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 .
Application Status
Claims 1-16 and 18 are pending and have been examined in this application.
While only claims 1-6 have been submitted in the RCE, claims 7-16 and 18 have not been canceled and as such, it is unclear as to whether it is the applicant's intent to cancel the withdrawn claims or maintain them. Therefore, for purposes of examination, claims 7-16 and 18 remain withdrawn.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 01/15/2026 has been entered.
Claim Rejections - 35 USC § 102
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 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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 and 3-5 are rejected under 35 U.S.C. 102(a)(1)/(2) as being anticipated by Tupin Jr. et al. (U.S. Pub. 20150181840).
In regard to claim 1, Tupin Jr. et al. discloses an intelligent dog collar for monitoring physiological parameters of a dog (Figs. 1-5 and Paragraphs [0104-0109], where there is a collar 402 which is used for monitoring physiological parameters of a dog), comprising: a movement sensor unit comprising at least one of an accelerometer or a gyrometer, wherein the movement sensor unit is configured to detect raw movement signals of the dog collar (Figs. 1-5 and Paragraphs [0050] and [0066-0069], where there is a movement sensor unit with at least both an accelerometer (“readings from the accelerometer may be interpreted as the animal being currently engaged in walking, running, sleeping, drinking, barking, scratching, shaking, etc.”) and a UWB device for monitoring cardiac activity and where the movement sensor unit is configured to detect raw movement signals (acceleration signal 210 and signal indicating movement of a dielectric material) of the dog), a storage module storing a trained neural network, the neural network being configured to determine a physiologic information from raw movement signals detected by the movement sensor unit (Figs. 1-5 and Paragraphs [0048-0056], [0066-0071], and [0113], where there is at least a storage module for storing a trained neural network (“neural networks, regression analysis, and the like and their use to analyze the signal inputs”) configured to determine a physiologic information from raw movement signals (acceleration signal 210 and signal indicating movement of a dielectric material) detected by the movement sensor unit (at least both the accelerometer and the UWB device)), said raw movement signals provided as input to the neural network comprising signals output from said at least one of an accelerometer or a gyrometer (Figs. 1-5 and Paragraphs [0048-0056], [0066-0071], and [0113], where the raw movement signals (acceleration signal 210 and signal indicating movement of a dielectric material) provided as input to the neural network at least includes signals output from the accelerometer as well as the UWB device), a processing unit connected to the movement sensor unit and configured to operate the trained neural network (Figs. 1-5 and Paragraphs [0048-0056], [0066-0071], and [0104-0113], where there is a processing unit 100/118 at least connected to the movement sensor unit and configured to operate the trained neural network (“neural networks”)), a memory configured to store the identified physiologic information (Figs. 1-6 and Paragraphs [0048-0056] and [0066-0071], where there is a memory (in storage components) configured to store the identified physiologic information (“processor 118 writes directly and/or reads directly from storage”)), an interface for transmitting to a communication device the identified physiologic information (Figs. 1-6 and Paragraphs [0048-0056] and [0066-0071], where there is an interface (“local input/output connection 108”) for transmitting to a communication device the identified physiologic information (“information may be communicated to an owner or veterinarian directly (through sound emitter/status light/display 604 of FIG. 6)… directly to a smartphone”)).
In regard to claim 3, Tupin Jr. et al. discloses the dog collar according to claim 1, wherein the neural network performs a regression function, and wherein the physiologic information comprises a heart rate of the dog or a breathing rate of the dog (Figs. 1-6 and Paragraphs [0079] and [0113], where the neural network performs a regression function (“regression analysis, and the like and their use to analyze the signal inputs”) and where the physiologic information comprises a heart rate (“the UWB device may be used to transmit and receive UWB signals to non-invasively monitor operations of an animal's heart”) of the dog).
In regard to claim 4, Tupin Jr. et al. discloses the dog collar according to claim 1, wherein the neural network has an encode-decoder architecture enabling signal translation or segmentation, and wherein the physiologic information comprises at least one of a heart signal of the dog, a breathing signal of the dog, a heart peak probability signal of the dog, or a breathing peak probability signal of the dog (Figs. 1-6 and 25-27 and Paragraphs [0230], [0079], and [0113], where the neural network at least performs signal translation (“receiving reflected RF energy from an animal body, organ, or tissue and converting or translating the reflected RF energy into recordable voltage or current measurements by the transceiver”) and where the physiologic information comprises a heart signal (“the UWB device may be used to transmit and receive UWB signals to non-invasively monitor operations of an animal's heart”) of the dog).
In regard to claim 5, Tupin Jr. et al. discloses the dog collar according to claim 4, wherein the neural network performs a source separation, and the physiologic information comprises both a heart signal of the dog and a breathing signal of the dog (Figs. 1-6 and 25-27 and Paragraphs [0113] and [0232], where the neural network at least performs a source separation (signals must be separated to have “amount fetched may be dependent on the type of vital sign to be calculated”) and where the physiologic information comprises both a heart signal of the dog and a breathing signal (see chart in Fig. 25 showing “heart rate” and “breathing”) of the dog).
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 2 is rejected under 35 U.S.C. 103 as being unpatentable over Tupin Jr. et al. (U.S. Pub. 20150181840) in view of Shuqiong et al. ("Person-Specific Heart Rate Estimation With Ultra-Wideband Radar Using Convolutional Neural Networks", IEEE Access, 2019 November 19, Volume 7, Pages 168484-168494).
In regard to claim 2, Tupin Jr. et al. discloses the dog collar according to claim 1, further comprising a transmitting antenna adapted to emit a radiofrequency (RF) detecting signal (Figs. 1-5 and Paragraphs [0048-0052], [0078], and [0104-0109], where there is a radar unit (“UWB” uses radiofrequency) which has a transmitting antenna (“one or more antennas”) adapted to emit a radiofrequency detecting signal), and at least a receiving antenna adapted to detect a raw RF signal which comprises reflection of the RF detecting signal (Figs. 1-5 and Paragraphs [0052], [0078], [0082], [0104-0109], and [0213] where there is a receiving antenna (“antennas of the cardiopulmonary (e.g., UWB device)”) adapted to detect a raw RF signal which comprises reflection of the RF detecting signal (“UWB antenna or another receiver may receive RF energy reflected back to the antenna”)), wherein the neural network is further configured to determine the physiologic information from raw movement signals thanks to the raw RF signals (Figs. 1-6 and Paragraphs [0048-0056], [0066-0071], and [0113] where the neural network (“neural networks… to analyze the signal inputs”) is further configured to determine the physiologic information from raw movement signals thanks to the raw RF signals). Tupin Jr. et al. is silent on a transmitting antenna adapted to emit a radiofrequency detecting signal having a frequency comprised between 57 GHz and 81 GHz. Shuqiong et al. discloses a transmitting antenna adapted to emit a radiofrequency detecting signal having a frequency comprised between 57 GHz and 81 GHz (Page 168489, IV.A, where there is a “79 GHz UWB multiple-input multiple out-put (MIMO) radar system with four transmitting antennas”), and at least a receiving antenna adapted to detect a raw RF signal which comprises reflection of the RF detecting signal (Page 168489, IV.A, where there is at least a receiving antenna (“four receiving antennas”) adapted to detect a raw RF signal); a storage module storing a trained neural network, the neural network being configured to determine a physiologic information into raw RF signals detected by the radar unit (Page 168489 and Abstract, where there is a storage module storing a trained neural network which “estimates the heart rate automatically based on the already trained neural networks”); and a processing unit connected to the radar unit and configured to operate the trained neural network (Page 168489, IV.A, where there is a processing unit such as the “Parallel Computing Toolbox” connected to the radar unit and at least configured to operate the trained neural network by performing “training for each participant”). Tupin Jr. et al. and Shuqiong et al. are analogous because they are from the same field of endeavor which include animal monitoring devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device body of Tupin Jr. et al. such that a transmitting antenna adapted to emit a radiofrequency detecting signal having a frequency comprised between 57 GHz and 81 GHz in view of Shuqiong et al. The motivation would have been to use a radar system which has a high-range resolution and a relatively lower cost. Furthermore, a 79 GHz radar utilizes a short wavelength, making it more sensitive to vital signs (Shuqiong et al., Page 168489, IV.A).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Tupin Jr. et al. (U.S. Pub. 20150181840) in view of Zhou et al. (CN 111753697).
In regard to claim 6, Tupin Jr. et al. discloses the dog collar according to claim 3. Tupin Jr. et al. is silent on wherein the neural network is a multi-task neural network, the neural network comprising a backbone of shared layers and two heads of task-specific layers, one of the head comprising the regression function and the other head comprising the segmentation function, or the translation function. Zhou et al. discloses the neural network is a multi-task neural network, the neural network comprising a backbone of shared layers and two heads of task-specific layers, one of the head comprising the regression function and the other head comprising the segmentation function, or the translation function (Translated Specification Page 9 lines 18-35, where there is a multi-task neural network comprising two heads of task-specific layers with one of the head including the regression function (“regression network”) and the other head including the segmentation function (“redefining network” which uses segmentation to create a separation in the source data)). Tupin Jr. et al. and Zhou et al. are analogous because they are from the same field of endeavor which include animal monitoring devices. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the device body of Tupin Jr. et al. such that the neural network is a multi-task neural network, the neural network comprising a backbone of shared layers and two heads of task-specific layers, one of the head comprising the regression function and the other head comprising the segmentation function, or the translation function in view of Zhou et al. The motivation would have been to utilize a neural network model such as a multi-task neural network that allows the system to utilize shared features between two separate tasks, in order to improve the performance or efficiency of each individual task.
Response to Arguments
Applicant's arguments (filed 01/15/2026) have been fully considered but they are not persuasive. Tupin Jr. et al. (U.S. Pub. 20150181840) disclose the applicant’s claims 1 and 3-5, as specified under Claim Rejections - 35 USC § 102 above.
Based on claim 1, as currently recited, the raw movement signals are interpreted to include acceleration signals 210 and signals indicating movement of a dielectric material. The movement sensor unit is interpreted as at least both an accelerometer and UWB device for monitoring cardiac activity.
Tupin Jr. et al. teaches the neural network is configured to determine a physiologic information from raw movement signals detected by the movement sensor unit in Figs. 1-5 and Paragraphs [0048-0056], [0066-0071], and [0113], where the trained neural network (“neural networks, regression analysis, and the like and their use to analyze the signal inputs”) is configured to determine a physiologic information (which includes heart rate and any physiologic data, such as animal movement) from raw movement signals (acceleration signal 210 and signal indicating movement of a dielectric material) detected by the movement sensor unit (at least both the accelerometer and the UWB device).
Furthermore, claim 1 does not specify that the physiologic information is limited to heart rate. Therefore, the applicant’s arguments, that the prior art does not disclose an accelerometer which detects signals used to determine heart rate, are not applicable to claim 1 as currently written. The claim does not include a limitation which explicitly requires that the accelerometer signals are used to determine heart rate. Lastly, based on the disclosure of Tupin Jr. et al., the neural network receives “signal inputs” from the entire device, which includes accelerometer signals.
It is again emphasized that the claims do not require that accelerometer data is specifically used to determine heart rate, as is implied in the applicant’s arguments. The rejection is maintained, with mapping updated accordingly to address the amendments to claim 1.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Particularly the references were cited because they pertain to the state of the art of animal monitoring devices.
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/KEVIN M DENNIS/Examiner, Art Unit 3647
/KIMBERLY S BERONA/Supervisory Patent Examiner, Art Unit 3647