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
Claim(s) 1-20 has/have been examined.Claim(s) 1-20 have been rejected.
Response to Arguments
The arguments submitted March 18, 20226 have been fully considered but are not persuasive.
Regarding the 101 rejection, Applicant argues that the claimed invention presents a technical solution that improves data center technology and therefore fulfills Step 2A prong 2. The examiner respectfully disagrees. For an improvement to technology to integrate the judicial exception the improvement must be implemented in the claim. Claim 1 only recites “perform a mitigating action” which does not concretely implement the improvement. This claim limitation can be interpreted as displaying an alert or notifying a user of the result of an analysis. The examiner notes that dependent claims do recite more specific mitigating actions and have not been rejected as abstract ideas.
Applicant argues that the claim amounts to significantly more at step 2b because the system operates in an unconventional manner. The examiner respectfully disagrees. Step 2b is an evaluation of claimed system elements other than those which make up the abstract idea. This includes the data center process, data center sensor, database and computer-readable medium. Step 2b does not include an evaluation of an unconventional manner of utilizing data. Applicant argues that the office oversimplifies the technical nature of the claims. The examiner respectfully disagrees. While the claims have been simplified in their recitation in the rejection, no claim limitation has been improperly oversimplified in its evaluation.
Regarding the 103 rejection, Applicant argues that ambient air proximate to a component, as is claimed, is not the same as storage device temperature, as in the Sethi. Applicant asserts that Embleton does not remedy the deficiencies of Sethi. The examiner respectfully disagrees. Sethi determines health scores based on telemetry data associated with a component and related components (Figure 10). Embleton teaches determining corrosion rates of components based temperature of the environment proximate to the component (paragraph 134 and 135). The rejection below describes how Embleton renders obvious the use of ambient air temperature to determine corrosion rate and health score.
Claim Rejections - 35 USC § 101
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.
Claim(s) 1-5, 8-12 and 15-19 is/are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter. The claims are directed to an abstract idea without significantly more.
Below is an evaluation using the 2019 Revised Patent Subject Matter Eligibility Guidance.
As per claim 1, Step 1 is satisfied because operation steps are processes.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include receiving an indication that a current environmental condition of an ambient air proximate to a component in a data center exceeds an environment threshold level; based at least on the indication, determining, using the current environmental condition, a location of the component, and historical data of other components exposed to environmental conditions that exceed the environment threshold level, a corrosion rate for the component; based at least on the corrosion rate for the component, determining a time the component will fail.
At step 2a prong 2, additional elements that integrate the judicial exception into a practical application are not recited. Recited details about the nature of the received data and the determined corrosion rate data are not additional elements but instead describe steps of the mental process. The claim additionally recites performing a mitigating action. This step does not integrate the judicial exception into a practical application because it is vaguely claimed and is an extra-solution activity that does not impose meaningful limit on the judicial exception. The claim additionally recites a data center management system comprising a processor; a data center sensor; and a historical database. The processor only generally links the judicial exception to a particular field of use. The data center sensor and historical database contribute only nominally to the execution of the claimed process in a data gathering step.
At step 2b, additional elements that may amount to significantly more than the judicial exception are not recited. The claim recites performing a mitigating action. This limitation does not amount to significantly more than the judicial exception because it is not a unconventional step and only generally and vaguely links the abstract idea to a technological environment. The claim additionally recites a data center management system comprising a processor; a data center sensor; and a historical database. These are conventional, off the shelf components. The use of these elements contributes only nominally or insignificantly to the execution of the claimed method in a data gathering step. Furthermore the additional element is directed to electronic recordkeeping or storing and retrieving information in memory, which have recognized as well‐understood, routine, and conventional when they are claimed in a generic manner. See MPEP § 2106.05(d)(II).
As per claim 2, further limitations about the data utilized are further limitations of the abstract idea. These limitations are considered part of the mental process, and their inclusion does not push the complexity of the process beyond what a human may perform using pen and paper (see MPEP 2106.04(a)(2)). This is not an additional element that is evaluated under step 2a prong 2 or step 2b.
As per claim 3, this claim recites various limitations that are considered part of the mental process, and their inclusion does not push the complexity of the process beyond what a human may perform using pen and paper (see MPEP 2106.04(a)(2)). This is not an additional element that is evaluated under step 2a prong 2 or step 2b.
As per claim 4, this claim recites a component failure prediction platform generating a machine learning failure prediction algorithm. The platform is only generally claimed and has a scope of a structure for applying a machine learning failure prediction algorithm. The limitation does not integrate the judicial exception into a practical application (step 2a prong 2) because the limitation does not present an element that applies, relies on, or uses the judicial exception in a manner that imposes any meaningful limit on the judicial exception. The limitation does not amount to significantly more than the judicial exception (step 2b) because utilizing a machine learning algorithm is well known (see, for example, (Sethi ('258) paragraph 28, Benson ('635) paragraph 16, and Delange (US Patent 10,613,962)) and only generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
As per claim 5, this claim recites that the mitigating action comprises migrating virtual machines. This limitation does not integrate the judicial exception into a practical application (step 2a prong 2) because the limitation does not present an element that applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The limitation does not amount to significantly more than the judicial exception (step 2b) because manipulation and migration virtual machines is well known (see, for example, Wikipedia's Virtualization and Kumar (2018/0157532)) and only generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
As per claim 8, Step 1 is satisfied because method steps are processes.
At step 2a prong 1, an abstract idea is recited: steps of the claim could be performed as a mental process. These steps include receiving an indication that a current environmental condition of ambient air proximate to a component in a data center exceeds an environment threshold level; based at least on the indication, determining, using the current environmental condition, a location of the component, and historical data of other components exposed to environmental conditions that exceed the environment threshold level, a corrosion rate for the component; based at least on the corrosion rate for the component, determining a time the component will fail.
At step 2a prong 2, additional elements that integrate the judicial exception into a practical application are not recited. Recited details about the nature of the received data and the determined corrosion rate data are not additional elements but instead describe steps of the mental process. The claim additionally recites performing a mitigating action. This step does not integrate the judicial exception into a practical application because it is vaguely claimed and is an extra-solution activity that does not impose meaningful limit on the judicial exception.
At step 2b, additional elements that may amount to significantly more than the judicial exception are not recited. The claim recites performing a mitigating action. This limitation does not amount to significantly more than the judicial exception because it is not a unconventional step and only generally and vaguely links the abstract idea to a technological environment.
As per claim 9, this claim recites accessing historical data from a historical database. The database contributes only nominally or insignificantly to the execution of the claimed method (e.g., in a data gathering step) and so does not integrate the judicial exception into a practical application or amount to significantly more than the judicial exception.
As per claims 10-11, further limitations about the data utilized are further limitations of the abstract idea. These limitations are considered part of the mental process, and their inclusion does not push the complexity of the process beyond what a human may perform using pen and paper (see MPEP 2106.04(a)(2)). This is not an additional element that is evaluated under step 2a prong 2 or step 2b.
As per claim 12, this claim recites that the mitigating action comprises migration virtual machines. This limitation does not integrate the judicial exception into a practical application (step 2a prong 2) because the limitation does not present an element that applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The limitation does not amount to significantly more than the judicial exception (step 2b) because manipulation and migration virtual machines is well known (see, for example, Wikipedia's Virtualization and Kumar (2018/0157532)) and only generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
As per claims 15, 16 and 17, these claims recite limitations found in claims 8, 9 and 11 and are rejected on the same grounds as claims 8, 9 and 11. The claims also recites a computer storage medium storing instructions. This limitation does not integrate the judicial exception into a practical application (step 2a prong 2) because it merely uses a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2). This limitation does not amount to significantly more than the judicial exception (step 2b) because it merely recites use of a computer in its ordinary capacity as a tool to perform an existing process. See MPEP §§ 2106.04(d), 2106.05(f)(2).
As per claim 18, the claim recites applying a machine learning failure prediction algorithm. This limitation does not integrate the judicial exception into a practical application (step 2a prong 2) because the limitation does not present an element that applies, relies on, or uses the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The limitation does not amount to significantly more than the judicial exception (step 2b) because utilizing a machine learning algorithm is well known (see, for example, (Sethi ('258) paragraph 28, Benson ('635) paragraph 16, and Delange (US Patent 10,613,962)) and only generally links the use of the judicial exception to a particular technological environment or field of use. See MPEP §§ 2106.04(d), 2106.05(h).
As per claim 19, this claim recites limitations found in claim 12 and is rejected on the same grounds as claim 12.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-4, 8-11,15-18 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Sethi (PG-PUB 2023/0342258) in view of Embleton (PG-PUB 2022/0026870).
Regarding claim 1, Sethi discloses a system for predicting a failure of a data center component based on environmental conditions, the system comprising:
a data center management system, the data center management system comprising a processor (Figure 2);
a data center sensor (paragraph 49, CPU temperature and hard drive temperature data is determined);
a record comprising component state data (paragraph 49, the health score may be based on storage device metrics such as error rate), environment state data (paragraph 49, the health score may be based on storage device temperature), and component corrosion rates (paragraphs 51-54, a change in health score of a component is determined),
the component state data comprising a metric that represents a health status or an attribute of components in data centers (paragraph 49, the health score may be based on storage device metrics such as error rate),
the environment state data comprising a temperature proximate the components in the data (paragraph 49, the health score may be based on storage device temperature),
a computer-readable medium comprising computer-executing instructions that, when executed by the processor, cause the processor to perform the following operations:
receiving, from the data center sensor, an indication that a current environmental condition of a component in a data center exceeds an environment threshold level (paragraphs 30 and 31, checkpoints are performed during a migration; the checkpoints include calculating health scores; paragraph 49, the health score may be based on storage device temperature);
based at least on the indication, using the current environmental condition, the historical component state data, the historical environment state data, the component corrosion rates, determine a rate of degradation of a component (paragraphs 51-53, a change in health score is determined)
based at least on the rate of degradation of the component, determining a time the component will fail (paragraph 55, the system determines that the health score will cross a threshold during a migration event); and
in response to determining the time the component will fail, performing a mitigation action for the component prior to a failure of the component (paragraph 59, action is taken to prevent data loss).
Sethi does not expressly disclose the method wherein the data includes a record of component state data, environmental state data and component corrosion rates and comprises a historical database of historical data, that the data elements are from a period of time prior to the component failure. Sethi also does not expressly disclose the historical data including humidity level and component corrosion rates and location data comprising information on a location of the components. Sethi does not disclose that the current environmental condition includes a determined rate of forming corrosion for a component, and that the determining a time the component will fail is based on the rate of forming of corrosion.
Embleton teaches a system for managing a computing device in an information handling system (paragraph 4), the system includes repositories of historical environmental conditions and corrosion rates (paragraph 110), and corrosion-based lifecycles of components (paragraph 116). The system receives spatial data regarding relative locations of components (paragraph 113). A predictive model is trained on past environmental conditions and subsequently receives current conditions including temperature, humidity level and corrosion rates as inputs to generate future rates of corrosion or absolute amounts of corrosion that will occur in a future time period for a component (paragraphs 141-143).
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the data center management system disclosed by Sethi such that components of the data center are evaluated based on historical repositories of environmental conditions, location data, and determined corrosion rates and amounts, as taught by Embleton. This modification would have been obvious because components are known to corrode (Embleton paragraphs 23 and 24) and adjustments to the environment can be taken to reduce corrosion and reduce the likelihood of premature failure (Embleton paragraphs 26 and 27).
Sethi does not expressly disclose the method wherein the received condition of a component includes an environmental condition of ambient air proximate to a component.
Embleton teaches determining corrosion rates of components based on a determined temperature near a corrosion detector and a determined temperature differential that indicates the temperature of the environment proximate to the component (paragraphs 134-136). Corrosion rate is thus estimated based on relative location of the component to the corrosion detector and a temperature differential between the component and the detector (paragraph 124 and 125).Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the data center management system disclosed by Sethi such that components of the data center are evaluated based on an indirect measurement of temperature proximate to the component, as taught by Embleton. This modification would have been obvious because some components may be located away from corrosion detectors (paragraph 113) and thus, away from the temperature determined by the corrosion detector (paragraphs 133-135), and it may not be possible to directly measure the temperature of or corrosion of these components (paragraph 113).
Regarding claim 2, Sethi in view of Embleton discloses the system of Claim 1, wherein the current environmental condition comprises a temperature level and a humidity level (Embleton paragraph 143), and wherein corrosion is a chemical reaction on the component (Embleton paragraph 37), and wherein the corrosion is caused by the environmental condition (Embleton paragraph 36).
Regarding claim 3, Sethi in view of Embleton discloses the system of Claim 2, wherein the rate of forming corrosion comprises one or more of the following:
determining a total amount of corrosion based on previous exposure to the environmental conditions exceeding thresholds,
calculating a current rate of continued corrosion based on current environmental conditions (Embleton paragraphs 83 and 84), and
comparing a total corrosion and current rate against historical failure thresholds to determine time of failure.
Regarding claim 4, Sethi in view of Embleton discloses the system of Claim 1, further comprising a component failure prediction platform coupled to the historical database, the component failure prediction platform generating a machine learning failure prediction algorithm that determines the time the component will fail (Sethi paragraph 28, a machine learning algorithm is used for the component health scores and related failure prediction; also Embleton paragraphs 143 and 144).
Regarding claim 8, Sethi discloses a computerized method comprising:
Receiving, by a processor from a datacenter sensor, an indication that a current environmental condition of an environment proximate to a component in a data center exceeds an environment threshold level (paragraphs 30 and 31, checkpoints are performed during a migration; the checkpoints include calculating health scores; paragraph 49, the health score may be based on storage device temperature);
based at least on the indication, determining, using the current environmental condition (paragraph 49, the health score may be based on storage device temperature, paragraphs 51-54, past health scores are stored and used in later analysis), a corrosion rate for the component (paragraphs 51-54, a change in health score is determined);
based at least on the corrosion rate for the component, determining a time the component will fail (paragraph 55, the system determines that the health score will cross a threshold during a migration event); and
in response to determining the time the component will fail, performing a mitigation action for the component prior to a failure of the component (paragraph 59, action is taken to prevent data loss).
Sethi does not expressly disclose the method wherein the data includes a record of component state data, environmental state data and component corrosion rates and comprises a historical database of historical data, that the data elements are from a period of time prior to the component failure. Sethi also does not expressly disclose the historical data including humidity level and component corrosion rates and location data comprising information on a location of the components. Sethi does not disclose that the current environmental condition includes a determined rate of forming corrosion for a component, and that the determining a time the component will fail is based on the rate of forming of corrosion.
Embleton teaches a system for managing a computing device in an information handling system (paragraph 4), the system includes repositories of historical environmental conditions and corrosion rates (paragraph 110), and corrosion-based lifecycles of components (paragraph 116). The system receives spatial data regarding relative locations of components (paragraph 113). A predictive model is trained on past environmental conditions and subsequently receives current conditions including temperature, humidity level and corrosion rates as inputs to generate future rates of corrosion or absolute amounts of corrosion that will occur in a future time period for a component (paragraphs 141-143).
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the data center management system disclosed by Sethi such that components of the data center are evaluated based on historical repositories of environmental conditions, location data, and determined corrosion rates and amounts, as taught by Embleton. This modification would have been obvious because components are known to corrode (Embleton paragraphs 23 and 24) and adjustments to the environment can be taken to reduce corrosion and reduce the likelihood of premature failure (Embleton paragraphs 26 and 27).
Sethi does not expressly disclose the method wherein the received condition of a component includes an environmental condition of ambient air proximate to a component.
Embleton teaches determining corrosion rates of components based on a determined temperature near a corrosion detector and a determined temperature differential that indicates the temperature of the environment proximate to the component (paragraphs 134-136). Corrosion rate is thus estimated based on relative location of the component to the corrosion detector and a temperature differential between the component and the detector (paragraph 124 and 125).Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the data center management system disclosed by Sethi such that components of the data center are evaluated based on an indirect measurement of temperature proximate to the component, as taught by Embleton. This modification would have been obvious because some components may be located away from corrosion detectors (paragraph 113) and thus, away from the temperature determined by the corrosion detector (paragraphs 133-135), and it may not be possible to directly measure the temperature of or corrosion of these components (paragraph 113).
Regarding claim 9, Sethi in view of Embleton discloses the computerized method of claim 8, further comprising accessing the historical data from a historical database, the historical database comprising historical component state data, historical environment state data (Embleton paragraph 110), component corrosion rates (Embleton paragraph 110), and location data (Embleton paragraph 113), the historical component state data comprising a metric that represents a health status or an attribute of components in data centers during a period of time prior to a component failure (Sethi paragraphs 51-53), the historical environment state data comprising a temperature and a humidity proximate the components in the data centers during the period of time prior to the component failure (Embleton paragraph 110), the component corrosion rates providing a rate of forming corrosion of the components during the period of time prior to the component failure based at least on the environment data with respect to the component (Embleton paragraph 116, corrosion-based lifecycles of components are recorded), and the location data comprising information on a location of the components (Embleton paragraph 113, spatial data regarding relative locations of components).
Regarding claim 10, Sethi in view of Embleton discloses the computerized method of claim 8, wherein the current environmental condition comprises a temperature level and a humidity level (Embleton paragraph 143).
Regarding claim 11, Sethi in view of Embleton discloses the computerized method of claim 8, wherein the component is a solid state drive (Sethi paragraph 39 describes a health score based on parameters of a storage device; while use of a solid state disk is not expressly disclosed, the examiner takes official notice that solid state disks are well known in the art. The inclusion of SSDs in the data center and their monitoring would have been obvious because, as would be clear to one of ordinary skill in the art, SSDs provide faster random access rates than conventional storage devices. See, for example, attached NPL reference regarding SSDs).
Regarding claims 15 and 16, these claims recites limitations found in claims 8 and 9, respectively, and are respectively rejected on the same grounds as claims 8 and 9.
Regarding claim 17, this claim recites limitations found in claim 11 and is rejected on the same grounds as claim 11.
Regarding claim 18, Sethi in view of Embleton discloses the computer storage medium of claim 15, wherein the computer-executable instructions, upon execution by the processor, further cause the processor to at least: apply a machine learning failure prediction algorithm that determines the time the component will fail (Sethi paragraph 28, a machine learning algorithm is used for the component health scores and related failure prediction; also Embleton paragraphs 143 and 144).
Regarding claim 20, this claim recites limitations found in claims 13 or 14, in the alternative, and is rejected on the same grounds as claim 13.
Claims 5, 12 and 19 are rejected under 35 U.S.C. 103 as being unpatentable Sethi, Embleton and Wikipedia's Virtualization (historical version published June 10, 2023).
Regarding claim 5, Sethi in view of Embleton discloses the system of Claim 1.
Sethi in view of Embleton does not expressly disclose the system wherein the mitigating action comprises migrating virtual machines hosted on the component to another component that has a risk of failure below a risk threshold.
Wikipedia's Virtualization teaches use of virtual machines to perform hardware virtualization.
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the network migration monitoring system disclosed by Sethi in view of Embleton and such that data being migrated includes virtual machines, as taught by Wikipedia's Virtualization. This modification would have been obvious because virtualization allows centralized administrative tasks with improved scalability and overall hardware-resource utilization (Wikipedia's Virtualization, page 2 paragraph 2).
Regarding claims 12 and 19, these claims recite limitations found in claim 5 and are rejected on the same grounds as claim 5.
Claims 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable Sethi in view of Embleton and Klein (PG-PUB 2020/0249850)
Regarding claim 6, Sethi in view of Embleton discloses the system of Claim 1. Sethi in view of Embleton does not expressly disclose the method wherein the mitigation action comprises replacing the component with a healthy component, or reallocating or reconfiguring the healthy component near the component.
Klein ('850) teaches predicting SSD failure using prior knowledge of behavior of SSDs (paragraph 4) in a data center environment (paragraph 5). The prediction is based on correlations between a drive failure of a SSD device and erase count, drive write, media write, utilization, bad block count, BER histogram, ECC state histogram, and temperature of the same SSD device (paragraph 42). The system mitigates a predicted failure by migrating data from a failing SSD to another SSD (abstract).
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the monitoring system disclosed by Sethi in view of Embleton such that a predicted failing component is migrated to another health component, as taught by Klein. This modification would have been obvious because predicting drive failure and migrating data to another drive in advance improves reliability of a system by reducing failure overhead (Klein paragraph 4).
Regarding claim 13, this claim recites limitations found in claim 6 and is rejected on the same grounds as claim 6.
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable Sethi in view of Embleton and Benson (US Patent Application Publication 2005/0283635).
Regarding claim 7, Sethi in view of Embleton discloses the system of claim 1. Sethi in view of Embleton does not expressly disclose the system wherein the mitigation action comprises applying a particular airflow approximate the component to reduce a temperature level and a humidity level below an environmental threshold level.
Benson teaches monitoring a computer system via multiple sensors (Figure 1) including temperature and humidity sensors (paragraph 12) and generating a prediction of a component malfunction (paragraph 6). If the system predicts that a computer system fan is about to fail, a new fan can be installed preemptively to avoid the predicted failure (paragraph 18)
Prior to the effective filing date of the claimed invention it would have been obvious to a person of ordinary skill in the art to modify the network migration monitoring system disclosed by Sethi in view of Embleton such that a cooling component malfunction is identified and mitigated by installing a new fan, as taught by Benson. This modification would have been obvious because cooling fan failures are known to occur (Benson paragraph 16) and installing a new fan preemptively may avoid the predicted failure (Benson paragraph 18).
Regarding claim 14, this claim recites limitations found in claim 7 and is rejected on the same grounds as claim 7.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Klein teaches a survey of corrosion generation and effects in data centers. Torresani teaches a system that monitors sensor data from remotely located devices and determines that a device competent failure has a threshold likelihood of failure.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOSEPH SCHELL whose telephone number is (571) 272-8186. The examiner can normally be reached on Monday through Friday 9AM-5:00PM (Pacific Time).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ashish Thomas can be reached at (571) 272-0631. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. The fax phone number for the examiner is 571-273-8186. The examiner may be e-mailed at joseph.schell@uspto.gov though communications via e-mail are not permitted without a written authorization form (see MPEP 502.03).
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JS/JOSEPH O SCHELL/Primary Examiner, Art Unit 2114
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