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 § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 4-6, 8-10, 13-15, 17-18 and 21 are rejected under 35 U.S.C. 102 (a)(2) as being anticipated by Zeng et al. (US 20230209390 A1).
Regarding claim 1,
Zeng discloses “A method applicable to a communication system” (See [0004] a communication method, to introduce artificial intelligence AI in a radio access network RAN), “wherein the communication system supports establishments of a user plane-based first connection, a control plane-based second connection” (See [0209] FIG. 1A is an example diagram of a protocol stack used when a gNB and UE perform user plane data exchange. [0210] FIG. 1B is an example diagram of a protocol stack used when a gNB and UE perform control plane data exchange. [0212] for a network side or a UE side, when a control plane and a user plane include protocol layers with a same name, for example, PDCP layers, RLC layers, MAC layers, or PHY layers, it indicates that the corresponding protocol layer supports both a user plane function and a control plane function), “and a third connection for transmitting an artificial intelligence (AI) data stream for a network element in the communication system” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. [0302] the AIC layer is configured to implement one or more of the following AI functions in non-real-time functions: data collection, model training, model downloading, model publishing, inference, and inference result publishing. [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information), “wherein a plurality of network elements of the communication system are involved on a transmission path of the Al data stream, and the plurality of network elements comprise a first network element and a second network element” (See [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information. [0484] FIG. 9A shows an example 2 of an architecture (network architecture + protocol stack) between a base station and UE. FIG. 9B is an example diagram of an information exchange procedure between a RAN and UE based on the architecture shown in FIG. 9A. In example 2, there is an independent AIC protocol layer. As described in the foregoing description of FIG. 7B, the AIC layer performs an AI function in non-real-time functions), “the third connection is established between the plurality of network elements” (See [0293] the RAN and the UE communicate based on a protocol architecture. The following describes a protocol architecture in which an AI function is introduced. [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer that is parallel to an RRC layer is added); “and the method comprises: sending to or receiving from the second network element, by the first network element, the Al data stream over the third connection” (See [0487] The RIC module of the RAN may further indicate the UE to collect data, indicate the UE to perform federated learning, publish a model (to be used by the UE to perform inference) to the UE, or send an inference result to the UE through the AIC layer).
Regarding claim 4,
Zeng discloses “The method according to claim 1, wherein a third generation partnership project (3GPP) protocol stack of the network element on the transmission path comprises a protocol layer corresponding to the third connection” (See [0221] In a RAN-related protocol, including but not limited to a 3rd generation partnership project (3GPP)-related protocol, a new function may be introduced into the RAN. [0222] To introduce an AI function that is easy to extend in the RAN, embodiments of this application provide the following four parts: Part 3 is a protocol stack for implementing the AI function. Part 4 is a communication method for implementing the AI function between the RAN and UE. [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added).
Regarding claim 5,
Zeng discloses “The method according to claim 4, wherein the protocol layer corresponding to the third connection is on a top layer of the 3GPP protocol stack” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. Note: In FIG.7B, the AIC layer is on top of the protocol stack).
Regarding claim 6,
Zeng discloses “The method according to claim 4, wherein there is at least one of: the network element on the transmission path comprises a user equipment, and the protocol layer corresponding to the third connection is above a Packet Data Convergence Protocol (PDCP) layer of a protocol stack of the user equipment” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. [0302] the AIC layer is above a PDCP layer. Note: In FIG.7B, the protocol stack for a RAN/UE has the AIC layer above a PDCP layer).
Regarding claim 8,
Zeng discloses “The method according to claim 1, wherein the network elements on the transmission path comprise network elements between which a control plane connection is established and/or network elements between which a user plane connection is established” (See [0209] FIG. 1A is an example diagram of a protocol stack used when a gNB and UE perform user plane data exchange. [0210] FIG. 1B is an example diagram of a protocol stack used when a gNB and UE perform control plane data exchange. [0212] for a network side or a UE side, when a control plane and a user plane include protocol layers with a same name, for example, PDCP layers, RLC layers, MAC layers, or PHY layers, it indicates that the corresponding protocol layer supports both a user plane function and a control plane function).
Regarding claim 9,
Zeng discloses “The method according to claim 1, wherein the Al data stream comprises at least one of: input data of an Al model; model parameters of the Al model; a final result output by the Al model; an intermediate result output by the Al model; or configuration parameters of the Al model” (See [0096] receiving model parameter information or model parameter gradient information from the terminal through the first protocol layer. [0097] the first protocol layer is an artificial intelligence control AIC layer above a packet data convergence protocol PDCP layer. [0487] The RIC module of the RAN may further indicate the UE to collect data, indicate the UE to perform federated learning, publish a model (to be used by the UE to perform inference) to the UE, or send an inference result to the UE through the AIC layer).
Regarding claim 10,
Zeng discloses “A communication apparatus, wherein the communication apparatus is disposed in a communication system” (See Fig. 13), “the communication system supports establishments of a user plane-based first connection, a control plane-based second connection” (See [0209] FIG. 1A is an example diagram of a protocol stack used when a gNB and UE perform user plane data exchange. [0210] FIG. 1B is an example diagram of a protocol stack used when a gNB and UE perform control plane data exchange. [0212] for a network side or a UE side, when a control plane and a user plane include protocol layers with a same name, for example, PDCP layers, RLC layers, MAC layers, or PHY layers, it indicates that the corresponding protocol layer supports both a user plane function and a control plane function), “and a third connection for transmitting an artificial intelligence (Al) data stream for a network element in the communication system” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. [0302] the AIC layer is configured to implement one or more of the following AI functions in non-real-time functions: data collection, model training, model downloading, model publishing, inference, and inference result publishing. [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information), “wherein a plurality of network elements of the communication system are involved on a transmission path of the Al data stream , the plurality of network elements comprises a first network element and a second network element, and the communication apparatus is the first network element” (See [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information. [0484] FIG. 9A shows an example 2 of an architecture (network architecture + protocol stack) between a base station and UE. FIG. 9B is an example diagram of an information exchange procedure between a RAN and UE based on the architecture shown in FIG. 9A. In example 2, there is an independent AIC protocol layer. As described in the foregoing description of FIG. 7B, the AIC layer performs an AI function in non-real-time functions); “the third connection is established between the plurality of network elements” (See [0293] the RAN and the UE communicate based on a protocol architecture. The following describes a protocol architecture in which an AI function is introduced. [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer that is parallel to an RRC layer is added), “and the communication apparatus comprises a memory and a processor, wherein the memory is configured to store a program, and the processor is configured to call the program from the memory to perform: sending to or receiving from the second network element, by the first network element, the Al data stream over the third connection” (See [0487] The RIC module of the RAN may further indicate the UE to collect data, indicate the UE to perform federated learning, publish a model (to be used by the UE to perform inference) to the UE, or send an inference result to the UE through the AIC layer).
Regarding claim 13,
Zeng discloses “The communication apparatus according to claim 10, wherein a third generation partnership project (3GPP) protocol stack of the network element on the transmission path comprises a protocol layer corresponding to the third connection” (See [0221] In a RAN-related protocol, including but not limited to a 3rd generation partnership project (3GPP)-related protocol, a new function may be introduced into the RAN. [0222] To introduce an AI function that is easy to extend in the RAN, embodiments of this application provide the following four parts: Part 3 is a protocol stack for implementing the AI function. Part 4 is a communication method for implementing the AI function between the RAN and UE. [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added).
Regarding claim 14,
Zeng discloses “The communication apparatus according to claim 13, wherein the protocol layer corresponding to the third connection is on a top layer of the 3GPP protocol stack” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. Note: In FIG.7B, the AIC layer is on top of the protocol stack).
Regarding claim 15,
Zeng discloses “The communication apparatus according to claim 13, wherein there is at least one of: the communication apparatus is a user equipment, and the protocol layer corresponding to the third connection is above a Packet Data Convergence Protocol (PDCP) layer of a protocol stack of the user equipment” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. [0302] the AIC layer is above a PDCP layer. Note: In FIG.7B, the protocol stack for a RAN/UE has the AIC layer above a PDCP layer).
Regarding claim 17,
Zeng discloses “The communication apparatus according to claim 10, wherein the network elements on the transmission path comprise network elements between which a control plane connection is established and/or network elements between which a user plane connection is established” (See [0209] FIG. 1A is an example diagram of a protocol stack used when a gNB and UE perform user plane data exchange. [0210] FIG. 1B is an example diagram of a protocol stack used when a gNB and UE perform control plane data exchange. [0212] for a network side or a UE side, when a control plane and a user plane include protocol layers with a same name, for example, PDCP layers, RLC layers, MAC layers, or PHY layers, it indicates that the corresponding protocol layer supports both a user plane function and a control plane function).
Regarding claim 18,
Zeng discloses “The communication apparatus according to claim 10, wherein the Al data stream comprises at least one of: input data of an Al model; model parameters of the Al model; a final result output by the Al model; an intermediate result output by the Al model; or configuration parameters of the Al model” (See [0096] receiving model parameter information or model parameter gradient information from the terminal through the first protocol layer. [0097] the first protocol layer is an artificial intelligence control AIC layer above a packet data convergence protocol PDCP layer. [0487] The RIC module of the RAN may further indicate the UE to collect data, indicate the UE to perform federated learning, publish a model (to be used by the UE to perform inference) to the UE, or send an inference result to the UE through the AIC layer).
Regarding claim 21,
Zeng discloses “A chip, comprising: a processor configured to call a program from a memory to cause a device provided with the chip to perform a method applicable to a communication system” (See [0186] The chip system includes a processor, and may further include a memory, configured to implement the method in any one of the foregoing method embodiments. The chip system may include a chip, or may include a chip and another discrete device), “wherein the communication system supports establishments of a user plane-based first connection, a control plane-based second connection” (See [0209] FIG. 1A is an example diagram of a protocol stack used when a gNB and UE perform user plane data exchange. [0210] FIG. 1B is an example diagram of a protocol stack used when a gNB and UE perform control plane data exchange. [0212] for a network side or a UE side, when a control plane and a user plane include protocol layers with a same name, for example, PDCP layers, RLC layers, MAC layers, or PHY layers, it indicates that the corresponding protocol layer supports both a user plane function and a control plane function), “and a third connection for transmitting an artificial intelligence (Al) data stream for a network element in the communication system” (See [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer is added. [0302] the AIC layer is configured to implement one or more of the following AI functions in non-real-time functions: data collection, model training, model downloading, model publishing, inference, and inference result publishing. [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information), “wherein a plurality of network elements of the communication system are involved on a transmission path of the Al data stream, and the plurality of network elements comprise a first network element and a second network element” (See [0229] to implement an AI function in a RAN, information may be transmitted between different network elements. The information may be referred to as AI data, AI information. [0484] FIG. 9A shows an example 2 of an architecture (network architecture + protocol stack) between a base station and UE. FIG. 9B is an example diagram of an information exchange procedure between a RAN and UE based on the architecture shown in FIG. 9A. In example 2, there is an independent AIC protocol layer. As described in the foregoing description of FIG. 7B, the AIC layer performs an AI function in non-real-time functions); “the third connection is established between the plurality of network elements” (See [0293] the RAN and the UE communicate based on a protocol architecture. The following describes a protocol architecture in which an AI function is introduced. [0301] FIG. 7B shows that an artificial intelligence control (artificial intelligence control, AIC) layer that is parallel to an RRC layer is added), “and the method comprises: sending to or receiving from the second network element, by the first network element, the Al data stream over the third connection” (See [0487] The RIC module of the RAN may further indicate the UE to collect data, indicate the UE to perform federated learning, publish a model (to be used by the UE to perform inference) to the UE, or send an inference result to the UE through the AIC layer).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
Claims 2-3 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng et al. (US 20230209390 A1) in view of Tanach et al. (US 20230130964 A1).
Regarding claims 2 and 11,
Zang discloses claim 2 of “The method according to claim 1”, and claim 11 of “The communication apparatus according to claim 10”, but does not explicitly disclose a header portion of the AI data stream comprising parameters.
However, Tanach discloses “wherein a data packet of the Al data stream comprises a header portion and a data portion” (See Fig. 2), “wherein the header portion comprises one or more of: a first parameter indicative of a source network element of the Al data stream; a second parameter indicative of a destination network element of the Al data stream” (See [0047] The header portion 210 includes a number of fields designating, in part, the AI task type, the length of the payload data, a source address (or identifier), and a destination address (or identifier). [0060] FIG. 6 illustrates an example flow diagram illustrating the dataflow between an AI client 120 and an AI server 130 using the AIoF protocol to transport AI computing tasks according to an embodiment).
Zeng discloses exchanging AI data between a RAN and UE, but does not disclose header addressing, and Tanach discloses including a source and destination addresses in the header of an AI packet for routing between an AI client and an AI server. A POSITA would understand an AI client or AI server to be a type of network element that participates in network communication.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zeng with the teachings of Tanach, to incorporate the addressing mechanism into the AI packet structure, and the motivation to do so would have been to facilitate correct routing of AI data between network elements.
Regarding claims 3 and 12,
Zeng in view of Tanach discloses claim 3 of “The method according to claim 2”, and claim 12 of “The communication apparatus according to claim 11”, “wherein there is at least one of: the first parameter is an address of the source network element; or the second parameter is an address of the destination network element” (See Tanach [0047] The header portion 210 includes a number of fields designating, in part, the AI task type, the length of the payload data, a source address (or identifier), and a destination address (or identifier). [0060] FIG. 6 illustrates an example flow diagram illustrating the dataflow between an AI client 120 and an AI server 130 using the AIoF protocol to transport AI computing tasks according to an embodiment).
Zeng discloses exchanging AI data between a RAN and UE, but does not disclose header addressing, and Tanach discloses including a source and destination addresses in the header of an AI packet for routing between an AI client and an AI server. A POSITA would understand an AI client or AI server to be a type of network element that participates in network communication.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zeng with the teachings of Tanach, to incorporate the addressing mechanism into the AI packet structure, and the motivation to do so would have been to facilitate correct routing of AI data between network elements.
Claims 7, 16 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Zeng et al. (US 20230209390 A1) in view of ZHANG et al. (US 20220322111 A1).
Regarding claims 7 and 16,
Zeng discloses claim 7 of “The method according to claim 1” and claim 16 of “The communication apparatus according to claim 10”, but does not explicitly disclose parsing and modification of a data portion in the AI data stream.
However, ZHANG discloses “wherein the third connection supports parsing and modification of content of a data portion in the Al data stream by the network element on the transmission path” (See [0244] Based on the communication method shown in FIG. 3, an independent AI protocol layer is introduced above the RRC protocol layer or the PDCP protocol layer of the first access network device. For example, the AI functions such as generating the AI parameter, receiving and parsing the AI data reported by the terminal device, and completing a network optimization operation based on the AI data are completed by the AI protocol layer of the first access network device). Note: The act of completing a network optimization operation indicates that the original AI data is processed into updated or newly generated parameters, which constitutes a modification of the AI data.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zeng with the teachings of ZHANG, and the motivation to do so would have been to improve diversity and flexibility of the AI function supported by the first access network device, and improve the accuracy and the efficiency of the AI training (ZHANG [0244]).
Regarding claim 20,
Zeng in view of ZHANG discloses “The method according to claim 7, wherein the third connection supports parsing and modification of content of a data portion in the Al data stream by all the network elements on the transmission path; or the third connection supports parsing and modification of content of a data portion in the Al data stream by a part of the network elements on the transmission path” (See [0244] Based on the communication method shown in FIG. 3, an independent AI protocol layer is introduced above the RRC protocol layer or the PDCP protocol layer of the first access network device. For example, the AI functions such as generating the AI parameter, receiving and parsing the AI data reported by the terminal device, and completing a network optimization operation based on the AI data are completed by the AI protocol layer of the first access network device). Note: The act of completing a network optimization operation indicates that the original AI data is processed into updated or newly generated parameters, which constitutes a modification of the AI data.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Zeng with the teachings of ZHANG, and the motivation to do so would have been to improve diversity and flexibility of the AI function supported by the first access network device, and improve the accuracy and the efficiency of the AI training (ZHANG [0244]).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SALMA A AYAD whose telephone number is (571)270-0285. The examiner can normally be reached Monday-Friday 8:00 to 5:30 ET.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Yemane Mesfin can be reached at 5712723927. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SALMA AYAD/Examiner, Art Unit 2462 /YEMANE MESFIN/Supervisory Patent Examiner, Art Unit 2462