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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 – 5, 7, 12 – 16, 18 and 20 are rejected under 35 U.S.C. 103 as being obvious over Ian (US 20230168364 A1) in view of Zeng (US 12139087 B1).
As to claim 1, 12 and 18, Ian discloses the method of localizing one or more objects within an enclosed environment with radar modules including a plurality of antennas pointed at different positions within the enclosed environment (Figs. 3 – 5), the method comprising:
receiving output from receivers of the radar modules based on reflected radar signals within the enclosed environment as detected by the plurality of antennas (Fig. 7 step 706);
generating a preprocessed data set for each of the plurality of antennas (Figs. 7 any one of steps 708 and 710);
localizing movements of the one or more objects within the enclosed environment based on the preprocessed data sets for the plurality of antennas (Para. 356 “localized energy” and Para. 22 “macro and minor movements” and Fig. 7 step 716 “each seat”); and
determining occupancy of the one or more objects at the different positions based on the localized movements of the one or more objects within the enclosed environment (Fig. 7 step 716).
In addition to claim 1, claims 12 and claim 18 include the feature adjusting the environmental feature associated with the enclosed environment based on the localized movements (Para. 214 “The occupancy information from the matching unit 318 may be used to detect when a passenger is in difficulty or a baby is left behind in a vehicle, and after a crash to inform emergency personnel that there is life in a vehicle and in which seat. Where, after a crash, the breathing or heat beat of one or more passengers is detected intermittently or as labored, this information may be used by emergency personnel in deciding which passenger should be rescued and evacuated first.”)
The issue is whether the primary reference meets the scope of the claimed feature radar modules including a plurality of antennas pointed at different positions. Technically, each antenna element of an array is going to have different line-of-sights (LOS’s) thus meeting scope of different positions. The term radar module is broader in scope than the term radar because module is a generic term that can be associated with any component of said radar system. The transmitter 306 and receiver 310 are two modules of the radar thus constituting the scope of a plurality of radar modules. Nonetheless, the claims require a plurality of receivers. In fact, Applicant shows different, separate radars 40 positioned throughout the interior of a vehicle as shown in Fig. 1. In another embodiment, Ian’s Fig. 5 shows a seat head rest having a radar. Ian do not explicitly say whether there are other separate radars.
One of ordinary skill understands that a radar transceiver array and separate radars have the same ability to resolver targets in an angular dimension based on MIMO techniques.
In the same field of endeavor, Zeng shows multiple radar nodes 110B located throughout a vehicle cabin as shown in Fig. 3A – 3B.
In view of the teachings of Zeng, it would have been obvious to the ordinarily skilled before filing to substitute several radars for a single radar in order to reduce the risk of complete failure in event of an error or degraded status because having one radar fail does not mean all of the other radars would fail.
As to claims 2, Ian in view of Zeng teaches the method of claim 1, further comprising adjusting an environmental feature of the enclosed environment based on the determined occupancy (Ian: Fig. 7 step 718. Para. 113 “warning light” meets the scope as provided by Applicant at Spec. Para. 17).
As to claims 3, 15 and 20 Ian in view of Zeng teaches the method of claim 2, wherein the enclosed environment is a vehicle cabin including one or more subsystems, and wherein the one or more subsystems include an infotainment system, vehicle controls, climate controls, safety features, or any combination thereof (Ian: Para. 213 “air bag”).
As to claim 4, Ian in view of Zeng teaches the method of claim 1, further comprising classifying a movement category for each of the one or more objects based on the localized movements, wherein the movement category is one of gross movements of the one or more objects and breathing patterns of the one or more objects (Ian: Para. 22 “the data detected which includes both macro and minor movements over time, can monitor posture, hand gestures, breathing and heart rate”).
As to claims 5 and 16, Ian in view of Zeng teaches the method of claim 1/12, further comprising generating the preprocessed data sets at different time slices for each of the plurality of antennas based on the radar signals collected by each of the plurality of antennas within a preset duration defining each of the different time slices (Ian: Para. 254 “If multiple antennas are used to transmit, the transmission can be done either sequentially (antenna-by-antenna) or simultaneously”).
As to claim 7, Ian in view of Zeng teaches the method of claim 1, further comprising applying a function to the preprocessed data to determine a number of scalar values equal to a number of the different positions within the enclosed environment (Ian: Para. 319 “The magnitude of a complex value may indicate the probability that a reflecting object is located in that coordinate.” At Spec. Para. 11, Applicant describes scalar value as a confidence score, which is another term for probability. Ian’s Fig. 7 step 718 makes clear that the detection occurs for each seat, which would the probability.).
As to claim 13, Ian in view of Zeng teaches the method of claim 12, wherein the enclosed environment is a vehicle cabin, and wherein the localized movements are from one or more occupants within the vehicle cabin, or one or more doors being opened (Ian: Para. 22 as previously cited.).
As to claim 14, Ian in view of Zeng teaches the method of claim 12, wherein the one or more objects are one or more vehicle occupants, and wherein a change in the radar signals detected by the plurality of antennas is based on at least one of gross movements of the one or more vehicle occupants and breathing patterns of the one or more vehicle occupants (Ian: Para. 22 as previously cited.).
Claims 6 and 17 are rejected under 35 U.S.C. 103 as being obvious over Ian in view of Zeng and in further view of Stadelmayer (US 20230108140 A1).
As to claims 6 and 17, Ian in view of Zeng does not teach the method of claim 1, wherein the preprocessed data sets are range-Doppler plots.
In the same field of endeavor, Stadelmayer teaches range-Doppler plots at Para. 76.
In view of the teachings of Stadelmayer, it would be obvious to the ordinarily skilled before filing to plot data including peaks in two dimensions namely range and Doppler in order increase resolution to better resolve the targets thereby improving accuracy.
Claim 8 is rejected under 35 U.S.C. 103 as being obvious over Ian in view of Zeng and in further view of Miller (US 10829072 B2).
As to claim 8, Ian in view of Zeng does not teach the method of claim 7, wherein each of the scalar values is a confidence score indicative of whether a respective one of the different positions is occupied, the method further comprising: determining the confidence score for one of the scalar values exceeds a threshold; and classifying a respective one of the different positions as occupied.
In addition to Para. 319, Ian at Para. 380 disclosed “method 2 maintained only coordinates with energy above a threshold which could be relative to a peak value or an absolute value.” Although Ian discloses a threshold and a probability as cited, Ian does not teach the confidence score (probability) being above a threshold.
In the same field of endeavor, Miller teaches “The score is compared to a threshold value stored in the memory of the image processing ECU 110 to identify an occupant in the one or more images (col. 6 ll. 30 – 40).”
Claims 9 – 10 are rejected under 35 U.S.C. 103 as being obvious over Ian in view of Zeng and in further view of Choi (US 20240372755 A1) or Bicais (US 20210377083 A1).
As to claim 9, Ian in view of Zeng does not teach the method of claim 1, wherein the step of localizing the movement is performed by a trained deep neural network, wherein a number of input channels of the trained deep neural network is equal to a number of the plurality of antennas.
In the same field of endeavor, Choi teaches “wherein the receiver is configured to receive the superposition of sounding reference signals via each of a plurality of receive antennas resulting in a superposition of sounding reference signals for each receive antenna and wherein the processor is configured to generate the input to the neural network from the superpositions of sounding reference signals received for the receive antennas (Para. 292).”
In the same field of endeavor, Bicais shows an input from each receive antenna fed into a neural network as shown in Fig. 4.
In view of the teachings of either one of Choi or Bicais, it would have been obvious to the ordinarily skilled before filing to provide an input to each receive antenna of Ian, as modified by Zeng, so that positional data including spatial differences can be accounted for thereby improving accuracy.
As to claim 10, Ian in view of Zeng, Stadelmayer and one of either Choi or Bicais teaches the method of claim 9, wherein a number of output neurons of the trained deep neural network is equal to a number of the different positions within the enclosed environment (Ian discloses localizing movements for each seat at Fig. 7 step 716 & Para. 150. The fact that Ian is determining an occupancy for “each” seat is indicative of having a number of inputs equal to number of seats equal to the number of outputs.).
Claim 11 is rejected under 35 U.S.C. 103 as being obvious over Ian in view of Zeng and in further view of either one of Choi or Bicais and in further view of Stadelmayer.
As to claim 11, Ian in view of Zeng and one of either Choi or Bicais does not teach the method of claim 9, wherein the trained deep neural network includes convolutional layers, pooling layers, a linear layer, and a classifier model, the method further comprising: producing, via the convolutional layers and the pooling layers, refined output data based on the preprocessed data sets; serializing the refined output data to a linear layer that includes a number of output neurons equal to a number of conditions of the enclosed environment to be determined; applying a function with a classifier model to map the linear layer to a number of scalar values equal to a number of output neurons; and localizing movements within the enclosed environment based on the scalar values.
Ian discloses a convolutional neural network CNN at Para. 334, but does not provide the particulars. The Examiner believes the claimed features resemble that of a standard CNN as evidenced by Ian at Para. 322 which cites to papers regarding neural networks.
In the same field of endeavor, Stadelmayer teaches “For instance, the classification algorithm 111 could be feedforward implemented as a CNN. Here, since compact input ward ar- data - e.g., the one or more 1-D time series or data architecture derived therefrom - is used as an input, a compact implementation of the CNN would be possible. For instance, it has been observed that an implementation that includes 5 or fewer convolutional layers performing 1-D convolutions only for processing each one of the one or more 1-D time series yields accurate results. For example, a 3-layered 1-D CNN may be used. The first layer uses 32 filters with a kernel size of 64, the second and third layer are using 64 kernels with a filter width of 32. After each convolutional layer, an average pooling of size 2 can be performed and a rectified linear unit (ReLU) is applied as activation function. After the convolutional block, the tensor is flattened and fed into a fully connected layer with 32 output dimensions (Table 0001).”
In view of the teachings Stadelmayer, it would have been obvious to a person having ordinary skill to implement the CNN as taught by Stadelmayer in order to better determine a motion class thereby improving accuracy.
Claim 19 is rejected under 35 U.S.C. 103 as being obvious over Ian in view of Zeng and in further view of Cortambert (US 20140240166 A1).
As to claim 19, Ian in view of Zeng does not teach the system of claim 18, wherein the radar modules are mounted in the enclosed environment in a cruciform arrangement.
In the same field, Cortambert teaches “FIG. 1 represents a basic diagram of a 3D radar with cruciform antenna making it possible to analyze the data regarding distances of the target and position in space by measuring bearing and elevation.”
In view of the teachings of Cortambert, it would have been obvious to a person having ordinary skill in the art before filing to apply a cruciform arrangement in order to determine both bearing and elevation thereby improving resolution thus accuracy.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL W JUSTICE whose telephone number is (571)270-7029. The examiner can normally be reached 7:30 - 5:30 M-F.
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/MICHAEL W JUSTICE/Examiner, Art Unit 3648
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