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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8/18/25 has been entered.
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 1-4, 12-17, 19-24, 26-29, 37-42, 44-49, and 51-56 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Yang et al (“Vital Sign and Sleep Monitoring Using Millimeter Wave” -cited by applicant).
Re claims 1, 26, 51: Yang discloses a millimeter wave (mmWave) mapping system configured to generate one or more point clouds and to determine one or more vital signs for defining a human psychological state, the mmWave mapping system comprising:
an mmWave sensor configured to transmit and receive one or more mmWave waveforms within a physical space (figure 2, section 3.1; see the mmWave sensor);
one or more processors commutatively coupled to the mmWave sensor, wherein a digital signal processor of the one or more processors is configured to analyze the one or more mmWave waveforms (figure 2, section 3.3; see the algorithm and classification run on a computing system); and
an application (app) comprising a set of computing instructions, the set of computing instructions configured for execution by the one or more processors, and the set of computing instructions, when executed by the one or more processors (figure 2, section 3.3; see the algorithm and classification run on a computing system), cause the one or more processors to:
generate, based on one or more mmWave waveforms of the mmWave sensor, a point cloud that maps at least a portion of the physical space, wherein the point cloud comprises point cloud data defining a person detected within the physical space (figure 2, section 5.1; see the mmWave mapping which implies a point cloud is generated),
determine, based on the point cloud data, a posture of the person within the portion of the physical space (section 5.1; see the posture detection and classification),
determine, based on the one or more mmWave waveforms of the mmWave sensor, one or more vital signs of the person (sections 4.1 and 5.2; see the breathing and heart rate determination and apnea detection),
determine a human psychological state of the person as defined by the point cloud data and the one or more vital signs of the person (page 14.2 last paragraph and section 5; see the heart rate and respiration rate measurements and change of posture and breathing disorders to monitor sleep quality and see monitoring of sleep via user posture and apnea events), and
provide an electronic feedback representing the human psychological state as defined by the point cloud data and the one or more vital signs of the person (Abstract; see the implied output of the monitoring algorithm).
Yang further discloses the above mentioned functions as steps of method and which are performed as non-transitory computer readable media (see the above citations and algorithms performed by a processing device).
Re claims 2, 27: The point cloud comprises a two-dimensional (2D) point cloud, a three-dimensional (3D) point cloud, or a four-dimensional (4D) point cloud, wherein the 4D point cloud comprises a space dimension and a time value (section 5.1; see that it is implied that the mapping is at least 2D or 3D).
Re claims 3, 28: The human psychological state comprises one or more of: a sleep state, a restfulness state, a restlessness state, a stress state, a sadness state, or a happiness state (page 14.2; see the quality of sleep).
Re claims 4, 29: The set of computing instructions include: generate a user-specific score defining a quality or quantity of the human psychological state as defined by one or more of: the point cloud data or the one or more vital signs of the person (section 5; see the sleep quality output as a metric that is defined by the point cloud mapping of posture and vital signs).
Re claims 12, 13, 37, 38: The instructions include: determine a chest displacement of the person based on the vital signs of the person and determine a breathing rate or heart rate based on the displacement (section 4.1; see the breathing detection based on chest displacement).
Re claims 14, 39: The instructions include: determine at least one of: a type of the posture of the person, a volumetric size of the person, an area of the person, or a representation of a current posture of the person (section 5.1; see the posture detection).
Re claims 15-17, 40-42: The system comprising an artificial intelligence (Al) model, wherein the Al model is trained with each of training point cloud data generated based on mmWave waveforms and vital sign data determined based on mmWave waveforms, and wherein the Al model is configured to receive the point cloud data of the person and the one or more vital signs of the person as input and is further configured to output a sleep phase classification corresponding to the human psychological state as defined by the point cloud data of the person and the one or more vital signs of the person (section 5.1; see the machine learning classifier which is an AI model that outputs a sleep classification defined by the posture (point cloud) and vital signs), wherein the Al model is further trained with a second training data set as generated by a second sensor, wherein the Al model is configured to receive a new second data set generated by a second sensor as input and further configured to output the sleep phase classification further based on the new second data set, wherein the Al model is further configured to output a sleep quality classification, the sleep quality classification based on the sleep phase classification determined at a first time and a second sleep phase classification determined, by the Al model, at a second time (section 5.1; see the machine learning classifier that uses second training data as subsequent data input into the model to generate another quality classification).
Re claims 19, 44: The Al model is further trained with a plurality of user feedback responses defining sleep quality of respective users, wherein the Al model is configured to receive one or more feedback responses of the person as input and output the sleep quality classification further based on the one or more feedback responses of the person (section 5.1; see the machine learning classifier which is capable of receiving feedback responses of respective users).
Re claims 20, 45: The instructions include: generate a user-specific sleep recommendation based on the sleep quality classification (section 5.1; wherein the output determinations correspond to a recommendation).
Re claim 21, 46: The instructions include: update, based on the sleep quality classification, a state of a device communicatively coupled to the one or more processors (section 5.1; wherein the classifying is performed for multiple data sets which is an updating).
Re claims 22, 47: The instructions include: receive an input specifying control of the electronic feedback, where the input causes one or more of: a starting or stopping of the electronic feedback, a frequency of the electronic feedback, or a pattern of providing or displaying the electronic feedback (section 5.1; wherein the input includes at least a starting/stopping of feedback).
Re claims 23, 24, 48, 49: The object detected within the physical space further includes a non-person object, wherein the mmWave sensor or the one or more processors is positioned within or is in a proximity to a furniture within the physical space (section 6; see the metal objects such as furniture).
Re claim 52: The mmWave sensor is configured to transmit the one or more mmWave waveforms comprising one or more omnidirectional mmWave waveforms within the physical space and wherein the mmWave sensor is configured to receive the one or more mmWave waveforms based on reflection of the one or more omnidirectional mmWave waveforms from the person within the physical space (section 3.3; see the “omni-sweep procedure” which corresponds to omnidirectional waveforms).
Re claims 53-55: The point cloud data further defines an object (i.e. a second person or a non-person object) other than the person (i.e. a first person) detected within the physical space, wherein the point cloud data defining the person within the physical space is based on the transmitted one or more omnidirectional mmWave waveforms reflected from the person to the mmWave sensor and wherein the point cloud data defining the object within the physical space is based on the transmitted one or more omnidirectional mmWave waveforms reflected from the object to the mmWave sensor (sections 6 and 7; see the other objects as a metal object or another person which, if present, are part of the point cloud data).
Re claim 56: The point cloud includes a set of coordinates to define the portion of the physical space and wherein the point cloud data defines an outline or presence of the person within the portion of the physical space such that the outline or presence of the person encompasses an area that is less than the portion of the physical space (section 3.3 and 7; see that the signals that generate the point cloud include signals from areas in the room/space that include the person and areas that do not include the person, resulting in a physical space that is larger than an outline of the 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.
Claims 5-9, 18, 25, 30-34, 43, and 50 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Chayat et al (US Pub 2019/0254544 -cited by applicant).
Re claims 5-7, 30-32: Yang discloses all features except that the user-specific score is rendered on a graphical user interface (GUI), and wherein the electronic feedback comprises visual feedback as displayed by the GUI or audio feedback by speakers. However, Chayat teaches of a radar system including a graphical user interface (GUI), and wherein the electronic feedback comprises visual feedback as displayed by the GUI or audio feedback by speakers [0054, 0106-0113; see the visual or audio feedback]. It would have been obvious to the skilled artisan to modify Yang, as taught by Chayat, in order to effectively permit feedback to an operator].
Re claims 8, 9, 18, 33, 34, 43: Yang dislcoses the instructions: generate a point cloud that maps at least a portion of the physical space, wherein the point cloud comprises point cloud data defining the person or the object detected within the physical space, and wherein the point cloud is generated at a time period based on one or more mmWave waveforms of the mmWave sensor, determine, based on the point cloud data, a posture of the person within the portion of the physical space, determine, based on the one or more mmWave waveforms of the mmWave sensor, a one or more vital signs of the person, determine the human psychological state as further defined by the point cloud data and the one or more vital signs of the person, and provide a electronic feedback representing the human psychological state as defined by the point cloud data at the time period and the one or more vital signs of the person (see the above citations for claim 1). However, Yang does not disclose that such is generated for a second point cloud in a second period of time to determine a second posture and second vital signs to provide second feedback or generate a state measurement value measuring a difference between a quality or quantity of the human psychological state between a first time period and the second time period and to generate a sleep deviation value. However, Chayat teaches of monitoring breathing rate, heart rate and heart rate variability for changes [0078]. It would have been obvious to the skilled artisan to modify Yang, as taught by Chayat, to determine ‘second’ data to determine change in a human state over time along with the difference between the first and second time which would provide an improved diagnosis to determine a sleep deviation value.
Re claims 25, 50: Yang discloses all features except for a baby crib. However, Chayat teaches on monitoring in proximity to a baby crib [0106]. It would have been obvious to the skilled artisan to modify Yang as taught by Chayat, in order to permit monitoring when a crib is present.
Claims 10, 11, 35, and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Yang in view of Santra et al (US 10,576,328 -cited by applicant).
Re claims 10, 11, 35, 36: Yang discloses all features except for a second sensor such as a motion sensor communicatively coupled to the one or more processors, wherein the one or more processors are configured to analyze a second data set generated by the second sensor, and wherein the set of computing instructions, when executed by the one or more processors, further cause the one or more processors to: determine, further based on the second data set, the human psychological of the person detected within the physical space. However, Santra teaches of a second sensor such as a motion sensor used to detect user characteristics (col 3, lines 26-28; see the “different sensor”). It would have been obvious to the skilled artisan to modify Yang, to use a second sensor as taught by Santra, in order to facilitate collection of addition measurements.
Response to Arguments
Applicant's arguments filed 8/18/25 have been fully considered but they are not persuasive. Applicant has submitted an RCE with amendments that obviate the 112 rejection, and only presents arguments for independent claims 1, 26, and 51. Specifically, Applicant argues that Yang does not expressly or inherently disclose the point cloud. Respectfully, the Examiner disagrees and maintains his position. It has been established that Yang does not explicitly refer to a ‘point cloud’. However, a point cloud is a known term in this field that is used to describe the data that is gathered from 60GHz mmWave sensors. Additionally, Yang’s radar signals are transmitted and reflected off objects in the sensor’s FOV and then the reflected signals are transformed into electric signals. The reflected signals (and subsequently the electric signals) contain information about the physical space including motion, speed, and position. As a ‘point cloud’ is defined as a set of data points in space, it follows that the collection of information is a point cloud because it comprises a set of data points from the indoor environment. As the collected data in Yang is a point cloud that comprises data points from a scanning of the indoor environment, the collected data in Yang maps at least a portion of the physical space. Yang is also not limited to obtaining a signal from a human, as the reference refers to a scan across the indoor environment and the tracking of any changes to that profile (see page 14:8) which includes humans and non-human objects such as walls and furniture (see page 14:7). While the point cloud that is collected is processed to “find” a human to analyze vital information, the mapping of a person and non-person is still performed.
In response to Applicant’s additional arguments regarding inherency, the Examiner’s position is not that the generation of a point cloud is inherent but rather that it is implied. In other words, it is not something that “may” occur but rather it is something that actually occurs in Yang. The lack of usage of the term “point cloud” is not indicative that the limitation is not present. A point cloud is specifically the return data that is visualized, wherein the points that are clustered allow identification of objects in the scene (see https://www.d3embedded.com/mmwave-radar-sensing/ for reference). Yang’s disclosure is not limited to collection of return signals. This data is visualized such that data points of the visualization that are clustered data correspond to a person or object, as shown in Figures 8 and 11. Contrary to Applicant assertions, Yang does disclose a point cloud because the collected data maps a portion of the physical space. Therefore, the limitation is met as Yang discloses the map which is consistent with what ‘point cloud data’ means, and goes beyond the mere collection of return signals.
Conclusion
All claims are identical to or patentably indistinct from, or have unity of invention with claims in the application prior to the entry of the submission under 37 CFR 1.114 (that is, restriction (including a lack of unity of invention) would not be proper) and all claims could have been finally rejected on the grounds and art of record in the next Office action if they had been entered in the application prior to entry under 37 CFR 1.114. Accordingly, THIS ACTION IS MADE FINAL even though it is a first action after the filing of a request for continued examination and the submission under 37 CFR 1.114. See MPEP § 706.07(b). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/MICHAEL T ROZANSKI/Primary Examiner, Art Unit 3797