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 .
Response to Amendment
The Amendment filed on 12/4/2025 has been entered. Claims 1-6 and 8-29 remain pending in the application.
Response to Arguments
Applicant's arguments filed on 12/4/2025 have been fully considered but they are not persuasive.
First, in response to applicant’s argument on pages 7-9 that Kumar (US PGPub 2023/0093963) in view of Cheng (WO 2024/040476) fails to teach the limitations of claims 1 and 28, especially the limitation of “…detecting, by the UE, one or more preconfigured trigger events for transferring an AI-ML model, wherein the one or more preconfigured trigger events comprising a new AI-ML model available at the AI server, an updated Al- ML model at the AI server, a new AI-ML model at the RAN node, an updated Al- ML model at the RAN node, a new AI-ML model at the UE, an updated AI-ML model at the UE, and a UE mobility event; …”, the examiner cannot concur with the applicant because of the reasons described below.
Applicant argues that the combination of references teaches Al Model transfer triggered by network "Instruction," not UE detection. And, applicant further argues that the trigger in Kumar is receipt of a network-initiated AI model configuration message. The AI model configuration message is not generated because "a new model is available" or "a model has been updated" at the AI server, the RAN node, or the UE; nor does it has anything to do with a UE mobility event. Nor does Kumar (and the combination of references) disclose that the UE locally monitors or preconfigures events such as "new AI-ML model available at the Al server" or "updated AI-ML model at the UE" and, upon detecting those conditions, initiates model transfer (see pages 7-8 of the imminent arguments filed 12/4/2025).
On the contrary to the applicant’s arguments, the claim set does not specify that the triggering of the UE should not involve a network instruction, nor UE locally monitors or preconfigurs events within the claims. Rather, Kumar teaches that the UE receives AI model configuration information from a base station, and the UE determines an AI model based on the received AI model configuration information. Then the UE determines whether the determined AI model is available locally. If the AI model is available locally, then the UE may continue operating in the RRC connected state with the base station, and if the AI model is not available at the UE, the UE is to obtain the AI model from the network (the base station or a remote storage like MR of Kumar) (see paragraphs 0119-0121 of Kumar). Therefore, the UE of Kumar is triggered to obtain the AI model from the network based on the received AI model configuration information as well as the UE’s determination (or detection) that a new/updated AI model exists on the base station or the remote storage (MR).
The current claims set fails to specify the key features of the applicant’s arguments, such as without being triggered by network instruction or UE locally monitors or preconfigures events. Examiner suggests to further amend the claims 1 and 28 to include the key inventive features of the imminent application.
Second, in response to applicant’s argument on pages 10-11 that Kumar (US PGPub 2023/0093963) in view of Cheng (WO 2024/040476) fails to teach the limitations of claim 14, especially the limitation of “…transferring the AI-ML dataset through the AI plane connection in the wireless network.”, the examiner cannot concur with the applicant because of the reasons described below.
Applicant argues that the combination of references fails to teach about transferring the AI-ML dataset, but to teach about only transferring the AI-ML model. Applicant further adds that AL-ML dataset is different from Al model data, thereby, transferring Al model through UP does NOT leach transferring AI-ML dataset. AI-ML Dataset: is the input for learning. It consists of raw training examples (e.g., thousands of images, sensor logs, channel impulse responses). It is typically voluminous, unstructured, and raises privacy concerns. AI model(s) is the output of the learning process (see page 10 of the imminent argument filed 12/4/2025).
On the contrary to the applicant’s arguments, the specification of the imminent application as well as the present claim set does NOT disclose how the AI-ML model is different from the AI-ML dataset. Examiner cannot locate any details or explanation of the differences between the AI-ML model and the AI-ML dataset. Applicant’s argument is totally based on the information which is NOT disclosed by the specification or the claims set of the imminent application. The terms of a AI-ML model or a AI-ML dataset, without further specification and on the eyes of an ordinary person in the skilled art, is merely a kind of data structure and does not differentiate from each other.
Examiner suggests to further amend the claim 14 to include the functional or structural features of the AI-ML dataset enough to be differentiated from the AI-ML model.
Third, in response to applicant’s argument on pages 10-11 that Kumar (US PGPub 2023/0093963) in view of Cheng (WO 2024/040476) fails to teach the limitations of claim 18, especially the limitation of “…setting up, by the RAN node, an AI plane connection for AI between a user equipment (UE) and an AI server in the wireless network; and transferring the AI-ML model through the AI plane connection for AI among the UE, the RAN node, and the Al server in the wireless network.”, the examiner cannot concur with the applicant because of the reasons described below.
Applicant argues that the combination of references does not teach an AI plane for the AI transfer because Cheng discloses transferring the specifically discloses transferring the AI model data as application-layer data through UP. In another word, under the combination of references, the Al model(s) are transferring as regular (application-layer) data WITHOUT any specificity for the AI models, which is recited in the amended claim 18 (see page 12 of the imminent argument filed 12/4/2025).
On the contrary to the applicant’s arguments, the amended features of claim 18, especially the limitation “an AI plane connection for AI between a user equipment (UE) and an AI server in the wireless network” does not sound that the AI plane connection is used for the specificity for transferring the AI models. Rather, examiner interprets the AI plane connection for AI as a connectivity between network entities (the UE and the AI server) to transfer AI models and may be able to be used for transferring other regular data.
Examiner suggests to further amend the claim 18 to specify the function of the AI plane connection as used for the specificity of transferring the AI models or only used for transferring the AI models.
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.
Claims 1-6 and 8-29 are rejected under 35 U.S.C. 103 as being unpatentable over Kumar (US PGPub 2023/0093963) in view of Cheng (WO 2024/040476).
Regarding claims 1 and 28, Kumar teaches a method for a user equipment (UE) using artificial intelligence-machine learning (AI-ML) model in a wireless network (Kumar, see abstract, This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements) comprising:
detecting, by the UE, one or more preconfigured trigger events for transferring an AI-ML model (Kumar, see paragraph 0121, the UE 115 determines whether the determined AI model is available. For example, the AI manager 415 of the UE 115 determines if the determined AI model (e.g., AI model information, such as input and output parameters) is available at the UE. If the AI model is available, the UE 115 may set the determined AI model for future RRC IDLE/INACTIVE state procedures and continue operating in the RRC connected state with the base station 105. The UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505) or from another wireless communication device (e.g., a second UE)), wherein the one or more preconfigured trigger events comprising a new AI-ML model available at the AI server, an updated AI-ML model at the AI server, a new AI-ML model at the RAN node, an updated AI-ML model at the RAN node, a new AI-ML model at the UE, an updated AI-ML model at the UE, and a UE mobility event (Kumar, see paragraph 0121, the UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505) or from another wireless communication device (e.g., a second UE)).
Kumar teaches the above yet fails to teach setting up an AI plane connection for AI between the UE and an AI server through a radio access network (RAN) node and a core network (CN) node in the wireless network; and transferring the AI-ML model with AI-ML model packets through the AI plane connection for AI in the wireless network.
Then Cheng teaches setting up an AI plane connection for AI between the UE and an AI server through a radio access network (RAN) node and a core network (CN) node in the wireless network (Cheng, see figure 1 and paragraphs 0037, the CN 124 may be an EPC, and the RAN 106 may be connected with the CN 124 via an S1 interface 128. In embodiments, the S1 interface 128 may be split into two parts, an S1 user plane (S1-U) interface, which carries traffic data between the base station 112 or base station 114 and a serving gateway (S-GW), and the S1-MME interface, which is a signaling interface between the base station112 or base station 114 and mobility management entities (MMEs)); and
transferring the AI-ML model with AI-ML model packets through the AI plane connection for AI in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kumar with RRC procedure design for wireless AI/ML of Cheng, because doing so would make Kumar more efficient in enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead (Cheng, see paragraph 0053).
Regarding claim 2, Kumar in view of Cheng teaches wherein the UE downloads the AI-ML model from the AI server, and wherein the AI-ML model is trained and stored at the AI server (Cheng, see paragraph 0055, The storage and management of the AI/ML models may also be vendor specific and is possibly out of 3GPP scope. For example, a vendor may build its own AI/ML model library server to store and manage AI/ML models, while another vendor may rent from OТТ).
Regarding claim 3, Kumar in view of Cheng teaches wherein the UE downloads the AI-ML model from the AI server (Cheng, see paragraph 0055, The storage and management of the AI/ML models may also be vendor specific and is possibly out of 3GPP scope. For example, a vendor may build its own AI/ML model library server to store and manage AI/ML models, while another vendor may rent from OТТ), and wherein the AI-ML model is trained or updated at the RAN node, and wherein the AI-ML model is transferred from the RAN node to the AI server (Cheng, see paragraph 0068, in step S21, a gNB may download an AI/ML model from its model server which is possibly built or rent by a gNB vendor. The downloading of the AI/ML may be initiated by a request from the gNB (S20), or the AI/ML may be pushed to the gNB by the model server (not shown)).
Regarding claim 4, Kumar in view of Cheng teaches wherein the AI-ML model is trained or updated at the UE, and wherein the AI-ML model is transferred from the UE to the RAN node through the AI server (Cheng, see paragraph 0065, After performing the training, in step S13, the UE may send the trained model to the gNB via an uplink RRC message. For example, the trained AI/ML model may be transmitted as UE Assistance Information (UAI)).
Regarding claim 5, Kumar in view of Cheng teaches wherein the UE transfers the AI-ML model to the AI server through the AI plane connection for AI directly (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 6, Kumar in view of Cheng teaches wherein the UE sends upload model request to the RAN node and uploads the AI-ML model to the AI server upon receiving upload model response from the RAN node (Cheng, see paragraph 0065, After performing the training, in step S13, the UE may send the trained model to the gNB via an uplink RRC message. For example, the trained AI/ML model may be transmitted as UE Assistance Information (UAI)).
Regarding claim 8, Kumar in view of Cheng teaches wherein the triggering event is a UE mobility event indicating the UE successfully switching from a source RAN node to a target RAN node (Kumar, see paragraphs 0119 and 0121, At 515, the base station 105 transmits AI model configuration information [one or more preconfigured trigger events for transferring an AI-ML model] to the UE 115. the AI manager 439 of the base station 105 generates and transmits a AI model configuration message (e.g., configuration transmission 452) to the UE 115 which includes the AI model configuration information (e.g., 442). the UE 115 determines whether the determined AI model is available. the UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505)).
Regarding claim 9, Kumar in view of Cheng teaches wherein the UE downloads the AI-ML model from the target RAN node or directly from the AI server (Kumar, see paragraphs 0119 and 0121, At 515, the base station 105 transmits AI model configuration information [one or more preconfigured trigger events for transferring an AI-ML model] to the UE 115. the AI manager 439 of the base station 105 generates and transmits a AI model configuration message (e.g., configuration transmission 452) to the UE 115 which includes the AI model configuration information (e.g., 442). the UE 115 determines whether the determined AI model is available. the UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505)).
Regarding claim 10, Kumar in view of Cheng teaches wherein the AI-ML model packets includes one or more AI-ML model elements comprising an AI-ML model, and an AI-ML model description (Cheng, see paragraph 0061, The model file may have a format depending on the machine learning framework that is used., such as a.h5 format, a . ONNX format or the like. Optionally, the AI/ML may also include a unique model ID and metadata).
Regarding claim 11, Kumar in view of Cheng teaches wherein the format of AI-ML model is determined based on one or more elements comprising a use case description, an update method, a size of the AI-ML model, and a proprietary setting for the AI-ML model (Cheng, see paragraph 0061, The model file may have a format depending on the machine learning framework that is used., such as a.h5 format, a . ONNX format or the like. Optionally, the AI/ML may also include a unique model ID and metadata).
Regarding claim 12, Kumar in view of Cheng teaches wherein the format of AI-ML model is explicit or implicit (Cheng, see paragraph 0061, The model file may have a format depending on the machine learning framework that is used., such as a.h5 format, a . ONNX format or the like. Optionally, the AI/ML may also include a unique model ID and metadata).
Regarding claim 13, Kumar in view of Cheng teaches wherein the AI-ML model description includes one or more elements comprising a use case description, an indication of delta update, and an indication of implicit or explicit AI-ML model format (Cheng, see paragraph 0061, The model file may have a format depending on the machine learning framework that is used., such as a.h5 format, a . ONNX format or the like. Optionally, the AI/ML may also include a unique model ID and metadata).
Regarding claim 14, Kumar teaches a method for a user equipment (UE) using artificial intelligence-machine learning (AI-ML) model in a wireless network (Kumar, see abstract, This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements) comprising:
detecting, by the UE, one or more preconfigured trigger events for transferring an AI-ML dataset (Kumar, see paragraph 0121, the UE 115 determines whether the determined AI model is available. For example, the AI manager 415 of the UE 115 determines if the determined AI model (e.g., AI model information, such as input and output parameters) is available at the UE. If the AI model is available, the UE 115 may set the determined AI model for future RRC IDLE/INACTIVE state procedures and continue operating in the RRC connected state with the base station 105. The UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505) or from another wireless communication device (e.g., a second UE)).
Kumar teaches the above yet fails to teach setting up an AI plane connection to an AI server through a radio access network (RAN) node and a CN node in the wireless network, wherein the AI plane connection enables AI-ML dataset transfer; and transferring the AI-ML dataset through the AI plane connection in the wireless network.
Then Cheng teaches setting up an AI plane connection to an AI server through a radio access network (RAN) node and a CN node in the wireless network, wherein the AI plane connection enables AI-ML dataset transfer (Cheng, see figure 1 and paragraphs 0037, the CN 124 may be an EPC, and the RAN 106 may be connected with the CN 124 via an S1 interface 128. In embodiments, the S1 interface 128 may be split into two parts, an S1 user plane (S1-U) interface, which carries traffic data between the base station 112 or base station 114 and a serving gateway (S-GW), and the S1-MME interface, which is a signaling interface between the base station112 or base station 114 and mobility management entities (MMEs)); and
transferring the AI-ML dataset through the AI plane connection in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kumar with RRC procedure design for wireless AI/ML of Cheng, because doing so would make Kumar more efficient in enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead (Cheng, see paragraph 0053).
Regarding claim 15, Kumar in view of Cheng teaches wherein the AI plane is a user plane (UP) in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 16, Kumar in view of Cheng teaches wherein the AI plane is a new plane established in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 17, Kumar in view of Cheng teaches wherein new resource blocks (RBs) are configured for the transfer of the AI-ML dataset through the AI plane in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 18, Kumar teaches a method for a radio access network (RAN) node in a wireless network (Kumar, see abstract, This disclosure provides systems, methods, and devices for wireless communication that support AI model-based enhancements) comprising:
detecting one or more preconfigured trigger events for transferring an AI-ML model (Kumar, see paragraph 0121, the UE 115 determines whether the determined AI model is available. For example, the AI manager 415 of the UE 115 determines if the determined AI model (e.g., AI model information, such as input and output parameters) is available at the UE. If the AI model is available, the UE 115 may set the determined AI model for future RRC IDLE/INACTIVE state procedures and continue operating in the RRC connected state with the base station 105. The UE 115 determines that the AI model is not available at the UE 115 and that the UE 115 is to obtain the AI model from the network (e.g., MR 505) or from another wireless communication device (e.g., a second UE)).
Kumar teaches the above yet fails to teach setting up, by the RAN node, an AI plane connection for AI between a user equipment (UE) and an AI server in the wireless network; and transferring the AI-ML model through the AI plane connection for AI among the UE, the RAN node, and the AI server in the wireless network.
Then Cheng teaches setting up, by the RAN node, an AI plane connection for AI between a user equipment (UE) and an AI server in the wireless network (Cheng, see figure 1 and paragraphs 0037, the CN 124 may be an EPC, and the RAN 106 may be connected with the CN 124 via an S1 interface 128. In embodiments, the S1 interface 128 may be split into two parts, an S1 user plane (S1-U) interface, which carries traffic data between the base station 112 or base station 114 and a serving gateway (S-GW), and the S1-MME interface, which is a signaling interface between the base station112 or base station 114 and mobility management entities (MMEs)); and
transferring the AI-ML model through the AI plane connection for AI among the UE, the RAN node, and the AI server in the wireless network (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Kumar with RRC procedure design for wireless AI/ML of Cheng, because doing so would make Kumar more efficient in enabling improved support of AI/ML based algorithms for enhanced performance and/or reduced complexity/overhead (Cheng, see paragraph 0053).
Regarding claim 19, Kumar in view of Cheng teaches wherein the AI server is an over-the-top (OTT) server (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB). The UE receives and decapsulates protocol data units (PDUs) carrying the model data, and forwards the model data to its application layer).
Regarding claim 20, Kumar in view of Cheng teaches wherein the RAN node transfers the AI-ML model received from the AI server to the UE, and wherein the AI-ML model is trained at the AI server (Cheng, see paragraph 0055, The storage and management of the AI/ML models may also be vendor specific and is possibly out of 3GPP scope. For example, a vendor may build its own AI/ML model library server to store and manage AI/ML models, while another vendor may rent from OТТ).
Regarding claim 21, Kumar in view of Cheng teaches wherein the RAN node parses the AI-ML model before transferring to the UE (Cheng, see paragraph 0068, in step S21, a gNB may download an AI/ML model from its model server which is possibly built or rent by a gNB vendor. The downloading of the AI/ML may be initiated by a request from the gNB (S20), or the AI/ML may be pushed to the gNB by the model server (not shown)).
Regarding claim 22, Kumar in view of Cheng teaches wherein the AI-ML model is trained or updated at the RAN node, and wherein, the RAN node uploads the AI-ML model to the AI server (Cheng, see paragraph 0065, After performing the training, in step S13, the UE may send the trained model to the gNB via an uplink RRC message. For example, the trained AI/ML model may be transmitted as UE Assistance Information (UAI)).
Regarding claim 23, Kumar in view of Cheng teaches wherein the AI-ML model is received from the UE through the AI server, wherein the AI-ML model is trained or updated at the UE (Cheng, see paragraph 0065, Then in step S12, the UE may train the AI/ML model with its local data by using various methods, such as a back propagation method).
Regarding claim 24, Kumar in view of Cheng teaches further comprising: receiving an upload model request from the UE; and sending an upload model response to the UE (Cheng, see paragraph 0065, After performing the training, in step S13, the UE may send the trained model to the gNB via an uplink RRC message. For example, the trained AI/ML model may be transmitted as UE Assistance Information (UAI)).
Regarding claim 25, Kumar in view of Cheng teaches further comprising: sending a model transfer request to the AI server; and receiving the AI-ML model from the AI server (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 26, Kumar in view of Cheng teaches further comprising:
receiving a model transfer request from a target RAN node when the UE switches to the target RNA node (Kumar, see paragraph 0073, The mobility pattern information may be useful in enhancing cell selection and reselection procedures, selecting target measurement object and measurement periodicity in IDLE or INACTIVE (IDLE/INACTIVE) states, and other IDLE or INACTIVE state procedures); and
transferring the AI-ML model to the target RAN node (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
Regarding claim 27, Kumar in view of Cheng teaches wherein the transferring of the AI-ML model is triggered upon detecting the UE switches from the RAN node to the target RAN node (Kumar, see paragraph 0073, The mobility pattern information may be useful in enhancing cell selection and reselection procedures, selecting target measurement object and measurement periodicity in IDLE or INACTIVE (IDLE/INACTIVE) states, and other IDLE or INACTIVE state procedures).
Regarding claim 29, Kumar in view of Cheng teaches wherein the UE transfers the AI-ML model from the AI server using downlink of the AI plane connection for AI when the AI-ML model is updated at the AI server or at the RAN node, and the UE transfers the AI-ML model to the AI server using uplink of the AI plane connection for AI when the AI-ML model is updated at the UE (Cheng, see paragraph 0063, the downloading of the AI/ML model may employ conventional Over the Top (OTT) solution. The AI/ML model data is transmitted as application-layer data via User Plane (UP) of the operator network, which provides a tunnel transparent to the network (e.g., to the gNB)).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHONG G KIM whose telephone number is (571)270-0619. The examiner can normally be reached Mon-Fri @ 9am - 5pm.
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/CHONG G KIM/Examiner, Art Unit 2443
/NICHOLAS R TAYLOR/Supervisory Patent Examiner, Art Unit 2443
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