DETAILED ACTION
This Non-Final Office Action is in response to the application filed on 08/14/2024, the Amendment & Remark filed on 02/27/2026 and the Request for Continued Examination filed on 03/16/2026.
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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 03/16/2026 has been entered.
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 5-11 and 18-19 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.
An original claim may lack written description support when (1) the claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved or (2) a broad genus claim is presented but the disclosure only describes a narrow species with no evidence that the genus is contemplated. See Ariad Pharms., Inc. v. Eli Lilly & Co., 598 F.3d 1336, 1349-50 (Fed. Cir. 2010) (en banc).
While the Applicant specifies in claims 5 and 18 that “determining, using the first incident model associated with the first type of climate event, the climate event incidence score associated with the property”, there is no written content as to how or what specific “incident model” is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to determine the climate event incidence score described. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 5 and 18 that “determining, using the damage model, the climate event damage score associated with the property of interest”, there is no written content as to how or what specific “damage model” is used (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to determine the climate event damage score described. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claims 6 and 19 that “training the incident model, wherein training the incident model includes a gradient boosted machine and is based at least in part on the one or more features associated with the plurality of properties and sequentially splitting training data based on one or more features determined to maximize information gain”, there is no written content as to how or what specific model training procedure are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to train the incident model to generate climate event incidence score described. The examiner noted that generic training methods such as gradient boost and information gain are recited but the generic training methods are not sufficient disclosure for training a model with a specific function of determining “a climate event incidence score representing a relative probability of a first type of climate event occurring at the property of interest”. As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
While the Applicant specifies in claim 9 that “training the damage model, wherein training the damage model is based at least in part on the one or more extracted features associated with the plurality of properties”, there is no written content as to how or what specific training procedure are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to train the incident model to generate climate event damage score described. The examiner noted that generic training methods are recited in the Specification but the generic training methods are not sufficient disclosure for training a model with a specific function of determining “a climate event damage score representing a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest” As such, the disclosure does not objectively demonstrate that the applicant actually invented—was in possession of—the claimed subject matter.
The written description requirement can be satisfied if the particular steps, i.e., algorithm, necessary to perform the claimed function were “described in the specification.” In re Hayes Microcomputer Prods, Inc. Patent Litigation, 982 F.2d 1527, 1533-34, 25 USPQ2d 1241, (Fed. Cir. 1992).
As such, claims 5-11 and 18-19 are rejected as failing the written description requirement.
Claim 6 and 19 recite “sequentially splitting training data based on one or more features determined to maximize information gain”. However, the Original Disclosure does not support “sequentially splitting training data based on one or more features determined to maximize information gain”. Specification paragraph 0098 recites “The climate event trainer 422, a component 434/436/438/440, or a subcomponent 506/526 thereof, fits an initial decision tree on a subset of the training data with features used to split data, and splits the training data by determining which features maximize information gain (or minimize cross entropy loss). Following the initial decision tree, additional trees may be fit to the residuals of the loss function using the above methodology in a sequential manner.” Thus, the Original Disclosure supports splitting training data to maximize information gain for one decision tree model and repeating the information gain maximization sequentially for additional trees model, while the claim is limited to training a incident model.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As an initial matter, the claims as a whole are to a method and a system, which falls within one or more statutory categories. (Step 1: YES) The recitation of the claimed invention is then further analyzed as follow, in which the abstract elements are boldfaced.
Claim 1 recites:
A method comprising:
generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes:
a first portion identifying a location of a property of interest input by a user;
a second portion including an image of the property of interest;
a third portion representing a climate event incidence score, wherein the claim event incidence score is determined usage an incident model and represents a relative probability of a first type of climate event occurring at the property of interest;
a fourth portion representing with a climate event damage score specific to the property of interest and distinct from the climate event incidence score, wherein the climate event damage score is based on one or more features visible in the image presenting the property of interest, wherein the climate damage score is determined using a damage model and represents a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest;
wherein one or more of the incident model and the damage model are trained based on image data representing a plurality of properties and one or more extracted features extracted from the image data representing the plurality of properties includes images representing at least a subset of the plurality of properties before and after a prior incident of the first type of climate event; and
sending, using one or more processors, the graphical user interface, including the first portion, the second portion, the third portion and the fourth portion for simultaneous presentation to the user.
Claims 2 and 15 recite:
wherein the third portion includes one or more sub-portions indicating a first set of top features associated with the property of interest, wherein the first set of top features include a first set of features associated with the property of interest with a greatest relative impact on the climate event incidence score.
Claims 3 and 16 recite:
wherein the fourth portion includes one or more sub-portions indicating a second set of top features associated with the property of interest, wherein the second set of top features include a second set of features associated with the property of interest with a greatest relative impact on the climate event damage score.
Claims 4 and 17 recite:
wherein the graphical user interface further comprises one or more of: a fifth portion associated with a confidence level; and a sixth portion associated with one or more remedial actions that, when performed, may affect one or more of the climate event incidence score and the climate event damage score.
Claims 5 and 18 recite:
receiving the location of the property of interest;
obtaining property data associated with the property of interest, wherein the property data includes image data associated with the property of interest;
determining, using a incident model associated with the first type of climate event, the climate event incidence score associated with the property; and
determining, using a damage model, the climate event damage score associated with the property of interest.
Claims 6 and 19 recite:
obtaining image data of a plurality of properties;
automatically extracting, using computer vision, one or more features associated with the plurality of properties; and
training the incident model, wherein training the incident model includes a gradient boosted machine and is based at least in part on the one or more features associated with the plurality of properties and sequentially splitting training data based on one or more features determined to maximize information gain; and
validating, based on a binary classifier, the incident model based on one or more of a geographic location hold-out and a temporal hold-out, wherein the geographic location hold-out held out a geographic location including a location associated with the property.
Claim 7 recites:
wherein validation of the incident model is based on the temporal hold-out, the method further comprising:
determining whether the incident model is predictive of held-out data associated with a first time period of time, wherein the first climate is trained on a second time period distinct from the first time period associated with the held-out data;
iteratively training and validating the incident model with different temporal hold-outs to determine, based on an accuracy of the incident model:
a minimum period of most recent training data; and
a maximum period of most recent training data, wherein one or more of the minimum period and the maximum period of most recent training data are associated with a pattern change.
Claim 8 recites:
wherein a set of validation metrics is used to validate against a held-out population, the set of validation metrics including one or more of:
a sum of a target divided by a sum observed by the incident model;
an F1 score; and
a receiver operating characteristic,
wherein the F1 score and the receiver operating characteristic are indicative of an ability of the incident model to discriminate between areas likely to have an incident of the first type of climate event or not; and
wherein the first climate is adapted based on a bias associated with the incident model.
Claim 9 recites:
obtaining property image data associated with a plurality of properties;
automatically extracting, using computer vision, one or more features associated with the plurality of properties; and
training the damage model, wherein training the damage model is based at least in part on the one or more extracted features associated with the plurality of properties; and
validating the second model climate based on one or more of a geographic location hold-out and a temporal hold-out, wherein the geographic location hold-out held out a geographic location including a location associated with the property.
Claim 10 recites:
wherein the validation is based on the temporal hold-out and further comprise one or more of:
determining whether the damage model is predictive of held-out data associated with a first time period of time, wherein the damage model is trained on a second time period distinct from the first time period associated with the held-out data; and
iteratively training and validating the damage model with different temporal hold-outs to determine, based on an accuracy of the damage model:
a minimum period of most recent training data; and
a maximum period of most recent training data, wherein one or more of the minimum period and the maximum period of most recent training data are associated with a pattern change.
Claim 11 recites:
wherein a set of validation metrics including one or more of:
a sum of a target divided by a sum observed by the damage model;
an F1 score; and
a receiver operating characteristic,
wherein the F1 score and the receiver operating characteristic are indicative of an ability of the damage model to discriminate between structures likely to be damaged or not, and
wherein the second climate is adapted based on a bias associated with the damage model.
Claims 12 and 20 recite:
wherein first climate event is one of a wildfire, a flood, hail, lightning, tornado, hurricane, drought, and wind.
Claim 13 recites:
wherein:
the first type of climate event includes wildfire;
the climate event incidence score representing a likelihood of wildfire occurring at the location of the property;
the climate event damage score representing a likelihood of damage from wildfire to the property;
the climate event incidence score is based on a first set of features including:
a distance or the property to a historic fire perimeter, a distance of the property to an area with high wildfire suppression difficulty, a fuel type associated with the property, a wildfire suppression difficulty associated with the property, a topography associated with the property, an average temperature associated with the property, a distance of the property to a nearest fire station, and an average annual precipitation associated with the property; and
the climate event damage score is based on a second set of features including: a neighboring vegetation density, a year built, a surrounding vegetation density, a roof material associated with the property, a fuel type, the fuel type associated with the property, an overhanging vegetation density, and a land slope.
Claim 14 recites:
A system comprising: a processor; and a memory, the memory storing instructions that, when executed by the processor, cause the system to:
generate a graphical user interface, wherein the graphical user interface includes:
a first portion identifying a location of a property of interest input by a user;
a second portion including an image of the property of interest;
a third portion representing a climate event incidence score, wherein the claim event incidence score is determined usage an incident model and represents a relative probability of a first type of climate event occurring at the property of interest;
a fourth portion representing with a climate event damage score specific to the property of interest and distinct from the climate event incidence score, wherein the climate event damage score is based on one or more features visible in the image presenting the property of interest, wherein the climate damage score is determined using a damage model and represents a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest;
wherein one or more of the incident model and the damage model are trained based on image data representing a plurality of properties and one or more extracted features extracted from the image data representing the plurality of properties includes images representing at least a subset of the plurality of properties before and after a prior incident of the first type of climate event; and
send the graphical user interface, including the first portion, the second portion, the third portion and the fourth portion for simultaneous presentation to the user.
Based on the limitations above, the claims describe a process that covers collecting, analyzing and displaying climate risk data. The collecting, analyzing and displaying of climate risk data to a user is considered to be a commercial interaction between at least a data analyst and a user. The collecting, analyzing and displaying of climate risk data is also considered risk assessment, which is a component of fundamental economic practices such as hedge and insurance. Both subgroupings fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas.. As such, the claim(s) recite(s) a Judicial Exception. (Step 2A prong one: Yes)
This analysis then evaluates whether the claims as a whole integrates the recited Judicial Exception into a practical application of the exception. In particular, the claims recite the additional element(s) of “one or more processor” as a mere tool to perform the steps of the Judicial Exception, which encompasses no more than Mere Instruction to Apply.
For example, the limitation “generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes a first portion identifying a location of a property of interest input by a user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating data identifying a location of a property of interest input by a user for presentation;
the limitation “generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes: a second portion including an image representing the property of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating an image representing the property of interest for presentation;
the limitation “generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes: a third portion representing a climate event incidence score, wherein the claim event incidence score is determined usage an incident model and represents a relative probability of a first type of climate event occurring at the property of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating the climate event incidence score for presentation;
the limitation “generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes: a fourth portion representing with a climate event damage score specific to the property of interest and distinct from the climate event incidence score, wherein the climate event damage score is based on one or more features visible in the image presenting the property of interest, wherein the climate damage score is determined using a damage model and represents a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest;” encompasses no more than generically invoking a processor to apply the Judicial Exception step of generating data the climate event damage score for presentation;
the limitation “generating, using one or more processors, a graphical user interface, wherein the graphical user interface includes: wherein one or more of the incident model and the damage model are trained based on image data representing a plurality of properties and one or more extracted features extracted from the image data representing the plurality of properties includes images representing at least a subset of the plurality of properties before and after a prior incident of the first type of climate event” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training one or more of models with extract features from image data representing a plurality of properties;
the limitation “sending, using one or more processors, the graphical user interface, including the first portion, the second portion, the third portion and the fourth portion for simultaneous presentation to the user” encompasses no more than generically invoking a processor to apply the Judicial Exception step of sending the generated data to the user for simultaneous presentation;
the limitation “receiving the location of the property of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of receiving the location of the property of interest;
the limitation “obtaining property data associated with the property of interest, wherein the property data includes image data associated with the property of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining property data associated with the property of interest;
the limitation “determining, using an incident model associated with the first type of climate event, the climate event incidence score associated with the property” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the climate event incidence score using a incident model;
the limitation “determining, using a damage model, the climate event damage score associated with the property of interest” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining the climate event damage score using a damage model;
the limitation “obtaining image data of a plurality of properties,” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining a plurality of properties;
the limitation “automatically extracting, using computer vision, one or more features associated with the plurality of properties” encompasses no more than generically invoking a processor to apply the Judicial Exception step of extracting features associated with properties;
the limitation “training the incident model, wherein training the incident model includes a gradient boosted machine and is based at least in part on the one or more features associated with the plurality of properties and sequentially splitting training data based on one or more features determined to maximize information gain” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training the incident model;
the limitation “validating, based on a binary classifier, the incident model based on one or more of a geographic location hold-out and a temporal hold-out, wherein the geographic location hold-out held out a geographic location including a location associated with the property” encompasses no more than generically invoking a processor to apply the Judicial Exception step of validating the incident model;
the limitation “determining whether the first climate is predictive of held-out data associated with a first time period of time, wherein the first climate is trained on a second time period distinct from the first time period associated with the held-out data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining whether the first climate is predictive of held-out data;
the limitation “iteratively training and validating the first climate with different temporal hold-outs to determine, based on an accuracy of the first climate” encompasses no more than generically invoking a processor to apply the Judicial Exception step of iteratively training and validating the incident model;
the limitation “obtaining property image data associated with a plurality of properties, wherein the property image data associated with the plurality of properties includes images associated with the plurality of the properties visually representing at least a subset of the plurality of properties before and after a prior incident of the first climate event” encompasses no more than generically invoking a processor to apply the Judicial Exception step of obtaining image data of a plurality of properties;
the limitation “automatically extracting, using computer vision, one or more features associated with the plurality of properties” encompasses no more than generically invoking a processor to apply the Judicial Exception step of extracting features associated with properties;
the limitation “training the damage model, wherein training the damage model is based at least in part on the one or more features associated with the plurality of properties” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training the damage model;
the limitation “validating the damage model based on one or more of a geographic location hold-out and a temporal hold-out, wherein the geographic location hold-out held out a geographic location including a location associated with the property” encompasses no more than generically invoking a processor to apply the Judicial Exception step of training the damage model;
the limitation “determining whether the incident model is predictive of held-out data associated with a first time period of time, wherein the first climate is trained on a second time period distinct from the first time period associated with the held-out data” encompasses no more than generically invoking a processor to apply the Judicial Exception step of determining whether the second climate is predictive of held-out data;
the limitation “iteratively training and validating the incident model with different temporal hold-outs to determine, based on an accuracy of the first climate” encompasses no more than generically invoking a processor to apply the Judicial Exception step of iteratively training and validating the damage model;
Other than being generally linked to the steps of the Judicial Exception, the additional elements in the above step(s) is/are recited at a high-level of generality, without technological detail of how the particular steps are performed technologically.
The additional element(s) of “memory” and/or “non-transitory storage medium” are generically recited to store data and/or instructions of the Judicial Exception.
The additional element(s) of “computer vision” are generically recited to obtain image data described only by a result-oriented solution with insufficient detail for how the computer vision accomplish it.
The additional element(s) of “incident model” and “damage model” are generically recited to perform the score determining steps described only by a result-oriented solution with insufficient detail for how the models accomplish it.
The additional element(s) of “training … model”, “wherein training the … model includes a gradient boosted machine” and “sequentially splitting training data based on one or more features determined to maximize information gain” are mathematical operation recited to perform the training steps. However, using mathematical operation to train model to generating climate risk score is adding an ineligible concept to another, which does not result in eligibility;
The additional element(s) of “Graphical User Interface” are generically recited to perform input/output steps described only by a result-oriented solution with insufficient detail for how the interface accomplish it.
The examiner further noted generic computer affixes such as “automatically”, are appended to abstract elements such as “extracting”, but found that to be mere instructions to implement the Judicial Exception idea on a computer.
Indeed, the instant claims (1) attempted to cover a solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result; (2) used of a computer or other machinery in its ordinary capacity for economic or other tasks or simply added a general purpose computer or computer components after the fact to the Judicial Exception and (3) generally applied the Judicial Exception to a generic computing environment without limitation indicative of practical application (See MPEP 2106.04(d)I). Thus, the claims are no more than Mere Instruction to Apply the Judicial Exception (See MPEP 2106.05(f)) or adding insignificant extra-solution activity to the judicial exception (See MPEP 2106.05(g)), which do not integrate the cited Judicial Exception into practical application (Step 2A prong two: No) The claims are directed to a Judicial Exception.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using generic computing elements such as processor, ML models and GUI to collect, analyze and display climate risk data amounts and to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Dependent claims 2-4, 8, 11, 12, 13, 15-17 and 20 merely limit the abstract idea but do not recite any additional element beyond the cited abstract idea, thus, do not amount to significantly more. No additional element currently recited in the claims amount the claims to be significantly more than the cited abstract idea. (Step 2B: No)
Therefore, claims 1-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 102
The previous rejection under 35 USC 102 is withdrawn in view of the Amendment filed on 02/27/2026.
Response to Arguments
Applicant's arguments filed on 02/27/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that cited paragraphs in the Specification provides adequate written description support for the limitations rejected under 35 USC 112(a), the examiner respectfully disagrees. In particular, the applicant contends that 0095-0098 supports how models including the claimed incident model is trained, pointing to the description of the model builder 506. The examiner noted that 0095 nominally discloses a model builder uses a training set to train a model in a circular fashion similar “trainer trains the model”; that 0096 discloses model trained by model builder 506 may vary based on implementation, absence of training methodology; that 0097 listed numerous machine learning technique that “may be used” without any details of how the model is trained for a particular machine learning technique in the list; that 0098 provides generic description of training GBM to minimize loss function but does not specify to the claimed incident model and damage model, which are claimed to have specific function unlike a generic GBM. As such, the cited paragraphs does not objectively demonstrate possession of the claimed invention at the time of filing.
Regarding the applicant’s argument that the claims do not recite a Judicial Exception, the examiner respectfully disagrees. As responded previously, the claims include the very recitation such as “generating … graphical user interface includes: a first portion ... a location of a property of interest”, “a climate event incidence score representing a relative probability of a first type of climate event occurring at the property of interest”, “a climate event damage score representing a relative severity of damage to the property of interest were the first type of climate event to occur at the property of interest”, “obtaining property data associated with the property of interest, wherein the property data includes image data associated with the property of interest”, “determining, using a first climate model associated with the first type of climate event, the climate event incidence score associated with the property” and “using a second climate model, the climate event damage score associated with the property of interest”. The limitations support the Step 2A prong one determination that claims recite “collecting, analyzing and displaying of climate risk data to a user”. The rejection also explained that the activities of “collecting, analyzing and displaying of climate risk data to a user” is considered to be a commercial interaction between at least a data analyst and a user. The current Office Action also supplemented the rejection, in view of the 11/26/2025 amendment, that analyzing and displaying of climate risk data are risk assessment, which is a component fundamental economic practices including hedging, insurance and mitigating risk. As such, the examiner maintains that the claims recite a Judicial Exception.
Regarding the applicant’s argument that the claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. The applicant contended that the claims are directed to “a particular way of achieving the desired outcome(s) of a climate event incidence score and/or a climate event damage score, integrate any alleged judicial exception into a practical application or provide significantly more”. However, the examiner noted that the only claimed outcome of the scores are to be displayed to a user via graphical user interface. The mere invoking of a GUI to display scores of the Judicial Exception of “collecting, analyzing and displaying of climate risk data to a user”. As such, the examiner maintains that the claims do not integrate the Judicial Exception into practical application.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, MICHAEL W ANDERSON can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHO YIU KWONG/Primary Examiner, Art Unit 3693
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