DETAILED ACTION
This action is made FINAL in response to the amendments filed on 4/29/2026.
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 of wireless communications (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;
facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration.
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 claim further recites one or more wireless devices which is 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)).
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;
facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration.
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 claim further recites one or more wireless devices which is 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)).
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:
wherein facilitating the wireless communications with the one or more wireless devices comprises transmitting one or more wireless signals to the one or more wireless devices according to the wireless communication configuration.
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:
wherein facilitating the wireless communications with the one or more wireless devices comprises transmitting one or more wireless signals to the one or more wireless devices according to the wireless communication configuration.
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, processing system, and one or more devices 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, processing system, and one or more devices 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 § 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) 1 - 4, 6, 11 - 14 ,16, 21- 24, and 30 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liang et al (US 2020/0025935) in view of UCHIDA et al (US 2023/0097466).
As to claim 1, Liang et al figure 2 shows and teaches a processor-implemented method of wireless communications (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 ; paragraph [0074]… The communications network 108 can exchange (send or receive) signals (e.g., electronic signals) or data (e.g., data from a computing device) and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology (or topologies)), 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”).
Liang et al fails to explicitly show/teach facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration.
However, UCHIDA et al teaches facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration (paragraph [0056]…Hereinafter, an embodiment of the present invention will be described with reference to drawings. FIG. 1 is a block diagram illustrating a configuration of a wireless communication system 1 of a first embodiment. The wireless communication system 1 includes a mobile station device 2, a plurality of ground station devices 3-1, 3-2, . . . , a bridge device 4, and a communication network 5. The mobile station device 2 is a device that is allowed to move by being carried by a person or mounted on a vehicle)
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 facilitate the wireless communications with one or more wireless devices according to the wireless communication configuration, as in UCHIDA et al, for the purpose of high-volume data transfer for transferring storage data such as map data, video data, sensor data,
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”).
As to claim 6, UCHIDA et al teaches wherein facilitating the wireless communications with the one or more wireless devices comprises transmitting one or more wireless signals to the one or more wireless devices according to the wireless communication (paragraph [0056]…Hereinafter, an embodiment of the present invention will be described with reference to drawings. FIG. 1 is a block diagram illustrating a configuration of a wireless communication system 1 of a first embodiment. The wireless communication system 1 includes a mobile station device 2, a plurality of ground station devices 3-1, 3-2, . . . , a bridge device 4, and a communication network 5. The mobile station device 2 is a device that is allowed to move by being carried by a person or mounted on a vehicle).
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 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 16 has similar limitations as claim 6. 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(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 UCHIDA et al (US 2023/0097466) and in further view of Howard (US 2020/0057965).
As to claim 3, Liang et al teaches performing the feature extraction.
Liang et al and UCHIDA et al both fail 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, 15, 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 UCHIDA et al (US 2023/0097466) and in further view of MOHAMMADI et al (US 2021/0270930).
As to claim 5, Liang et al teaches the wireless communication configuration.
Liang et al and UCHIDA et al both fail 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.
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 UCHIDA et al (US 2023/0097466) and in further view of Holt (US 2010/0310175).
As to claim 7, Liang et al teaches the machine learning model.
Liang et al and UCHIDA et al both fail 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.
Response to Arguments
Applicant's arguments filed 4/29/2026 have been fully considered but they are not persuasive.
Claim Rejections - 35 USC § 101
The 101 Rejection still has not been overcome. The claims are abstract and the steps in the claims can be completed with a mental process and/or generic computer components. Additionally, the steps in the claims do not describe an improvement of technology in any way.
The applicant argues:
In the present case, the claims are directed to a technical solution to problems arising in the field of wireless communication, and more specifically to improving the performance of wireless networks in handling multiple wireless devices. For example, as the Specification explains:
Wireless communication systems are widely deployed to provide various
telecommunication services such as telephony, video, data, messaging, broadcasts, etc. The current and future demands on wireless communication networks continue to grow. For example, Sixth Generation (6G) systems are expected to support applications such as augmented reality, multisensory communications, and high-fidelity holograms. These systems are further expected to serve a continuously growing number of devices while also accomplishing high standards regarding performance.
Specification [0003] (emphasis added). The claimed techniques provide a solution to this problem, namely through using machine learning to generate an optimal wireless communication configuration (e.g., one or more parameters of a beam). Id. 11 [0022]-[0025]. More specifically, the claimed techniques "leverage multimodal data and context awareness in order to provide improved communications (such as through more optimal beam selection)." Id. 1 [0022]. Further, because "different data modalities generally have significantly different characteristics, fusion of these different data modalities involves targeted feature extraction and fusion operations using machine learning." Id. Advantageously, the techniques claimed herein allow for the prediction or selection of the most suitable (or among the most suitable) wireless communication configuration
(e.g., a beam with the most robustness and/or highest throughput), thereby improving wireless communications (e.g., between a base station and a wireless device). Id. " [0022], [0049]-[0050].
The claims embody the solution described in the Specification (e.g., include the components or steps that provide the solution) by reciting, for example: (1) "accessing a plurality of data samples corresponding to a plurality of data modalities"; (2) "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"; (3) "fusing the plurality of features using one or more attention-based models"; (4) "generating a wireless communication configuration based on processing the fused plurality of features using a machine learning model"; and (5) "facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration."
The examiner disagrees with the applicant’s position, as the arguments presented rely on limitations that are neither explicitly recited in the claims nor reasonably inferred from them. At no point in the pending claims does the applicant assert, describe, or even suggest the limitations of:
using machine learning to generate an optimal wireless communication configuration (e.g., one or more parameters of a beam)
different data modalities generally have significantly different characteristics, fusion of these different data modalities involves targeted feature extraction and fusion operations using machine learning
the prediction or selection of the most suitable (or among the most suitable) wireless communication configuration (e.g., a beam with the most robustness and/or highest throughput), thereby improving wireless communications (e.g., between a base station and a wireless device)
Rather, the applicant appears to have introduced this language as part of the argument, but such a limitation cannot be read into the claims when it is not supported by the actual claim language.
Without clear support in the claim language the examiner cannot give weight to arguments premised on these alleged limitations.
The applicant argues:
Step 2A, Prong 2 - The Claims Integrate any Alleged Abstract Idea into a Practical Application
Applicant has elected to skip the Step 2A, Prong 1 analysis and move to Prong 2. Applicant asserts that any alleged abstract idea is integrated into a practical application. Under the second prong of the Step 2A inquiry, "Examiners evaluate integration into a practical application by evaluating additional elements individually and in combination to determine whether they integrate the exception into a practical application," such as by considering whether an additional element reflects "[a]n improvement in the functioning of a computer, or an improvement to other technology or technical field. M.P.E.P. § 2106.04(d); see also M.P.E.P. § 2106.05 (discussing improvements to computer functionality in the scope of the eligibility analysis). A claim comprising any additional element that integrates the judicial exception into a practical application of that exception is directed to eligible subject matter. Id.; see also Thales Visionix Inc. v. United States, 850 F.3d 1343, 1347 (Fed. Cir. 2017) (stating that claim elements are considered individually and as a whole so as to "articulate what the claims are directed to with enough specificity to ensure the [Step 2A] inquiry is meaningful"); Ex parte Donovan, Appeal No. 2017-005993 (P.T.A.B. Mar. 5, 2019) at pp. 9-15 (finding claims patent-eligible under Step 2A, Prong 2 of the Alice/Mayo test where the claim as a whole requires specific interoperation of hardware and specially configured computing modules).
Applicant submits that the claims are eligible under Step 2A, Prong 2 because various features of the claims, in fact, integrate any alleged abstract idea into a practical application-namely, generating an optimal wireless communication configuration by using machine learning to leverage multimodal data and context awareness (e.g., feature extraction and fusion). In particular, Applicant submits that this practical application reflects an improvement to other technology or a technical field, namely the field of wireless communication and, more specifically, the technology of facilitating wireless communications with wireless devices (e.g., transmitting a wireless signal to a device based on the optimal beam between the device and a base station).
Applicant respectfully directs the Examiner's attention to Example 48, claim 3. See July 2024 Subject Matter Eligibility Examples, p. 18. Example 48 describes an artificial intelligence- based method of analyzing speech signals and separating desired speech from extraneous or background speech. The Office explained that while claim 3 of Example 48 recites abstract ideas, the claim is directed to an improvement to existing speech-to-text technology. Specifically, the Office noted how the claim recited that the deep neural network, which was trained on speech source separation, could be used to make "individual transcription of each separated speech signal possible." Id., p. 28 (citing M.P.E.P. § 2106.05(a)). Accordingly, the USPTO said that this claim was patent eligible under Step 2A, Prong 2.
Likewise, here, the features of the present claims reflect an improvement to a technology or technical field. For example, the practical application improves the technical field of wireless communication by using a "machine learning (ML) and/or artificial intelligence (AI) to leverage multimodal data and context awareness in order to provide improved communications (such as through more optimal beam selection)." Specification 1 [0022]. As the Specification explains, because "different data modalities generally have significantly different characteristics, fusion of these different data modalities involves targeted feature extraction and fusion operations using machine learning." Id. In doing so, the machine learning system may provide the optimal wireless communication configuration (e.g., a beam with the most robustness and/or highest throughput). Id. 11 [0022], [0049]-[0050]. Accordingly, the system facilitates wireless communication using the optimal configuration, thereby improving wireless systems. Id. 11 [0022], [0129]. The pending claims reflect these improvements (e.g., include the components or steps that provide the improvements (see M.P.E.P. 2106.04(d)(1))) presented above (e.g., by performing feature extraction on data from a plurality of modalities, fusing the features, generating a wireless communication configuration based on processing the fused features, and then facilitating wireless communications with one or more devices based on the configuration). Therefore, the present claims are eligible under Step 2A, Prong 2.
Accordingly, Applicant submits that the claims are directed to a specific improvement in wireless communication technology and the field of selecting or predicting an optimal wireless communication configuration, and are thus eligible subject matter under Step 2A, Prong 2 of the Alice/Mayo test.
The examiner disagrees. The applicant’s reliance on Example 48, claim 3 is misplaced. Unlike Example 48, where the claimed invention improved the operation of speech-to-text technology through a specifically trained neural network that enabled individual transcription of separated speech signals, the pending claims do not recite a specific technological improvement to wireless communication technology or to the operation of a communication device. Instead, the claims broadly recite obtaining multimodal data, extracting and fusing features, processing the fused features using machine learning, and generating a wireless communication configuration. These limitations just employ generic machine learning techniques to analyze information and generate a result. Such data analysis, evaluation, and prediction constitute an abstract idea.
Further, the claims do not recite a particular machine learning architecture, feature extraction technique, feature fusion mechanism, beamforming algorithm, communication protocol improvement, or other technological mechanism that improves the functioning of a wireless communication system. Any alleged improvement to wireless communications comes from the generated information from the configuration rather than from an improvement to the underlying communication technology itself.
The claims just use machine learning as a tool to process information and select or predict a communication configuration. The additional elements do not impose anyu meaningful limit on the judicial exception and instead amount to applying the abstract idea in a generic environment. Accordingly, the claims do not integrate the recited abstract idea into a practical application.
The applicant argues:
Step 2B - The Claims Recite an Inventive Concept that Amounts to Significantly More than an Abstract Idea
Similar to BASCOM, the present claims recite non-conventional and non-generic methods and systems, in this case for facilitating wireless communications with devices based on an optimal configuration. For example, the Specification describes how wireless communication networks face growing demands in serving increasingly more devices while maintaining high performance standards. Specification 1 [0003]. Applicant's claimed solution improves how wireless systems
connect to devices, for example, by accounting for and processing data of different modalities. Id. [0022]. Specifically, because data from different modalities may have significantly different characteristics, the claimed solution includes targeted feature extraction and fusion using machine learning. Id. Based on the fused features, the system generates an optimal wireless communication configuration (e.g., one or more parameters of a beam), which is used to facilitate wireless communications with one or more devices. Id. " [0030], [0049]. Consequently, the techniques described herein significantly improve wireless communication by predicting and using the optimal configuration (e.g., the beam with the highest throughput and/or most robustness). Id." [0022], [0050]. The improvements are reflected in particular features in the claims, as presented above, such as (1) "accessing a plurality of data samples corresponding to a plurality of data modalities"; (2) "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"; (3) "fusing the plurality of features using one or more
attention-based models"; (4) "generating a wireless communication configuration based on processing the fused plurality of features using a machine learning model"; and (5) "facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration."
Applicant further respectfully directs the Examiner's attention to the Federal Circuit's decision in Cosmokey Solutions GMBH & Co. KG V. Duo Security LLC, in which a finding of ineligibility under Section 101 was reversed by the Federal Circuit. 15 F.4th 1091, 1097-98 (Fed. Cir. 2021). In that case, and in support of holding that the claims were directed to eligible subject matter under Step 2B of the Alice/Mayo test, the Federal Circuit noted that the specification "describes how the particular arrangement of steps in claim 1 provides a technical improvement" and "emphasizes the inventive nature" of the steps recited in the claims. Id. at 1099.
Likewise, as discussed supra, the Specification discusses how the particular arrangement of steps recited in the claims provide a technical improvement. For example, the solution involves accessing (e.g., collecting, generating, and/or receiving) data from different modalities. Specification I [0027]. The multimodal data undergoes feature extraction, and the extracted features are fused (e.g., concatenated, summed, and/or aggregated). Id. " [0042]-[0044], [0047].
The fused features become input to a machine learning model, and the model outputs data or features to a classifier (e.g., a multilayer perceptron). Id. 1 [0049]. The classifier, in turn, outputs a wireless communication configuration, such as a beam predicted to provide the best available communications between a base station and a wireless device (e.g., most robustness and/or highest throughput). Id. This particular arrangement of steps improves wirele communication at least
because it leverages multimodal data and context awareness, thereby improving how wireless systems facilitate connecting to devices. Id. 11 [0022], [0030], [0031], [0037], [0095], [0129].
Thus, like the patents at issue in Cosmokey, the claims provide a technical improvement to a technical field (e.g., a technical improvement to the field of selecting the optimal wireless communication configuration with wireless devices).
Applicant further submits that the claims are generally directed to facilitating wireless communications with (e.g., transmitting wireless signals to) wireless devices. Wireless communication with devices reflects significantly more than any abstract idea. If not, then the claims of every wireless communication patent application (e.g., WiFi, WLAN, WAN, 3G, 4G, and 5G communications) should likely analogously be rejected for ineligible subject matter. However, this has not been the case from the USPTO. The claims of this application should be no different just because they include features related to machine learning. Rather, the Examiner
should consider the claims as a whole. See M.P.E.P. § 2106.04. The claims as a whole improve wireless systems, such as by facilitating wireless communication according to the optimal configuration. See, e.g., Specification 1 [0022].
The examiner disagrees. The pending claims do not recite a specific technological solution that improves the functioning of a computer, wireless communication device, or communication network. The limitations simply describe the analysis of information and the use of the resulting information to make a decision, which constitutes an abstract idea.
The claims do not recite a particular feature extraction technique, attention model architecture, classifier architecture, beamforming algorithm, network protocol enhancement, or other technological mechanism that improves the operation of wireless communication technology itself. Instead, the machine learning model is invoked at a high level of generality as a tool for evaluating information and select a desired result.
Unlike BASCOM, the claims do not recite a non-conventional arrangement of technological components that produces a technological improvement. Unlike CosmoKey, the claims do not recite a specific technical implementation that solves a technological improvement. Any alleged improvement arises from the content of the information generated by the abstract analysis (the selected communication configuration) rather than from an improvement to the underlying technology used to perform wireless communications.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. See MPEP § 2106.04(d) (discussing Finjan, Inc. v. Blue Coat Sys., Inc., 879 F.3d 1299, 1303-04, 125 USPQ2d 1282, 1285-87 (Fed. Cir. 2018))
It is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology (MPEP 2106.05(a)(II).
Claim Rejections - 35 USC § 102 & 103
Liang et al fails to explicitly show/teach facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration.
However, UCHIDA et al teaches facilitating the wireless communications with one or more wireless devices according to the wireless communication configuration (paragraph [0056]…Hereinafter, an embodiment of the present invention will be described with reference to drawings. FIG. 1 is a block diagram illustrating a configuration of a wireless communication system 1 of a first embodiment. The wireless communication system 1 includes a mobile station device 2, a plurality of ground station devices 3-1, 3-2, . . . , a bridge device 4, and a communication network 5. The mobile station device 2 is a device that is allowed to move by being carried by a person or mounted on a vehicle)
Therefore, Liang et al in view of UCHIDA et al shows all the limitations as claimed.
Conclusion
THIS ACTION IS MADE FINAL. 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON S COLE whose telephone number is (571)270-5075. The examiner can normally be reached Mon - Fri 7:30pm - 5pm EST (Alternate Friday's Off).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Omar Fernandez can be reached at 571-272-2589. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRANDON S COLE/ Primary Examiner, Art Unit 2128
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