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 .
DETAILED ACTION
This office action is in response to the amendments filed on 07/21/2026.
Claims 1-13 and 15-21 are currently pending.
Claims 1-13 and 15-21 are rejected.
Claims 1, 13 and 20 are independent claims.
Response to Amendment
Claim Rejections - 35 USC § 103
5. 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 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.
6. 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 of this title, 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.
7. The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under pre-AIA 35 U.S.C. 103(a) 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.
8. Claims 1-2, 4, 6-13 and 15-21 are rejected under 35 U.S.C. 103 as being unpatentable over Taesang YOO et al. (US 2021/0273707 A1) hereinafter YOO.
For claim 1, Yoo teaches a terminal device (YOO, Fig. 2 item 120), comprising:
a transceiver (YOO, Fig. 2 item 252);
a memory (YOO, Fig. 2 item 282) configured to store a computer programs; and
a processor (YOO, Fig. 2 item 280) configured to execute the computer programs stored in the memory,
to input first information into a channel state discriminative model, to obtain corresponding channel state discriminative information output by the channel state discriminative model (YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the CSI sequence encoder taught by YOO to be or have the channel state discriminative model because they both process the encoded information to output the corresponding channel state discriminative information. See also summary and paragraphs 79-83 and 85-90.); wherein the first information comprises at least one of: encoded channel information, encoded channel state information (CSI), or channel state indication information obtained through encoding (YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. In other words, the m(t) is the first information which is the encoded CSI.);
wherein the transceiver is configured to send the channel state discriminative information (YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. In other words, the m(t) is the first information which is the encoded CSI. In other words, the UE transmits the n(t) which is the channel state discriminative information.).
For claim 2, Yoo further teaches the terminal device of claim 1, wherein the first information is implemented as at least one of: indication information of CSI or indication information of channel information output by an encoding unit; or actual CSI corresponding to the channel state indication information obtained after encoding of the channel information by the encoding unit (YOO, Fig. 6 and paragraph 84 teach the CSI instance encoder output n(t) which is the encoded H(t). See also paragraph 86.).
For claim 4, Yoo further teaches the terminal device of claim 1, wherein the first information indicates a corresponding channel state, the channel state discriminative information is used for determining a type of the corresponding channel state and/or a type of a decoding unit and the type of the corresponding channel state comprises at least one of: a channel state indication information type, a CSI type, or a channel type (YOO, Fig. 6 and paragraphs 79-90).
For claim 6, Yoo further teaches the terminal device of claim 1, wherein the processor is further configured to invoke and execute the computer programs stored in the memory to: input channel information and/or CSI into an encoding unit, to obtain channel state indication information (YOO, Fig. 6 and paragraphs 79-90).
For claim 7, Yoo further teaches the terminal device of claim 1, wherein the channel state discriminative information is sent by means of at least one of:
a random access procedure, uplink control information (UCI), a physical uplink control channel (PUCCH), a physical uplink shared channel (PUSCH), an RRC message, uplink data transmission, or uplink data transmission with regard to artificial intelligence (Al) service transmission requirements (YOO, Fig. 6 and paragraph 84.)
For claim 8, Yoo further teaches the terminal device of claim 1, wherein a manner to send the channel state discriminative information comprises at least one of:
sending periodically;
sending according to at least one of the following indicated by a network device: a period, a time within a period, or a frequency-domain resource;
sending when at least one of a current channel state indication information type, a current CSI type, a current channel type, a current scenario type, or a current channel feature index type is changed;
sending when an encoding scheme and/or a decoding scheme needs to be changed; or
sending according to an instruction of the network device (YOO, Fig. 6 and paragraphs 77-78.)
For claim 9, Yoo further teaches the terminal device of claim 1, wherein the processor is further configured to invoke and execute the computer programs stored in the memory to: train the channel state discriminative model by using sample information and channel state discriminative information corresponding to the sample information (YOO, Figs. 2, 6 and paragraphs 46-47 and 79-90.).
For claim 10, Yoo further teaches the terminal device of claim 1, wherein the transceiver is configured to: send the channel state discriminative model (YOO, Figs. 6, 8 and paragraphs 92-95. See also Fig. 9 and paragraphs 79-90, 98-99.).
For claim 11, Yoo further teaches the terminal device of claim 10, wherein the channel state discriminative model is carried in at least one of: uplink control signaling, an RRC message, uplink data transmission, or uplink data transmission with regard to artificial intelligence (AI) service transmission requirements (YOO, Fig. 6 and paragraph 79-90.)
For claim 12, Yoo further teaches the terminal device of claim 1, wherein the transceiver is configured to: receive the channel state discriminative information, and selecting a corresponding encoding unit according to the channel state discriminative information received (YOO, Figs. 4, 6 and paragraphs 66-69, 79-90.)
For claim 13, Yoo teaches a network device (YOO, Fig. 2 item 110), comprising:
a transceiver (YOO, Fig. 2 item 234);
a memory (YOO, Fig. 2 item 242) configured to store a computer programs; and
a processor (YOO, Fig. 2 item 240) configured to execute the computer programs stored in the memory, to
receive channel state discriminative information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may receive encoded CSI on the PUSCH or PUCCH.);
determine, according to the channel state discriminative information, a decoding unit corresponding to channel state indication information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may determine that only the change n(t) of CSI is received as the encoded CSI. CSI sequence decoder 710 may determine an intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of a previous intermediate decoded CSI instance h(t−1) from memory 720 and the change. CSI instance decoder 730 may decode the intermediate decoded CSI m(t) into decoded CSI. CSI sequence decoder 710 and CSI instance decoder 720 may use neural network decoder weights ϕ. The intermediate decoded CSI may be represented by [{circumflex over (m)}(t), h.sub.dec(t)]custom-characterg.sub.dec,ϕ(n(t), h.sub.dec(t−1)). CSI sequence decoder 710 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t−1). The gNB may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as H{circumflex over ( )}(t)custom-characterf_(dec, ϕ) (m{circumflex over ( )}(t)). CSI sequence decoder 710 may save the decoded CSI h(t) in memory 720. In other words, the CSI sequence decoder is the decoding unit, n(t) is the coded channel state discriminative information);
decode the channel state indication information by using the decoding unit corresponding to the channel state indication information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may determine that only the change n(t) of CSI is received as the encoded CSI. CSI sequence decoder 710 may determine an intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of a previous intermediate decoded CSI instance h(t−1) from memory 720 and the change. CSI instance decoder 730 may decode the intermediate decoded CSI m(t) into decoded CSI. CSI sequence decoder 710 and CSI instance decoder 720 may use neural network decoder weights ϕ. The intermediate decoded CSI may be represented by [{circumflex over (m)}(t), h.sub.dec(t)]custom-characterg.sub.dec,ϕ(n(t), h.sub.dec(t−1)). CSI sequence decoder 710 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t−1). The gNB may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as H{circumflex over ( )}(t)custom-characterf_(dec, ϕ) (m{circumflex over ( )}(t)). CSI sequence decoder 710 may save the decoded CSI h(t) in memory 720. In other words, the m(t) is the channel state indication information, the CSI instance decoder is the decoding unit corresponding to the channel state indication information.);
wherein the channel state discriminative information is obtained by inputting, by a terminal device, first information into a channel state discrimination model and being output by the channel state discrimination model(YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the CSI sequence encoder taught by YOO to be or have the channel state discriminative model because they both process the encoded information to output the corresponding channel state discriminative information. See also summary and paragraphs 79-83 and 85-90.);
wherein the first information comprises at least one of: encoded channel information, encoded channel state information (CSI), or channel state indication information obtained through encoding (YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. In other words, the m(t) is the first information which is the encoded CSI.).
For claim 15, Yoo further teaches the network device of claim 13, wherein the channel state discriminative information comprises at least one of:
an indication of a channel type;
an indication of a channel state information (CSI) type;
an indication of a channel state indication information type;
an indication of a scenario type;
an indication of a channel feature index type;
an indication of an encoding scheme; or
an indication of a decoding scheme (YOO, Fig. 4 and paragraphs 66-69, 82-90.).
For claim 16, Yoo further teaches the network device of claim 13, wherein the processor is configured to invoke and execute the computer programs stored in the memory to:
train a channel state discriminative model by using sample information and channel state discriminative information corresponding to the sample information (YOO, Fig. 4 and paragraphs 66-69. Figs. 2, 5 and paragraphs 46-47, 82-90.).
For claim 17, Yoo further teaches the network device of claim 16, wherein the channel state discriminative model comprises at least one of: a classification neural network, a classification algorithm, or a classification model (YOO, Figs. 5-7 and paragraphs 71-80, 86-87.).
For claim 18, Yoo further teaches the network device of claim 16, wherein the transceiver is configured to: send the channel state discriminative model (YOO, Fig. 7 and paragraphs 85-86.).
For claim 19, Yoo further teaches the network device of claim 18, wherein the channel state discriminative model is carried in at least one of: downlink control signaling, a media access control-control element (MAC CE) message, a radio resource control (RRC) message, a broadcast message, downlink data transmission, or downlink data transmission with regard to artificial intelligence (AI) service transmission requirements (YOO, Fig. 7 and paragraphs 85-86.).
For claim 20, Yoo teaches a method for information processing, comprising:
receiving, by a network device, channel state discriminative information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may receive encoded CSI on the PUSCH or PUCCH.);
determining, according to the channel state discriminative information, a decoding unit corresponding to channel state indication information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may determine that only the change n(t) of CSI is received as the encoded CSI. CSI sequence decoder 710 may determine an intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of a previous intermediate decoded CSI instance h(t−1) from memory 720 and the change. CSI instance decoder 730 may decode the intermediate decoded CSI m(t) into decoded CSI. CSI sequence decoder 710 and CSI instance decoder 720 may use neural network decoder weights ϕ. The intermediate decoded CSI may be represented by [{circumflex over (m)}(t), h.sub.dec(t)]custom-characterg.sub.dec,ϕ(n(t), h.sub.dec(t−1)). CSI sequence decoder 710 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t−1). The gNB may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as H{circumflex over ( )}(t)custom-characterf_(dec, ϕ) (m{circumflex over ( )}(t)). CSI sequence decoder 710 may save the decoded CSI h(t) in memory 720. In other words, the CSI sequence decoder is the decoding unit, n(t) is the coded channel state discriminative information);
decoding, by the network device, the channel state indication information by using the decoding unit corresponding to the channel state indication information (YOO, Fig. 7 and paragraph 85 teach CSI sequence decoder 710 may determine that only the change n(t) of CSI is received as the encoded CSI. CSI sequence decoder 710 may determine an intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of a previous intermediate decoded CSI instance h(t−1) from memory 720 and the change. CSI instance decoder 730 may decode the intermediate decoded CSI m(t) into decoded CSI. CSI sequence decoder 710 and CSI instance decoder 720 may use neural network decoder weights ϕ. The intermediate decoded CSI may be represented by [{circumflex over (m)}(t), h.sub.dec(t)]custom-characterg.sub.dec,ϕ(n(t), h.sub.dec(t−1)). CSI sequence decoder 710 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t−1). The gNB may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as H{circumflex over ( )}(t)custom-characterf_(dec, ϕ) (m{circumflex over ( )}(t)). CSI sequence decoder 710 may save the decoded CSI h(t) in memory 720. In other words, the m(t) is the channel state indication information, the CSI instance decoder is the decoding unit corresponding to the channel state indication information.);
wherein the channel state discriminative information is obtained by inputting, by a terminal device, first information into a channel state discrimination model and being output by the channel state discrimination model(YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the CSI sequence encoder taught by YOO to be or have the channel state discriminative model because they both process the encoded information to output the corresponding channel state discriminative information. See also summary and paragraphs 79-83 and 85-90.);
wherein the first information comprises at least one of: encoded channel information, encoded channel state information (CSI), or channel state indication information obtained through encoding (YOO, Fig. 6 and paragraph 84 teach CSI instance encoder 610 may use neural network encoder weights θ. The intermediate encoded CSI may be represented as m(t) custom- characterƒ.sub.enc,θ(H(t)). CSI sequence encoder 620 may determine a previously encoded CSI instance h(t−1) from memory 630 and compare the intermediate encoded CSI m(t) and the previously encoded CSI instance h(t−1) to determine a change n(t) in the encoded CSI. The change n(t) may be a part of a channel estimate that is new and may not be predicted by the decoder. The encoded CSI at this point may be represented by [n(t), h.sub.enc(t)]custom-characterg.sub.enc,θ(m(t), h.sub.enc(t−1)). CSI sequence encoder 620 may provide this change n(t) on the PUSCH or PUCCH, and the UE may transmit the change (e.g., information indicating the change) n(t) as the encoded CSI on the UL channel to the gNB. Because the change is smaller than an entire CSI instance, the UE may send a smaller payload for the encoded CSI on the UL channel, while including more detailed information in the encoded CSI for the change. CSI sequence encoder 620 may generate encoded CSI h(t) based at least in part on the intermediate encoded CSI m(t) and at least a portion of the previously encoded CSI instance h(t−1). CSI sequence encoder 620 may save the encoded CSI h(t) in memory 630. In other words, the m(t) is the first information, the CSI sequence encoder is the channel state discriminative model, the n(t) is the corresponding channel state discriminative information. In other words, the m(t) is the first information which is the encoded CSI.).
For claim 21, Yoo further teaches the method of claim 20, wherein the corresponding channel state discriminative information is used to be output to an adaptive decoding unit, to obtain an indication of a type of the corresponding channel state and/or an indication of a type of a decoding unit; wherein the adaptive decoding unit comprises a plurality of decoding units adapted to different channel states, and a decoding unit is determined according to the indication of the type of the corresponding channel state and/or the indication of the type of the decoding unit (YOO, Fig. 7 and paragraph85 teach As shown in FIG. 7, CSI sequence decoder 710 may receive encoded CSI on the PUSCH or PUCCH. CSI sequence decoder 710 may determine that only the change n(t) of CSI is received as the encoded CSI. CSI sequence decoder 710 may determine an intermediate decoded CSI m(t) based at least in part on the encoded CSI and at least a portion of a previous intermediate decoded CSI instance h(t−1) from memory 720 and the change. CSI instance decoder 730 may decode the intermediate decoded CSI m(t) into decoded CSI. CSI sequence decoder 710 and CSI instance decoder 720 may use neural network decoder weights ϕ. The intermediate decoded CSI may be represented by [{circumflex over (m)}(t), h.sub.dec(t)]custom-characterg.sub.dec,ϕ(n(t), h.sub.dec(t−1)). CSI sequence decoder 710 may generate decoded CSI h(t) based at least in part on the intermediate decoded CSI m(t) and at least a portion of the previously decoded CSI instance h(t−1). The gNB may reconstruct a DL channel estimate from the decoded CSI h(t), and the reconstructed channel estimate may be represented as H{circumflex over ( )}(t)custom-characterf_(dec, ϕ) (m{circumflex over ( )}(t)). CSI sequence decoder 710 may save the decoded CSI h(t) in memory 720. See also paragraphs 86-89.).
9. Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Taesang YOO et al. (US 2021/0273707 A1) hereinafter YOO in view of Salah Eddine HAJRI et al. (US 2023/0379029 A1), hereinafter HAJRI.
For claim 3, YOO teaches all the limitations of parent claim 1. YOO does not explicitly teach wherein a format of the first information is specified in a protocol and/or configured by a network device; and wherein the format of the first information being configured by the network device comprises: the format of the first information being configured by the network device by means of at least one of: broadcast, a downlink control information (DCI) message, a media access control-control element (MAC CE) message, a radio resource control (RRC) message, downlink data transmission, or downlink data transmission with regard to artificial intelligence (AI) service transmission requirements.
However, HAJRI explicitly teaches wherein a format of the first information is specified in a protocol and/or configured by a network device; and wherein the format of the first information being configured by the network device comprises: the format of the first information being configured by the network device by means of at least one of: broadcast, a downlink control information (DCI) message, a media access control-control element (MAC CE) message, a radio resource control (RRC) message, downlink data transmission, or downlink data transmission with regard to artificial intelligence (AI) service transmission requirements (HAJRI, Fig. 8 and paragraphs 92-96.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught in YOO with wherein a format of the first information is specified in a protocol and/or configured by a network device; and wherein the format of the first information being configured by the network device comprises: the format of the first information being configured by the network device by means of at least one of: broadcast, a downlink control information (DCI) message, a media access control-control element (MAC CE) message, a radio resource control (RRC) message, downlink data transmission, or downlink data transmission with regard to artificial intelligence (AI) service transmission requirements taught in HAJRI order to support more advanced technologies as the number of hypotheses for which CSI reporting is performed may increase. [HAJRI: background].
10. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Taesang YOO et al. (US 2021/0273707 A1) hereinafter YOO in view of Sungho PARK et al. (US 2016/0173174 A1), hereinafter PARK.
For claim 5, YOO teaches all the limitations of parent claim 4. YOO does not explicitly teach wherein the type of the corresponding channel state is determined according to an environmental scenario and/or an index feature corresponding to the environmental scenario; wherein the environmental scenario comprises at least one of: an indoor environment, an outdoor environment, a dense neighborhood, an open wild field, line of sight (LOS), non LOS (NLOS), high-speed movement, or low-speed movement; and wherein the index feature comprises at least one of: time-domain feature information, frequency-domain feature information, or spatial feature information.
However, PARK explicitly teaches wherein the type of the corresponding channel state is determined according to an environmental scenario and/or an index feature corresponding to the environmental scenario; wherein the environmental scenario comprises at least one of: an indoor environment, an outdoor environment, a dense neighborhood, an open wild field, line of sight (LOS), non LOS (NLOS), high-speed movement, or low-speed movement; and wherein the index feature comprises at least one of: time-domain feature information, frequency-domain feature information, or spatial feature information (PARK, Fig. 6 and claim 1, abstract.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the method taught in YOO with wherein the type of the corresponding channel state is determined according to an environmental scenario and/or an index feature corresponding to the environmental scenario; wherein the environmental scenario comprises at least one of: an indoor environment, an outdoor environment, a dense neighborhood, an open wild field, line of sight (LOS), non LOS (NLOS), high-speed movement, or low-speed movement; and wherein the index feature comprises at least one of: time-domain feature information, frequency-domain feature information, or spatial feature information taught in PARK thereby generating a downlink signal suitable for transmission through a radio channel.. [HAJRI: paragraph 51].
Response to Arguments
11. Applicant's arguments filed 07/21/2026 have been fully considered but they are moot because of the new ground of rejection.
Conclusion
12. 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.
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILL W LIN whose telephone number is (571)272-8749. The examiner can normally be reached M-F 8:00-5:00.
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/WILL W LIN/Primary Examiner, Art Unit 2412
