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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step One
The claims are directed to a method (claims 1 - 10), a processing system with structural components (claims 11 - 20), and a non-transitory computer readable medium (21 – 29). Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
As to claim 1,
Step 2A, Prong One
The claim recites in part:
generating a wireless communication configuration based on processing the fused plurality of features using a machine learning model
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. “Generating” is a mental process involving the creation of new mental representations, ideas, or thoughts that go beyond previously stored memory.
Additionally, As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
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Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
accessing a plurality of data samples corresponding to a plurality of data
modalities;
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
The claim further recites:
generating a plurality of features by, for each respective data sample of the plurality of data samples, performing feature extraction based at least in part on a respective modality of the respective data sample;
fusing the plurality of features using one or more attention-based models;
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The recitation of data sample and data modalities amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
accessing a plurality of data samples corresponding to a plurality of data
modalities;
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
The claim further recites:
generating a plurality of features by, for each respective data sample of the plurality of data samples, performing feature extraction based at least in part on a respective modality of the respective data sample;
fusing the plurality of features using one or more attention-based models;
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of data sample and data modalities amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 2, The limitations “the plurality of data modalities comprises at least one of: (i) image data, (ii) radio detection and ranging (radar) data, (iii) light detection and ranging (LIDAR) data, or (iv) relative positioning data” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
As to claim 3,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein performing the feature. extraction comprises, for a first data sample of the plurality of data samples:
determining a first modality, from the plurality of data modalities, of the first data sample;
selecting a trained feature extraction model based on the first modality; and
generating a first set of features by processing the first data sample using the trained feature extraction model.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein performing the feature. extraction comprises, for a first data sample of the plurality of data samples:
determining a first modality, from the plurality of data modalities, of the first data sample;
selecting a trained feature extraction model based on the first modality; and
generating a first set of features by processing the first data sample using the trained feature extraction model.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception
As to claim 4,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the plurality of data samples comprises, for each respective data modality of the plurality of data modalities, a sequence of data samples;
the fused plurality of features comprises a sequence of fused features; and
the machine learning model comprises a time-series-based machine learning model that processes the sequence of fused features to generate the wireless communication configuration.
these elements are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f)). These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)).
The recitation of “the plurality of data samples comprises, for each respective data modality of the plurality of data modalities, a sequence of data sample” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the plurality of data samples comprises, for each respective data modality of the plurality of data modalities, a sequence of data samples;
the fused plurality of features comprises a sequence of fused features; and
the machine learning model comprises a time-series-based machine learning model that processes the sequence of fused features to generate the wireless communication configuration.
are recited at a high-level of generality and amounts to no more than adding the words “apply it” to the judicial exception. These limitations also amount to extra solution activity because it is a mere nominal or tangential addition to the claim, amounting to mere data output (see MPEP 2106.05(g)). The courts have similarly found limitations directed to displaying a result, recited at a high level of generality, to be well-understood, routine, and conventional. See (MPEP 2106.05(d)(II), "presenting offers and gathering statistics.", “determining an estimated outcome and setting a price”).
The recitation of “the plurality of data samples comprises, for each respective data modality of the plurality of data modalities, a sequence of data sample” amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 5,
Step 2A, Prong One
The claim recites in part:
wherein the wireless communication configuration comprises a selection of a beam for performing wireless communications with one or more wireless devices.
As drafted and under its broadest reasonable interpretation, this limitation covers performance of the limitation in the mind (including an observation, evaluation, judgment, opinion) or with the aid of pencil and paper but for the recitation of generic computer components. “Selecting” is a fundamental process that allows humans to prioritize, interpret, and make choices based on goals or stimuli.
Additionally, As per MPEP 2106.04(a)(2)(III)(C)), a claim that requires a computer may still recite a mental process. In evaluating whether a claim that requires a computer recites a mental process, examiners should carefully consider the broadest reasonable interpretation of the claim in light of the specification. For instance, examiners should review the specification to determine if the claimed invention is described as a concept that is performed in the human mind and applicant is merely claiming that concept performed 1) on a generic computer, or 2) in a computer environment, or 3) is merely using a computer as a tool to perform the concept. In these situations, the claim is considered to recite a mental process.
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Accordingly, at Step 2A, Prong One, the claim is directed to an abstract idea.
Step 2A, Prong Two
Further the claim does not include additional elements that integrate this abstract idea into a practical application. “Selecting” is performed using generic computer components performing their typical functions and does not provide a meaningful technological improvement.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
Nothing in the claim adds “significantly more” beyond generic computing.
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 6,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
facilitating wireless communications with the one or more wireless devices using the selected beam.
which amounts to extra-solution activity of gathering data for use in the claimed process. As described in MPEP 2106.05(g), limitations that amount to merely adding insignificant extra-solution activity to a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application.
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
facilitating wireless communications with the one or more wireless devices using the selected beam.
are recited at a high level of generality and amounts to extra-solution activity of receiving data i.e. pre-solution activity of gathering data for use in the claimed process. The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 7,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the machine learning model is trained using a pre-training operation comprising:
generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and
training the machine learning model based on the first plurality of predicted beams and the first relative angle information.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of received power simulator and relative angle information amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the machine learning model is trained using a pre-training operation comprising:
generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and
training the machine learning model based on the first plurality of predicted beams and the first relative angle information.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of received power simulator and relative angle information amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 8,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
the machine learning model is refined using an adaptation operation comprising:
generating a second plurality of predicted beams based on the received power simulator and second relative angle information;
measuring actual received power information based on the second plurality of predicted beams; and
training the machine learning model based on the actual received power information and the second relative angle information.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
the machine learning model is refined using an adaptation operation comprising:
generating a second plurality of predicted beams based on the received power simulator and second relative angle information;
measuring actual received power information based on the second plurality of predicted beams; and
training the machine learning model based on the actual received power information and the second relative angle information.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein the adaptation operation further comprises:
in response to determining that the actual received power information differs from predicted received power information beyond a threshold, measuring actual received power information for at least one additional beam; and
training the machine learning model based on the actual received power information for the at least one additional beam and the
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of actual received power information amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein the adaptation operation further comprises:
in response to determining that the actual received power information differs from predicted received power information beyond a threshold, measuring actual received power information for at least one additional beam; and
training the machine learning model based on the actual received power information for the at least one additional beam and the
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
The recitation of actual received power information amounts to generally linking the use of the judicial exception to a particular environment of field of use (See MPEP 2106.05(h)).
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
As to claim 9,
Step 2A, Prong One
The claim does not recite an abstract idea or any other judicial exception and therefore passes Step 2A, Prong One of the Alice/Mayo analysis.
Step 2A, Prong Two
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
wherein training the machine learning model comprises:
generating a plurality of weights for the first plurality of predicted beams based on received power for each of the first plurality of predicted beams;
generating a binary cross-entropy loss based on the plurality of weights; and
updating one or more parameters of the machine learning model based on the binary cross-entropy loss.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2A, Prong Two, the additional elements individually or in combination do no integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of:
wherein training the machine learning model comprises:
generating a plurality of weights for the first plurality of predicted beams based on received power for each of the first plurality of predicted beams;
generating a binary cross-entropy loss based on the plurality of weights; and
updating one or more parameters of the machine learning model based on the binary cross-entropy loss.
which is recited at a high-level of generality with no detail of the training process and amounts to no more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (See MPEP 2106.05(f))
Accordingly, at Step 2B the additional elements individually or in combination do not amount to significantly more than the judicial exception.
Claim 11 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The non-transitory computer readable medium, one or more processors, and processing system are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 18 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 19 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim 20 has similar limitations as claim 10. Therefore, the claim is rejected for the same reasons as above.
Claim 21 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
The non-transitory computer readable medium, one or more processors, and processing system are recited at a high-level of generality and amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Claim 22 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 23 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 24 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 25 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 27 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 28 has similar limitations as claim 8. Therefore, the claim is rejected for the same reasons as above.
Claim 29 has similar limitations as claim 9. Therefore, the claim is rejected for the same reasons as above.
Claim Rejections - 35 USC § 102
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.
Claim(s) 1, 2, 4, 11, 12, 14, 21, 22, 24, and 30, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Liang et al (US 2020/0025935).
As to claim 1, Liang et al figure 2 shows and teaches a processor-implemented method (paragraph [0005]… present disclosure is directed to an object detection system that includes a LIDAR system, a map system, a fusion system, and a detector system), comprising:
accessing a plurality of data samples corresponding to a plurality of data
modalities (paragraph [0093]…one or more sensor systems (e.g., LIDAR system 202 and camera system 204), map system 206, fusion system 208, and detector system 210) (Examiner’s Note: “one or more sensor systems” reads on “plurality of modalities”);
generating a plurality of features by, for each respective data sample of the
plurality of data samples, performing feature extraction based at least in part on a
respective modality of the respective data sample (paragraph [0140]… Our overall architecture includes two streams, with one stream extracting image features and another one extracting features from LIDAR BEV);
fusing the plurality of features using one or more attention-based models (paragraph [0102]… The fusion system 208 can be further configured to execute at a machine-learned neural network within BEV system 226, one or more continuous convolutions to fuse the image features from the first data stream (e.g., image stream 222) with the LIDAR features from the second data stream (e.g., BEV stream 224). The fusion system 208 can also be configured to generate a feature map 228 that includes the fused image features and LIDAR features) (Examiner’s Note: “machine-learned neural network” reads on “attention-based model”); and
generating a wireless communication configuration based on processing the
fused plurality of features using a machine learning model (paragraph [0103]… the detector system 210 can include a machine-learned detector model 230 configured to receive the map-modified LIDAR data and/or feature map as input and, in response to receiving the map-modified LIDAR data, to generate as output a plurality of detector outputs 232. Detector outputs 232 can correspond to detections of identified objects of interest within the map-modified LIDAR data and/or feature map ; paragraph [0105]… detector output(s) 232 can be provided to one or more of the perception system 124, prediction system 126, motion planning system 128, and vehicle control system 138 to implement additional autonomy processing functionality based on the detector output(s) 232. For example, motion planning system 128 of FIG. 1 can determine a motion plan for the autonomous vehicle (e.g., vehicle 102) based at least in part on the detection output(s) 232 of FIG. 2) (Examiner’s Note: “motion planning system” reads on “wireless communication configuration”).
As to claim 2, Liang et al figure 2 shows and teaches the processor-implemented method, wherein the plurality of data modalities comprises at least one of: (i) image data, (ii) radio detection and ranging (radar) data, (iii) light detection and ranging (LIDAR) data, or (iv) relative positioning data (paragraph [0093]…Object detection system 200 can include, for example, one or more sensor systems (e.g., LIDAR system 202 and camera system 204), map system 206).
As to claim 4, Liang et al figure 2 shows and teaches the processor-implemented method, wherein:
the plurality of data samples comprises, for each respective data modality of the
plurality of data modalities, a sequence of data samples (paragraph [0103)… first data stream (e.g., image stream 222) with the LIDAR features from the second data stream (e.g., BEV stream 224)(Examiner’s Note: “data stream” reads on “sequence of data samples”);
the fused plurality of features comprises a sequence of fused features (paragraph [0102]…The fusion system 208 can also be configured to generate a feature map 228 that includes the fused image features and LIDAR features. In some implementations, the feature map 228 is configured as a bird's eye view representation for subsequent analysis, which can advantageously maintain a data structure native to the 3D sensors such as LIDAR and facilitate training of machine-learned models employed in the corresponding fusion system 208)(Examiner’s Note: “bird's eye view representation” reads on “sequence of fused features”); and
the machine learning model comprises a time-series-based machine learning model that processes the sequence of fused features to generate the wireless communication configuration (paragraph [0103]… the detector system 210 can include a machine-learned detector model 230 configured to receive the map-modified LIDAR data and/or feature map as input and, in response to receiving the map-modified LIDAR data, to generate as output a plurality of detector outputs 232. Detector outputs 232 can correspond to detections of identified objects of interest within the map-modified LIDAR data and/or feature map.)(Examiner’s Note: “map-modified LIDAR data” reads on “time-series-based”).
Claim 11 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 12 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim 14 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 21 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
Claim 22 has similar limitations as claim 2. Therefore, the claim is rejected for the same reasons as above.
Claim24 has similar limitations as claim 4. Therefore, the claim is rejected for the same reasons as above.
Claim 30 has similar limitations as claim 1. Therefore, the claim is rejected for the same reasons as above.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3, 13, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (US 2020/0025935) in view of Howard (US 2020/0057965).
As to claim 3, Liang et al teaches performing the feature extraction.
Liang et al fails to explicitly show/teach performing the feature extraction comprises, for a first data sample of the plurality of data samples: determining a first modality, from the plurality of data modalities, of the first data sample; selecting a trained feature extraction model based on the first modality; and generating a first set of features by processing the first data sample using the trained feature extraction model.
However, Howard figure 4a shows and teaches performing the feature extraction comprises, for a first data sample of the plurality of data samples; determining a first modality, from the plurality of data modalities, of the first data sample; selecting a trained feature extraction model based on the first modality; and generating a first set of features by processing the first data sample using the trained feature extraction model.
(paragraph [0072]…. Data from data channels 401, such as text data 410, image data 411, video data 412, audio data 413 and sensor data 414 may be input to data schema 402 ; paragraph [0074]… Models processor 404 may include a plurality of models such as models 423-1, 423-2, 423-3 through 423-N. For example, each model 423-1-423-N may be a particular type of model, such as is described below, and may handle a particular type of data ;.Models processor 404 may select models 424 from model database 448 for advanced feature extraction and processing depending on the available events that may be stored in events database 403. Further, models processor 403 may use sequences of models. The models selected and processed 435 by models processor 404 may be sent to models output schema 424.) (Examiner’s Note: “data channels” reads on “plurality of modalities” ; “select models” reads on “selecting a trained feature extraction model” ; “models output schema” reads on “generate”).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Liang et al to perform the feature extraction comprises, for a first data sample of the plurality of data samples; determining a first modality, from the plurality of data modalities, of the first data sample; selecting a trained feature extraction model based on the first modality; and generating a first set of features by processing the first data sample using the trained feature extraction model, as in Howard, for the purpose of automating techniques that may provide enhanced security, safety, and reduced costs.
Claim 13 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim 23 has similar limitations as claim 3. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 5, 6, 15, 16, 25, and 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (US 2020/0025935) in view of MOHAMMADI et al (US 2021/0270930).
As to claim 5, Liang et al teaches the wireless communication configuration.
Liang et al fails to explicitly show/teach the wireless communication configuration comprises a selection of a beam for performing wireless communications with one or more wireless devices.
However, MOHAMMADI et al teaches a wireless communication configuration comprises a selection of a beam for performing wireless communications with one or more wireless devices (paragraph [0061]…FIG. 5 illustrates an exemplary embodiment utilizing beam management. In the exemplary embodiment, the future route of a terminal device may be known, and the route information may be provided to a network entity, for example to a base station such as a gNB, as one or more future position estimates of the terminal device. The base station may then use the one or more future position estimates for example to predict which beam should be selected next for the terminal device, and/or to predict what content should be available for pre-caching in the next base station. By using the one or more future position estimates of the terminal device for example for beam management, beam sweeping for finding the terminal device may be reduced, thus resulting in more efficient use of the resources of the network).
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Liang et al’s wireless communication configuration to comprise a selection of a beam for performing wireless communications with one or more wireless devices, as in MOHAMMADI et al, for the purpose predicting a future position of the terminal device based on the one or more future position estimates.
As to claim 6, MOHAMMADI et al teaches facilitating wireless communications with the one or more wireless devices using the selected beam (paragraph [0061]…FIG. 5 illustrates an exemplary embodiment utilizing beam management. In the exemplary embodiment, the future route of a terminal device may be known, and the route information may be provided to a network entity, for example to a base station such as a gNB, as one or more future position estimates of the terminal device. The base station may then use the one or more future position estimates for example to predict which beam should be selected next for the terminal device, and/or to predict what content should be available for pre-caching in the next base station. By using the one or more future position estimates of the terminal device for example for beam management, beam sweeping for finding the terminal device may be reduced, thus resulting in more efficient use of the resources of the network)
It would have been obvious for facilitating wireless communications with the one or more wireless devices using the selected beam, for the same reasons as above.
Claim 15 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 16 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim 25 has similar limitations as claim 5. Therefore, the claim is rejected for the same reasons as above.
Claim 26 has similar limitations as claim 6. Therefore, the claim is rejected for the same reasons as above.
Claim(s) 7, 17, and 27 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (US 2020/0025935) in view of Holt (US 2010/0310175).
As to claim 7, Liang et al teaches the machine learning model.
Liang et al fails to explicitly show/teach that the machine learning model is trained using a pre-training operation comprising: generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and
training the machine learning model based on the first plurality of predicted beams and the first relative angle information.
However, Holt teaches a machine learning model is trained using a pre-training operation comprising: generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and training the machine learning model based on the first plurality of predicted beams and the first relative angle information (paragraph [0040]… machine learning model is trained using a pre-training operation comprising: generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and training the machine learning model based on the first plurality of predicted beams and the first relative angle information)
Therefore, it would have been obvious for one having ordinary skill in the art, at the time the invention was made for Liang et al’s machine learning model to be trained using a pre-training operation comprising: generating a first plurality of predicted beams based on a received power simulator and first relative angle information; and training the machine learning model based on the first plurality of predicted beams and the first relative angle information, as in Holt, for the purpose of providing views of objects that are otherwise occluded from visual inspection.
Claim 17 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Claim 27 has similar limitations as claim 7. Therefore, the claim is rejected for the same reasons as above.
Conclusion
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128