DETAILED CORRESPONDENCE
This action is in response to the filing of the Amendments on 01/15/2026. Claims 2 and 17 are cancelled. Claims 21 – 22 are new.
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
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.
Claims 1, 16, 21 and 22 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.
The Applicants specification does not specifically define the word “dynamically.” However, the specification does state in para 0007 of the Published App “The dynamic setting of the time window permits the computer to perform the identification with radar data from a single time window.” The Examiner will use the known definition of real-time to understand the time is set dynamically (or in real time).
Dependent claims are rejected as well.
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.
Claim(s) 1, 3 – 6, 12 – 16 and 18 - 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cong (US 20240175982) in view of Wang (UWB – radar – based synchronous motion recognition using time varying range-Doppler images, IET 2019).
Claim 1, Cong discloses a computer comprising a processor and a memory, the memory storing instructions executable by the processor to: receive radar data from an ultrawideband radar in a passenger compartment of a vehicle [see p0008 – p0016 - classification of an object within a vehicle using RADAR signal processing, the method may comprise identifying an object, such as preferably a human or other living vehicle occupant, within a vehicle using RADAR signals. The object may be assigned to a location within the vehicle by processing RADAR signals. One or more features about the object may be extracted by processing RADAR signals. The object may then be classified by processing RADAR signals];
identify a motion inside the passenger compartment based on the radar data received during the time-window; and actuate a component of the vehicle based on the identification of the motion [see p0037, p0160 - sensor(s) may be used to identify one or more occupants present in the cabin and, in some embodiments, identify features, such as vital signs, about such occupant(s); to assist in monitoring a possible health condition or other condition concerning for the safety of the vehicle occupants, detection of a particular vital sign or vital sign change associated with a particular occupant may trigger actuation of another sensor and/or monitor, such as a camera, within the vehicle].
Cong does not specifically teach dynamically set a starting-time for a time-window based on a second derivative of fast time in the radar data with respect to slow time in the radar data.
However, Wang discloses a novel UWB radar motion recognition architecture that can run simultaneously with actions. This architecture takes time-varying range–Doppler images (TRDI) as input and uses the conventional algorithms, specifically principal component analysis or deep learning-based algorithms, for feature extraction, and finally gated recurrent unit (GRU) is employed to dynamically model the features and get the classification result.
Further discloses, since there are many complex shaped static objects in the detection space, and the spatial extent of the target human body is much larger than the UWB radar transmit pulse width, the received signals inevitably contains multipath components. According to the UWB multipath channel model proposed by Tsao et al.; the received architecture of the UWB radar is represented as a matrix R, the elements of which are R[m, n] = ∑ i where s(t) ais(n Tf − τi) + avs(nTf − τv(mTs)) represents the elementary waveform, amplitude and propagation time delay of static objects. (M ×N) (1) ai and τi av are the and τv(t) correspond to human target reflection's amplitude and propagation time delay, while t is the slow time in which the reflected signal is attained. Sampling interval and fast-time sampling points are marked as Tf and n, respectively. Each row of the data in R is formed by fast-time sampling a pulse reflection signal, which is called a radar scan. Points generated by the fast-time sampling can be mapped to the range bins in which the reflected signal strength information at a specific distance is stored [see Section 2, Signal model, See Figs 1 – 2, page 2132].
Wang also discloses The radar received data can be directly used to demonstrate a range-time image (RTI), from which each blob's pixel intensity corresponds to that scattering centre's slow time and radial distance (or range). It is intuitive to observe the change in the distance of the target with slow time on RTIs. The range Doppler images (RDI) can be attained by applying Fourier transform across the range bins over a period of slow time, which maps received energy into a two dimensional space of range and frequency [see Section 2.2, page 2132 and Figs 1a – 1b].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include dynamically set a starting-time for a time-window based on a second derivative of fast time in the radar data with respect to slow time in the radar data, as suggested and taught by Wang, with a reasonable expectation of success, for the purpose of providing is the ability to recognize the motion with low time delay while ensuring performance. These results show the potential of the UWB radar motion recognition system for indoor monitoring and assisted living; automatically identifying the motion event time interval and the classification of motions in different directions.
Claim 16 is similarly rejected as Claim 1, see above.
Claim 3, Cong discloses the computer of claim 1, wherein the instructions further include instructions to refrain from using the radar data received before the starting-time to identify the motion inside the passenger compartment [see p0053 - if the vital sign data from the RADAR or other electromagnetic sensor has classified the occupant as an infant or child, rather than immediately sending a notification, the vehicle may be configured to monitor other data, such as temperature and/or time since the child has been left within the vehicle. Upon detecting a temperature within the cabin beyond a threshold temperature, such as 90 degrees Fahrenheit, for example, in combination with data indicative of a child being left in the vehicle, the vehicle may be configured to send a notification to a user].
Claim 18 is similarly rejected as Claim 3, see above.
Claim 4, Cong discloses the computer of claim 1, but is silent to wherein the instructions further include instructions to convert the radar data received during the time-window to a frequency domain, and identify the motion in the passenger compartment based on the radar data in the frequency domain.
However, Wang discloses a novel UWB radar motion recognition architecture that can run simultaneously with actions. This architecture takes time-varying range–Doppler images (TRDI) as input and uses the conventional algorithms, specifically principal component analysis or deep learning-based algorithms, for feature extraction, and finally gated recurrent unit (GRU) is employed to dynamically model the features and get the classification result [see page 2131].
Further teaching, a single RDI characterizes the overall performance of the range and frequency of the target's action over a period of time, but it does not contain time-varying information of the target's motion. In order to model the dynamic process of the motion, we propose a new data representation named TRDI. The whole process can be clearly seen in Fig. 2. The TRDI is composed of a series of RDIs, which are obtained by applying Fourier transform to the sub signals, and these sub-signals can be synchronously received as the action proceeds. This mechanism of synchronous signal reception and processing provides the basis for real-time processing. From the perspective of the entire motion signal, this process is equivalent to applying the short-time Fourier transform (STFT) to the complete motion signal. STFT is one of the most classical time–frequency analysis, given that s(t) represents the time-domain signal, STFT(n,k) is obtained by applying window function w(τ) s(t) to and computing the Fourier transform of the windowed signal [see Section 2, Signal model, page 2132].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the instructions further include instructions to convert the radar data received during the time-window to a frequency domain, and identify the motion in the passenger compartment based on the radar data in the frequency domain, as suggested and taught by Wang, with a reasonable expectation of success, for the purpose of providing is the ability to recognize the motion with low time delay while ensuring performance. These results show the potential of the UWB radar motion recognition system for indoor monitoring and assisted living; automatically identifying the motion event time interval and the classification of motions in different directions.
Claim 19 is similarly rejected as Claim 4, see above.
Claim 5, Cong discloses the computer of claim 4, but is silent to wherein the instructions further include instructions to convert the radar data received during the time-window to the frequency domain by applying a fast Fourier transform.
However, Wang discloses a single RDI characterizes the overall performance of the range and frequency of the target's action over a period of time, but it does not contain time-varying information of the target's motion. In order to model the dynamic process of the motion, we propose a new data representation named TRDI. The whole process can be clearly seen in Fig. 2. The TRDI is composed of a series of RDIs, which are obtained by applying Fourier transform to the sub signals, and these sub-signals can be synchronously received as the action proceeds. This mechanism of synchronous signal reception and processing provides the basis for real-time processing. From the perspective of the entire motion signal, this process is equivalent to applying the short-time Fourier transform (STFT) to the complete motion signal. STFT is one of the most classical time–frequency analysis, given that s(t) represents the time-domain signal, STFT(n,k) is obtained by applying window function w(τ) s(t) to and computing the Fourier transform of the windowed signal [see Section 2, Signal model, page 2132].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the instructions further include instructions to convert the radar data received during the time-window to the frequency domain by applying a fast Fourier transform, as suggested and taught by Wang, with a reasonable expectation of success, for the purpose of providing is the ability to recognize the motion with low time delay while ensuring performance. These results show the potential of the UWB radar motion recognition system for indoor monitoring and assisted living; automatically identifying the motion event time interval and the classification of motions in different directions.
Claim 6, Cong discloses the computer of claim 1, but is silent to wherein the time-window has a preset duration.
However, Wang discloses the TRDI is composed of a series of RDIs, which are obtained by applying Fourier transform to the sub-signals, and these sub-signals can be synchronously received as the action proceeds. This mechanism of synchronous signal reception and processing provides the basis for real-time processing. From the perspective of the entire motion signal, this process is equivalent to applying the short-time Fourier transform (STFT) to the complete motion signal. STFT is one of the most classical time–frequency analysis, given that s(t) represents the time-domain signal, STFT(n,k) is obtained by applying window function w(τ) s(t) to and computing the Fourier transform of the windowed signal [see Section 2, Fig 1B and page 2132].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the time-window has a preset duration, as suggested and taught by Wang, with a reasonable expectation of success, for the purpose of providing is the ability to recognize the motion with low time delay while ensuring performance. These results show the potential of the UWB radar motion recognition system for indoor monitoring and assisted living; automatically identifying the motion event time interval and the classification of motions in different directions.
Claim 12, Cong discloses the computer of claim 1, wherein the instructions further include instructions to classify the motion as a type of animate bulk motion based on the radar data received during the time-window [see p0008 - a method for classification of an object within a vehicle using RADAR signal processing, the method may comprise identifying an object, such as preferably a human or other living vehicle occupant, within a vehicle using RADAR signals. The object may be assigned to a location within the vehicle by processing RADAR signals].
Claim 14, Cong discloses the computer of claim 1, wherein the instructions further include instructions to identify a number of respirating individuals based on the radar data received during the time- window [see Cong p0006, p0037, p0065, features that may be extracted from RADAR or other electromagnetic signal data from within the cabin of a vehicle include variance of frequency-difference between Doppler peaks, spread bandwidth, number of valid detections, number of valid Doppler spectrum peaks, range, range extent, and occupant vital signs, such as breathing rate and heart rate].
Claim 15, Cong discloses the computer of claim 1, wherein the instructions further include instructions to command a user interface to output an alert to an occupant of the passenger compartment based on the identification of the motion [see p0158 - some vehicles/systems/methods may be configured to automatically take an action based upon a detected change in vital sign, such as a sufficiently dramatic increase or decrease in the vital sign. Such action(s) may comprise, for example, triggering a warning/notification/action, either within the vehicle or to a device remote from the vehicle, such as a smartphone. In some embodiments, if a dramatic increase or decrease in the vital sign, or another vital sign condition that is indicative of danger, is detected, the vehicle may be configured to take control from the driver (or, in the case of an autonomous vehicle, simply reconfigure a current driving instruction set) to slow the vehicle, pull the vehicle to the side of the road, and/or stop the vehicle].
Claim 20 is similarly rejected as Claim 15, see above.
Claim(s) 7 – 9 are rejected under 35 U.S.C. 103 as being unpatentable over Cong (US 20240175982) in view of Wang (UWB – radar – based synchronous motion recognition using time varying range-Doppler images, IET 2019) and Tiron (US 20230190140).
Claim 7, Cong discloses the computer of claim 1, wherein the instructions further include instructions to apply a filter to the radar data, and the second derivative is of the radar data after the application of the filter.
However, Tiron discloses a method for monitoring the physiological condition of a person, comprising: identifying coughing by a person by (i) accessing a passive signal generated with a microphone by passive non-contact sensing in a vicinity of the person, the passive signal representing acoustic information detected by the microphone, (ii) deriving one or more cough related features from the signal, and (iii) classifying, or transmitting for classification, the one or more cough related features to generate an indication of one or more events of coughing or coughing type by the person; and receiving physiological data associated with one or more physiological parameters of the person [see Summary of Inv].
Further teaching, The RF transmitter generates and/or emits radio waves having a predetermined frequency and/or a predetermined amplitude (e.g., within a high frequency band, within a low frequency band, long wave signals, short wave signals, etc.). The RF receiver detects the reflections of the radio waves emitted from the RF transmitter, and this data can be analyzed by the control system to determine a location of the user, one or more cough events, one or more physiological parameters, and/or one or more of the sleep-related parameters; respiration and any respiratory conditions [see Summary].
Further teaching, The method of the one or more processors may include pre-processing a sound signal generated by the microphone to produce the passive signal. The pre-processing may include any one, more or all of: (a) filtering with an infinite impulse response filter; (b) baseline removal comprising subtraction of a minimum over a sliding window; (c) artefact removal employing a percentile limit; (d) normalization with a standard deviation over a sliding window; and (e) integration and high-pass filtering.
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the instructions further include instructions to apply a filter to the radar data, and the second derivative is of the radar data after the application of the filter, as suggested and taught by Tiron, with a reasonable expectation of success, for the purpose of providing improvement to signal quality, removing unwanted noise, and optimizing system performance.
Claim 8, Cong discloses the computer of claim 7, but is silent to wherein the filter is a smoothing filter.
However, Tiron discloses if the active cluster proportion is large, the frequency feature may be computed as the mean over the active cluster. From the passive sensing stream (e.g., fpeak_median_passive); (a) a 3-point median filter maybe applied to the modulation frequency time series for smoothing; (b) the median over passive clusters may be taken as the feature [see p0452].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the filter is a smoothing filter, as suggested and taught by Tiron, with a reasonable expectation of success, for the purpose of providing with a reasonable expectation of success, for the purpose of providing improvement to signal quality, removing unwanted noise, and optimizing system performance.
Claim 9, Cong discloses the computer of claim 7, but is silent to wherein the filter is a bandpass filter isolating frequencies for human respiration.
However, Tiron discloses one or more processors may be configured to detect a peak with a pre-defined respiration range of the autocorrelated, pre-processed sound signal. The one or more processors may be configured to determine a respiration rate estimate from peaks of a plurality of signals, each of the plurality of signals being a sound signal of a discrete frequency band and processed by the pre-processing and the autocorrelating…[see p0086]; signal pre-processing (e.g., of a generated acoustic sound signal) can involve applying a digital bandpass filter, retaining frequency content in the range 100 Hz to 4,000 Hz (or higher). This can be implemented with a direct form FIR filter using a Kaiser window or by other means [see p0530].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the filter is a bandpass filter isolating frequencies for human respiration, as suggested and taught by Tiron, with a reasonable expectation of success, for the purpose of providing with a reasonable expectation of success, for the purpose of providing improvement to signal quality, removing unwanted noise, and optimizing system performance.
Claim(s) 13 is rejected under 35 U.S.C. 103 as being unpatentable over Cong (US 20240175982) in view of Wang (UWB – radar – based synchronous motion recognition using time varying range-Doppler images, IET 2019) and Podkamien (US 20230168364).
Claim 13, Cong discloses the computer of claim 12, but is silent to wherein the instructions further include instructions to classify the motion as a type of animate bulk motion by executing a neural network classifier with the radar data received during the time-window as an input.
However, Podkamien discloses systems and methods for radar based monitoring of the cabin of a vehicle. In particular, but not exclusively, the disclosure relates to detecting occupancy, posture and classification of occupants of vehicles and controlling the vehicle's system based on the monitored parameters such as mass or size or orientation of occupying objects.
Further teaching, a radar sensor array that is installed in a position allowing monitoring of the cabin of the vehicle and its occupants. The radar unit comprises of an array of transmitters and receivers which are configured to transmit a beam of electromagnetic radiations towards the vehicle passengers and receive the electromagnetic waves reflected by the passengers, respectively. Signals may be clustered by synchronized movements, to identify individual passengers and to detect hand gestures and the like. An aspect of the invention is to provide a radar system that comprises a central sensor providing multidimensional time dependent tracking within the cabin of a vehicle for determining passengers within the cabin. Thus a RADAR sensor unit is located in the ceiling of a vehicle to monitors passengers in the front and back seats of the vehicle is shown. Typically the sensor operates in a pulsed mode [see Summary and p0157 – p0161].
The pre-processing unit receives the electromagnetic signal from the radar receiver and extract person key points using a trained deep neural network (DNN). See Figs 10B – 11E, the sensor has been positioned close to a human subject, the two elements represent two modes of motion, one originating from the respiratory motion of the human subject, and the other originating from the heartbeat motion of the human subject. As can be seen, the elements represent motions which have different periodicity from one another [see at least p0194, p0273 – p0284].
It would have been obvious before the effective date of the claimed invention to one of ordinary skill in the art to modify the device in Cong, to include wherein the instructions further include instructions to classify the motion as a type of animate bulk motion by executing a neural network classifier with the radar data received during the time-window as an input, as suggested and taught by Podkamien, with a reasonable expectation of success, for the purpose of providing simpler system which replace the vehicle sensors and operate safety systems according to the passengers seating position.
Allowable Subject Matter
If 112 overcome properly, Claims 10 and 11 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
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.
The examiner has pointed out particular references contained in the prior art of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. Applicant should consider the entire prior art as applicable as to the limitations of the claims. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RENEE LAROSE whose telephone number is (313)446-4856. The examiner can normally be reached on Monday - Friday 8:30am - 5:00pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abby Lin can be reached on (571) 270-3976. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Renee LaRose/Examiner, Art Unit 3657
/ABBY LIN/ Supervisory Patent Examiner, Art Unit 3657