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 application filed on 04/23/2024.
Claims 1-20 are currently pending.
Claims 1-20 are rejected.
Claims 1 and 14-16 are independent claims.
- Claim Objection
5. Claim 14 is objected to because of the following informalities: “and and” in lines 4-6 should be “and”; “access stratum” in line 9 should be “access stratum;”. Appropriate correction is required.
6. Claim 15 is objected to because of the following informalities: “(UE” in line 2 should be “(UE)”. Appropriate correction is required.
Claim Rejections - 35 USC § 103
7. 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.
8. 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.
9. 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.
10. Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jibing Wang et al. (US 2022/0353803 A1), hereinafter Wang, in view of WEN TONG et al. (US 2024/0022927 A1), hereinafter TONG.
For claim 1, Wang teaches a user equipment (UE) for wireless communication (Wang, Fig. 5 item 110.), comprising:
at least one memory (Wang, Fig. 5 item 512.); and
at least one processor (Wang, Fig. 5 item 510.) coupled to the at least one memory and configured to cause the UE to:
receive, from a network entity, first configuration information (Wang, Fig. 8 and paragraphs 90-111.);
receive, from the network entity, indication information on a set of AI operations (Wang, Fig. 8 and paragraphs 90-111.); and
transmit, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information (Wang, Fig. 8 and paragraphs 90-111.).
TONG further teaches a protocol layer responsible for artificial intelligence (AI) management in access stratum (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer.).
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 Wang with a protocol layer responsible for artificial intelligence (AI) management in access stratum taught in TONG, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.
For claim 2, Wang and TONG further teach the UE of claim 1, wherein, the first configuration information indicates at least one of the following:
a list of descriptions about AI operations that a base station can provide or want the UE to support; a list of indexes about AI operations that a base station can provide or want UE to support; an index identifying a current entity of the protocol layer; associated radio bearer IDs to identify an associated protocol data convergence protocol (PDCP) entity; an indication to update an AI operations list in a base station; or rules to provide AI model performance feedback to a base station (Wang, Fig. 8 steps 810-820 and paragraphs 94-97.).
For claim 3, Wang and TONG further teach the UE of claim 1, wherein, the at least one processor is further configured to cause the UE to:
receive third configuration information on a radio link control (RLC) entity associated with an entity of the protocol layer, wherein the RLC entity is always configured in a RLC acknowledged mode or is in a default RLC acknowledged mode (Wang, Fig. 7 and paragraph 85.).
For claim 4, Wang and TONG further teach the UE of claim 1, wherein, for the set of AI operations, at least one entity of the protocol layer is configured as follows:
when there is at least one AI task in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI task, and when there is at least one AI model in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI model; or only one entity of the protocol layer is configured for the set of AI operations (TONG, Fig. 7A and paragraph 400.).
For claim 5, Wang and TONG further teach the UE of claim 1, wherein, the indication information indicates a requirement on the set of AI operations in a radio resource control (RRC) message or a message of the protocol layer, and the at least one of the set of AI operations is adopted by the UE based on implementation of the UE (Wang, Fig. 7 and paragraph 88. See also Fig. 8 and paragraph 98.).
For claim 6, Wang and TONG further teach the UE of claim 1, wherein, the at least one processor is further configured to cause the UE to:
receive a first message including indication information on the set of AI operations; transmit a second message indicating to subscribe at least part of the set of AI operations; and receive a third message indicating the at least one of the set of AI operations (Wang, Fig. 8 and paragraphs 90-111.).
For claim 7, Wang and TONG further teach the UE of claim 6, wherein, when that the protocol layer is configured for the UE before receiving the first message, the first message is one of a radio resource control (RRC) message, a system information block (SIB), and a message of the protocol layer; the second message is one of an RRC message and a message of the protocol layer; and the third message is a message of the protocol layer (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer. See also Wang, Fig. 3 and paragraph 48.).
For claim 8, Wang and TONG further teach the UE of claim 6, wherein, when the protocol layer is configured for the UE after receiving the first message and before transmitting the second message, the first message is one of a radio resource control (RRC) message and system information block (SIB) (Wang, Fig. 7 and paragraph 88); the second message is one of a RRC message and a message of the protocol layer; and the third message is a message of the protocol layer (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer. See also Wang, Fig. 3 and paragraph 48.).
For claim 9, Wang and TONG further teach the UE of claim 6, wherein, when the protocol layer is configured for the UE after transmitting the second message and before receiving the third message, the first message is one of a radio resource control (RRC) message and system information block (SIB) (Wang, Fig. 7 and paragraph 88); the second message is a RRC message (Wang, Fig. 7 and paragraph 88); and the third message is a message of the protocol layer (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer. See also Wang, Fig. 3 and paragraph 48.).
For claim 10, Wang and TONG further teach the UE of claim 6, wherein, when the protocol layer is configured for the UE after receiving the third message, the first message is one of a radio resource control (RRC) message and system information block (SIB) (Wang, Fig. 7 and paragraph 88); and the second message and the third message are RRC messages (Wang, Fig. 7 and paragraph 88).
For claim 11, Wang and TONG further teach the UE of Claim 1, wherein, a control protocol data unit (PDU) of the protocol layer comprises information indicating at least one of the following: message type; subscribe or unsubscribe; index of AI task wanted to subscribe or unsubscribe; index of AI model wanted to subscribe or unsubscribe; and list of index of available AI operations from a base station (Wang, Fig. 8 and paragraph 94. See also TONG, Fig. 7A and paragraph 400. 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 Wang with TONG to have a control PDU of the AI-related protocol, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results).
For claim 12, Wang and TONG further teach the UE of claim 1, wherein, a data protocol data unit (PDU) of the protocol layer comprises information indicating at least one of the following: message type; AI task index; AI model index; input AI task index input AI model index; output AI task index; output AI model Index; and model payload (Wang, Fig. 8 and paragraph 94. See also TONG, Fig. 7A and paragraph 400. 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 Wang with TONG to have a data PDU of the AI-related protocol, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.).
For claim 13, Wang and TONG further teach the UE of claim 1, wherein, a message format of the protocol layer comprises: control protocol data unit (PDU) and data PDU, wherein the control PDU and data PDU are sent over different logical channels (LCHs) and radio bearers (Wang, Fig. 8 and paragraph 94. See also TONG, Fig. 7A and paragraphs 400, 478. 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 Wang with TONG to have control PDU and data PDU of the AI-related protocol, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.).
For claim 14, Wang teaches a network apparatus (Wang, Fig. 5 item 120), comprising:
at least one memory (Wang, Fig. 5 item 562); and
at least one processor (Wang, Fig. 5 item 560) coupled to the at least one memory and configured to cause the network apparatus to:
transmit, to a user equipment (UE), first configuration information (Wang, Fig. 8 and paragraphs 90-111.);
transmit, to the UE, indication information on a set of AI operations (Wang, Fig. 8 and paragraphs 90-111.); and
receive, from the UE, feedback on at least one of the set of AI operations, via a message on the protocol layer based on the first configuration (Wang, Fig. 8 and paragraphs 90-111.).
TONG further teaches a protocol layer responsible for artificial intelligence (AI) management in access stratum (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer.).
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 Wang with a protocol layer responsible for artificial intelligence (AI) management in access stratum taught in TONG, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.
For claim 15, Wang teaches a method performed by a user equipment (UE), the method comprising:
receiving, from a network entity, first configuration information (Wang, Fig. 8 and paragraphs 90-111.);
receiving, from the network entity, indication information on a set of AI operations (Wang, Fig. 8 and paragraphs 90-111.); and
transmitting, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information (Wang, Fig. 8 and paragraphs 90-111.).
TONG further teaches a protocol layer responsible for artificial intelligence (AI) management in access stratum (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer.).
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 Wang with a protocol layer responsible for artificial intelligence (AI) management in access stratum taught in TONG, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.
For claim 16, Wang teaches a processor for wireless communication (Wang, Fig. 5 item 512 and paragraph 69.), comprising:
at least one controller (Wang, Fig. 5 item 516 and paragraphs 69-70.) coupled to at least one memory (Wang, Fig. 5 item 512 paragraph 69.) and configured to cause the processor to:
receive, from a network entity, first configuration information (Wang, Fig. 8 and paragraphs 90-111.);
receive, from the network entity, indication information on a set of AI operations (Wang, Fig. 8 and paragraphs 90-111.); and
transmit, to the network entity, feedback on at least one of the set of AI operations via a message on the protocol layer based on the first configuration information (Wang, Fig. 8 and paragraphs 90-111.).
TONG further teaches a protocol layer responsible for artificial intelligence (AI) management in access stratum (TONG, Fig. 7A and paragraph 400 teach communication on AI-related protocol (AIP) layer.).
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 Wang with a protocol layer responsible for artificial intelligence (AI) management in access stratum taught in TONG, since both references operate within the same 3GPP 5G NR framework and both aim to support AI/ML operations between UE and network with predictable results.
For claim 17, Wang and TONG further teach the processor of claim 16, wherein the first configuration information indicates at least one of the following: a list of descriptions about AI operations that a base station can provide or want the processor to support; a list of indexes about AI operations that a base station can provide or want the processor to support; an index identifying a current entity of the protocol layer; associated radio bearer IDs to identify an associated protocol data convergence protocol (PDCP) entity; an indication to update an AI operations list in a base station; or rules to provide AI model performance feedback to a base station (Wang, Fig. 8 steps 810-820 and paragraphs 94-97.).
For claim 18, Wang and TONG further teach the processor of claim 16 wherein the at least one controller is further configured to cause the processor to: receive third configuration information on a radio link control (RLC) entity associated with an entity of the protocol layer, wherein the RLC entity is always configured in a RLC acknowledged mode or is in a default RLC acknowledged mode (Wang, Fig. 7 and paragraph 85.).
For claim 19, Wang and TONG further teach the processor of claim 16, wherein, for the set of AI operations, at least one entity of the protocol layer is configured as follows: when there is at least one AI task in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI task, and when there is at least one AI model in the set of AI operations, a corresponding entity of the protocol layer is configured for each AI model; or only one entity of the protocol layer is configured for the set of AI operations (TONG, Fig. 7A and paragraph 400.).
For claim 20, Wang and TONG further teach the processor of claim 16, wherein the indication information indicates a requirement on the set of AI operations in a radio resource control (RRC) message or a message of the protocol layer, and the at least one of the set of AI operations is adopted by the processor based on implementation of the processor (Wang, Fig. 7 and paragraph 88. See also Fig. 8 and paragraph 98.).
Conclusion
11. 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.
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, Charles Jiang can be reached at 571-270-7191. 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.
/WILL W LIN/Primary Examiner, Art Unit 2412
Prosecution Insights