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 .
Status
This action is in response to the amendment filed on 3/31/2026. Claims 2-9 are pending. No claims are amended. Claims 2-9 have been added. Claim 1 has been cancelled.
Response to Arguments
Applicant’s arguments with respect to claim(s) 1 have been considered but are moot because the previous claim(s) have been cancelled and new claims have been filed. An updated rejection of the new claims can be found below.
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.
Claims 2-9 are rejected under 35 USC 101 because the claimed invention is directed to a judicial exception (i.e. abstract idea) without anything significantly more.
Step 1: Claims 2-9 are directed to a system. Therefore, claims 2-9 are directed to patent eligible categories of invention.
Step 2A, Prong 1: Claim 2 recites building a churn model from historical data, scoring customers against the model, recommending a retention action, and displaying the data, constituting an abstract idea based on a “Mental Process”, “Mathematical Calculation,” and “Certain Methods of Organizing Human Activity” related to managing personal behavior or interactions between individuals including social activities.
mental process: As drafted, the claim recites collecting information, analyzing it, and using the analysis to reach a conclusion and select a course of action which is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “one or more processors,” nothing in the claim precludes the determining step from practically being performed in the human mind. For example, but for the “one or more processors” language, the claim encompasses a user collecting and analyzing data to reach a probabilistic conclusion. The mere nominal recitation of a generic processing circuitry does not take the claim limitation out of the mental processes grouping. This limitation is a mental process.
certain methods of organizing human activity: The claim as a whole recites a method of organizing human activity. The claimed invention is a method that allows for users to predict and attempt to mitigate customer churn which is a method of managing human interaction. Determining a churn risk mitigation action (i.e. a promotional offer) and presenting it is a form of target marketing customer relation management which is a fundamental economic practice. Thus, the claim recites an abstract idea. Managing Personal Behavior or Relationships or Interactions between People”; According to the 2019 PEG, “managing personal behavior or relationships or interactions between people” includes social activities, teaching, and following rules or instructions.
mathematical formula: The claim recites a mathematical concept (which can include a mathematical relationships, mathematical formulas or equations, and mathematical calculations), and in this case a formula or calculation that is used to correlate features, generate a risk weighting factor, compute a churn likelihood value, and predict an accuracy score. Thus, the claim recites a mathematical concept. Note that, in this example, the “encoding” step is determined to recite a mathematical concept because the claim explicitly recites a mathematical formula or calculation.
Dependent claims 4, 6-9, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration.
Dependent claims 3, 5, will be evaluated under Step 2A, Prong 2 below.
Step 2A, Prong 2: Independent claim 2 does not integrate the judicial exception into a practical application. Claim 2 is a system comprising “a non-transitory computer-readable medium configured to store interactions data …a set of electronic communications …one or more processors configured to execute a plurality of operations… a screen of a device, a graphical user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to store data, collect data, analyze data, receive data, determine data, generate and evaluate data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity, mathematical concept) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not sufficient to prove integration into a practical application. See MPEP 2106.05(h).
Therefore, the additional elements of the independent claims, when considered both individually and in combination, are not sufficient to prove integration into a practical application.
Dependent claims 4, 6-9, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which does not integrate the judicial exception into a practical application.
Dependent claim 3 introduces the additional element of “generate a set of model validity metrics based on the product renewal data of the first transaction data and the first prediction data; wherein to further train the one or more churn prediction models comprises providing the set of model validity metrics for training.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f). This limitation does not integrate the judicial exception into a practical application because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 5 introduces the additional element of “receiving, via the graphical user interface, an indication of application of the churn risk mitigation action; and updating a set of response recommendation features corresponding to the churn risk mitigation action responsive to the indication of application.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) does not integrate a judicial exception into a practical application. See MPEP 2106.05(f).
Therefore, the additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not sufficient to prove integration into a practical application.
Step 2B: Independent claim 2 does not comprise anything significantly more than the judicial exception. As can be seen above with respect to Step 2A, Prong 2, Claim 2 is a system comprising “a non-transitory computer-readable medium configured to store interactions data …a set of electronic communications …one or more processors configured to execute a plurality of operations… a screen of a device, a graphical user interface.” These additional elements are mere instructions to implement an abstract idea using a computer in its ordinary capacity, or merely uses the computer as a tool to perform the identified abstract idea. Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to access, identify, determine, and present data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity and/or mathematical concept) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). The claim employs generic computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment. This type of generally linking is not anything significantly more than the judicial exception. See MPEP 2106.05(h).
The additional elements of the independent claims, when considered both individually and in combination, do not comprise anything significantly more than the judicial exception.
Dependent claims 4, 6-9, further narrow the abstract idea identified in the independent claims and do not introduce further additional elements for consideration, which is not anything significantly more than the judicial exception.
Dependent claim 3 introduces the additional element of “generate a set of model validity metrics based on the product renewal data of the first transaction data and the first prediction data; wherein to further train the one or more churn prediction models comprises providing the set of model validity metrics for training.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f). This limitation is not anything significantly more than the judicial exception because it is nothing more than generally linking the use of the judicial exception to a particular technological environment. See MPEP 2106.05(h).
Dependent claim 5 introduces the additional element of “receiving, via the graphical user interface, an indication of application of the churn risk mitigation action; and updating a set of response recommendation features corresponding to the churn risk mitigation action responsive to the indication of application.” Use of a computer or other machinery in its ordinary capacity for performing the steps of the abstract idea or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., certain methods of organizing human activity) is not anything significantly more than the judicial exception. See MPEP 2106.05(f).
The additional elements of the dependent claims, when considered both individually and in the context of the independent claims, are not anything significantly more than the judicial exception.
Accordingly, claims 2-9 are rejected under 35 USC 101.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 2-9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. The applicant has added claim 2, which includes the language of “determine, using the first customer data and the one or more churn prediction models, first prediction data representing a set of customer risk weighting factors, wherein each customer risk weighting factor corresponds to a respective relationship factor of one or more relationship factors.” Although the applicant has support in the originally filed disclosure for risk weights as an aggregate score percentage with separate additional weightings applied for external factors (¶ 30, 33), the applicant does not have support for a respective factor to factor mapping the risk weight factor to the respective relationship factor as claimed.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 4 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 4 recites the limitation “the model validity metrics.” There is insufficient antecedent basis for this limitation in the claim.
The dependent claims inherit the rejections of the claims from which they depend.
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) 2-5, 7-9, is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharp et al. (US 20160203509 A1) in view of Han et al. (US 20170061344 A1).
Regarding claim 2, Sharp teaches a non-transitory computer-readable medium configured to store interactions data (¶ 53, 66, 73, 74) comprising a set of support interactions, each support interaction representing support for a respective subscriber of a plurality of subscribers on behalf of a respective provider (¶ 81-82, discloses various historical behavioral data. ¶ 41, discloses users interacting with the system. ¶ 29, 34-36, discloses offering services from a provider to a subscriber. ¶ 3, 78-80, abstract ¶ 31, 48, 72, Fig. 11);
and a set of electronic communications representing messages between a respective provider of the plurality of providers and a respective subscriber of the plurality of subscribers (¶ 30, 32, discloses messages for making an offering. ¶ 53, discloses communication between providers and clients. ¶ 78-81, 112);
and one or more processors configured to execute a plurality of operations, the operations comprising (¶ 246, 73-74)
collect a plurality of provider attributes comprising, for each provider of the plurality of providers, a set of provider attributes comprising a plurality of product characteristics and a plurality of provider characteristics (¶ 100-103, 110-112, discloses provider characteristics. ¶ 90-91),
collect a plurality of subscriber attributes comprising, for each subscriber of the plurality of subscribers, a respective set of subscriber attributes comprising demographic information (¶ 32, 86, 87, discloses subscriber attributes ¶ 69, 112),
analyze patterns within the interactions data in view of the plurality of provider attributes and the plurality of subscriber attributes to identify a plurality of churn correlation features correlated to increased risk of subscriber churn each churn correlation feature of the plurality of churn correlation features comprising one or more explanatory variables, each explanatory variable corresponding to one of a respective provider attribute of the plurality of provider attributes or a respective subscriber attribute of the plurality of subscriber attributes, (¶ 103-108, disclose the use of user data to make churn determinations. ¶ 34, discloses analyzing subscriber behavior to determine churn risk. ¶ 82, 113-114, 166-177, 223-225),
using the plurality of churn correlation features, the interactions data, the plurality of provider attributes, and the plurality of subscriber attributes, train one or more churn prediction models to predict a relative propensity for a given subscriber of the plurality of subscribers, defined by the respective set of subscriber attributes of the given subscriber, to switch from a current provider of the plurality of providers to a different provider of the plurality of providers (¶ 114-120, 224-225, disclose training churn models with various input values. ¶ 233-237, disclose training using subscriber and provider information. ¶ 192-204, 2-3, 141, 185),
receive first customer data representing a first customer from the plurality of subscribers that is associated with a first subscription provider of the plurality of providers (¶ 80-83, discloses customer/subscriber data that is received. ¶ 26, 31-32, 49, 57, 76),
determine, using the first customer data and the one or more churn prediction models, first prediction data representing a set of customer risk factors, wherein each customer risk factor corresponds to a respective relationship factor of one or more relationship factors (¶ 34-37, disclose churn risk in view of company and usage features. ¶ 114, 211, 223-225, 100-101),
determine, using the first prediction data, that a churn likelihood value for the first customer exceeds a churn threshold value (¶ 37, 122, discloses filtering thresholds in churn scoring. ¶ 211-213, 224-225, disclose churn risk thresholds ¶ 110),
in response to determining that a churn likelihood value for the first customer exceeds the churn threshold value, determine a churn risk mitigation action based at least in part on one or more relationship factors corresponding to the set of customer risk factors (¶ 111-113, discloses who should receive marketing offers),
receive first transaction data representing in part product renewal data of one or more subscription products for the first customer (¶ 100-101, discloses a renewal period. ¶ 3, 121),
evaluate the churn risk mitigation action in light of the first transaction data to determine a prediction accuracy score (¶ 212-214, monitor the risk and determine if the data is accurate. ¶ 116-119, 223-225. Fig. 16),
using the first transaction data, further train the one or more churn prediction models (¶ 212-214, 244, 227-228, disclose triggering a churn model, Fig. 18).
Sharp does not specifically teach a plurality of providers of subscription products, customer risk weighting factors, or generate a GUI.
However, Han teaches each support interaction representing support for a respective subscriber of a plurality of subscribers on behalf of a respective provider of a plurality of providers of subscription products (¶ 31-34, discloses multiple users, providers, and products. ¶ 53);
determine, using the first customer data and the one or more churn prediction models, first prediction data representing a set of customer risk weighting factors, wherein each customer risk weighting factor corresponds to a respective relationship factor of one or more relationship factors (¶ 56-57, disclose updating risks and weighting factors. ¶ 41);
generate, for display on a screen of a device, a graphical user interface including the churn risk mitigation action for the first customer (¶ 48-53, 71-72, 39, disclose generating a GUI).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Sharp to include/perform a plurality of subscription products, as taught/suggested by Han. This known technique is applicable to the system of Sharp as they both share characteristics and capabilities, namely, they are directed to evaluating and making determinations with regards to customer churn and churn risk. One of ordinary skill in the art would have recognized that applying the known technique of Han would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Han to the teachings of Sharp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such product features into similar systems. Further, applying a plurality of subscription products would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow further data points and analysis.
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Sharp to include/perform customer risk weighting factors, as taught/suggested by Han. This known technique is applicable to the system of Sharp as they both share characteristics and capabilities, namely, they are directed to evaluating and making determinations with regards to customer churn and churn risk. One of ordinary skill in the art would have recognized that applying the known technique of Han would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Han to the teachings of Sharp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such weighting risk features into similar systems. Further, applying customer risk weighting factors would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow a user to assigning specific scores or weights to behavioral and engagement metrics, so they can proactively identify at-risk accounts and prioritize retention efforts.
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Sharp to include/perform generate a GUI, as taught/suggested by Han. This known technique is applicable to the system of Sharp as they both share characteristics and capabilities, namely, they are directed to evaluating and making determinations with regards to customer churn and churn risk. One of ordinary skill in the art would have recognized that applying the known technique of Han would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Han to the teachings of Sharp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such GUI features into similar systems. Further, applying generating a GUI would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to take raw data and have an actionable visual experience.
Regarding claim 3, Sharp teaches generate a set of model validity metrics based on the product renewal data of the first transaction data and the first prediction data; wherein to further train the one or more churn prediction models comprises providing the set of model validity metrics for training (¶ 112, discloses a validated assignment. ¶ 163, discloses cross-validation. ¶ 191, discloses a validation set that is used for training. ¶ 213-214, discloses used training models for validation. ¶ 241-244).
Regarding claim 4, Sharp teaches wherein the model validity metrics include at least one of percentage of accurate predictions, percentage accurate negative predictions, percentage inaccurate positive predictions, or percentage inaccurate negative predictions (¶ 112, discloses a validated assignment. ¶ 163, discloses cross-validation. ¶ 191, discloses a validation set that is used for training. ¶ 213-214, discloses used training models for validation. The models are a percentage computed. ¶ 129, 241-244).
Regarding claim 5, Sharp teaches updating a set of response recommendation features corresponding to the churn risk mitigation action responsive to the indication of application (¶ 82-83, discloses making a marketing determination to minimize churn risk. ¶ 112-113, discloses updating/ directing marketing campaigns in a specific direction. ¶ 121, 228-230).
Sharp does not specifically teach however, Han teaches receiving, via the graphical user interface, an indication of application of the churn risk mitigation action (¶ 48-53, 71-72, 39, disclose generating a GUI displaying churn risk).
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Sharp to include/perform generate a GUI, as taught/suggested by Han. This known technique is applicable to the system of Sharp as they both share characteristics and capabilities, namely, they are directed to evaluating and making determinations with regards to customer churn and churn risk. One of ordinary skill in the art would have recognized that applying the known technique of Han would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Han to the teachings of Sharp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such GUI features into similar systems. Further, applying generating a GUI would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow the user to take raw data and have an actionable visual experience.
Regarding claim 7, Sharp teaches wherein each support interaction of the set of support interactions represents one of a technical support interaction, a product support interaction, a billing support interaction, or a user information support interaction (¶ 82, 86, 104, discloses support interactions which include billing history. Fig. 8-11).
Regarding claim 8, Sharp teaches wherein the churn risk mitigation action represents one of a personal contact interaction, a marketing email, a promotional offer, a discount subscription offer, or a benefit to remain with the first subscription provider (¶ 111-113, 57, 72, 78, 92, disclose personal communication sent to the customer to mitigate churn.) Also taught by Han (¶ 53, 71-72).
Regarding claim 9, Sharp teaches wherein the product renewal data represents one of renew same product with previous provider, changed product with previous provider, added products with previous provider, changed provider, and did not renew product subscription (¶ 100-101, the concept of renewing, Fig. 8-11). Also taught by Han (¶ 34, 37, 40, 50-52, 75, 83).
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Sharp et al. (US 20160203509 A1) in view of Han et al. (US 20170061344 A1) in further view of Wright (US 20030200135 A1).
Regarding claim 6, the combination of Sharp and Han teach the limitations of claim 2. The combination does not specifically teach wherein the set of customer risk weighting factors are based on one or more of customer-provider relationship, fluctuations in market status, seasonal factors, or environmental factors.
However, Wright teaches wherein the set of customer risk weighting factors are based on one or more of customer-provider relationship, fluctuations in market status, seasonal factors, or environmental factors (¶ 48, discloses fluctuations based on market changes and external factors.)
It would have been obvious to one of ordinary skill in the art at the time of filing to modify Sharp to include/perform wherein the set of customer risk weighting factors are based on one or more of customer-provider relationship, fluctuations in market status, seasonal factors, or environmental factors, as taught/suggested by Han. This known technique is applicable to the system of Sharp as they both share characteristics and capabilities, namely, they are directed to evaluating and making determinations with regards to customer churn. One of ordinary skill in the art would have recognized that applying the known technique of Han would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Han to the teachings of Sharp would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such weighting factor features into similar systems. Further, applying wherein the set of customer risk weighting factors are based on one or more of customer-provider relationship, fluctuations in market status, seasonal factors, or environmental factors would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow for a more dynamic and forward-looking data assessment.
Other pertinent prior art includes Higgins et al. (US 20190213511 A1) discloses a customer relationship management (CRM) system or other type of customer-engagement management platform to develop and implement a strategy for customer retention. Maga et al. (US 20070156673 A1) discloses analyzing and predicting churn within a business's customer base so that steps may be taken to limit or otherwise manage churn. Gupta (US 20190102820 A1) discloses providing recommendations for software to prevent churn or provide prioritized insights to a user. Eskandari (US 20040073520 A1) discloses categorizing customers based on the likelihood of a customer being lost and to further segment customers that have a high likelihood-to-be-lost into smaller, more homogenous groups. Goldberg et al. (US 20160055496 A1) discloses predicting customer churn.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMIE H AUSTIN whose telephone number is (571)272-7363. The examiner can normally be reached Monday, Tuesday, Thursday, Friday 7am-2pm.
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JAMIE H. AUSTIN
Examiner
Art Unit 3625
/JAMIE H AUSTIN/Primary Examiner, Art Unit 3625
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