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 Arguments
Applicant's arguments filed 11/24/2025 have been fully considered but they are not persuasive.
In response to applicant’s argument in pages 9-12, the applicant asserts that “TANG is combined with DECARREAU and NOH, one skilled in the art cannot achieve the solution of current claim 1 for obviousness purposes.” Examiner respective disagrees.
The applicant further asserts in pages 11, 12 that “DECARREAU at least fails to disclose or teach the features "wherein said reporting the capability of supporting the Al network model using the time-frequency resource for transmitting the preamble comprises: determining subsets of Physical Random Access Channel Occasions (ROs) and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a User Equipment (UE) that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the ROs based on the capability of supporting the AI network model and the types of the subsets, and initiating random access using any RO in the to-be-used subset of the ROs; or wherein said reporting the capability of supporting the AI network model using a time-frequency resource for transmitting a preamble comprises: determining subsets of preambles and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a UE that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the preambles based on the capability of supporting the Al network model and the types of the subsets, and reporting the capability of supporting the Al network model using a preamble in the to-be-used subset of the preambles" as recited in current claim 1.” Examiner respectively disagrees.
“During examination, the claims must be interpreted as broadly as their terms reasonably allow." MPEP § 2111.01 (I) (citing to In re American Academy of Science Tech Center, 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004)).
"Though understanding the claim language may be aided by explanations contained in the written description, it is important not to import into a claim limitations that are not part of the claim. For example, a particular embodiment appearing in the written description may not be read into a claim when the claim language is broader than the embodiment." MPEP 2111.01 (11) citing to Superguide Corp. v. DirecTV Enterprises, Inc., 358 F.3d 870, 875, 69 USPQ2d 1865, 1868 (Fed. Cir. 2004).
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., “the UE can indirectly inform the base station whether the UE supports the AI network model by using different types of subsets of preambles or ROs, without occupying additional resources or signaling to report the capability, thereby saving resources and signaling overhead.”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Therefore, given the broadest reasonable interpretation (BRI), DECARREAU would teach "wherein said reporting the capability of supporting the Al network model using the time-frequency resource for transmitting the preamble comprises: determining subsets of Physical Random Access Channel Occasions (ROs) and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a User Equipment (UE) that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the ROs based on the capability of supporting the AI network model and the types of the subsets, and initiating random access using any RO in the to-be-used subset of the ROs; or wherein said reporting the capability of supporting the AI network model using a time-frequency resource for transmitting a preamble comprises: determining subsets of preambles and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a UE that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the preambles based on the capability of supporting the Al network model and the types of the subsets, and reporting the capability of supporting the Al network model using a preamble in the to-be-used subset of the preambles". As indicated by par. 90, 91 of DECARREAU, “Normal reporting could imply that the UE must report RACH related information for the last successful RACH procedure and it is used for instance at BSs without ML capability… the UE may keep or store a RACH information log on the involved parameters separately for different RACH trigger types, and store them in a RACH report …the UE may indicate in a response to the network its ability to create a (an extended) RACH report for RACH or ML optimization or not… it indicates that normal reporting is sent back to the network”, the UE would indicating the ML or AI supporting or not through normal reporting or an extended RACH report or ML optimization with parameters with different RACH trigger types. In par. 47 of DECARREAU, “…the RACH procedure involves several parameters which are given to the UE by the network (or BS). Such parameters include (among others) the RACH (or PRACH) configuration index (which may identify a RACH preamble format, a subframe number, a slot number, a starting symbol, etc., and thus specifies the available set of PRACH occasions)…”, and further in par. 49, “…a RACH procedure may be triggered (or caused to be performed) by a number of events or RACH trigger types, such as, for example: Initial access from RRC_IDLE…”, the parameters indicating available set of PRACH occasions or subsets of Physical Random Access Channel Occasions (ROs) and trigger by RACH trigger types including initial access. Therefore, DECARREAU indicates determining subsets of Physical Random Access Channel Occasions (ROs) and types of the subsets and the RACH report with the parameters indicating normal report or extended report or ML optimization would indicating the capability of supporting the ML or AI. Therefore, given the broadest reasonable interpretation, DECARREAU teaches "wherein said reporting the capability of supporting the Al network model using the time-frequency resource for transmitting the preamble comprises: determining subsets of Physical Random Access Channel Occasions (ROs) and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a User Equipment (UE) that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the ROs based on the capability of supporting the AI network model and the types of the subsets, and initiating random access using any RO in the to-be-used subset of the ROs; or wherein said reporting the capability of supporting the AI network model using a time-frequency resource for transmitting a preamble comprises: determining subsets of preambles and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a UE that supports the AI network model and being used for initiating random access by a UE that does not support the AI network model; and determining a to-be-used subset of the preambles based on the capability of supporting the Al network model and the types of the subsets, and reporting the capability of supporting the Al network model using a preamble in the to-be-used subset of the preambles". Therefore, the combination of the reference would teach the claims.
The rejection is maintained.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1, 4, 18, 19, 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over TANG (US 20220124836 with continuation no. PCT/CN2019/098035 filed on 07/26/2019) in view of DECARREAU et al. (US 20220217781).
Regarding claims 1, 18, 19, TANG (US 20220124836) teaches a method for reporting Artificial Intelligence (Al) network model support capability, comprising:
determining capability of supporting an Al network model (par. 55, 57), wherein the capability of supporting the Al network model comprises whether to support using the Al network model for channel estimation (par. 55, When the UE indicates that it has the capability of AI-based channel state information, the UE can use an AI method to determine channel states or determine channel state indication information, and can send the channel state information indication information); and
reporting the capability of supporting the Al network model using an uplink resource in a random access procedure (par. 56, 58-62, the UE sends first information, and the first information indicates that the UE supports AI-based target information indication. When the UE supports the AI-based target information indication, the UE can use an AI method to determine channel states or determine channel state indication information, and can send the indication information); wherein said reporting the capability of supporting the Al network model using an uplink resource in a random access procedure (par. 55, When the UE indicates that it has the capability of AI-based channel state information, the UE can use an AI method to determine channel states or determine channel state indication information, and can send the channel state information indication information);
However, TANG does not teach comprises: reporting the capability of supporting the Al network model using a time- frequency resource for transmitting a preamble;
wherein said reporting the capability of supporting the Al network model using a time-frequency resource for transmitting a preamble comprises:
determining subsets of Physical Random Access Channel Occasions (ROs) and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a User Equipment (UE) that supports the Al network model and being used for initiating random access by a UE that does not support the Al network model; and
determining a to-be-used subset of the ROs based on the capability of supporting the Al network model and the types of the subsets and initiating random access using any RO in the to-be-used subset of the Ros; or
wherein said reporting the capability of supporting the Al network model using a time-frequency resource for transmitting a preamble comprises:
determining subsets of preambles and types of the subsets, wherein the types of the subsets comprise being used for initiating random access by a UE that supports the Al network model and being used for initiating random access by a UE that does not support the Al network model; and
determining a to-be-used subset of the preambles based on the capability of supporting the Al network model and the types of the subsets and reporting the capability of supporting the Al network model using a preamble in the to-be-used subset of the preambles.
But, DECARREAU et al. (US 20220217781) in a similar or same field of endeavor teaches comprises: reporting the capability of supporting the Al network model using a time- frequency resource for transmitting a preamble (par. 90, 91, 119, 120, 122, based on trigger-specific cost information provided by each RACH trigger-specific ML submodule, the RACH optimization coordinator (e.g., see FIG. 3) may determine a new (e.g., optimal) set of RACH parameters and/or RACH resources, such as an optimal preamble or set of preambles and a RACH resource (e.g., a frequency, time and beam related parameters) to be used for one or more (or all) RACH trigger types);
wherein said reporting the capability of supporting the Al network model using a time-frequency resource for transmitting a preamble (par. 90, 91, 119, 120, 122, based on trigger-specific cost information provided by each RACH trigger-specific ML submodule, the RACH optimization coordinator (e.g., see FIG. 3) may determine a new (e.g., optimal) set of RACH parameters and/or RACH resources, such as an optimal preamble or set of preambles and a RACH resource (e.g., a frequency, time and beam related parameters) to be used for one or more (or all) RACH trigger types) comprises:
determining subsets of Physical Random Access Channel Occasions (ROs) (par. 47, 54, Such parameters include (among others) the RACH (or PRACH) configuration index (which may identify a RACH preamble format, a subframe number, a slot number, a starting symbol, etc., and thus specifies the available set of PRACH occasions)) and types of the subsets (par. 47, 54, 90, 91, 119, 120, 122), wherein the types of the subsets comprise being used for initiating random access by a User Equipment (UE) that supports the Al network model and being used for initiating random access by a UE that does not support the Al network model (par. 90, 91, 97, 119, 120, 122, Normal reporting could imply that the UE must report RACH related information for the last successful RACH procedure and it is used for instance at BSs without ML capability… the UE may indicate in a response to the network its ability to create a (an extended) RACH report for RACH or ML optimization or not…); and
determining a to-be-used subset of the ROs based on the capability of supporting the Al network model and the types of the subsets and initiating random access using any RO in the to-be-used subset of the Ros (par. 54, 57, 95, number of RACH preamble transmissions required for a successful RACH procedure for the RACH trigger type…the BS may adjust one or more RACH parameters or adjust allocation of resources (e.g., RACH resources, such as slots, RACH occasions…Each ML submodule may, for example, determine a RACH cost for a RACH trigger type. For example, based on the costs of a plurality of RACH trigger types, RACH parameters and/or resource allocation may be adjusted… for physical layer RACH (also referred to as PRACH) configuration indexes that involve more than 1 number of PRACH occasions within a PRACH slot, the number of used PRACH occasions can also be reported within the PRACH slot; par. 90, whether the RACH report will be used for RACH optimization (or ML optimization)… Normal reporting could imply that…it is used for instance at BSs without ML capability; par. 49, 67, a RACH procedure may be triggered (or caused to be performed) by a number of events or RACH trigger types, such as, for example: Initial access from RRC_IDL); or
wherein said reporting the capability of supporting the Al network model using a time-frequency resource for transmitting a preamble (par. 54, 55, 71, 78, 91, 119) comprises:
determining subsets of preambles (par. 54, 55, the BS may adjust one or more RACH parameters or adjust allocation of resources (e.g., RACH resources, such as slots, RACH occasions, beams, RACH preambles, number of RACH preambles allocated to the trigger type, a length of a RACH preamble, . . . ); par. 71, average number of RACH preamble transmissions required for a successful RACH procedure; par. 119, an optimal preamble or set of preambles… allocating more preambles for trigger types where the cost is too high or decreasing the number of preambles accordingly) and types of the subsets (par. 47, 54, 90, 91, 119, 120, 122), wherein the types of the subsets comprise being used for initiating random access by a UE that supports the Al network model and being used for initiating random access by a UE that does not support the Al network model (par. 90, 91, 119, 120, 122, Normal reporting could imply that the UE must report RACH related information for the last successful RACH procedure and it is used for instance at BSs without ML capability… the UE may indicate in a response to the network its ability to create a (an extended) RACH report for RACH or ML optimization or not…); and
determining a to-be-used subset of the preambles based on the capability of supporting the Al network model and the types of the subsets (par. 54, 55, 71, 119, the BS may adjust one or more RACH parameters or adjust allocation of resources (e.g., RACH resources, such as slots, RACH occasions, beams, RACH preambles, number of RACH preambles allocated to the trigger type, a length of a RACH preamble, . . . )) and reporting the capability of supporting the Al network model using a preamble in the to-be-used subset of the preambles (par. 78, The variable c.sub.n.sup.(k)(t) depends on channel related parameters reported at the UE (e.g., preamble selection, RACH configuration index, BWP index, etc.); par. 91, the UE may indicate in a response to the network its ability to create a (an extended) RACH report for RACH or ML optimization or not).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by DECARREAU in the system of TANG for reporting the capability.
The motivation would have been to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security (DECARREAU par. 4).
Regarding claims 4, 24, TANG does not teach the method according to claim 1, wherein prior to determining the subsets of ROs and the types of the subsets, the method further comprises: receiving the subsets of ROs and the types of the subsets which are configured by a base station.
But, DECARREAU et al. (US 20220217781) in a similar or same field of endeavor teaches wherein prior to determining the subsets of ROs and the types of the subsets (par. 51, 66, 68, the UE 132 may later receive from BS 134 one or more updated (or adjusted) RACH parameters and/or updated resource allocation(s), e.g., based at least in part on the RACH report sent (at 322) by the UE 132 to BS 134… RACH report (sent at 322) may include various RACH-related information, such as, for example, one or more of the following:… a number of RACH preambles; par. 47, 54, 95, he RACH procedure involves several parameters which are given to the UE by the network (or BS)… specifies the available set of PRACH occasions)… the number of used PRACH occasions can also be reported within the PRACH slot), the method further comprises: receiving the subsets of ROs and the types of the subsets which are configured by a base station (par. 54, 57, 95, number of RACH preamble transmissions required for a successful RACH procedure for the RACH trigger type…the BS may adjust one or more RACH parameters or adjust allocation of resources (e.g., RACH resources, such as slots, RACH occasions).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by DECARREAU in the system of TANG for reporting the capability.
The motivation would have been to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security (DECARREAU par. 4).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over TANG (US 20220124836 with continuation no. PCT/CN2019/098035 filed on 07/26/2019) and DECARREAU et al. (US 20220217781) as applied to claims 1 above, and further in view of NOH et al. (US 20210014931).
Regarding claim 10, TANG teaches the method according to claim 1, wherein following reporting the capability of supporting the Al network model using the uplink resource in the random access procedure (par. 55, When the UE indicates that it has the capability of AI-based channel state information, the UE can use an AI method to determine channel states or determine channel state indication information, and can send the channel state information indication information; par. 56, 58-62, The information included in the random access process includes one of the following:…Msg3 in the four-step random access process), the method further comprises:
based on that the capability of supporting the Al network model indicates supporting using the Al network model for channel estimation (par. 55, When the UE indicates that it has the capability of AI-based channel state information, the UE can use an AI method to determine channel states or determine channel state indication information, and can send the channel state information indication information, receiving an Al model size reporting trigger instruction from a base station, wherein the support capability reporting trigger instruction indicates to report an input size of all the Al network model; and
reporting the input size of all the Al network model using a PDSCH scheduled by a PDCCH in response to the Al model size reporting trigger instruction.
But, DECARREAU et al. (US 20220217781) in a similar or same field of endeavor teaches receiving an Al model size reporting trigger instruction from a base station par. 47, 54, 56, 57, 84, 61, 128, 129, the BS using a RACH optimization procedure to select a set of RACH parameters to be used for RACH procedures, wherein the set of RACH parameters, for example, may be selected or designed to reduce a number of RACH failures and/or to decrease a number of RACH preamble transmissions (e.g., on average) required to achieve RACH procedure success (e.g., for one or more RACH trigger types); par. 99-102), wherein the support capability reporting trigger instruction indicates to report an input size of all the Al network model par. 47, 54, 56, 57, 84, 61, 128, 129, a UE to report RACH information for all possible combinations or subsets of the k RACH trigger types; par. 58, a new (e.g., extended) set of RACH reporting parameters may be used by UEs to send extended RACH reports to a BS, e.g., for the purpose of RACH optimization; par. 99-102); and
reporting the input size of all the Al network model in response to the Al model size reporting trigger instruction (par. 47, 54, 56, 57, 84, 61, 128, 129, a UE to report RACH information for all possible combinations or subsets of the k RACH trigger types; par. 99-102).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by DECARREAU in the system of TANG for reporting the capability.
The motivation would have been to provide significant improvement in wireless performance, which may include new levels of data rate, latency, reliability, and security (DECARREAU par. 4).
However, TANG does not teach using a PDSCH scheduled by a PDCCH;
But, NOH et al. (US 20210014931) in a similar or same field of endeavor teaches reporting using a PDSCH scheduled by a PDCCH in response to the reporting trigger instruction (par. 21, 223, receiving, from a UE, UE capability information including whether the UE supports cooperative communication for receiving PDSCHs from a plurality of TRPs in a particular time-frequency resource…when one PDCCH allocates at least two PDSCHs to a same serving cell and a same BWP at a same time point, at least two TCI states may be allocated respectively to PDSCHs or DMRS ports via one PDCCH).
Thus, it would have been obvious to the person of ordinary skill in the art before the effectively filing date of the claimed invention to implement the system or method as taught by NOH in the system of TANG and DECARREAU for reporting the capability.
The motivation would have been to improve cooperative communication for transmitting and receiving of the network.
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.
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/THINH D TRAN/for /Thinh Tran/, Patent Examiner of Art Unit 2466 03/04/2026