Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
The amendment filed November 24, 2025 has been entered. Claims 1-30 remain pending in the application.
Response to Arguments
Applicant's arguments with respect to claims 1-30 filed on November 12, 2025, have been fully considered but they are not persuasive.
On pp. 9 and 10 of Applicant’s response, Applicant argues that Zhou does not teach “each different dimension in the set of dimensions corresponds to a different number of real values to output” of claim 1. In particular, Applicant argues that Zhou does not teach that SNR has different number of real values to output when compared to words of Zhou. Examiner disagrees.
In Zhou, SNR (i.e., second dimension) and L words (first dimension) are different because L words correspond to words of a sentence, whereas the SNR corresponds to noise in the sentence separate from the words of the sentence. (See p. 1078, section II, right column, 2nd paragraph, lines 24-34, and p. 1080, right column, first paragraph, lines 2 and 3) Indeed, Zhou shows that the SNR is provided separately from the words used in the encoder. (See FIG. 4). Since SNR and L words are different, values they would output can be different. Thus, SNR and L words of Zhou teach that "each different dimension in the set of dimensions corresponds to a different number of real values to output" of claim 1. Therefore, Zhou in view of Xu teaches all of the limitations of claim 1.
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.
Claims 1, 2, 5-8, 10, 11, 13, 14, 17-20, 22, 24, 25, 28-30 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou et al. (“Adaptive Bit Rate Control in Semantic Communication with Incremental Knowledge-based HARQ,” July 5 2022) and in view of Xu et al. (“Wireless Image Transmission Using Deep Source Channel Coding With Attention Modules,” April 12, 2022).
Regarding claim 1, Zhou teaches “[a]n apparatus for wireless communications, comprising: a semantic encoder configured to: obtain a set of real values for transmission to a receiving device” (see p. 1078, section II, right column, 2nd paragraph, lines 10-12, p. 1080, right column, last paragraph, lines 11-13, and FIG. 4; semantic encoder SCen; the semantic encoder receives (i.e., obtains) input sentence s with L words, s = [w1, w2, . . . , wL] (i.e., a set of real values), which is being encoded for transmission (i.e., for transmission to a receiving device)); and
Zhou further teaches “encode the set of real values based on a semantic model and a first dimension of a set of dimensions, wherein each different dimension in the set of dimensions corresponds to a different number of real values to output” (see p. 1078, section II, right column, 2nd paragraph, lines 24-31; semantic encoding process sen = SCen (i.e., a semantic model) of input sentence s (i.e. the set of real values); the input sentence s has L words (i.e., a first dimension of a set of dimensions), and after completing the coding, the sen will be converted to RLxB, where B is the number of bits for each word according to a quantization module (i.e., each different dimension in the set of dimensions corresponds to a different number of real values to output)); and
Zhou further teaches “output an encoded set of real values” (see p. 1078, section II, right column, 2nd paragraph, lines 32-34; output b = Q(sen; thetaen) of the quantization process, which is the output of the encoded set of real values));
Zhou also teaches “a transmitter configured to output the encoded set of real values for transmission to the receiving device over a wireless communication channel” (see p. 1080, left column, first section, lines 6-7; p. 1083, section 4, left column, last paragraph, lines 2-4; transmitter chooses to transmit (i.e., output) b (i.e., the encoded set of real values) over a channel (i.e., a wireless communication channel); the wireless communication channel is additive white Gaussian noise (AWGN)); and
Zhou also teaches “wherein the semantic encoder is further configured to use a second dimension of the set of dimensions” (see p. 1080, right column, first paragraph, lines 2 and 3, and FIG. 4; semantics and SNR (i.e., a second dimension of the set of dimensions) are considered, and the SNR is provided as an input to the semantic encoder to determine appropriate bit length; thus, the semantic encoder uses a second dimension).
Zhou does not appear to explicitly disclose “a receiver configured to obtain feedback from the receiving device” and the semantic encoder uses the second dimension “based on the feedback” of claim 1. However, the foregoing limitations are well known in the art prior to the effective filing data of the claimed invention. For example, Xu teaches “a receiver configured to obtain feedback from the receiving device . . . based on the feedback” (see p. 2317, section II, left column, last paragraph, lines 2–4, and FIG. 2; joint source-channel encoder (i.e., a receiver) receives (i.e., obtains) SNR feedback from the joint source-channel decoder; thus, the receiver obtains the SNR feedback, which is used (i.e., based on the feedback) by the encoder). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou to incorporate the teachings of Xu to obtain SNR feedback from a receiving device, and based on the SNR feedback, the semantic encoder uses it as a second dimension. The suggestion to do so would have been to use SNR feedback for a large performance improvement (Xu at p. 2316, right column, 2nd paragraph, lines 40 and 41).
Regarding claim 2, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic model is trained based on a model of the wireless communication channel abstracted as an additive white Gaussian noise (AWGN) channel on top of an existing physical (PHY) layer and a medium access control (MAC) layer” (see Zhou at p. 1083, section 4, left column, last paragraph, lines 2-4, and p. 1084, Table 1; the wireless communication channel is additive white Gaussian noise (AWGN), where the PHY and the MAC layers are part of apparatus of a wireless communication system).
Regarding claim 5, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic model is trained based on target specific training data” (see Zhou at p. 1083, right column, last paragraph, lines 4 – 6, and p. 1084, left column, first paragraph, line 1; the training SNR for the semantic systems is in the range of -2dB to 6dB (i.e., target specific training data), which means during the training, each transmission will choose a channel SNR between -2dB and 6dB; thus, the semantic model is trained for target range of -2dB and 6dB SNR).
Regarding claim 6, the combination of Zhou and Xu teaches the apparatus of claim 5, and further teaches “wherein the target specific training data is based on an application or a downstream task associated with the wireless communication, a configuration of the apparatus, a configuration of the receiving device, or a combination thereof” (see Zhou at p. 1083, right column, last paragraph, lines 4 – 6, and p. 1084, left column, first paragraph, line 1; the target training data is based on choosing a channel SNR between -2dB and 6dB; thus, choosing a channel SNR is a downstream task associated with the wireless communication).
Regarding claim 7, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic model for the semantic encoder is jointly trained with a semantic model for a semantic decoder at the receiving device” (see Zhou at p. 1078, right column, second paragraph, lines 3-5; p. 1079, left column, 1st paragraph, line 12; the described semantic coder includes an encoder and a decoder, and the semantic coder (encoder & decoder) is trained based on deep learning (DL) techniques, such as transformers or LTSM; thus, the semantic decoder and encoder are jointly trained).
Regarding claim 8, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic encoder comprises a neural network” (see Zhou at p. 1078, left column, 1st paragraph, lines 2-4, and FIG. 5; deep neural network (DNN) structure is adapted for semantic coding, which includes semantic encoder; thus, the semantic encoder comprises a neural network structure).
Regarding claim 10, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “further comprising a source encoder configured to: obtain the encoded set of real values; and generate a set of coded symbols based on a third dimension, wherein: the source encoder being configured to generate the set of coded symbols comprises the source encoder being configured to map the encoded set of real values to a set of resource elements; and the transmitter being configured to output the encoded set of real values for transmission to the receiving device over a wireless communication channel comprises the transmitter being configured to output the set of coded symbols for transmission on the set of resource elements” (see Xu at p. 2318, right column, first paragraph, lines 2-6, and left column, first paragraph, lines 3-9; neural encoder includes multiple layers, where first few layers are source encoder and remaining layers are channel encoder (i.e., source encoder); the channel encoder (the source encoder) obtains the encoded values (i.e., the encoded set of real values) and the channel encoder (the source encoder) generates channel symbols (i.e., a set of coded symbols) based on channel SNR conditions (i.e., a third dimension); the channel encoder is configured to code according to the SNR and the resource assignment (i.e., resource elements) is based on the SNR to achieve the optimal quality of reconstructed images. Therefore, the transmitted encoded values of the neural encoder are based on the resource assignment/resource elements used by the channel encoder, and the transmission is based on the used resource assignment/resource elements. Thus, the channel symbols (set of coded symbols) map the encoded real values to resource elements and the channel symbols are transmitted over the resource assignment used). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou to incorporate the teachings of Xu to use a channel encoder based on channel SNR conditions to map the encoded set of real values to a set of resource elements and transmit the encoded set of real values based on the set of resource elements. The suggestion to do so would have been to use SNR feedback for a large performance improvement (Xu at p. 2316, right column, 2nd paragraph, lines 40 and 41).
Regarding claim 11, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic encoder is configured with a priori information, and wherein the semantic encoder is configured to encode the set of real values further based on the a priori information” (see Zhou at p. 1080, right column, first paragraph, lines 2 and 3, and FIG. 4; the SNR (i.e., a priori information) is provided as an input to the semantic encoder to determine appropriate bit length; thus, the semantic encoder is configured to encode the set of real values further based on the a priori information).
Regarding claim 13, Zhou teaches “[a]n apparatus for wireless communications, comprising: a receiver configured to: receive, from a transmitting device, a first encoded set of real values having a first dimension of a set of dimensions, wherein each different dimension in the set of dimensions corresponds to a different number of real values of the set of real values” (see p. 1078, section II, right column, 2nd paragraph, lines 10-12 and 24-34, p. 1080, right column, last paragraph, lines 11-13, and FIG. 4; semantic encoder SCen; the semantic encoder receives (i.e., obtains) input sentence s with L words, s = [w1, w2, . . . , wL] (i.e., a set of real values), which is being encoded for transmission; semantic encoding process sen = SCen (i.e., a semantic model) of input sentence s (i.e. the set of real values); the input sentence s has L words (i.e., a first dimension of a set of dimensions), and after completing the coding, the sen will be converted to RLxB, where B is the number of bits for each word according to a quantization module (i.e., each different dimension in the set of dimensions corresponds to a different number of real values to output); output b = Q(sen; thetaen) of the quantization process, which is the output of the encoded set of real values; thus, the received encoded values have a first dimension of a set of dimensions, wherein each different dimension in the set of dimensions corresponds to a different number of real values of the set of real values);
Zhou also teaches “a semantic decoder configured to: decode the first encoded set of real values based on a semantic model; and attempt to infer a set of real values” (see p. 1079, left column, first paragraph, lines 1-6; the decoding process is similar to the encoding process, and can be divided into dequantization and semantic decoding (i.e., decoding the set of real values based on a semantic model to infer a set of real values), and see equation (6)); and
Zhou does not explicitly teach “a transmitter configured to output feedback to the transmitting device, wherein the semantic decoder is further configured to receive, from the transmitting device, a second encoded set of real values having a second dimension of the set of dimensions in response to the feedback” of claim 1. However, the foregoing limitations are well known in the art prior to the effective filing data of the claimed invention. For example, Xu teaches “a transmitter configured to output feedback to the transmitting device, wherein the semantic decoder is further configured to receive, from the transmitting device, a second encoded set of real values having a second dimension of the set of dimensions in response to the feedback” (see p. 2317, section II, left column, last paragraph, lines 2–4, and FIG. 2; joint source-channel encoder (i.e., a transmitting device) receives SNR feedback from the joint source-channel decoder (i.e., a transmitter outputs feedback to the transmitting device); the joint source-channel encoder transmits encoded values (i.e., a second encoded set of real values) based on the SNR feedback (i.e., second dimension) to the joint source-channel decoder; thus, the second encoded set of real values have a second dimension based on the feedback provided the decoder). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou to incorporate the teachings of Xu to obtain SNR feedback from a receiving device, and based on the SNR feedback, the semantic encoder uses it as a second dimension. The suggestion to do so would have been to use SNR feedback for a large performance improvement (Xu at p. 2316, right column, 2nd paragraph, lines 40 and 41).
Regarding claim 14, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic model is trained based on a model of the wireless communication channel abstracted as an additive white Gaussian noise (AWGN) channel on top of an existing physical (PHY) layer and a medium access control (MAC) layer” (see Zhou at p. 1083, section 4, left column, last paragraph, lines 2-4, and p. 1084, Table 1; the wireless communication channel is additive white Gaussian noise (AWGN), where the PHY and the MAC layers are part of apparatus of a wireless communication system).
Regarding claim 17, the combination of Zhou and Xu teaches the apparatus of claim 13, and further teaches “wherein the semantic model is trained based on target specific training data” (see Zhou at p. 1083, right column, last paragraph, lines 4 – 6, and p. 1084, left column, first paragraph, line 1; the training SNR for the semantic systems is in the range of -2dB to 6dB (i.e., target specific training data), which means during the training, each transmission will choose a channel SNR between -2dB and 6dB; thus, the semantic model is trained for target range of -2dB and 6dB SNR).
Regarding claim 18, the combination of Zhou and Xu teaches the apparatus of claim 17, and further teaches “wherein the target specific training data is based on an application or a downstream task associated with the wireless communication, a configuration of the apparatus, a configuration of the receiving device, or a combination thereof” (see Zhou at p. 1083, right column, last paragraph, lines 4 – 6, and p. 1084, left column, first paragraph, line 1; the target training data is based on choosing a channel SNR between -2dB and 6dB; thus, choosing a channel SNR is a downstream task associated with the wireless communication).
Regarding claim 19, the combination of Zhou and Xu teaches the apparatus of claim 13, and further teaches “wherein the semantic model for the semantic encoder is jointly trained with a semantic model for a semantic decoder at the receiving device” (see Zhou at p. 1078, right column, second paragraph, lines 3-5; p. 1079, left column, 1st paragraph, line 12; the described semantic coder includes an encoder and a decoder, and the semantic coder (encoder & decoder) is trained based on deep learning (DL) techniques, such as transformers or LTSM; thus, the semantic decoder and encoder are jointly trained).
Regarding claim 20, the combination of Zhou and Xu teaches the apparatus of claim 13, and further teaches “wherein the semantic decoder comprises a neural network” (see Zhou at p. 1078, right column, second paragraph, lines 3-5; the described semantic coder includes an encoder and a decoder, and the semantic coder (encoder & decoder) is trained based on deep learning (DL) techniques, such as transformers or LTSM (i.e., a neural network); thus, the semantic decoder comprises a neural network structure).
Regarding claim 22, the combination of Zhou and Xu teaches the apparatus of claim 1, and further teaches “wherein the semantic encoder is configured with a priori information, and wherein the semantic decoder is configured to decode the set of real values further based on the a priori information” (see Zhou at p. 1080, right column, first paragraph, lines 2 and 3, and FIG. 4; the SNR (i.e., a priori information) is provided as an input to the semantic encoder to determine appropriate bit length; thus, the semantic encoder is configured to encode the set of real values further based on the a priori information, and the semantic decoder decodes the received set of encoded real values based on the a priori information).
Regarding claims 24, 25, 28, and 29, they are the method claims corresponding to the apparatus claims of claims 1, 2, 5 and 6 that have been rejected above. Applicant’s attention is directed to the rejection of claims 1, 2, 5 and 6. Claims 24, 25, 28, and 29 are rejected under the same rationale as claims 1, 2, 5 and 6.
Regarding claim 30, it is the method claim corresponding to the apparatus claim of claim 13 that has been rejected above. Applicant’s attention is directed to the rejection of claim 13. Claim 30 is rejected under the same rationale as claim 13.
Claims 3, 4, 15, 16, 26, and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Xu and further in view of Zhang et al. (“Semantic Communications with Variable-Length Coding for Extended Reality,” February 17, 2023).
Regarding claim 3, the combination of Zhou and Xu teaches the apparatus of claim 1, and the combination of Zhou teaches training using a cross-entropy (CE) loss function (see Zhou at p. 1079, left column, 1st paragraph, lines 15 and 16), but does not explicitly disclose “wherein the semantic model is trained based on one or more target specific perceptual loss values” of claim 3. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Zhang teaches “wherein the semantic model is trained based on one or more target specific perceptual loss values” (see p. 4, right column, second paragraph, lines 1–5; to improve the perceptual quality of reconstructed XR images, perceptual-friendly semantic losses are used (i.e., trained based on one or more perceptual loss values) to enhance the semantic information extraction and reasoning process in semantic coding modules (i.e., semantic model)). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Zhang to train the semantic model based on one or more perceptual values. The suggestion to do so would have been to improve quality of reconstructed images (Zhang at p. 4, right column, 2nd paragraph, lines 1 and 2).
Regarding claim 4, the combination of Zhou, Xu, and Zhang teaches the apparatus of claim 3, and further teaches “wherein the target specific perceptual loss values are based on an application or a downstream task associated with the wireless communication, a configuration of the apparatus, a configuration of the receiving device, or a combination thereof” (see Zhang at p. 4, right column, second paragraph, lines 1–5; to improve the perceptual quality of reconstructed XR images, perceptual-friendly semantic losses are used; thus, a downstream task is reconstruction of the XR images associated with the described wireless communication, and the perceptual loss values are based on improving reconstructing XR images). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Zhang to have the perceptual values based on a downstream task. The suggestion to do so would have been to improve quality of reconstructed images (Zhang at p. 4, right column, 2nd paragraph, lines 1 and 2).
Regarding claim 15, the combination of Zhou and Xu teaches the apparatus of claim 13, and the combination of Zhou teaches training using a cross-entropy (CE) loss function (see Zhou at p. 1079, left column, 1st paragraph, lines 15 and 16), but does not explicitly disclose “wherein the semantic model is trained based on one or more target specific perceptual loss values” of claim 15. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Zhang teaches “wherein the semantic model is trained based on one or more target specific perceptual loss values” (see p. 4, right column, second paragraph, lines 1–5; to improve the perceptual quality of reconstructed XR images, perceptual-friendly semantic losses are used (i.e., trained based on one or more perceptual loss values) to enhance the semantic information extraction and reasoning process in semantic coding modules (i.e., semantic model)). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Zhang to train the semantic model based on one or more perceptual values. The suggestion to do so would have been to improve quality of reconstructed images (Zhang at p. 4, right column, 2nd paragraph, lines 1 and 2).
Regarding claim 16, the combination of Zhou, Xu, and Zhang teaches the apparatus of claim 15, and further teaches “wherein the target specific perceptual loss values are based on an application or a downstream task associated with the wireless communication, a configuration of the apparatus, a configuration of the receiving device, or a combination thereof” (see Zhang at p. 4, right column, second paragraph, lines 1–5; to improve the perceptual quality of reconstructed XR images, perceptual-friendly semantic losses are used; thus, a downstream task is reconstruction of the XR images associated with the described wireless communication, and the perceptual loss values are based on improving reconstructing XR images). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Zhang to have the perceptual values based on a downstream task. The suggestion to do so would have been to improve quality of reconstructed images (Zhang at p. 4, right column, 2nd paragraph, lines 1 and 2).
Regarding claims 26 and 27, they are the method claims corresponding to the apparatus claims of claims 3 and 4 that has been rejected above. Applicant’s attention is directed to the rejection of claims 3 and 4. Claims 26 and 27 are rejected under the same rationale as claims 3 and 4.
Claims 9, 12, 21, and 23 are rejected under 35 U.S.C. 103 as being unpatentable over Zhou in view of Xu and further in view of Finkelstein (U.S. Publication No. 2021/0083942).
Regarding claim 9, the combination of Zhou and Xu teaches the apparatus of claim 1, but does not explicitly disclose “wherein the output encoded set of real values comprises an analog waveform” of claim 9. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Finkelstein teaches “wherein the output encoded set of real values comprises an analog waveform” (see ¶ [0082]; QAM tuner configured to communicate using QAM protocols, and can generate signal using amplitude modulation (AM) analog modulation scheme by changing (modulating) the amplitudes of carrier waves. Therefore, the generated signal is an encoded signal and it comprises analog waveform). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Finkelstein to have the output encoded set of real values of Zhou to include analog waveform. The suggestion to do so would have been to improve quality of communication between wireless-communication capable devices (Finkelstein at ¶ [0080]).
Regarding claim 12, the combination of Zhou and Xu teaches the apparatus of claim 1, but does not explicitly disclose “wherein the apparatus comprises a user equipment (UE) or a base station (BS)” of claim 12. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Finkelstein teaches “wherein the apparatus comprises a user equipment (UE) or a base station (BS)” (see ¶¶ [0063] and [0106] and FIG. 4; user device is a user equipment (UE) and refers to a wireless communication device). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Finkelstein to have user equipment (UE) used for semantic communication. The suggestion to do so would have been to improve quality of communication between wireless-communication capable devices (Finkelstein at ¶ [0080]).
Regarding claim 21, the combination of Zhou and Xu teaches the apparatus of claim 13, but does not explicitly disclose “wherein the first encoded set of real values comprises an analog waveform” of claim 21. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Finkelstein teaches “wherein the first encoded set of real values comprises an analog waveform” (see ¶ [0082]; QAM tuner configured to communicate using QAM protocols, and can generate signal using amplitude modulation (AM) analog modulation scheme by changing (modulating) the amplitudes of carrier waves. Therefore, the generated signal is an encoded signal (i.e., the first encoded set of real values) and it comprises analog waveform). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Finkelstein to have the output encoded set of real values of Zhou to include analog waveform. The suggestion to do so would have been to wirelessly communicate between wireless-communication capable devices (Finkelstein at ¶ [0061]).
Regarding claim 23, the combination of Zhou and Xu teaches the apparatus of claim 13, but does not explicitly disclose “wherein the apparatus comprises a user equipment (UE) or a base station (BS)” of claim 23. However, the foregoing limitation is known in the art prior to the filing date of the claimed invention. For example, Finkelstein teaches “wherein the apparatus comprises a user equipment (UE) or a base station (BS)” (see ¶¶ [0063] and [0106] and FIG. 4; user device is a user equipment (UE) and refers to a wireless communication device). Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the invention of Zhou and Xu to incorporate the teachings of Finkelstein to have user equipment (UE) used for semantic communication. The suggestion to do so would have been to wirelessly communicate between wireless-communication capable devices (Finkelstein at ¶ [0061]).
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 SRIHARSHA REDDY VANGAPATY whose telephone number is (571)272-7655. The examiner can normally be reached M-F 8-5 EST.
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, Khaled Kassim can be reached at (571) 270-3770. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SRIHARSHA REDDY VANGAPATY/Examiner, Art Unit 2475
/HASHIM S BHATTI/Primary Examiner, Art Unit 2475
Prosecution Insights