DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
The amendment filed 02/02/2026 is entered.
Claims 1-20 are amended.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, filed 02/02/2026, with respect to Claim Objections, Claims Rejections under 35 USC 112(b), and Claim Rejections under 35 USC 101 have been fully considered and are persuasive. The objections and rejections have been overcome.
Applicant’s arguments, filed 02/02/2026, with respect to Claim Rejections under 35 USC 102 and 103 have been considered but are moot because the arguments do not apply to the specific combination of references being used in the current rejection.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C.
102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the
statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a
new ground of rejection if the prior art relied upon, and the rationale supporting the rejection,
would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness
rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the
claimed invention is not identically disclosed as set forth in section 102, if the
differences between the claimed invention and the prior art are such that the
claimed invention as a whole would have been obvious before the effective filing
date of the claimed invention to a person having ordinary skill in the art to which
the claimed invention pertains. Patentability shall not be negated by the manner in
which the invention was made.
Claims 1-4, 10, 12-13, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) in view of Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021).
Regarding Claim 1, Breed teaches: An integrated occupancy sensing system for detecting human occupancy in a residential building with accuracy, comprising:
one or more battery-free radio frequency identification (RFID) sensor nodes, each of the one or more battery-free RFID sensor nodes including at least one of (1) an image sensor, (2) an acoustic energy sensor, (3) a temperature sensor, (4) an illuminance sensor, or (5) a relative humidity sensor ([0108]: “RFID devices”; [0110]: “the objects equipped with the RFID devices may include sensors... These sensors may be temperature, optical, flow, humidity, ..., acoustic”); and
one or more base station units, each of which is configured to be connected to a power source ([0108]: “interrogator”; [0204]: “an interrogator associated with the component control system”), wherein:
when the one or more base station units are connected to the power source, the one or more base station units are configured to emit a continuous wave carrier signal ([0142]: “the interrogator can continuously broadcast the carrier frequency”), and the one or more battery-free RFID sensor nodes are configured to receive and reflect the continuous wave carrier signal ([0108]: “The interrogator controls transmission of RF signals from the antennas to cause these RFID devices to generate return signals.”), and
the one or more base station units are also configured to:
receive the reflected signal from the one or more battery-free RFID sensor nodes ([0108]: “Analysis of these return signals by a processor associated with the interrogator”); and
based on the reflected signal, infer a likelihood of … occupancy … ([0108]; [0192]: “The RFID and SAW tag(s) can be constructed to provide information on the occupancy of the child seat, i.e., whether a child is present, based on the weight, temperature, and/or any other measurable parameter.”).
Breed also teaches that the disclosed invention could be applied to residential buildings ([Abstract]: “monitoring a structure at a fixed location, e.g., a house”; [0178]: “many of these advances are equally applicable to …, in some cases, homes and buildings.”).
Breed does not explicitly teach – but Sharma teaches: based on the reflected signal, infer a likelihood of multiple individual occupancy in the residential building (Sharma [Abstract]: “we have employed passive RFID tags in the ambient for occupant counting”; “smart homes”; [p. 8565]: “occupant counting model”; [p. 8566]: “occupant-reflected signal”).
It would have been obvious to one of ordinary skill in the art to modify Breed to infer a likelihood of multiple individual occupancy in a residential building based on reflected RFID signals, as taught by Sharma. Modifying Breed to infer a likelihood of multiple individual occupancy in a residential building is beneficial for improving the accuracy of occupancy detection in residential applications (Sharma [Abstract]) and is an application of a known technique to yield predictable results.
Regarding Claim 12, Breed teaches: A method for detecting human occupancy with a wireless sensing platform in a residential building with accuracy, the method comprising:
emitting from one or more base station units a continuous wave carrier signal, the one or more base station units configured to be connected to a power source ([0142]: “the interrogator can continuously broadcast the carrier frequency”), wherein:
the continuous wave carrier signal is configured to be received by one or more battery-free radio frequency identification (RFID) sensor nodes that are configured to receive and reflect the continuous wave carrier signal, the one or more battery-free RFID sensor nodes each including at least one of (1) an image sensor, (2) an acoustic energy sensor, (3) a temperature sensor, (4) an illuminance sensor, or (5) a relative humidity sensor ([0108]: “The interrogator controls transmission of RF signals from the antennas to cause these RFID devices to generate return signals.”; [0110]: “the objects equipped with the RFID devices may include sensors... These sensors may be temperature, optical, flow, humidity, ..., acoustic”);
receiving, at the one or more base station units, the reflected signal from the one or more battery-free RFID sensor nodes ([0108]: “Analysis of these return signals by a processor associated with the interrogator”); and
inferring, based on the reflected signal, a likelihood of … occupancy … ([0192]: “The RFID and SAW tag(s) can be constructed to provide information on the occupancy of the child seat, i.e., whether a child is present, based on the weight, temperature, and/or any other measurable parameter.”).
Breed also teaches that the disclosed invention could be applied to residential buildings ([Abstract]: “monitoring a structure at a fixed location, e.g., a house”; [0178]: “many of these advances are equally applicable to …, in some cases, homes and buildings.”).
Breed does not explicitly teach – but Sharma teaches: inferring, based on the reflected signal, a likelihood of multiple individual occupancy in the residential building (Sharma [Abstract]: “we have employed passive RFID tags in the ambient for occupant counting”; “smart homes”; [p. 8565]: “occupant counting model”; [p. 8566]: “occupant-reflected signal”).
The rationale to modify Breed with the teachings of Sharma persists from Claim 1.
Regarding Claim 2, Breed discloses: wherein at least one of the one or more battery-free RFID sensor nodes further includes a photovoltaic cell, and the at least one battery-free RFID sensor node is powered by a combination of the continuous wave carrier signal and the photovoltaic cell ([0072]: “The RFID tags can be active, passive or a combination of both”; [0202]: “photo cell”).
Regarding Claim 3, Breed discloses: wherein at least one of the one or more battery-free RFID sensor nodes does not include an energy storage component ([0072]: “The RFID tags can be ... passive”).
Regarding Claims 4 and 13, Breed discloses: wherein each of the one or more battery-free RFID sensor nodes includes an identical motherboard that provides power and communication to a corresponding battery-free RFID sensor node ([0187]: “A variation of this design is to use an RF circuit such as in an RFID to serve as an energy source. One design could be for the RFID to operate with directional antennas at a relatively high frequency such as 2.4 GHz.”).
Regarding Claims 10 and 19, Breed discloses: wherein at least one of the base station units is configured to:
detect an electromagnetic interference signal within an electric distribution system of a building caused by electrical devices in the building ([0110]: “the objects equipped with the RFID devices may include sensors... These sensors may be … electric field … sensors”; [0345]: “sensors sensitive to other frequencies in the electromagnetic spectrum as the need arises”); and
Breed does not explicitly teach – but the combination of Breed and Sharma teaches: infer the likelihood of multiple individual occupancy based on the electromagnetic interference signal. Breed teaches detecting electromagnetic frequencies in the environment, and using “any measurable parameter” to determine occupancy ([0192]), and Sharma teaches inferring a likelihood of multiple individual occupancy in a residential building (Sharma [Abstract]; [p. 8565-8566]).
Therefore, it would have been obvious to one of ordinary skill in the art to modify Breed to infer the likelihood of multiple individual occupancy based on the electromagnetic interference signal. Detecting interference and inferring occupancy based on interference is considered ordinary and well-known for use in the art, and inferring occupancy based on interference improves occupancy detection by providing an additional method to detect occupants. The rationale to modify Breed with the teachings of Sharm persists from Claim 1.
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), as applied to Claims 4 and 13 above, and further in view of Tan (S. Y. Tan et al., “A flexible framework for building occupancy detection using spatiotemporal pattern networks,” 2019 American Control Conference (ACC), 2019).
Regarding Claims 5 and 14, Breed teaches: wherein:
at least one of the one or more battery-free RFID sensor node includes (1) a temperature sensor, (2) an illuminance sensor, and (3) a relative humidity sensor ([0108-0110]) and a computer-readable storage that stores a machine-learned Al model for inferring likelihood of … occupancy based on data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor … ([0316]: “neural network”; [0336]: “occupancy state”; “This data can then be used to train a pattern recognition system such as a neural network”).
Breed does not explicitly teach – but Sharma teaches: inferring the likelihood of multiple individual occupancy (Sharma [Abstract]; [p. 8565]; [p. 8566]).
Breed does not explicitly teach – but Tan teaches: the machine learned Al model is a trained spatiotemporal pattern network (STPN) (Tan [section 1]: “occupancy detection spatiotemporal pattern network (Occ-STPN)”).
The rationale to modify Breed with the teachings of Sharma persists from Claim 1.
It would have been obvious to one of ordinary skill in the art to modify Breed and use a STPN to infer occupancy, as taught by Tan. Using a STPN to infer occupancy is beneficial for improving the accuracy of occupancy detection (Tan [section 1]).
Claims 6 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), as applied to Claims 4 and 13 above, and further in view of Maloney (US 2004/0095241).
Regarding Claims 6 and 15, Breed does not explicitly teach – but Maloney teaches: wherein each of the one or more battery-free RFID sensor nodes further includes one or more daughterboards, each of which provides a specific sensing modality (Maloney [0067]: “an upstanding daughter board 172 corresponding to the receptacle 170”; Fig. 13).
It would have been obvious to one of ordinary skill in the art to modify Breed and use one or more daughterboards, each of which provides a specific sensing modality, as taught by Maloney. Daughterboards are considered ordinary and well-known for use in the art. Using daughterboards with specific sensing modalities is beneficial for reducing the cost and improving the reliability of the system (Maloney [0016]).
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817), Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), and Maloney (US 2004/0095241), as applied to Claims 6 and 15 above, and further in view of Ranasinghe (US 2021/0256267).
Regarding Claims 7 and 16, Breed does not explicitly teach – but Ranasinghe teaches: wherein:
at least one of the one or more battery-free RFID sensor nodes includes an image sensor and a computer-readable storage that stores a machine-learned model for inferring likelihood of multiple individual occupancy based on data generated by the image sensor (Ranasinghe [0172]: “The ID tag can be an RFID tag, a Bluetooth or Bluetooth Low Energy, also known as “BLE” (for example, by having two Bluetooth receivers in the camera) tag”; [0173]: “convolutional neural network”; [0215]: “track one or more people in the room”; [0254]: “output a probabilistic occupancy map”), and
the machine-learned model is a trained convolutional neural network (Ranasinghe [0173]: “convolutional neural network”).
It would have been obvious to one of ordinary skill in the art to modify Breed and include and image sensor in an RFID sensor node, and use a CNN to infer likelihood of multiple individual occupancy based on data generated by the image sensor, as taught by Ranasinghe. Using a camera and a CNN to determine occupancy is beneficial for improving the accuracy of occupancy detection (Ranasinghe [0214]).
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817), Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), and Maloney (US 2004/0095241), as applied to Claims 6 and 15 above, and further in view of Candanedo (Luis M. Candanedo et al., “Accurate occupancy detection of an office room from light, temperature, humidity and CO2 measurements using statistical learning models,” Energy and Buildings, Volume 112, 2016, Pages 28-39).
Regarding Claims 8 and 17, Breed teaches: wherein:
at least one of the one or more battery-free RFID sensor nodes includes an acoustic energy sensor and a computer-readable storage that stores a machine-learned Al model for inferring the likelihood of … occupancy based on data generated by the acoustic energy sensor … ([0110]: “the objects equipped with the RFID devices may include sensors... These sensors may be ... acoustic”; [0316]: “neural network”).
Breed does not explicitly teach – but Sharma teaches: inferring the likelihood of multiple individual occupancy (Sharma [Abstract]; [p. 8565]; [p. 8566]).
Breed does not explicitly teach – but Candanedo teaches: and the machine-learned Al model is a trained random forest classifier (Candanedo [section 1]: “occupancy detection”: “random forest”; [section 3.2]: “Random Forests are models that make an effort to improve the accuracy of the prediction by creating many classification trees.”).
The rationale to modify Breed with the teachings of Sharma persists from Claim 1.
It would have been obvious to one of ordinary skill in the art to modify Breed and use a random forest classifier, as taught by Candanedo. Using a random forest classifier model to determine occupancy is beneficial for improving the accuracy of occupancy detection (Candanedo [section 3.2]).
Claims 9 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817), Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), and Maloney (US 2004/0095241), as applied to Claims 6 and 15 above, and further in view of Tan (S. Y. Tan et al., “A flexible framework for building occupancy detection using spatiotemporal pattern networks,” 2019 American Control Conference (ACC), 2019).
Regarding Claims 9 and 18, Breed teaches: wherein:
wherein at least one of the one or more battery-free RFID sensor node includes (1) a temperature sensor, (2) an illuminance sensor, (3) a relative humidity sensor, and (4) either an image sensor or an acoustic energy sensor, and a computer-readable storage that stores a machine-learned Al model for inferring the likelihood of … occupancy based on data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor … ([0110]: “the objects equipped with the RFID devices may include sensors... These sensors may be temperature, optical, flow, humidity, ..., acoustic”; [0316]: “neural networks”).
Breed does not explicitly teach – but Sharma teaches: inferring the likelihood of multiple individual occupancy (Sharma [Abstract]; [p. 8565]; [p. 8566]).
Breed does not explicitly teach – but Tan teaches: the machine-learned Al model is a trained spatiotemporal pattern network (STPN) (Tan [section 1]: “occupancy detection spatiotemporal pattern network (Occ-STPN)”).
The rationale to modify Breed with the teachings of Sharma persists from Claim 1, and the rationale to modify Breed with the teachings of Tan persists from Claim 5.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Sharma (Sharma et al., “Deep-Learning-Based Occupant Counting by Ambient RF Sensing,” March 15, 2021), as applied to Claims 1 and 12 above, and further in view of Sahragard (Sahragard, H.P., Keshtegar, B., Chahouki, M.A.Z. et al. “Modeling spatial distribution of plant species using autoregressive logistic regression method-based conjugate search direction,” Plant Ecol 220, pgs. 267–278, 2019).
Regarding Claims 11 and 20, Breed teaches: wherein:
at least one of the one or more base station unit also includes a computer readable storage that stores a machine learned Al model configured to infer an overall likelihood of … occupancy based on the inferences of occupancy received from the one or more battery-free RFID sensor nodes … ([0204]: “an interrogator associated with the component control system”; [0316]: “The control system 628 also processes the return signals to provide information about the vehicle or the component. The processing of the return signals can be any known processing including the use of pattern recognition techniques, neural networks, fuzzy systems and the like.”).
Breed does not explicitly teach – but Sharma teaches: inferring the likelihood of multiple individual occupancy (Sharma [Abstract]; [p. 8565]; [p. 8566]).
Breed does not explicitly teach – but Sahragard teaches: and the machine learned Al model is trained using an autoregressive logistic regression technique. (Sahragard [Abstract]: “autoregressive logistic regression”).
The rationale to modify Breed with the teachings of Sharma persists from Claim 1.
It would have been obvious to one of ordinary skill in the art to modify Breed and use an AL model trained using an autoregressive logistic regression technique, as taught by Sahragard. Training an AI model using an autoregressive logistic regression technique is beneficial for improving the prediction accuracy of the model (Sahragard [Abstract]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to NOAH Y. ZHU whose telephone number is (571)270-0170. The examiner can normally be reached Monday-Friday, 8AM-4PM.
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/NOAH YI MIN ZHU/Examiner, Art Unit 3648
/William Kelleher/Supervisory Patent Examiner, Art Unit 3648
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