Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This office action is in response to the amendment filed on 7/23/2026. Claims 1-18 are canceled, claims 19-38 are newly added and claims 1-18 are pending and have been considered below.
Election/Restrictions
3. Newly submitted claims 19-38 are directed to an invention that is independent or distinct from the invention originally claimed for the following reasons: the newly submitted claims 19-38 are directed to a capability-aware selection and dynamic reconfiguration of an ML model for obtaining and applying multiple inferences whereas the original claims 1-18 are directed to ML model capability discovery followed by remote configuration.
Since applicant has received an action on the merits for the originally presented invention, this invention has been constructively elected by original presentation for prosecution on the merits. Accordingly, claims 19-38 are withdrawn from consideration as being directed to a non-elected invention. See 37 CFR 1.142(b) and MPEP § 821.03.
To preserve a right to petition, the reply to this action must distinctly and specifically point out supposed errors in the restriction requirement. Otherwise, the election shall be treated as a final election without traverse. Traversal must be timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are subsequently added, applicant must indicate which of the subsequently added claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Claim Objections
4. Claims 1, 6, 10 and 15 are objected to because of the following informalities: “article “a or an” is missing in front the claims preamble”. Appropriate correction is required.
Claim 2 is objected to because of the following informalities: “monitorwhether the…” line 3 and should read “monitor whether the…”. Appropriate correction is required.
Claim 5 is objected to because of the following informalities: “the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause, for the at least one of the one or more machine learning models the apparatus” and should read “the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause, for the at least one of the one or more machine learning models, the apparatus to:”. Appropriate correction is required.
Claim Rejections - 35 USC § 101
5. 35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claimed invention, when the claims are taken as a whole, is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, the claim recites an apparatus which falls into one of the statutory categories.
2A – Prong 1: Claim 1, in part, recites
“monitor whether the first node receives, from the second node for at least one of the one or more machine learning models, a configuration request requesting to configure the respective machine learning model according to a respective requested configuration”; “configure the at least one of the one or more machine learning models according to the respective requested configuration if the first node receives the configuration request for the at least one of the one or more machine learning models from the second node” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 1 further recites “provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The courts have found limitations directed to obtaining information electronically, recited at a high level of generality, to be well-understood routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory"). As explained by the Supreme Court, a claim directed to a judicial exception cannot be made eligible "simply by having the applicant acquiesce to limiting the reach of the patent for the formula to a particular technological use." Diamond v. Diehr, 450 U.S. 175, 192 n.14, 209 USPQ 1, 10 n. 14 (1981). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 6
2A – Prong 1: Claim 6, in part, recites
“determine, for at least one machine learning model of the one or more machine learning models, a respective requested configuration of the respective machine learning model if the second node receives the support indication for each of the one or more machine learning models, wherein the respective requested configuration is based on the at least one capability of the respective machine learning model” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 6 further recites “provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
“one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to” amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: Relevant court decision: the followings are examples of court decisions demonstrating well-understood, routine and conventional activities, see e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): Computer readable storage media comprising instructions to implement a method, e.g., see Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015). Thus, limitations that amount to merely indicating a field of use or technological environment in which to apply a judicial exception do not amount to significantly more than the exception itself, and cannot integrate a judicial exception into a practical application. Accordingly, these additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Claim 10
Claim 10, the claim recites a method which falls into one of the statutory categories.
2A – Prong 1: Claim 10, in part, recites
“monitoring whether the first node receives, from the second node for at least one of the one or more machine learning models, a configuration request requesting to configure the respective machine learning model according to a respective requested configuration”; “configuring the at least one of the one or more machine learning models according to the respective requested configuration if the first node receives the configuration request for the at least one of the one or more machine learning models from the second node” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 10 further recites “providing, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “providing, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 15
2A – Prong 1: Claim 15, in part, recites
“determine, for at least one machine learning model of the one or more machine learning models, a respective requested configuration of the respective machine learning model if the second node receives the support indication for each of the one or more machine learning models, wherein the respective requested configuration is based on the at least one capability of the respective machine learning model” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
2A – Prong 2: This judicial exception is not integrated into a practical application. In particular, claim 6 further recites “providing, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Step 2B: The claim does not contain significantly more than the judicial exception. The claim further recites “provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 2 recites “monitor whether the first node receives, from the second node, a request to provide a first inference generated by a first machine learning model of the one or more machine learning models after the first node receives the configuration request” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).; “generate the first inference by the first machine learning model configured according to the requested configuration for the first machine learning model” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f), and “provide the first inference to the second node if the first node receives the request to provide the first inference” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Claim 3 recites “wherein the instructions, when executed by the one or more processors, further cause the apparatus to: inhibit, for at least one of the one or more machine learning models, transferring the respective machine learning model to the second node” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 4 recites “wherein, for each of the one or more machine learning models, the capability of the respective machine learning model comprises at least one input that may be used by the respective machine learning model, at least one output that may be provided by the respective machine learning model, and for each of the at least one output, a characteristic of the respective output dependent on the at least one input.” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claim 5 recites “decide if the configuration request for the respective machine learning model is accepted, and inhibit the configuring the respective machine learning model if the configuration request is not accepted.” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Claim 7 recites “request, from the first node, to provide a first inference generated by a first machine learning model of the at least one machine learning model after the configuration request for the first machine learning model is provided to the first node” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network). “supervise whether the second node receives the first inference from the first node, and apply, by the second node, the first inference if the second node receives the first inference from the first node.” under broadest reasonable interpretation covers a mental process including an observation, evaluation, judgment or opinion that could be performed in the human mind or with the aid of pencil and paper.
Claim 8 recites “storing, for each of the one or more machine learning models, the identifier of the respective machine learning model and the at least one capability of the respective machine learning model at the second node if the second node receives the support indication for the respective machine learning model.” insignificant extra-solution activity of mere data gathering and output. MPEP § 2106.05(g). This limitation is directed to the well-understood, routine, and conventional activity of receiving or transmitting data over a network. MPEP § 2106.05(d)(II); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network).
Claim 9 recites “wherein, for each of the one or more machine learning models, the capability of the respective machine learning model comprises at least one input that may be used by the respective machine learning model, at least one output that may be provided by the respective machine learning model, and for each of the at least one output, a characteristic of the respective output dependent on the at least one input” However, these are mere instructions to apply the judicial exception using a generic computer programmed with a generic class of computer algorithm. MPEP § 2106.05(f).
Claims 11-15 contains subject matter similar to claims 2-5 and are rejected under the same rationale.
Claims 16-18 contains subject matter similar to claims 7-9 and are rejected under the same rationale.
Claim Rejections - 35 USC § 102
6. 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.
Claim(s) 1-18 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Soldati et al. (US 2023/0189049).
Claim 1. Soldati discloses apparatus comprising:
one or more processors, and memory storing instructions that, when executed by the one or more processors ([0007],[0159]), cause the apparatus to:
provide, to a second node different from a first node, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that the first node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model (receiving, from the wireless device, information indicating whether the wireless device is capable of executing a Machine Learning (ML) model that is operable to provide an output on the basis of which at least one RAN operation performed by the wireless device may be configured) ([0005])…(..information 266 about an ML model that the wireless device is or has been configured to execute may comprise.. model type… model identifier…indication of training of the model performed…) ([0049]-[0061]);
monitor whether the first node receives, from the second node for at least one of the one or more machine learning models, a configuration request requesting to configure the respective machine learning model according to a respective requested configuration, and configure the at least one of the one or more machine learning models according to the respective requested configuration if the first node receives the configuration request for the at least one of the one or more machine learning models from the second node (Steps 232 to 254 illustrate different actions that may be taken by the RAN node, in accordance with the method 200, as a consequence of the information received at step 210 and/or step 214….the RAN node may update a model for execution by the wireless device in response to information received from the wireless device, and may send information about the updated model to the wireless device in step 234… In some examples, the updating of a model at step 232 may be performed on the basis of the received information, which may be combined with other information available to the first RAN node, including changes in the radio network environment since the model was last configured or executed, changes in the network including new RAN nodes, etc. The model may also be updated for example to take account of a capability of the wireless device. Sending information about the updated model may comprise sending a complete updated model, sending only updates to the model, sending information about when the updated model should be used, etc..) ([0041], fig. 2b).
Claim 2. Soldati discloses the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: monitor whether the first node receives, from the second node, a request to provide a first inference generated by a first machine learning model of the one or more machine learning models after the first node receives the configuration request ([0074]); generate the first inference by the first machine learning model configured according to the requested configuration for the first machine learning model ([0075]), and provide the first inference to the second node if the first node receives the request to provide the first inference ([0075],[0078]).
Claim 3. Soldati discloses the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: inhibit, for at least one of the one or more machine learning models, transferring the respective machine learning model to the second node (…a negative acknowledgement indicates that the wireless device cannot report the requested information to the network node) ([0070]-[0071], [0104],[0123]).
Claim 4. Soldati discloses the apparatus according to claim 1, wherein, for each of the one or more machine learning models, the capability of the respective machine learning model comprises at least one input (instruction from RAN) that may be used by the respective machine learning model, at least one output that may be provided by the respective machine learning model, and for each of the at least one output, a characteristic of the respective output dependent on the at least one input ([0079]) .
Claim 5. Soldati discloses the apparatus according to claim 1, wherein the instructions, when executed by the one or more processors, further cause, for the at least one of the one or more machine learning models the apparatus: decide if the configuration request for the respective machine learning model is accepted ([0123]), and inhibit the configuring the respective machine learning model if the configuration request is not accepted (…a negative acknowledgement indicates that the wireless device cannot report the requested information to the network node) ([0123], [0104]).
Claims 10-14 represent the method of claims 1-5 and are rejected under the same rationale.
Claim 6. Soldati discloses an apparatus comprising: one or more processors, and memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
monitor whether a second node receives, for each of one or more machine learning models, a respective support indication, wherein the respective support indication indicates that a first node different from the second node supports the respective machine learning model, and the respective support indication comprises an identifier of the respective machine learning model and at least one capability of the respective machine learning model ([0154]);
determine, for at least one machine learning model of the one or more machine learning models, a respective requested configuration of the respective machine learning model if the second node receives the support indication for each of the one or more machine learning models, wherein the respective requested configuration is based on the at least one capability of the respective machine learning model ([0155]-[0156]), and provide, to the first node, a configuration request requesting to configure the at least one machine learning model according to the respective requested configuration, wherein the configuration request comprises the identifiers of the at least one machine learning model ([0156]-[0157]).
Claim 7. Soldati disclose the apparatus according to claim 6, wherein the instructions, when executed by the one or more processors, further cause the apparatus to: request, from the first node, to provide a first inference generated by a first machine learning model of the at least one machine learning model after the configuration request for the first machine learning model is provided to the first node ([0154]); supervise whether the second node receives the first inference from the first node (…the wireless device transmits a response message indicating whether it is either capable or configured to use (or it is using) machine learning models and algorithms for its operation. The response message may include information such as what network node configured the ML model, when the model was received or trained, a performance measure associated to the model) ([0154]), and apply, by the second node, the first inference if the second node receives the first inference from the first node ([0075],[0078]).
Claim 8. Soldati disclose the apparatus according to claim 6, wherein the instructions, when executed by the one or more processors, further cause the apparatus to perform: storing, for each of the one or more machine learning models, the identifier of the respective machine learning model and the at least one capability of the respective machine learning model at the second node if the second node receives the support indication for the respective machine learning model ([0154]-[0155]).
Claim 9. Soldati discloses the apparatus according to claim 6 wherein, for each of the one or more machine learning models, the capability of the respective machine learning model comprises at least one input (instruction from RAN)that may be used by the respective machine learning model, at least one output that may be provided by the respective machine learning model, and for each of the at least one output, a characteristic of the respective output dependent on the at least one input ([0079]).
Claims 15-18 represent the method of claims 6-9 and are rejected under the same rationale.
Response to Arguments
7. Applicant’s arguments and amendments filed on 07/23/2026 have been fully considered
but are moot in view of new ground of rejection(s).
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 extension fee 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.
9. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure (See PTO-892).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Phenuel S. Salomon whose telephone number is (571) 270-1699. The examiner can normally be reached on Mon-Fri 7:00 A.M. to 4:00 P.M. (Alternate Friday Off) EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached on (571) 272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-3800.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/PHENUEL S SALOMON/Primary Examiner, Art Unit 2146
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