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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 07/27/2026 has been entered.
Response to Amendments
Claims 1, 5, 7-9, 11-12, 14, 16-18, and 20 are amended.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, filed 07/27/2026, with respect to Claim Rejections under 35 USC 103 have been considered but are moot because they do not apply to the specific combination of references being used in the current rejections.
Claim Objections
Claim(s) 1 and 12 is/are objected to because of the following informalities:
In Claim 1, the phrase “utilizing backscatter communication technique” should be “utilizing a backscatter communication technique”
In Claim 12, line 4, the word “wherein:” should be deleted and replaced with a semicolon
In Claim 12, the phrase “sensors, which comprises” should be “sensors, which comprise”
In Claim 12, the phrase “utilizing backscatter communication technique” should be “utilizing a backscatter communication technique”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims
particularly pointing out and distinctly claiming the subject matter which the
inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out
and distinctly claiming the subject matter which the applicant regards as his
invention.
Claim(s) 1 and 12 is/are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding Claim 1, the claim recites the limitation “an overall likelihood of multiple individual occupancy.” It is unclear what “individual” means. Claim 1 previously recites the limitations “individual sensor data” and “individual occupancy inference,” which use “individual” to distinguish separate items. However, “an overall likelihood of multiple individual occupancy” could refer to a likelihood that is based on the multiple fused individual occupancy inferences, or could refer to a likelihood that multiple individuals/people are present. For examination purposes, the limitation is interpreted as referring to a likelihood that multiple people are present. This rejection also applies to the corresponding limitation in Claim 12.
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.
Claim(s) 1-4 and 12-13 is/are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) in view of Konrad (US 2019/0360714).
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 sensors, which comprise 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 ([0072]: “The RFID tags can be … passive”; [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 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.”; [0122]: “one or more interrogators can be used each having one or more antennas that transmit energy at radio frequency, or other electromagnetic frequencies, to the sensors”), generate individual sensor data from each sensor ([0122]: “… and receive modulated frequency signals from the sensors containing sensor and/or identification information”; [0124]: “the sensors will respond with an identification signal followed by or preceded by information relating to the sensed value, state and/or property”), and reflect the individual sensor data utilizing backscatter communication technique to the one or more base station units ([0122]: “information can be returned immediately to the interrogator in the form of a modulated backscatter RF signal”), and
the one or more base station units are also configured to:
receive the sensor data from the one or more battery-free RFID sensor nodes ([0108]: “Analysis of these return signals by a processor associated with the interrogator”; [0122]: “… and receive modulated frequency signals from the sensors containing sensor and/or identification information”); and
determine an individual occupancy inference based on the individual sensor data from each sensor ([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 further teaches that the disclosed invention may 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:
fusing individual occupancy inferences to infer an overall likelihood of multiple individual occupancy in the residential building.
However, Konrad is in the field of building occupancy sensing (Konrad [0002]) and teaches:
determining individual occupancy inferences based on individual sensor data from different sensors (Konrad [0041]: “Cross-modality fusion of estimates from sensors of different types”; [0042]: “Within-modality fusion (or cross-instance fusion) of estimates from different sensors of the same type”; [0054]: “Let … denote the estimated occupancy-counts produced by interior sensors/modalities/algorithms at time t.”); and
fusing individual occupancy inferences to infer an overall likelihood of multiple individual occupancy in the building (Konrad [0040]: “Combining data and/or decisions from multiple complementary information sources can not only improve the accuracy of occupancy estimates…”; [0055]: “Let … denote the estimated number of occupants within zone produced by fusion system 3 at time t.”; [0057]; [0067]: “final fused estimates … can be approximately modeled as Gaussian”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed and fuse individual occupancy inferences to infer an overall likelihood of multiple individual occupancy in the residential building, as taught by Konrad, with a reasonable expectation of success. Applying Konrad’s fusion-based occupancy sensing technique to Breed’s occupancy sensing system yields the predictable result of inferring an overall likelihood of multiple individual occupancy with improved accuracy and robusticity to environmental variability (Konrad [0040]).
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:
receiving, by one or more battery-free radio frequency identification (RFID) sensor nodes, the continuous wave carrier signal, the one or more battery-free RFID sensor nodes each including sensors, which comprise 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 ([0072]: “The RFID tags can be … passive”; [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”; [0122]);
generating, by each sensor, individual sensor data ([0122]: “… and receive modulated frequency signals from the sensors containing sensor and/or identification information”; [0124]: “the sensors will respond with an identification signal followed by or preceded by information relating to the sensed value, state and/or property”);
reflecting the individual sensor data utilizing backscatter communication technique to the one or more base station units ([0122]: “information can be returned immediately to the interrogator in the form of a modulated backscatter RF signal”;);
receiving, at the one or more base station units, the sensor data from the one or more battery-free RFID sensor nodes ([0108]: “Analysis of these return signals by a processor associated with the interrogator”; [0122]: “… and receive modulated frequency signals from the sensors containing sensor and/or identification information”); and
determining, at the one or more base station units, an individual occupancy inference based on the individual sensor data from each sensor ([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 further teaches that the disclosed invention may 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:
fusing individual occupancy inferences to infer an overall likelihood of multiple individual occupancy in the residential building.
However, Konrad is in the field of building occupancy sensing (Konrad [0002]) and teaches:
determining individual occupancy inferences based on individual sensor data from different sensors (Konrad [0041]: “Cross-modality fusion of estimates from sensors of different types”; [0042]: “Within-modality fusion (or cross-instance fusion) of estimates from different sensors of the same type”; [0054]: “Let … denote the estimated occupancy-counts produced by interior sensors/modalities/algorithms at time t.”); and
fusing individual occupancy inferences to infer an overall likelihood of multiple individual occupancy in the building (Konrad [0040]: “Combining data and/or decisions from multiple complementary information sources can not only improve the accuracy of occupancy estimates…”; [0055]: “Let … denote the estimated number of occupants within zone produced by fusion system 3 at time t.”; [0057]; [0067]: “final fused estimates … can be approximately modeled as Gaussian”).
The rationale to modify Breed with the teachings of Konrad persists from Claim 1.
Regarding Claim 2, Breed as modified teaches: 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 as modified teaches: 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 as modified teaches: 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.”).
Claims 5 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Konrad (US 2019/0360714), 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 as modified teaches: inferring the likelihood of multiple individual occupancy as discussed regarding Claims 1 and 12 above.
Breed as modified further teaches: wherein at least one of the one or more battery-free RFID sensor nodes includes a computer-readable storage that stores a machine-learned Al model for inferring occupancy based on environmental sensor data generated by the temperature sensor, the illuminance sensor, and the relative humidity sensor ([0108-0110]; [0316]: “The processing of the return signals can be any known processing including the use of … neural networks”; [0336]: “occupancy state”; “This data can then be used to train a pattern recognition system such as a neural network”).
Breed as modified does not explicitly teach: the machine-learned Al model is a trained spatiotemporal pattern network (STPN).
However, Tan is in the field of building occupancy detection using machine learning (Tan [Abstract]) and teaches: the machine-learned Al model is a trained spatiotemporal pattern network (STPN) (Tan [section 1]: “occupancy detection spatiotemporal pattern network (Occ-STPN)”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, and use a STPN to infer occupancy, as taught by Tan, with a reasonable expectation of success. 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 Konrad (US 2019/0360714), 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: 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.
However, Maloney is in the field of RFID (Maloney [Abstract]) and 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 before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, and use one or more daughterboards, each of which provides a specific sensing modality, as taught by Maloney, with a reasonable expectation of success. 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), Konrad (US 2019/0360714), 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 as modified teaches: inferring the likelihood of multiple individual occupancy as discussed regarding Claims 1 and 12 above.
Breed as modified further teaches: wherein at least one of the one or more battery-free RFID sensor nodes includes a computer-readable storage that stores a machine-learned model for inferring occupancy. based on image sensor data generated by the image sensor, and the machine-learned model is a trained convolutional neural network.
Breed as modified does not explicitly teach: inferring occupancy based on image sensor data generated by the image sensor, or the machine-learned model is a trained convolutional neural network.
However, Ranasinghe is in the field of occupancy detection using RFID (Ranasinghe [Abstract]; [0172]) and teaches: at least one of the one or more RFID sensor nodes includes a computer-readable storage that stores a machine-learned model for inferring likelihood of multiple individual occupancy based on image sensor 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 before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, 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, with a reasonable expectation of success. 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), Konrad (US 2019/0360714), 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 as modified teaches: inferring the likelihood of multiple individual occupancy as discussed regarding Claims 1 and 12 above.
Breed as modified further teaches: wherein at least one of the one or more battery-free RFID sensor nodes includes a computer-readable storage that stores a machine-learned Al model for inferring occupancy based on acoustic energy sensor 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 networks”).
Breed as modified does not explicitly teach: the machine-learned Al model is a trained random forest classifier.
However, Candanedo is in the field of occupancy detection using statistical learning models (Candanedo [Title]) and teaches: 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.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, and use a random forest classifier, as taught by Candanedo, with a reasonable expectation of success. Using a random forest classifier 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), Konrad (US 2019/0360714), 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 as modified teaches: inferring the likelihood of multiple individual occupancy as discussed regarding Claims 1 and 12 above.
Breed as modified further teaches: wherein at least one of the one or more battery-free RFID sensor node a computer-readable storage that stores a machine-learned Al model for inferring 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 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 Tan persists from Claim 5.
Claims 10 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Konrad (US 2019/0360714), as applied to Claims 1 and 12 above, and further in view of Agarwal (US 2018/0306609).
Regarding Claims 10 and 19, Breed as modified teaches: inferring the likelihood of multiple individual occupancy as discussed regarding Claims 1 and 12 above.
Breed as modified further teaches: wherein at least one of the base station units is configured to: detect an electromagnetic … signal within an electric distribution system of a building caused by electrical devices in the building.
Breed as modified does not explicitly teach: that the electromagnetic signal is an interference signal, or inferring the likelihood of multiple individual occupancy based on the electromagnetic interference signal.
However, Agarwal is in the field of occupancy sensing using data fusion (Agarwal [0008]; [0039]) and teaches: detecting an electromagnetic interference signal (Agarwal [0034]: “electromagnetic interference (EMI)”; [0041]: “a sensor assembly including an electromagnetic sensor (EMI sensor) can be installed near an appliance and/or it's power source to detect usage of the appliance”), and
inferring occupancy based on the electromagnetic interference signal (Agarwal [0064]: “a second order virtual sensor 124 could indicate whether an occupant is present within a home by analyzing the outputs of multiple human activity-related first order virtual sensors 120”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, and detect an electromagnetic interference signal and infer occupancy based on the electromagnetic interference signal, as taught by Agarwal, with a reasonable expectation of success. Inferring occupancy based on electromagnetic interference is beneficial for providing an additional method to detect occupants and thereby improving occupancy detection.
Claims 11 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Breed (US 2008/0282817) and Konrad (US 2019/0360714), 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 as modified 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 the overall likelihood of multiple individual occupancy ([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: that the machine learned AI model is configured to infer the overall likelihood of multiple individual occupancy based on the fused individual occupancy inferences, or
the machine learned AI model is trained using an autoregressive logistic regression technique.
However, Konrad teaches: a machine learned AI model configured to infer the overall likelihood of multiple individual occupancy based on the fused individual occupancy inferences (Konrad [0045]: “a fusion algorithm can combine both raw data and decisions generated by different sensors through a complex, generally nonlinear relationship, e.g., kernel support vector regression and neural networks which can be trained using machine learning techniques”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed and use a machine learned AI model configured to infer the overall likelihood of multiple individual occupancy based on the fused individual occupancy inferences, as taught by Konrad, with a reasonable expectation of success. Applying Konrad’s machine learning fusion algorithm to Breed’s occupancy sensing system yields the predictable result of inferring an overall likelihood of multiple individual occupancy with improved accuracy and robusticity to environmental variability (Konrad [0040]).
Furthermore, Sahragard is in the field of machine learning (Sahragard [Abstract] and teaches: training machine learned AI models using an autoregressive logistic regression technique (Sahragard [Abstract]: “autoregressive logistic regression (ALR)-based conjugate gradient training approach”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Breed, as modified by Konrad, and use an AI model trained using an autoregressive logistic regression technique, as taught by Sahragard, with a reasonable expectation of success. Training an AI model using an autoregressive logistic regression technique is beneficial for improving the prediction accuracy of the model (Sahragard [Abstract]).
Conclusion
The cited references made of record in the contemporaneously filed PTO-892 form and not relied upon in the instant office action are considered pertinent to Applicant’s disclosure, and may have one or more of the elements in Applicant’s disclosure and at least Claims 1 and 12.
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.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR).
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Vladimir Magloire, can be reached on (571) 270-5144. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NOAH YI MIN ZHU/Examiner, Art Unit 3648
/BRADY W FRAZIER/Primary Examiner, Art Unit 3648
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