically add at least some of the non-anchor daily clusters to the meta clusters according to first statistical correlations between each non-anchor daily cluster and each meta cluster; [see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters)]
g. automatically merge at least some of the meta clusters to form merged meta clusters according to second statistical correlations between the meta clusters; [see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups forming merged meta clusters]
I. Automatically assign the breathing signals associated with a first cluster to a first person based, at least in part, on one or more first respective distances between the first cluster and each predetermined sleep area; and [see [0059]; step 5; the cluster/group closest to a core point (data related to a person as disclosed in step 2) are assigned to that core point associated with the person)]
j. automatically assign the breathing signals associated with a second cluster to a second person based, at least in part, on one or more second respective distances between the second cluster and each predetermined sleep area. [see [0059]-[0062]; each group identified belongs to a different person]
Klevin does not disclose that the clusters formed are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period and h. automatically determine respective distances between each merged meta cluster and one or more predetermined sleep areas of one or more respective people in the room;
Wang, directed towards signal processing for vital sign detection of multiple persons in an environment [see abstract of Wang] further discloses that clusters formed are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period;[see [0099] of Wang disclosing that the clusters could correspond to daily clusters] and h. automatically determine respective distances between each merged meta cluster and one or more predetermined sleep areas of one or more respective people in the room;[see [0114] of Wang]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin further such that the clusters formed are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period; h. automatically determine respective distances between each merged meta cluster and one or more predetermined sleep areas of one or more respective people in the room according to the teachings of Wang in order to distinguish multiple people in the room with high accuracy [see [0033] of Wang]
Regarding claim 10, Klevin further discloses that for each respective non-anchor daily cluster not added to one of the meta clusters in step f, a respective new meta cluster is formed that includes the respective daily cluster. [see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters are formed)]
Regarding claim 13, Klevin as modified by Wang discloses all the limitation of claim 9 [see rejection of claim 9]
Klevin further discloses that the one or more predetermined sleep areas comprise a first predetermined sleep area for a first person in the room [see [0073]-[0076]; a focus area is determined for the person],
Wang further discloses that the computer-readable instructions further cause the processor to: automatically assign the breathing signals associated with the first merged meta cluster to the first person based on a first distance between the first merged meta cluster and the first predetermined sleep area; and automatically assign the breathing signals associated with the second merged meta cluster to the second person based on a second distance between the first merged meta cluster and the first predetermined sleep area, the first distance smaller than the second distance. [see [0114] of Wang disclosing using distances between the location of the person and the clusters to pick which cluster corresponds to which person]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin further such that the computer-readable instructions further cause the processor to: automatically assign the breathing signals associated with the first merged meta cluster to the first person based on a first distance between the first merged meta cluster and the first predetermined sleep area; and automatically assign the breathing signals associated with the second merged meta cluster to the second person based on a second distance between the first merged meta cluster and the first predetermined sleep area, the first distance smaller than the second distance according to the teachings of Wang in order to distinguish multiple people in the room with high accuracy [see [0033] of Wang]
Regarding claim 14, Klevin as modified by Wang discloses all the limitation of claim 9 [see rejection of claim 9]
Wang further discloses that the one or more predetermined sleep areas comprise: a first predetermined sleep area for a first person in the room, and a second predetermined sleep area for a second person in the room, [see [0073]-[0076]; a focus area is determined for the person] and the computer-readable instructions further cause the processor to: automatically determine first distances between each merged meta cluster and the first predetermined sleep area; automatically determine second distances between each merged meta cluster and the second predetermined sleep area[see [0114] of Wang disclosing using distances between the location of the person and the clusters to pick which cluster corresponds to which person]; automatically assign the breathing signals associated[see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array] with the first merged meta cluster to the first person, the first merged meta cluster having a smallest first distance with respect to the first distances, and automatically assign the breathing signals associated with the second merged meta cluster [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array] to the second person, the second merged meta cluster having a smallest second distance with respect to the second distances. [see [0114] of Wang]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin further such that the one or more predetermined sleep areas comprise: a first predetermined sleep area for a first person in the room, and a second predetermined sleep area for a second person in the room, and the computer-readable instructions further cause the processor to: automatically determine first distances between each merged meta cluster and the first predetermined sleep area; automatically determine second distances between each merged meta cluster and the second predetermined sleep area; automatically assign the breathing signals associated with the first merged meta cluster to the first person, the first merged meta cluster having a smallest first distance with respect to the first distances, and automatically assign the breathing signals associated with the second merged meta cluster to the second person, the second merged meta cluster having a smallest second distance with respect to the second distances according to the teachings of Wang in order to distinguish multiple people in the room with high accuracy [see [0033] of Wang]
Regarding claim 15, Kleven discloses a system for wireless detection of breathing from multiple people [see abstract of Keven and [0135]], comprising:
one or more transmitting antennas configured [see [0037]-[0039] of Kleven disclosing several antennas] to produce transmitted wireless signals over a time period; [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp]
one or more receiving antennas configured to receive reflected wireless signals over the time period, [see [0041]-[0042] disclosing that the antenna can transmit and then collect point cloud data wherein each frame is associated with a time-stamp] the reflected wireless signals being reflected from objects and/or people in a room; [see [0023] of Kleven] and
a computer in electrical communication with the one or more receiving antennas to receive the reflected wireless signals,[see [0036]-[0038]; the monitoring device comprises a computer system connected to the phased array] the computer including a processor and non-transitory computer memory in electrical communication with the processor [processor 23/24; see [00102]; it is inherent that a computer would have a processor and a memory] the non-transitory computer memory storing computer-readable instructions that, when executed by the processor [see [0023]-[0024] of Kleven], cause the processor to:
a. automatically extract breathing signals from the reflected wireless signals; [see [0027] and [0103]; the respiratory signals are extracted from 3D point cloud input data collected by the radar array]
b. automatically form feature maps for the breathing signals, [3D point cloud data map; see FIG. 3] each feature map representing a correlation of the breathing signals at each voxel in a plurality of voxels that represent three-dimensional locations within the room, each feature map representing a respective time interval of the time period, the time period subdivided into time intervals; [see [0042] and FIG. 3; each point represents a breathing signal at different voxels in a 3D space corresponding to different frames at different time-stamps (i.e. during a time interval)]
c. automatically group at least some of the feature maps into clusters, [see [0062]-[0063] disclosing that groupin of the points can be done using various clustering algorithms]
d. automatically identify a plurality of anchor clusters, from the daily clusters, having respective groups of the correlated breathing signals that mutually overlap in time; [see [0066]-[0067]; there are overlapping geometries (i.e. clusters) that overlap across two frames (overlap in time) making up a minimum bounding box (i.e. anchor clusters)]
e. automatically form a plurality of meta clusters, each meta cluster including a respective anchor cluster, wherein the daily clusters not including the anchor clusters comprise non-anchor daily clusters [see [0067]; step b. any group that does not overlap with the minimum bounding box (i.e anchor cluster are the equivalent of meta clusters]
f. automatically add at least some of the non-anchor daily clusters to the meta clusters according to first statistical correlations between each non-anchor daily cluster and each meta cluster; [see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters)]
i.automatically assign the breathing signals associated with a first cluster to a first person based, at least in part, on one or more first respective distances between the first cluster and each predetermined sleep area; and [see [0059]; step 5; the cluster/group closest to a core point (data related to a person as disclosed in step 2) are assigned to that core point associated with the person)]
j. automatically assign the breathing signals associated with a second cluster to a second person based, at least in part, on one or more second respective distances between the second cluster and each predetermined sleep area. [see [0059]-[0062]; each group identified belongs to a different person]
Kleven does not disclose that the clusters formed are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period; ; h. automatically assign the breathing signals associated with the first all-days meta cluster to a first person based, at least in part, on one or more first respective distances between a first in-bed center location of the first all-days meta cluster and each predetermined side of the bed area
Wang further disclose that the clusters forms are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period; [see [0099] of Wang disclosing that the clusters could correspond to daily clusters]h. automatically assign the breathing signals associated with the first all-days meta cluster to a first person based, at least in part, on one or more first respective distances between a first in-bed center location of the first all-days meta cluster and each predetermined side of the bed area [see [0114] of Wang]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin further such that the cluster formed are daily clusters; wherein each daily cluster representing a group of correlated breathing signals for one of the days of the time period; h. automatically assign the breathing signals associated with the first all-days meta cluster to a first person based, at least in part, on one or more first respective distances between a first in-bed center location of the first all-days meta cluster and each predetermined side of the bed area according to the teachings of Wang in order to distinguish multiple people in the room with high accuracy [see [0033] of Wang]
Regarding claim 19, Kleven as modified by Wang discloses all the limitations of claim 15 [see rejection of claim 15]
Klevin as modified by Wang further discloses that the daily clusters from a first day are grouped into only the first daily meta cluster for the first day, the first all-days meta cluster is formed with the first daily meta cluster from the first day, see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters are formed)] and the second all-days meta cluster is formed with the second daily meta cluster from a second day, wherein a respective second statistical correlation between the second daily meta cluster from the second day and the first all-days meta cluster is lower than or equal to a threshold value. see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters are formed)]
Regarding claim 20, Klevin further discloses that the first all-days meta cluster is formed with the first daily meta cluster from a first day, and the second all-days meta cluster is formed with the second daily meta cluster from the first day. [see [0067]; step b. the non-overlapping groups are added to previous non-overlapping groups (i.e. meta clusters are formed)]
Claims 11 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kleven (WO 2022101390) hereinafter “Kleven” in view of Wang et al. (U.S. Publication No. 2020/0302187) hereinafter “Wang” as applied to claim 9 above and further in view of Visvikis et al. (US Publication No. 2022/0378320) hereinafter “Visvikis”.
Regarding claim 11, Kleven as modified by Wang discloses all the limitations of claim 9 [see rejection of claim 9]
Kleven as modified by Wang does not disclose that the first statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates.
Visvikis further discloses that the first statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates. [see [0173] of Pearson]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin as modified by Wang further such that the first statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates according to the teachings of Pearson since doing so would have been a simple substitution of one type of statistical analysis with another equivalent one and would have been obvious to try by an ordinarily skilled in the art (KSR Rationale B)
Regarding claim 12, Kleven as modified by Wang discloses all the limitations of claim 9 [see rejection of claim 9]
Kleven as modified by Wang does not disclose that the second statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates.
the second statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates [see [0173] of Pearson]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin as modified by Wang further such that the first statistical correlations include: a Pearson correlation between average feature maps, and/or a difference between average breathing rates according to the teachings of Pearson since doing so would have been a simple substitution of one type of statistical analysis with another equivalent one and would have been obvious to try by an ordinarily skilled in the art (KSR Rationale B)
Claims 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kleven (WO 2022101390) hereinafter “Kleven” in view of Wang et al. (U.S. Publication No. 2020/0302187) hereinafter “Wang” as applied to claim 15 above and further in view of Hristov et al. (U.S. Publication No. 2021/0321938) hereinafter “Hristov”.
Regarding claim 16, Kleven as modified by Wang discloses all the limitations of claim 15 [see rejection of claim 15]
Kleven as modified by Wang does not disclose that the one or more predetermined sides of the bed area comprises a first predetermined side of the bed area for the first person, and the computer-readable instructions further cause the processor to: automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person based, at least in part, on a first/second distance between the first in-bed center location and the first predetermined side of the bed area;.
Hristov, directed towards vital sign detection using radar signals [see abstract of Hristov] further disclose that one or more predetermined sides of the bed area comprises a first predetermined side of the bed area for the first person,[see [0063]; side of the bed 410 is determined] and the computer-readable instructions further cause the processor to: automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person based, at least in part, on a first/second distance between the first in-bed center location and the first predetermined side of the bed area; [see [0063]-[0064] of Hristov disclosing defining a distance between the bed and the person’s signals]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin as modified by Wang further such that one or more predetermined sides of the bed area comprises a first predetermined side of the bed area for the first person, and the computer-readable instructions further cause the processor to: automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person based, at least in part, on a first/second distance between the first in-bed center location and the first predetermined side of the bed area according to the teachings of Hristov in order to accurately automate the detection of a person’s vital sign [see [0007] of Hristov]
Regarding claim 17, Kleven as modified by Wang discloses all the limitations of claim 15 [see rejection of claim 15]
Kleven as modified by Wang does not disclose that the one or more predetermined sides of the bed area comprises: a first/second predetermined side of the bed area for the first/second person, and the computer-readable instructions further cause the processor to: automatically determine first/second distances between each in-bed center location and the first/second predetermined sleep area; automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person, the first in-bed center location having a smallest first/second distance with respect to the first/second distances.
Hristov further discloses that the one or more predetermined sides of the bed area comprises: a first/second predetermined side of the bed area for the first/second person, ,[see [0063]; side of the bed 410 is determined] and the computer-readable instructions further cause the processor to: automatically determine first/second distances between each in-bed center location and the first/second predetermined sleep area;[see [0071]-[0072] of Hristav discloses that the reference of the distance is to the center of a patient’s bed] automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person, the first in-bed center location having a smallest first/second distance with respect to the first/second distances [see [0093]-[0094] of Hristov]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin as modified by Wang further such that the one or more predetermined sides of the bed area comprises: a first/second predetermined side of the bed area for the first/second person, and the computer-readable instructions further cause the processor to: automatically determine first/second distances between each in-bed center location and the first/second predetermined sleep area; automatically assign the breathing signals associated with the first/second all-days meta cluster to the first/second person, the first in-bed center location having a smallest first/second distance with respect to the first/second distances according to the teachings of Hristov in order to accurately automate the detection of a person’s vital sign [see [0007] of Hristov]
Regarding claim 18, Kleven as modified by Wang discloses all the limitations of claim 15 [see rejection of claim 15]
Kleven as modified by Wang discloses the first person is a primary person under observation, in steps h and i, the first all-days meta cluster is assigned to the first person and the second all-days meta cluster is assigned to the second person,[see [0099] disclosing that the clusters are tabbed based on daily activity to multiple people] and the computer-readable instructions further cause the processor to: automatically reassign the first all-days meta cluster to the second person; [see [0209]; a first signal associated with the first person] and automatically reassign the second all-days meta cluster to the first person second, wherein the first duration is greater than twice the second duration. [see [0429]-[0430] disclosing detecting multiple people in the room]
Hristov further discloses determine a first duration of the breathing signals associated with the first all-days meta cluster and a second duration of the breathing signals associated with the second all-days meta cluster; [see [0037] of Hristov disclosing that each cluster would be associated with a duration T]
It would have been obvious to a person of ordinary skill level in the art at the time of the filing of the invention to modify the teachings of Klevin as modified by Wang further such that determine a first duration of the breathing signals associated with the first all-days meta cluster and a second duration of the breathing signals associated with the second all-days meta cluster according to the teachings of Hristov in order to accurately automate the detection of a person’s vital sign [see [0007] of Hristov]
Conclusion
No claim is allowed.
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/MARJAN SABOKTAKIN/Examiner, Art Unit 3797
/MICHAEL J CAREY/Supervisory Patent Examiner, Art Unit 3795
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