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 .
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 06/13/2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
Claim 10 is objected to because of the following informalities:
Claim 10 has a period “.” in the wrong place: “… wherein the capability information at least indicates the quantization capability of the UE. or, obtaining …”
Appropriate correction is required.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless – (a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-4, 7-10, 13-15, 21-24 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2022/0284282 Al (Vitthaladevuni).
Regarding Claim 1:
Vitthaladevuni teaches An information processing method, performed by user equipment (UE) ([0088] UE 115) and comprising:
exchanging a quantized dataset with a first network device (ie. base station), wherein the dataset is quantized based on a quantization capability of the UE, ([0086] a UE 115 may conduct several measurements of channel conditions as part of communicating with a base station 105. These measurements may generate a significant amount of data to be transmitted to the base station 105 to assist the base station 105 in network management. [0088] The UE 115 may quantize the output of the neural network to provide data that is more capable of being communicated. Subsequently, after the neural network compression, the UE 115 may add the additional layers to further compress the data, where the UE 115 may use a differential encoding, entropy encoding, or both to further compress and encode the measurement data prior to sending the transmission to the base station.)
and the dataset is configured for at least one of training, optimization, or supervision of an artificial intelligence (Al) model. ([0084][0085] Before the machine learning models can be deployed on devices, the machine learning models may be prepared and trained, e.g., a using a dataset. To build and further train the machine learning models, a network device may collect different datasets to identify implications or effects of the datasets for the different applications. That is, a machine learning model may be trained on one or more datasets using the neural networks, and when real-world data is input to the machine learning model, the machine learning model may generate an output based on the dataset.)
Regarding Claim 8:
Vitthaladevuni teaches An information processing method, performed by a first network device ([0088] base station) and comprising:
exchanging a quantized dataset with user equipment (UE) (ie. UE 115), wherein the dataset is quantized based on a quantization capability of the UE, ([0086] a UE 115 may conduct several measurements of channel conditions as part of communicating with a base station 105. These measurements may generate a significant amount of data to be transmitted to the base station 105 to assist the base station 105 in network management. [0088] The UE 115 may quantize the output of the neural network to provide data that is more capable of being communicated. Subsequently, after the neural network compression, the UE 115 may add the additional layers to further compress the data, where the UE 115 may use a differential encoding, entropy encoding, or both to further compress and encode the measurement data prior to sending the transmission to the base station.)
and the dataset is configured for at least one of training, optimization, or supervision of an artificial intelligence (AI) model. ([0084][0085] Before the machine learning models can be deployed on devices, the machine learning models may be prepared and trained, e.g., a using a dataset. To build and further train the machine learning models, a network device may collect different datasets to identify implications or effects of the datasets for the different applications. That is, a machine learning model may be trained on one or more datasets using the neural networks, and when real-world data is input to the machine learning model, the machine learning model may generate an output based on the dataset.)
Regarding Claim 21:
Vitthaladevuni teaches A communication device user equipment (UE) ([0088] UE 115), comprising; a processor; a transceiver; and a memory, executable by the processor, ([0142] a processor and memory coupled with the processor may be configured to perform one or more of the functions described herein, e.g., by executing, by the processor, instructions stored in the memory.) wherein the processor is configured to:
exchange a quantized dataset with a first network device (ie. base station), wherein the dataset is quantized based on a quantization capability of the UE, ([0086] a UE 115 may conduct several measurements of channel conditions as part of communicating with a base station 105. These measurements may generate a significant amount of data to be transmitted to the base station 105 to assist the base station 105 in network management. [0088] The UE 115 may quantize the output of the neural network to provide data that is more capable of being communicated. Subsequently, after the neural network compression, the UE 115 may add the additional layers to further compress the data, where the UE 115 may use a differential encoding, entropy encoding, or both to further compress and encode the measurement data prior to sending the transmission to the base station.)
and the dataset is configured for at least one of training, optimization, or supervision of an artificial intelligence (AI) model. ([0084][0085] Before the machine learning models can be deployed on devices, the machine learning models may be prepared and trained, e.g., a using a dataset. To build and further train the machine learning models, a network device may collect different datasets to identify implications or effects of the datasets for the different applications. That is, a machine learning model may be trained on one or more datasets using the neural networks, and when real-world data is input to the machine learning model, the machine learning model may generate an output based on the dataset.)
Regarding Claim 22:
Vitthaladevuni teaches on the method of claim 1 as described.
Vitthaladevuni teaches on A non-transitory computer storage medium, storing an executable program, wherein when the executable program is executed by a processor, the processor is caused to perform the method of claim 1. ([0142] a processor and memory coupled with the processor may be configured to perform one or more of the functions described herein, e.g., by executing, by the processor, instructions stored in the memory.)
Regarding Claim 23:
Vitthaladevuni teaches on the method of claim 8 as described.
Vitthaladevuni teaches on A network device ([0088] UE 115), comprising: a processor; a transceiver; and a memory storing a program executable by the processor, wherein the processor is configured to perform the method of claim 8. ([0142] a processor and memory coupled with the processor may be configured to perform one or more of the functions described herein, e.g., by executing, by the processor, instructions stored in the memory.)
Regarding Claim 24:
Vitthaladevuni teaches on the method of claim 8 as described.
Vitthaladevuni teaches on A non-transitory computer storage medium, storing an executable program, wherein when the executable program is executed by a processor, the processor is caused to perform the method of claim 8. ([0142] a processor and memory coupled with the processor may be configured to perform one or more of the functions described herein, e.g., by executing, by the processor, instructions stored in the memory.)
Regarding Claim 2:
Vitthaladevuni teaches on the method of claim 1 as described.
Vitthaladevuni teaches wherein the exchanging a quantized dataset with a first network device, comprises: sending, according to the quantization capability of the UE, the quantized dataset to the first network device; ([0086] a UE 115 may conduct several measurements of channel conditions as part of communicating with a base station 105. These measurements may generate a significant amount of data to be transmitted to the base station 105 to assist the base station 105 in network management. [0088] the UE 115 may quantize the output of the neural network to provide data that is more capable of being communicated. Subsequently, after the neural network compression, the UE 115 may add the additional layers to further compress the data, where the UE 115 may use a differential encoding, entropy encoding, or both to further compress and encode the measurement data prior to sending the transmission to the base station.)
or, receiving the quantized dataset sent by the first network device, wherein the dataset is quantized by the first network device according to the quantization capability of the UE. ([0097] While UE 115-a is shown performing the different compression and encoding operations in the example of FIG. 2, any encoding device (e.g., a base station 105, a TRP, another type of UE 115, etc.) may perform the techniques described herein to compress and encode a dataset before transmitting the dataset to an additional device. Additionally, while base station 105-a is shown receiving and decoding compressed and encoded dataset 235 ( e.g., an encoded, quantized, and compressed dataset), any decoding device (e.g., a UE 115, a TRP, another type of base station 105, etc.) may perform the techniques described herein to decode compressed and encoded dataset 235.)
Regarding Claim 3:
Vitthaladevuni teaches on the method of claim 1 as described.
Vitthaladevuni teaches further comprising: sending capability information to the first network device, wherein the capability information (ie. encoded quantized dataset) indicates the quantization capability of the UE. ([0095] Once the encoded dataset has been quantized (e.g. , the dataset is now encoded, quantized, and compressed), UE 115-a may then perform the differential encoder operation, the entropy encoder operation, or both before sending the further encoded, quantized, and compressed dataset to base station 105-a. [0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
Regarding Claim 4:
Vitthaladevuni teaches on the method of claim 3 as described.
Vitthaladevuni teaches wherein the capability information comprises at least one of: first information, indicating a quantization manner (ie. compressed, encoded) supported by the UE; or second information, indicating a quantization precision (ie. differential values) supported by the UE. ([0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
Regarding Claim 7:
Vitthaladevuni teaches on the method of claim 4 as described.
Vitthaladevuni teaches further comprising: receiving a quantization configuration (ie. encoded dataset) sent by the first network device (ie. base station), wherein the quantization configuration indicates at least one of the quantization manner or the quantization precision (ie. differential values) for the dataset that is exchanged between the first network device and the UE; ([0097] While UE 115-a is shown performing the different compression and encoding operations in the example of FIG. 2, any encoding device (e.g., a base station 105, a TRP, another type of UE 115, etc.) may perform the techniques described herein to compress and encode a dataset before transmitting the dataset to an additional device. Additionally, while base station 105-a is shown receiving and decoding compressed and encoded dataset 235 ( e.g., an encoded, quantized, and compressed dataset), any decoding device (e.g., a UE 115, a TRP, another type of base station 105, etc.) may perform the techniques described herein to decode compressed and encoded dataset 235.)
or, sending a quantization parameter to the first network device, wherein the quantization parameter indicates at least one of the quantization manner (ie. compressed, encoded) or the quantization precision (ie. differential values) for the dataset that is exchanged between the first network device and the UE. ([0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
Regarding Claim 9:
Vitthaladevuni teaches on the method of claim 8 as described.
Vitthaladevuni teaches further comprising: determining the quantization capability supported by the UE, wherein the quantization capability of the UE comprises at least one of a quantization manner (ie. compressed, encoded) or a quantization precision (ie. differential values) supported by the UE. ([0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
Regarding Claim 10:
Vitthaladevuni teaches on the method of claim 8 as described.
Vitthaladevuni teaches wherein the determining the quantization capability supported by the UE, comprises: receiving capability information sent by the UE, wherein the capability information (ie. encoded quantized dataset) at least indicates the quantization capability of the UE; or, obtaining a predefined quantization capability (ie. initial values) associated with the UE. ([0095] Once the encoded dataset has been quantized (e.g. , the dataset is now encoded, quantized, and compressed), UE 115-a may then perform the differential encoder operation, the entropy encoder operation, or both before sending the further encoded, quantized, and compressed dataset to base station 105-a. [0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b. [0097] While UE 115-a is shown performing the different compression and encoding operations in the example of FIG. 2, any encoding device (e.g., a base station 105, a TRP, another type of UE 115, etc.) may perform the techniques described herein to compress and encode a dataset before transmitting the dataset to an additional device. Additionally, while base station 105-a is shown receiving and decoding compressed and encoded dataset 235 ( e.g., an encoded, quantized, and compressed dataset), any decoding device (e.g., a UE 115, a TRP, another type of base station 105, etc.) may perform the techniques described herein to decode compressed and encoded dataset 235.)
Regarding Claim 13:
Vitthaladevuni teaches on the method of claim 9 as described.
Vitthaladevuni teaches wherein the exchanging a quantized dataset with UE, comprises at least one of: sending, according to the quantization capability of the UE, the quantized dataset to the UE; ([0097] While UE 115-a is shown performing the different compression and encoding operations in the example of FIG. 2, any encoding device (e.g., a base station 105, a TRP, another type of UE 115, etc.) may perform the techniques described herein to compress and encode a dataset before transmitting the dataset to an additional device. Additionally, while base station 105-a is shown receiving and decoding compressed and encoded dataset 235 ( e.g., an encoded, quantized, and compressed dataset), any decoding device (e.g., a UE 115, a TRP, another type of base station 105, etc.) may perform the techniques described herein to decode compressed and encoded dataset 235.)
or receiving the quantized dataset sent by the UE based on the quantization capability of the UE. ([0086] a UE 115 may conduct several measurements of channel conditions as part of communicating with a base station 105. These measurements may generate a significant amount of data to be transmitted to the base station 105 to assist the base station 105 in network management. [0088] the UE 115 may quantize the output of the neural network to provide data that is more capable of being communicated. Subsequently, after the neural network compression, the UE 115 may add the additional layers to further compress the data, where the UE 115 may use a differential encoding, entropy encoding, or both to further compress and encode the measurement data prior to sending the transmission to the base station.)
Regarding Claim 14:
Vitthaladevuni teaches on the method of claim 9 as described.
Vitthaladevuni teaches further comprising: sending, according to the quantization capability of the UE, a quantization configuration (ie. encoded dataset) to the UE, wherein the quantization configuration indicates at least one of the quantization manner (ie. compressed, encoded) or the quantization precision (ie. differentials) for the dataset that is exchanged between the first network device and the UE. ([0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
Regarding Claim 15:
Vitthaladevuni teaches on the method of claim 8 as described.
Vitthaladevuni teaches further comprising: receiving a quantization parameter sent by the UE, wherein the quantization parameter (ie. reconstruction values) indicates at least one of a quantization manner (ie. compressed, encoded) or a quantization precision (ie. differentials) for the dataset that is exchanged between the first network device and the UE. ([0136] At 725, UE 115-b may transmit the encoded, quantized, and compressed dataset (e.g., a compressed and encoded dataset after a single shot encoder) to a second device (e.g., base station 105-b) after encoding the compressed dataset based on the one or more encoding operations. The encoded, quantized, and compressed dataset may include differential values for data in the dataset that are based on initial values for the data, previous reconstruction values for the data, initial reconstruction values for the data, or a combination thereof. [0137] At 730, base station 105-b may decode the encoded, quantized, and compressed dataset received from UE 115-b.)
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.
Claim(s) 5-6, 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0284282 Al (Vitthaladevuni) in view of US 2025/0378335 Al (Namgoong).
Regarding Claims 5, 16:
Vitthaladevuni teaches on the invention of claims 4, 9 as described.
Vitthaladevuni teaches on dataset quantization ([0084]-[0088]). However, Vitthaladevuni is silent on wherein the quantization manner comprises at least one of: scalar quantization; or codebook quantization.
Namgoong teaches, in the same field of endeavor, on a quantization schemes for encoder and decoder training, Abstract.
Namgoong teaches wherein the quantization manner comprises at least one of: scalar quantization; ([0137] a set of quantization schemes, e.g., scalar quantization, vector quantization, etc. [0151] At 1206 of FIG. 12, the first server 1202 and the second server 1204 communicate to identify a set of quantization schemes ( quantizing schemes) that may be used for an encoder and decoder training operation. For example, the identified set may include ( e.g., comprises) one or more types of scalar quantization.)
or codebook quantization. ([0046] For example, a user equipment vendor may apply a particular quantization scheme based on a codebook used during a training operation. Once the training operation is completed, the user equipment vendor may send, to the network entity, vendor information associated with the codebook and the quantization scheme that was determined during the learning operation.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to modify Vitthaladevuni per Namgoong to include wherein the quantization manner comprises at least one of: scalar quantization; or codebook quantization. This would have been advantageous as discussed above, as it would allow the modified system to provide flexibility per encoding datasets per types of quantization and vendors, allowing for greater customization and selection.
Regarding Claim 6:
Vitthaladevuni teaches on the method of claim 4 as described.
Vitthaladevuni teaches on dataset quantization ([0084]-[0088]). However, Vitthaladevuni is silent on wherein the second information comprises at least one of: a quantization order of scalar quantization; quantization codebook type of codebook quantization, wherein quantization codebooks of different types correspond to different quantization precisions; or a quantization factor of codebook quantization; wherein quantization factors of at least one of different types or different numbers correspond to different quantization precisions.
Namgoong teaches wherein the second information comprises at least one of: a quantization order of scalar quantization; ([0137] a set of quantization schemes, e.g., scalar quantization, vector quantization, etc. [0151] At 1206 of FIG. 12, the first server 1202 and the second server 1204 communicate to identify a set of quantization schemes (quantizing schemes) that may be used for an encoder and decoder training operation. For example, the identified set may include (e.g., comprises) one or more types of scalar quantization.)
quantization codebook type of codebook quantization, wherein quantization codebooks of different types correspond to different quantization precisions; or a quantization factor of codebook quantization; wherein quantization factors of at least one of different types or different numbers correspond to different quantization precisions. ([0113] During NN back propagation, the gradient is computed for the decoder 604, the codebook 610, and the encoder 602. Thus, the codebook 610 may be optimized during the back propagation because gradients are computed for the codebook 610, such that the outputs of the encoder 602 may be closer in value to the vectors of the codebook 610, and vice versa. [0125]-[0129] different vectors produced using different codebooks.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to modify Vitthaladevuni per Namgoong to include wherein the second information comprises at least one of: a quantization order of scalar quantization; quantization codebook type of codebook quantization, wherein quantization codebooks of different types correspond to different quantization precisions; or a quantization factor of codebook quantization; wherein quantization factors of at least one of different types or different numbers correspond to different quantization precisions. This would have been advantageous as discussed above, as it would allow the modified system to provide flexibility per encoding datasets per types of quantization and vendors, allowing for greater customization and selection.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0284282 Al (Vitthaladevuni) in view of WO 2019099024 A1 (Ahmed).
Regarding Claim 11:
Vitthaladevuni teaches on the method of claim 10 as described.
Vitthaladevuni teaches on dataset quantization ([0084]-[0088]). However, Vitthaladevuni is silent on wherein the obtaining a predefined quantization capability associated with the UE, comprises at least one of: querying, from a second network device, the predefined quantization capability subscribed by the UE; or determining, according to a protocol agreement, the predefined quantization capability supported by the UE.
Ahmed teaches, in the same field of endeavor, on methods for multi quantization codebook(s) for explicit channel state information (CSI) feedback in new radio (NR), Abstract.
Ahmed teaches wherein the obtaining a predefined quantization capability associated with the UE, comprises determining, according to a protocol agreement, the predefined quantization capability supported by the UE. ([0034] Fig. 4, a tree selection process may be performed based on some predetermined thresholds, T and T.sub.2, to determine the best or optimum quantization codebook for a UE, without the need to complete the whole chain of quantization (based on computed time domain coefficients) and MSE computation for all quantization options.)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to modify Vitthaladevuni per Ahmed to include wherein the obtaining a predefined quantization capability associated with the UE, comprises determining, according to a protocol agreement, the predefined quantization capability supported by the UE. This would have been advantageous as discussed above, as it would allow the modified system to provide determination of best quantization codebook for the UE, see Ahmed [0034].
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2022/0284282 Al (Vitthaladevuni) in view of US 2011/0222415 Al (Ramamurthi).
Regarding Claim 12:
Vitthaladevuni teaches on the method of claim 10 as described.
Vitthaladevuni teaches on dataset quantization ([0084]-[0088]). However, Vitthaladevuni is silent on obtaining, in a case where the capability information sent by the UE is not received, the predefined quantization capability associated with the UE.
Ramamurthi teaches, in the same field of endeavor, on A method comprising establishing a wireless link with at least one endpoint, Abstract.
Ramamurthi teaches obtaining, in a case where the capability information (ie. mobility indication) sent by the UE is not received, the predefined quantization capability (ie. mobility of endpoints) associated with the UE. ([0049] base station 210 may be able to determine the mobility of endpoints 270 on its own ( e.g., without receiving a mobility indication). In particular embodiments, the mobility indication may quantize the mobility of an endpoint into four classes: (1) indoor, (2) microcellular), (3) base coverage urban and ( 4) high speed. [0005] The method also includes receiving a mobility indication indicative of a rate of travel of the endpoint. The method further include dynamically selecting at least one parameter for the wireless link based on the received mobility indication.) The base station is able to obtain the predefined quantization capability (whether or not the capability information is received).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, to modify Vitthaladevuni per Ramamurthi to include obtaining, in a case where the capability information sent by the UE is not received, the predefined quantization capability associated with the UE. This would have been advantageous as discussed above, as it would allow the modified system to provide resiliency by incorporating alternative methods for obtain capability information in case of failure communication.
Conclusion & Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to RACHEL J HACKENBERG whose telephone number is (571)272-5417. The examiner can normally be reached 9am-5pm M-F.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Glenton B Burgess can be reached at (571)272-3949. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/RACHEL J HACKENBERG/Primary Examiner, Art Unit 2454
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