DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This action is responsive to the original application filed on 9/23/2022 and the Remarks and Amendments filed on 12/9/2025. Applicant elected Group I (claims 1-14 and 29) and has canceled the claims of Group II (claims 15-28 and 30). Therefore claims 1-14 and 29 are pending.
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 1-14 and 29 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50 (“2019 PEG”).
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The Step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g., mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in Step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined at step 2A, Prong 2 that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
Claim 1
Step 1: The claim recites a method; therefore, it is directed to the statutory category of a process.
Step 2A Prong 1: The claim recites, inter alia:
estimating a representation of a channel … to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of estimating a representation of a channel based on transmitter and receiver locations and a representation of the spatial environment, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
wherein estimating the representation of the channel using the machine learning model comprises estimating a set of vectors representing each ray of a plurality of rays in the channel, each set of vectors representing propagation of the ray over time: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of estimating vectors representing rays, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2: The claim does not recite any additional limitations which integrate the abstract idea into a practical application. Specifically, the additional elements consist of “using a machine learning model trained to” and “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel”.
The additional elements of “using a machine learning model trained to” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the trained machine learning model is broadly used to estimate representations of the channel. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)).
Thus, even when viewed individually and as an ordered combination, these additional elements do not integrate the abstract idea into a practical application and the claim is thus directed to the abstract idea.
Step 2B: Finally, the claim taken as a whole does not contain an inventive concept which provides significantly more than the abstract idea.
The additional elements of “using a machine learning model trained to” amount to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the trained machine learning model is broadly used to estimate representations of the channel. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)).
The additional element “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)), and is a well-understood, routine, conventional activity (see MPEP § 2106.05(d)(II)(i); “Receiving or transmitting data over a network”).
Taken alone or in combination, the additional elements of the claim do not provide an inventive concept and thus the claim is subject-matter ineligible.
Claim 2
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the machine learning model is trained to generate the estimated representation of the channel based on transmission of rays uniformly sampled on an ellipsoid representing the transmitter” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 3
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the machine learning model is trained to generate the estimated representation of the channel based on a set of rays selected based on the location of the transmitter and the location of the receiver in the spatial environment” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 4
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
estimate the set of rays: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of estimating rays, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
generate an embedding representing a relationship between the location of the transmitter and the location of the receiver in the spatial environment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating an embedding, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
encodes the location of the transmitter and the location of the receiver in the spatial environment into line-of-sight information: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating an encoding of locations, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
identify a plurality of rays to launch towards the receiver based on the embedding representing the relationship between the location of the transmitter and the location of the receiver in the spatial environment and the line-of-sight information: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying rays to launch, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “a ray launching model”, “an embedding model”, “an image prior model”, and “a ray launching model” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 5
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generate a probabilistic heat map associated with an ellipsoid around the location of the transmitter, flattened over a two-dimensional grid: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a heat map, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “model trained to” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 6
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
estimate signal attenuation due to reflections off surfaces in the spatial environment and a second sub-model configured to estimate signal attenuation due to free space in the spatial environment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of estimating signal attenuation, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “a first sub-model configured to” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 7
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
estimate a distance over which signals propagate relative to a transmission source or reflection point in the spatial environment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of estimating a distance, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The additional elements of “the first sub-model is further configured to” amount to generic computer components used as a tool to perform an existing process. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 8
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional elements of “each vector includes position information, direction information, gain information, time of flight information, and a validity score indicating whether a ray contributes to the channel” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 9
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein a respective set of vectors representing a respective ray of the plurality of rays is terminated based on one of: a location of the ray intersecting with the location of the receiver, or a power of the ray falling below a threshold value” amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP § 2106.05(h). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 10
Step 1: A process, as above.
Step 2A Prong 1: The claim recites the abstract ideas of the preceding claims from which it depends.
Step 2A Prong 2, Step 2B: The additional element of “wherein the machine learning model is trained to minimize a line of sight (LOS) between an estimated state of the channel and an actual state of the channel over time” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the model is trained to minimize a LOS. Thus, the additional elements amount to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). Taken alone or in combination, the additional elements of the claim do not provide an inventive concept, integrate the abstract ideas into a practical application, or provide significantly more than the abstract ideas of the claim and thus the claim is subject-matter ineligible.
Claim 11
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
generating a graphical rendering of the estimated representation of the channel in the three-dimensional representation of the spatial environment: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of generating a graphical representation of the channel, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception
Claim 12
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
selecting one or more beams for communications between the transmitter and the receiver based on the estimated representation of the channel: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of selecting a beam, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception
Claim 13
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
detecting presence and location of objects in the spatial environment based on the estimated representation of the channel: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of detecting presence of objects, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception
Claim 14
Step 1: A process, as above.
Step 2A Prong 1: The claim recites, inter alia:
identifying material properties of the spatial environment based on the estimated representation of the channel: Under its broadest reasonable interpretation in light of the specification, this limitation encompasses the mental process of identifying properties of the environment, which is an evaluation or observation that is practically capable of being performed in the human mind with the assistance of pen and paper.
Step 2A Prong 2, Step 2B: The claim does not recite any additional elements that are sufficient to integrate the judicial exceptions into a practical application or amount to significantly more than the judicial exception
Claim 29
Claim 29 recites a processing system (step 1: a machine) using a processor and memory to perform the steps of claim 1 which by MPEP 2106.05(f) (“apply it”) cannot integrate an abstract idea into a practical application or provide significantly more than the abstract idea by itself, and are thus rejected for the same reasons set forth in the rejection of claim 1.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 7-11, and 29 are rejected under 35 USC § 103 as being obvious over Zhu et al. (Zhu et al., “A Deep Learning and Geospatial Data-Based Channel Estimation Technique for Hybrid Massive MIMO Systems”, Jan. 29, 2022, arXiv:2201.12676v1, pp. 1-18, hereinafter “Zhu”) in view of Song et al. (US 20220376957 A1, hereinafter “Song”) and Rappaport et al. (US 20220163616 A1, hereinafter “Rappaport”).
Regarding claim 1, Zhu discloses [a] computer-implemented method of wireless communications, comprising: (Abstract’ “a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuzzy c-Means (FCM) clustering”)
estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of [[a transmitter]] in a spatial environment, a location [[of a receiver]] in the spatial environment, and a three-dimensional representation of the spatial environment; and (Page 2, §B; “a learning-based channel estimation approach for the MU-mMIMO hybrid precoding RF beamformer has been presented; and Page 9, §IV; and figures 4 and 11; the figures disclose the channel representation estimation using a ML model based on 3D geospatial data)
wherein estimating the representation of the channel using the machine learning model comprises estimating a set of vectors representing each ray of a plurality of rays in the channel (Page 145125, §B and C,1; “We assume that the topology of the one-step model that fits the mapping from the input vector to one of the six path parameters (i.e., AAoD) also fits the mapping from the input vector to all six path parameters …. For the uth path, 15 input features Fu are organized as a three-dimensional input vector fc u ∈ R1× 15× 1, and the corresponding output is the categorical class tag vu with two values, where vu = 1 if the uth path is existent and vu = 0 if the uth path is non-existent … The outpu tvector of the convolution operation Ic l ∈ R15× 1× Col is activated by the rectified linear unit (ReLU) φReLU(Ic l ). The output vector of the lth CONV layer is given by Oc l = φReLU(Ic l ) ∈ R15× 1× Col. After Lc consecutive CONV layers, the input vector of the following max pooling layer IP ∈ R15×1×CoLc is operated with a window of size 1 × 2 and stride s = 1, in order to extract the max value between the neighboring elements in each depth of the input vector”, which discloses estimating a set of vectors representing rays or paths or six path parameters as the output of the machine learning model for each propagation path or the “u-th path”).
Zhu fails to explicitly disclose but Song discloses a transmitter and a receiver ([0055]; “During operation, an estimate and prediction of a communication channel between a transmitter side and a receiver side is normally needed. Channel estimation is typically performed by sending a reference signal from the transmitter and measuring the reference signal at the receiver. The reference signal is made up of a sequence of symbols that are known a priori by both the transmitter and receiver. The receiver can thus estimate the communication channel based on the received symbols and the known symbols”)
taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel ([0129]; “In some example embodiments, the method further comprises determining a beamforming configuration for transmission from the first device to the second device based on the channel estimation information; and transmitting data over the communication channel from the first device to the second device based on the beamforming configuration”, which discloses, under a broadest reasonable interpretation of the claim language, taking an action based on an estimated representation of the channel, the action being transmitting beamforming information based on channel estimation information and further including transmitting a signal including the beamforming information from a transmitter based on the channel estimation information).
Zhu and Song are analogous art because both are concerned with the use of machine learning for estimating representations of channels in wireless communications. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and wireless communications to combine the transmitter and receiver and actions of Song with the method of Zhu to yield to the predictable result of estimating a representation of a channel using a machine learning model trained to generate the estimated representation of the channel based on a location of a transmitter in a spatial environment, a location of a receiver in the spatial environment, and a three-dimensional representation of the spatial environment … wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel. The motivation for doing so would be to adapt transmissions to current channel conditions and achieve reliable communication with high data rates (Song; [0002]).
Zhu fails to explicitly disclose but Rappaport discloses each set of vectors representing propagation of the ray over time (Abstract; “Each of the signals has a multipath component. Then, it is possible to determine time of flight (ToF) information and angle of arrival (AoA) information of the multipath components present in the signal”, which discloses per-ray or multipath parameters that represent a ray or path over time; and [0019]; and [0078]).
Zhu, Song, and Rappaport are analogous art because all are concerned with the use of machine learning for estimating representations of channels in wireless communications. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and wireless communications to combine the ray propagation over time of Rappaport with the method of Zhu and Song to yield to the predictable result of wherein estimating the representation of the channel using the machine learning model comprises estimating a set of vectors representing each ray of a plurality of rays in the channel, each set of vectors representing propagation of the ray over time. The motivation for doing so would be to provide for real time imaging and position location using a mobile or portable (e.g., moveable or attachable or handheld) device (Rappaport; [0007]).
Regarding claim 29, it is a system claim corresponding to the steps of claim 1 and is rejected for the same reasons as claim 1.
Regarding claim 7, the rejection of claims 1 and 6 are incorporated and Zhu discloses wherein the first sub-model is further configured to estimate a distance over which signals propagate relative to a transmission source or reflection point in the spatial environment (§III.B).
Regarding claim 8, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Rappaport discloses and each vector includes position information, direction information, gain information, time of flight information, and a validity score indicating whether a ray contributes to the channel ([0019]; and Figure 12B, Element 2030 and 2035).
The motivation to combine Zhu, Song, and Rappaport is the same as discussed above with respect to claim 1.
Regarding claim 9, the rejection of claims 1 and 6 are incorporated and Zhu discloses wherein a respective set of vectors representing a respective ray of the plurality of rays is terminated based on one of: a location of the ray intersecting with the location of the receiver, or a power of the ray falling below a threshold value (§III.B and IV).
Regarding claim 10, the rejection of claim 1 is incorporated and Zhu discloses wherein the machine learning model is trained to minimize an line of sight (LOS) between an estimated state of the channel and an actual state of the channel over time (§III.B and IV).
Regarding claim 11, the rejection of claim 1 is incorporated and Zhu discloses wherein the taking the one or more actions further comprises generating a graphical rendering of the estimated representation of the channel in the three-dimensional representation of the spatial environment (Figures 4, 9, and 10).
Claims 2-6 are rejected under 35 USC § 103 as being obvious over Zhu in view of Song and Rappaport and further in view of Dent et al. (US 20100309994 A1, hereinafter “Dent”).
Regarding claim 2, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Dent discloses wherein the machine learning model is trained to generate the estimated representation of the channel based on transmission of rays uniformly sampled on an ellipsoid representing the transmitter (Figure 1; the figure discloses estimating a channel representation based on rays sampled on an ellipsoid depicted as dotted lines in the figure; and [0016]).
Zhu, Song, Rappaport, and Dent are analogous art because all are concerned with the use of machine learning for estimating representations of channels in wireless communications. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and wireless communications to combine the set of rays of Dent with the method of Zhu and Song and Rappaport to yield to the predictable result of wherein the machine learning model is trained to generate the estimated representation of the channel based on transmission of rays uniformly sampled on an ellipsoid representing the transmitter. The motivation for doing so would be to improve the accuracy of scattering object characterizations used to determine channel estimates (Dent; [0001]).
Regarding claim 3, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Dent discloses wherein the machine learning model is trained to generate the estimated representation of the channel based on a set of rays selected based on the location of the transmitter and the location of the receiver in the spatial environment (Figure 1; and [0016]).
The motivation to combine Zhu, Song, Rappaport, and Dent is the same as discussed above with respect to claim 2.
Regarding claim 4, the rejection of claims 1 and 3 are incorporated and Zhu discloses a ray launching module trained to identify a plurality of rays to launch towards the receiver based on the embedding representing the relationship between the location of the transmitter and the location of the receiver in the spatial environment and the line-of-sight information (§III.B).
Zhu fails to explicitly disclose but Dent discloses wherein: the machine learning model comprises a ray launching model configured to estimate the set of rays, and the ray launching model comprises: an embedding model trained to generate an embedding representing a relationship between the location of the transmitter and the location of the receiver in the spatial environment; an image prior model that encodes the location of the transmitter and the location of the receiver in the spatial environment into line-of-sight information; and (Figure 1; and [0016]).
The motivation to combine Zhu, Song, Rappaport, and Dent is the same as discussed above with respect to claim 2.
Regarding claim 5, the rejection of claims 1 and 3 are incorporated and Zhu further discloses generate a probabilistic heat map [[associated with an ellipsoid around the location of the transmitter,]] flattened over a two-dimensional grid (Figure 4)
Zhu fails to explicitly disclose but Dent discloses associated with an ellipsoid around the location of the transmitter (Figure 1; and [0016]).
The motivation to combine Zhu, Song, Rappaport, and Dent is the same as discussed above with respect to claim 2.
Regarding claim 6, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Dent discloses a first sub-model configured to estimate signal attenuation due to reflections off surfaces in the spatial environment and a second sub-model configured to estimate signal attenuation due to free space in the spatial environment (Figure 1; and [0016]; and [0037]).
The motivation to combine Zhu, Song, Rappaport, and Dent is the same as discussed above with respect to claim 2.
Claims 12-14 are rejected under 35 USC § 103 as being obvious over Zhu in view of Song and Rappaport and further in view of Ji et al. (US 20210143879 A1, hereinafter “Ji”).
Regarding claim 12, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Ji discloses wherein the taking the one or more actions further comprises selecting one or more beams for communications between the transmitter and the receiver based on the estimated representation of the channel ([0061]).
Zhu, Song, Rappaport, and Ji are analogous art because all are concerned with the use of machine learning for estimating representations of channels in wireless communications. Before the effective filing date of the claimed invention, it would have been obvious to one skilled in machine learning and wireless communications to combine the selecting beam communications of Ji with the method of Zhu and Song and Rappaport to yield to the predictable result of wherein the taking the one or more actions comprises selecting one or more beams for communications between the transmitter and the receiver based on the estimated representation of the channel. The motivation for doing so would be to provide a method in which a device transmits and receives data to and from a terminal using directly obtained information (Ji; [0009]).
Regarding claim 13, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Ji discloses wherein the taking the one or more actions further comprises detecting presence and location of objects in the spatial environment based on the estimated representation of the channel ([0081]).
The motivation to combine Zhu, Song, Rappaport, and Ji is the same as discussed above with respect to claim 12.
Regarding claim 14, the rejection of claim 1 is incorporated and Zhu fails to explicitly disclose but Ji discloses wherein the taking the one or more actions further comprises identifying material properties of the spatial environment based on the estimated representation of the channel ([0081]).
The motivation to combine Zhu, Song, Rappaport, and Ji is the same as discussed above with respect to claim 12.
Response to Arguments
Applicant’s arguments and amendments, filed on 12/9/2025, with respect to the 35 U.S.C. 112(b) rejection of claim 10 have been fully considered and are persuasive. The 35 U.S.C. 112(b) rejection of claim 10 is withdrawn.
Applicant’s arguments and amendments, filed on 12/9/2025, with respect to the nonstatutory, non-provisional double patenting rejection of claim 1 have been fully considered and are persuasive. The nonstatutory, non-provisional double patenting rejection of claim 1 is withdrawn.
Applicant’s arguments and amendments, filed on 12/9/2025, with respect to the 35 U.S.C. 101 rejection of the pending claims have been fully considered and are not persuasive.
With respect to Step 2A, Prong 2, Applicant argues “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, transmitting and/or receiving a wireless signal (based on wireless channel estimation done using machine learning) … any alleged abstract idea has been integrated into the practical application of wireless communication (transmission and/or reception) based on improved channel estimation which accounts for information about an environment within which a transmitter and a receiver operate … the features of the present claims reflect an improvement to a technology or technical field … the pending claims are eligible because the claims as a whole improve wireless communication technology and thus integrate the exception into a practical application of ‘estimating a representation of a channel’ and ‘taking one or more actions’ comprising ’at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel.’”, and cites to paragraphs [0025] and [0031] as evidenced of an alleged technical improvement. Examiner respectfully disagrees.
Applicant has failed to provide evidence from the originally filed specification or otherwise as to why the specifically identified additional elements of the independent claims beyond the identified abstract ideas - “using a machine learning model trained to” and “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel” – integrate the abstract ideas of the independent claims into a practical application or provide a technical improvement. The additional element of “using a machine learning model trained to” amounts to reciting only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished because it is not clear how the trained machine learning model is broadly used to estimate representations of the channel. Using a generic neural network to implement abstract ideas does not render a claim eligible under 35 USC § 101. Thus, the additional element amounts to no more than a recitation of the words "apply it" (or an equivalent) or are more than mere instructions to implement an abstract idea or other exception on a computer (see MPEP § 2106.05(f)). The additional element “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel” is insignificant extra-solution activity required for any uses of the abstract ideas (see MPEP § 2106.05(g)). It appears that the claims are directed towards the improvement of abstract ideas, namely generating channel estimations and estimating vectors. Abstract ideas or judicial exceptions alone cannot reflect a technical improvement. See MPEP §2106.05(a).
With respect to Step 2B, Applicant argues “the Specification provides sufficient details to show how the claimed features improve the technical field of wireless communications (e.g., channel estimation which accounts for an environment within which a transceiver and receiver are positioned) … the Specification discusses how the particular arrangement of steps recited in the claims provides a technical improvement. Thus, like the patents at issue in Cosmokey, the claims clearly provide a technical improvement to a technical field (e.g., a technical improvement to the field of wireless communication and practical implementation thereof, for example, by generating channel estimations with machine learning models that account for an environment within which a receiver and transceiver are located and taking one or more actions including at least one of "transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel")”, and cites to paragraphs [0025] and [0031] as evidenced of an alleged technical improvement. Examiner respectfully disagrees.
As stated above with respect to Step 2A, Prong 2, it appears that the claims are directed towards the improvement of abstract ideas, namely generating channel estimations and estimating vectors. Abstract ideas or judicial exceptions alone cannot reflect a technical improvement. See MPEP §2106.05(a). Applicant has failed to provide evidence why the additional elements of the claims- “using a machine learning model trained to” and “taking one or more actions based on the estimated representation of the channel, wherein taking the one or more actions comprises at least one of transmitting a signal from the transmitter or receiving another signal at the receiver, based on the estimated representation of the channel” – reflect the alleged technical improvements that are disclosed in the originally filed specification.
Accordingly, Applicant’s arguments and amendments are not persuasive, and the 35 USC § 101 rejection of the pending claims is maintained.
Applicant’s arguments and amendments, filed on 12/9/2025, with respect to the 35 USC § 103 rejection of the pending claims have been fully considered but are moot because the arguments do not apply to the combination of references used to reject the independent claims. Zhu, Song, and Rappaport are now being used to render the independent claims obvious under 35 USC § 103.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/BRENT JOHNSTON HOOVER/Primary Examiner, Art Unit 2127