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 Arguments
Applicant's arguments filed 2/26/26 have been fully considered but they are not persuasive.
Applicant argues “Regarding the first solution of amended claim 1, Je discloses, in paragraph [0016], transmitting configuration information for Al-based handover to a UE. Je further discloses that the configuration information comprises information related to the neural network (structure information for a connection relation between nodes of the neural network and weight information for weights between the nodes). It can be seen that the "configuration information" in Je is defined as the structure and parameters of the neural network model, i.e., the model itself. Therefore, in Je, the network side transmits the model itself to the UE. For example, paragraph [0153] of Je discloses that the Al-based handover procedure may include a scheme for transmitting the configured neural network model to the UE; paragraph [0158] of Je discloses that the device may acquire an output result from the input value on the basis of the structure information and the weight information; and paragraph [0161] of Je discloses that a neural network structure to be used for the actual Al handover may be acquired according to structure information transmitted thereafter.” – The Examiner’s notes the Applicant’s interpretation of the prior art.
Applicant argues “Paragraph [0153] of Je discloses the existence of "learning," but Je does not disclose the connection between the learning mentioned in paragraph [0153] and the configuration information in paragraph [0016]. Moreover, this "learning" is for the purpose of "updating the configured neural network mode," meaning that the result of the learning is an "updated model," not "first information" obtained by a model by performing learning on input information, and transmissible to the terminal and independent of the model itself.
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., this "learning" is for the purpose of "updating the configured neural network mode," meaning that the result of the learning is an "updated model," not "first information" obtained by a model by performing learning on input information, and transmissible to the terminal and independent of the model itself.) 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).
The Examiner respectfully submits that the claims must be given the broadest reasonable interpretation. “First information” is given its plain meaning and/or information defined by the claim or specification. In the instant case the claim defines “first information” as first information transmitted by a network device… the first information being
information obtained by the first Al model deployed in the network device that is trained or learned from input information…. wherein the first information comprises at least one of a
handover command or a handover condition of a target cell. The Examiner respectfully submits that consider input and output information is the nature of a neural Network. Input is constantly updated based on learned output. This is the basis of what is called training. Carefully note that Je teaches this very concept 0153 – “The AI-based handover procedure may include a scheme for transmitting the configured neural network model to the UE, a procedure for making a request for a handover to a target cell identified according to the configured model to a serving cell, or a learning procedure for updating the configured neural network mode.”
0154 – “AI is a technology for implementing a learning ability, an inference ability, a perception ability…”. 0164 – “it is possible to apply learning in real time to the neural network structure designed through the measurement result of the UE through feedback”. 0172 - “UE may use an artificial intelligence (AI)-based method using a neural network (NN) for determining the handover.” 0179 – “the BS may transmit information related to the handover (hereinafter, referred to as handover information) to the UE in operation 620… the handover information may include” – see input and output particularly. 0186 – “UE generates NN output info value according to HO performance reference value which is performance value of feedback)” 0190 - “When the value is optional, it means that the handover through AI is supported and the UE may selectively use the AI-based handover” 0191 - “A feedback request is a value of which the BS informs in order to allow the UE to transmit learning information related to the handover for learning. The UE transmits learning information to the BS in the future on the basis of detailed information of the feedback format transmitted together”.
As noted above, the cited section highlight and is based on the context of the overall disclosure illustrates that learning input and output is continuously updated by the UE and BS based on an AI model and a handover is accomplished accordingly. What is illustrated in the prior art is an AI model for a network (BS and UE) to improve the conventional HO by using a NN to learn and train according to the conventional parameters to improve the performance of HO in real time.
Applicant argues “In rejecting original claim 3 (regarding the first information), the Examiner cited paragraphs [0016]-[0017] of Je for "measurement and configuration information" and paragraphs [0152]-[0154] for "condition." However, the condition involved in Je is the condition for transmitting a measurement report in the prior art (MR-based handover), not
a handover condition of the target cell. Furthermore, Je does not disclose a handover command obtained by the Al model deployed in the network device by training or learning from input information.
The Examiner respectfully submits that the Handover command is one of two options: The claim reads: wherein the first information comprises at least one of a handover command or a handover condition of a target cell. It can be noted that first information is met based on at least channel quality information for example as outlined in 0164 and 0165 which teaches the parameters required to construct a more accurate environment. …” it is possible to apply learning in real time to the neural network structure designed through the measurement result of the UE through feedback. Further, when the performance deteriorates due to an environment change, the performance of the AI-based handover can be improved through continuous feedback after switching to the general handover. An efficient handover may be achieved by applying an AI-based handover determination method to a wireless communication system between the BS and the UE through a combination of cell information and measurement information or environment information of the UE. A channel quality may be acquired by measuring a received signal. Hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP), reference signal received quality (RSRQ), received signal strength indicator (RSSI), signal to noise ratio (SNR), signal to interference and noise ratio (SINR), carrier to interference and noise ratio (CINR), error vector magnitude (EVM), bit error rate (BER), block error rate (BLER), other terms having the technical meaning equivalent thereto, or indexes indicating a channel quality may be additionally used.
Applicant argues “In rejecting original claim 4 (regarding the input information), the Examiner cited paragraphs [0158], [0192] and [0237]-[0242] of Je for the input. However, the input information in Je may be a channel quality for the cell, the radio signal intensity, throughput, or all radio signals which can be measured by a UE. The input information in Je does not involve the input information defined in amended claim 1 (location information of a terminal; uplink service information and downlink service information of the terminal; handover failure information of the terminal in a historical period; deployment information of a mobile network; cell load information; or auxiliary information provided by a third-party application in the terminal).”
The Examiner respectfully submits Claim 1 reads: wherein the input information comprises at least one of: location information of a terminal; uplink service information and downlink service information of the terminal; handover failure information of the terminal in a historical period; deployment information of a mobile network; cell load information; or auxiliary information provided by a third-party application in the terminal.
As can be seen from the prior art, The input information would read on e.g., channel quality which reflects at least the uplink service information and downlink service information of the terminal (i.e., the information on that which services the uplink and downlink which is channel quality).
Applicant argues Regarding the second solution of amended claim 1, in rejecting original claim 7 (regarding the second information), the Examiner cited paragraph [0299] of Je, which discloses that "it is possible to identify a target cell suitable for the actual UE situation by selecting the target cell in real time on the basis of the measured channel quality" and cited paragraph [0355], which discloses that "the UE may identify a target cell on the basis of an input value and a neural network model of the BS". In these paragraphs, Je discloses identifying a target cell, however Je fails to disclose the "second information" as defined in amended claim 1 (which includes at least one of: beam information of the target cell; a handover success probability of the target cell; a handover condition of the target cell; service prediction information of the terminal; or trajectory prediction information of the terminal).
However, the Examiner respectfully disagrees. 0162 – teaches – “the neural network for the AI-based handover may consider a channel quality of the current serving cell, a channel quality of the target cell, and a type of the serving cell (for example, whether the serving cell is a small cell or an RAT type) as an input. Further, according to an embodiment, the neural network 400 for the AI-based handover may provide a plurality of output values. For example, the neural network for the AI-based handover may indicate a plurality of target cells to which the handover can be performed”. this reads on service prediction because the target cell is where the service is likely ot be obtained based on the model. 0165 teaches -A channel quality may be acquired by measuring a received signal. Hereinafter, as metric indicating the channel quality, reference signal received power (RSRP) is described as an example, but beam reference signal received power (BRSRP),
Applicant argues “Furthermore, similar to the first solution, Je does not disclose the input information as defined in amended claim 1.” As noted above 0179-0190 teaches at least some of the input/output information that is cycled to learn and train the input. Weights are applied to the inputs to adjust the outputs relayed between the UE and BSs.
Independent claims 13 and 17 have been amended similarly to claim 1, and thus are also not allowable. The dependent claims are therefore also not allowable by virtue of their dependency, as well as for the additional distinguishing features recited therein.
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)(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.
Claim(s) 1, 3-10 and 12-17 and 19-25 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Je et al. US Patent Pub. No.: 2023/0014613 A1, hereinafter, ‘Je’.
Consider Claim 1, Je teaches a cell handover method, comprising: A cell handover method, comprising: determining at least one handover information in a cell handover process based on a first Artificial Intelligence (AI) model(e.g., this is met based on claim 1 which claims “a method performed by a BS of a serving cell in a wireless communication system, the method comprising: transmitting configuration information for an artificial intelligence (AI)-based handover to a UE; receiving a handover request to a target cell according to the AI-based handover from the UE; and transmitting a configuration message for access to the target cell to the UE, based on the handover request, wherein the target cell is identified based on a neural network (NN) configured for the AI-based handover and a measurement result of the UE”); wherein determining the at least one handover information based on the first AI model comprises: receiving first information transmitted by a network device (e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. ) the first information being information obtained by the first Al model deployed in the network device that is trained or learned from input information (i.e., input/output see input and output between BS and UE with respect to learning and training according to AI )(e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. ); and determining the at least one handover information based on the first information; wherein the first information comprises at least one of a handover command or a handover condition of a target cell(i.e., handover condition would read on at least the channel quality of the target cell amongst other thing as noted) (e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context.); or (note: optional context indicated by “or” )determining the at least one handover information based on second information, the second information being information obtained by the first AI model deployed in a terminal that is predicted from input information(i.e., note inference and prediction are defined by the purpose AI NNs. Consider based on this context the remarks above and the context of the following cited sections )(e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. ); wherein the second information comprises at least one of: beam information of the target cell; a handover success probability of the target cell; a handover condition of the target cell; service prediction information of the terminal; or trajectory prediction information of the terminal (e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. );wherein the input information comprises at least one of: location information of a terminal; uplink service information and downlink service information of the terminal; handover failure information of the terminal in a historical period; deployment information of a mobile network; cell load information; or auxiliary information provided by a third-party application in the terminal(note: please consider that in a neural network outputs are used as inputs to learn and train the network. Consider this understanding in the context of the BS and UE communicating said outputs to form improved inputs accordingly )(e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. ).
Consider Claim 13, Je teaches a cell handover method (e.g., this is met based on the context of at least 0008 –“The disclosure provides an apparatus and a method for performing an artificial intelligence (AI)-based handover in a wireless communication system”. ), comprising: training or learning input information based on a first Al model to obtain first information(e.g., see at least 0015 – “The disclosure provides an apparatus and a method for learning related to the AI-based handover in a wireless communication system” ); and transmitting the first information to a terminal, the terminal being configured to determine at least one handover information in a cell handover process based on the first information (e.g., this is met based on at least 0016 “According to various embodiments of the disclosure, a method performed by a BS of a serving cell in a wireless communication system includes transmitting configuration information for an artificial intelligence (AI)-based handover to a UE, receiving a handover request to a target cell according to the AI-based handover from the UE, and transmitting a configuration message for access to the target cell to the UE, based on the handover request, and the target cell is identified based on a neural network (NN) configured for the AI-based handover and a measurement result of the UE.” ) (The remaining amendments of this claim contains subject matter similar to claim 1 and is therefore rejected based on similar rationale).
Consider Claim 17, Je teaches a terminal (e.g., see at least figure 18 UE - 1800), comprising: one or more processors(e.g., see at least figure 18 -controller - 1805); and one or more transceivers connected to the one or more processors(e.g., see at least figure 18 -communication - 1801), wherein the one or more processors are configured to load and execute executable instructions, so as to perform: determining at least one handover information in a cell handover process based on a first Artificial Intelligence (AI) model (e.g., this is met based on at least claim 6 - “A method performed by a user equipment (UE) in a wireless communication system, the method comprising: receiving configuration information for an artificial intelligence (AI)-based handover from a BS of a serving cell; identifying a target cell according to the AI-based handover, based on a neural network (NN) configured for the AI-based handover and a measurement result; transmitting a handover request to the target cell to the BS; and receiving a configuration message for access to the target cell from the BS.”). (e.g., see at least 0153-0154, 0162, 0164-0165, 0179-0191 and context of at least figure 6 – the rationale/ interpretation is noted in response to arguments for further context. )
Consider Claims 3, 14 and 19, Je teaches wherein the first information comprises at least one of: a measurement configuration; or a configuration of a target cell (i.e., see response to arguments also)(e.g., see measurement and configuration information 0016-0017 and claims – e.g., see conditions in at least 152-154).
Consider Claims 4 and 15, Je teaches wherein the input information comprises a measurement report of the terminal (see also remarks above regarding NN inputs and outputs which contain measurement info)(e.g., see at least 0158 – “in the case of the AI-based handover, the input may be a channel quality for the cell and the output may be whether the corresponding cell is a handover target cell.”- see also inputs noted in 0192, and 0237-0242).
Consider Claims 5 and 16, Je teaches wherein the input information is obtained by at least one of following ways: being transmitted by a neighboring cell network device to the network device via an Xn interface; being transmitted by the neighboring cell network device to the network device via a first Al interface; being transmitted by a terminal to the network device via Radio Resource Control (RRC) signaling; or being transmitted by the terminal to the network device via a second Al interface (e.g., see at least 0075 and 0413 “ For example, the message including the learning data may be transmitted through an X2 interface.”).
Consider Claim 7, Je teaches wherein the second information comprises identification information of a target cell (e.g., see at least 0299- “it is possible to identify a target cell suitable for the actual UE situation by selecting the target cell in real time on the basis of the measured channel quality…” 0355 – “the UE may identify a target cell on the basis of an input value and a neural network model of the BS…” – context of figure 12 and claims).
Consider Claim 9, Je teaches wherein the input information is obtained by at least one of following ways: being transmitted from a first entity in the terminal to a second entity via inter-layer interaction, the second entity being an entity in which the first Al model is deployed; being transmitted from the first entity in the terminal to the second entity via a third Al interface; being transmitted by a network device to the terminal via RRC signaling; or being transmitted by the network device to the terminal via a fourth Al interface (e.g., see layers noted in at least 0115, RRC signaling – 0076, 0168-0169, 0227 and figure 5).
Consider Claim 10, Je teaches wherein the second entity is one of a Non-Access Stratum (NAS) entity, a Radio Resource Control (RRC) entity, a Service Data Adaptation Protocol (SDAP) entity, a Packet Data Convergence Protocol (PDCP) entity, a Radio Link Control (RLC) entity, a Medium Access Control (MAC) entity, a Physical layer (PHY) entity, or an Al protocol layer (e.g., see at least figures 3).
Consider Claim 12, Je teaches receiving an activation instruction transmitted by a network device, the activation instruction being used to activate the terminal to use the first Al model (e.g., see activation in at least 0237 and 0273).
Consider Claim 20. Je teaches a network device, comprising: one or more processors; and one or more transceivers connected to the one or more processors, wherein the one or more processors are configured to load and execute executable instructions, so as to perform the cell handover method according to claim 13 (e.g., see at least figure 17).
Consider Claim 21, Je teaches wherein the handover command
comprises a configuration of the target cell and a handover type supported by the target
cell; wherein the handover type supported by the target cell comprises at least one of:
conditional handover, dual active protocol stack (DAPS) handover, traditional handover,
or random access channel-less (RACH-LESS) handover (0179- The handover information may indicate the type of the handover (for example, MR-based handover or AI-based handover)(i.e., MR meets the traditional HO).
Consider claim 22, Je teaches the claimed invention further comprising:
determining, from the handover type supported by the target cell, a handover type in
the cell handover process based on the second information (see at least BS information and type noted in at least 0179).
Consider Claim 23, JE teaches the claimed information further comprising:
receiving a deactivation instruction transmitted by the network device, the
deactivation instruction being used to deactivate use of the first AI model by the terminal (e.g., this is met based on 0180 – which includes info regarding which type of HO should be should which could include excluding the AI).
Consider Claim 24, Je teaches the claimed invention further comprising:
etermining auxiliary information based on the first AI model, wherein the auxiliary
information is used to assist the terminal in triggering conditional handover, and the
auxiliary information comprises at least one of: an identifier of the target cell, a handover
success rate of the target cell, or weight information of the target cell (e.g., se at least 0172 which teaches information used for HO including weights used for calculation and 0190 - The AI HO informs whether the downloadable neural network (NN)-based handover is used. When the corresponding value is mandatory, it means that the NN and weights are downloaded and used.).
Consider Claim 25, wherein determining the at least one
handover information based on the second information comprises at least one of:
in a case that the second information comprises beam information of the target cell,
determining a target beam used after handover to the target cell based on the beam
information of the target cell (e.g., see beam – 0169 and 0165);
in a case that the second information comprises handover success probability of the
target cell, determining whether to perform cell handover based on the handover success
probability of the target cell, or selecting an appropriate target cell based on the handover
success probability of the target cell (the probability can be expressed by weights - 0157);
in a case that the second information comprises service prediction information of the
terminal, selecting an appropriate target cell according to a service predicted based on
the service prediction information( 0299 - The UE may predict a state/environment of the UE through a change in the channel quality (for example, a change in the RSRP/SNIR) which is an input value and the change in the input value may influence the determination of the handover by the UE.)(also noted the selected target is selected based on the predicted service which is the point of using the AI model amongst others); or in a case that the second information comprises trajectory prediction information of the terminal, selecting an appropriate target cell based on a trajectory predicted based on the trajectory prediction information (e.g., this would be net based on 0276 - The BS may generate an operation area (for example, the operation area 420 of the neural network 400 in FIG. 4) corresponding to the hidden layer between the input layer and the output layer through the configured nodes. The structure information may be cell-specifically configured. A measurement value, a distance from each cell, or a channel quality with each cell is frequently changed according to a movement condition of the UE, and the weight information transmitted to the UE may be UE-specifically configured. The weight information may be transmitted through RRC, MAC CE, or DCI.).
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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Je et al. US Patent Pub. No.: 2023/0014613 A1, hereinafter, ‘Je’ in view of Zhang et al. US 20230319585 A1, hereinafter, ‘Zhang’.
Consider Claim 11, Je teaches receiving configuration information of the first Al model transmitted by a network device (e.g., see at least configuration in 0016).
However, Je does not teach wherein the configuration information of the first AI model comprises at least one of: a model type of the first AI model, a number of neural network layers, a type of each neural network layer, a cascading relationship between adjacent neural network layers, or a network parameter in each neural network layer.
In analogous art, Zhang teaches 0158 - the system node 120 configures one or more local AI models in accordance with the model parameter(s) included in the first set of configuration information. For example, the model parameter(s) included in the first set of configuration information may include an identifier (e.g., a unique model identification number) identifying which local AI model(s) should be used at the AI execution module 220 (e.g., the AI management module 210 may configure the AI execution module 220 to local AI model(s) that are the same as the global AI model(s), for example by transmitting the identifier(s) of the global AI model(s)). The AI execution module 220 may then initialize the identified local AI model(s) using weights included in the model parameter(s). In some examples, such as when the system node 120 has requested a collaborative task for collaborative training of the local AI model(s), the model parameter(s) included in the first set of configuration information may be the collaboratively trained parameter(s) (e.g., weights) of the local AI model(s). The AI execution module 220 may then update the parameter(s) of the local AI model(s) according to the collaboratively trained parameter(s).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date to try wherein the configuration information of the first AI model comprises at least one of: a model type of the first AI model, a number of neural network layers, a type of each neural network layer, a cascading relationship between adjacent neural network layers, or a network parameter in each neural network layer for the purpose of providing information based on the particular AI model.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US Patent Pub. No.: 20240381199 teaches a method and apparatus for performing handover based on AI model in a wireless communication system is provided. The source node acquire a mobility information for a specific UE. The source node transmits, to the specific UE, a measurement configuration including a request for a location information. The source node receives, from the specific UE, the location information. The source node determine a target RAN node for the specific UE by using an AI model, based on the mobility information and the location information. The source node performs a handover procedure for the specific UE with the determined target RAN node.
US Patent Pub. No.: 20180176986 A1 teaches in 0156 the handover type information element included in the handover request message.
US Patent Pub. No.: 20230179490 A1 teaches in 0382 - there are two types of outputs of an AI model: one is a network decision related to a handover action and the other is a network decision related to the cell selection, reselection, and handover parameter.
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 CHARLES TERRELL SHEDRICK whose telephone number is (571)272-8621. The examiner can normally be reached 8A-5P.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew D Anderson can be reached at 571 272 4177. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHARLES T SHEDRICK/Primary Examiner, Art Unit 2646
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