DETAILED ACTION
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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2-7, 9, and 11 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
For Claims 2, 3, 9, it is unclear that the method steps are performed by the base station, as in the preamble of the independent claim, because the body of the independent claim recites that the UE determines the AI model.
For Claims 4 and 11, it is unclear what it means for the configuration information to further comprise “indication information indicating an AI model adopted by the UE”. In the independent claim, the AI model is determined by the UE based on RB indication information in the configuration information. At the point when the configuration information arrives, the AI model has not yet been adopted or determined.
For Claim 5 (line 5), “model indication information” appears to have antecedent basis in the claim.
Remaining claims are rejected as depending from a rejected claim.
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) 1-6, 10-14, and 20-21, as understood by rejections under 35 USC 112, is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 114079599, citing to the translation provided herewith) in view of Davydov et al. (US 2016/0227520).
For Claims 1, 20, and 21, Chen teaches an information processing method, performed by a base station and comprising: sending configuration information, wherein the configuration information comprises Resource Block (RB) indication information indicating a number of RBs for the UE, and the number of RBs is used for the UE to determine an Artificial Intelligence (AI) model to perform channel estimation (see underlined portions of page 12; the UE receives a transmission from the base station and thus the matter teaches that the base station transmits the matter).
Chen as applied above is not explicit as to, but Davydov teaches a communication device, comprising: a processor; and a memory configured to store instructions executable by the processor (see paragraphs 17, 63), and a non-transitory computer storage medium storing a computer executable program (see paragraphs 17, 63), and that a number of RBs have same precoding for a User Equipment (UE) (see paragraphs 45-46, 55).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to establish a groups of resource blocks with the same precoding as in Davydov when implementing the method of Chen. The motivation would be to optimize transmission parameters in accord with the network devices.
For Claim 2, Chen further teaches the method, further comprising:
based on the number of RBs, determining a target number of RBs (see page 12: steps 202 and 204); and
based on the target number of RBs, determining the AI model corresponding to the target number of RBs (see page 12: steps 203 and 205).
For Claim 3, Chen further teaches the method, wherein determining the target number of RBs based on the number of RBs comprises:
in response to the number of RBs being a first type of value, determining that the target number of RBs is equal to the number of RBs (see p. 14: underlined sections); or
in response to the number of RBs being a second type of value, determining the target number of RBs based on at least one of: computing capability information of the UE, storage capability information of the UE, channel quality information, or model deployment information of the UE (see p. 14: underlined sections; target adjusted based on available models).
For Claim 4, Chen further teaches the method, wherein the configuration information further comprises: model indication information indicating an AI model adopted by the UE (see p. 14: example 3, especially step 301: downloading model).
For Claim 5, Chen further teaches the method, wherein sending the configuration information comprises:
in response to determining, based on model deployment information of the UE, that an AI model corresponding to the target number of RBs exists in the UE, sending the configuration information comprising the RB indication information and model indication information (see p. 14: step 304: downloading in real time); or
in response to determining, based on the model deployment information of the UE, that the AI model corresponding to the target number of RBs does not exist in the UE, sending the configuration information comprising at least the RB indication information.
For Claim 6, Chen further teaches the method, further comprising: in response to determining, based on the model deployment information of the UE, that the AI model corresponding to the target number of RBs does not exist in the UE, sending model information to the UE, wherein the model information comprises the AI model corresponding to the target number of RBs (see p. 14: step 304).
For Claim 10, Chen teaches an information processing method, performed by a User Equipment (UE), and comprising: receiving configuration information, wherein the configuration information comprises Resource Block (RB) indication information indicating a number of RBs for the UE (see underlined portions of page 12); and
based on the number of RBs, determining an Artificial Intelligence (AI) model to perform channel estimation (see underlined portions of page 12).
Chen as applied above is not explicit as to, but Davydov teaches that a number of RBs have same precoding for a User Equipment (UE) (see paragraphs 45-46, 55).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to establish a groups of resource blocks with the same precoding as in Davydov when implementing the method of Chen. The motivation would be to optimize transmission parameters in accord with the network devices.
For Claim 11, Chen further teaches the method, wherein the configuration information further comprises model indication information indicating an AI model adopted by the UE (see p. 14: example 3, especially step 301: downloading model);
wherein determining the AI model to perform channel estimation based on the number of RBs comprises: determining to perform channel estimation based on the AI model indicated by the model indication information corresponding to the number of RBs (see p. 14: example 3, especially step 301: downloading model).
For Claim 12, Chen further teaches the method, wherein determining the AI model to perform channel estimation based on the number of RBs comprises: in response to the number of RBs being a first type of value, determining an AI model corresponding to the number of RBs to perform channel estimation (see p. 14: underlined sections).
For Claim 13, Chen further teaches the method, wherein determining the AI model to perform channel estimation based on the number of RBs comprises: in response to the number of RBs being a second type of value, determining an AI model to perform channel estimation based on at least one of: model deployment information of the UE, computing capability information of the UE, storage capability information of the UE, or channel quality information (see p. 14: underlined sections; target adjusted based on available models).
For Claim 14, Chen further teaches the method, wherein the number of RBs comprises a target number of RBs, and the method further comprises:
receiving model information, wherein the model information comprises an AI model corresponding to the target number of RBs (see p. 14: example 3, especially step 301: downloading model);
wherein determining the AI model to perform channel estimation based on the number of RBs comprises: determining the AI model corresponding to the target number of RBs to perform channel estimation (see page 12: steps 202-205).
Claim(s) 8 and 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 114079599, citing to the translation provided herewith) in view of Davydov et al. (US 2016/0227520) as applied to claims 1 and 10 above, and further in view of Noh et al. (US 2021/0266944).
For Claims 8 and 15, Chen further teaches the method, wherein sending the configuration information comprises:
in response to a PRB bundling configuration for the UE being a semi-static PRB bundling configuration, sending Radio Resource Control (RRC) signaling carrying the configuration information; or
in response to the PRB bundling configuration for the UE being a PRB bundling configuration, sending the configuration information through a Physical Downlink Control Channel (PDCCH) (see pages 9-10, 23-24: underlined portions, examples 1 and 2, PDCCH).
The references as applied above are not explicit as to, but Noh teaches a dynamic PRB bundling configuration (see paragraphs 104, 70-71).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to employ the PDCCH as in Chen for a dynamic PRB bundling configuration as in Noh. One of ordinary skill would have been able to do so with the reasonably predictable result of providing configuration information in a known manner for providing parameters appropriate for current conditions.
Claim(s) 9 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al. (CN 114079599, citing to the translation provided herewith) in view of Davydov et al. (US 2016/0227520) as applied to claims 1 and 10 above, and further in view of Echigo et al. (WO 2023/026413, citing to the translation provided herewith).
For Claims 9 and 17, the references as applied above are not explicit as to, but Echigo teaches the method, further comprising:
receiving suggestion information, wherein the suggestion information comprises at least one of: computing capability information of the UE, storage capability information of the UE, or channel quality information (see page 12, underlined portions, steps 101-102); and
based on the suggestion information, determining the AI model for the UE to perform channel estimation (see page 12, steps 101-102).
Thus it would have been obvious to one of ordinary skill in the art at the time the application was filed to determine an AI model as in Echigo when implementing the method of Chen and Davydov. The motivation would be to allow for the selection of a compatible AI model.
Allowable Subject Matter
Claim 16 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Claim 7 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Narayanan Thangaraj et al. (US 2025/0016593) teaches a system in which the input dimension for an AI model relates to the computing capability of a UE. Hu et al. (US 2024/0171428) teaches a system in which the number of references signals per resource block maps to a neural network model. Shi et al. (US 2020/0235962) teaches the same precoding applied across a PRB bundle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CASSANDRA L DECKER whose telephone number is (571)270-3946. The examiner can normally be reached 7:30 am - 4:00 pm.
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, Faruk Hamza can be reached at 571-272-7969. 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.
/CASSANDRA L DECKER/Examiner, Art Unit 2466 5/26/2026
/FARUK HAMZA/Supervisory Patent Examiner, Art Unit 2466
Prosecution Insights