DETAILED ACTION
This Final Office Action is in response to the application filed on 09/15/2021 and the Amendment & Remark filed on 05/11/2026.
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 .
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 1, 2, 14 and 21-27 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 1 and 21 that “generating a machine learning model configured to generate assessments of multiple metrics associated with reference IP assets, wherein the machine learning model utilizes predictive analytic techniques including at least one decision tree learning, association rule learning, support vector machines, clustering, Bayesian networks, reinforcement learning, or representation learning”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor to generate the machine learning model configured to generate assessments of multiple metrics associated with reference IP assets. It is noted that the claims nominally recite that what techniques the machine learning model may utilize. However, there is no specific detail discussing how the machine learning model for any one of the techniques result in the generation of the model generating assessments of multiple metrics associated with reference IP assets. 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 1 and 21 that “generating a training dataset including performance metrics associated with use of the machine learning model, the training dataset associated with IP assessment data, loan data, policy data and rating data”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor to generate the training dataset including performance metrics associated with use of the unspecified machine learning model. 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 1 and 21 that “training the machine learning model using the training dataset such that a trained machine learning model is generated that wherein training the machine learning model utilizes an artificial neural network including deep learning to determine associations between explanatory variables and predicted variables from past occurrences and utilizing these variables to predict unknown outcomes”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor to train the unspecified machine learning model such that it “amounts to an improvement in machine learning capabilities”. The examiner noted that the claims recite “wherein training the machine learning model utilizes an artificial neural network” but no disclosure is found regarding the training process of the unspecified machine learning model, let alone training utilizing an artificial neural network. 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 1 and 21 that “generating a second training dataset based additional feedback data indicating details of performance of past loans and insurance policies associated with prior ratings of the IP assets;”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor to generate the training dataset including performance metrics associated with use of the unspecified machine learning model. 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 1 and 21 that “retraining the machine learning model utilizing the second training dataset such that a retrained machine learning model is generated that learns, without human intervention, attributes of IP asset data, IP assessment data, and IP valuation data that are more likely or less likely to be associated with issuance of loans, insurance policies, and favorable ratings”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor to retrain the unspecified machine learning model. The examiner noted that the claims recite “wherein training the machine learning model utilizes an artificial neural network” but no disclosure is found regarding the training process of the unspecified machine learning model, let alone training utilizing an artificial neural network. 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 1 and 21 that “generating, utilizing the trained machine learning model and the IP data corresponding to IP assets including at least patents owned by the entity, wherein the assessment data includes at least one of claim breadth data indicating a breadth of rights confirmed by a patent claim, geographic reach indicating an applicability or strength of the IP assets in various geographic regions, timing data indicating a duration of coverage of the IP assets validity data indicating how likely the IP assets are to be invalidated, or exposure data indicating a likelihood that the IP assets or the entity will be associated with an exposure event; and generate, utilizing the assessment data, valuation data indicating a value of the IP assets”, there is no written content as to how or what specific process of determination are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order for the claimed processor utilize an unspecific trained machine learning model to generate the desired assessment data that could be used to generate valuation data indicating the value of the IP assets. 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 23 and 26 that “inputting a first training dataset into a machine learning model; inputting a second training dataset into the machine learning model; and training, without human intervention, the machine learning model to attribute attributes of the first training dataset and the second training dataset with likelihoods of insurance being issued or defaulted on, the first training dataset and the second training dataset being at least one of IP assessment data, loan data, policy data, or rating data, and the first training dataset and the second training dataset being different types of data”, there is no written content as to how or what specific process of training are performed (i.e. formulas, algorithms, sequence of mathematical steps, process of determination, for example) in order to for the claimed processor to train the machine learning model, particularly without human intervention. 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 1, 2, 14 and 21-27 are rejected as failing the written description requirement.
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, 2, 14 and 21-27 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 apparatus and process, 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.
The claims recite:
one or more processors; and non-transitory computer-readable media storing instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
receiving, from a first device associated with an entity, Intellectual Property (IP) data associated with entity, the IP data corresponding to IP assets including at least a patent owned by the entity, wherein the IP data includes at least one of claims data indicating patent claims of the IP assets, specification data indicating specifications of the IP assets, file wrapper data indicating information found in a file wrapper of the IP assets, products data indicating one or more items or services offered by the entity, assignment data indicating assignment information associated with the IP assets, or litigation data indicating litigation- related information associated with the IP assets;
generating a machine learning model configured to generate assessments of multiple metrics associated with reference IP assets, wherein the machine learning model utilizes predictive analytic techniques including at least one of decision tree learning, association rule learning, support vector machines, clustering, Bayesian networks, reinforcement learning, or representation learning;
generating a training dataset including performance metrics associated with use of the machine learning model, the training dataset associated with IP assessment data, loan data, policy data, and rating data;
training the machine learning model using the training dataset such that a trained machine learning model is generated wherein training the machine learning model utilizes an artificial neural network including deep learning to determine associations between explanatory variables and predicted variables from past occurrences and utilizing these variables to predict unknown outcomes;
generating a second training dataset based on additional feedback data indicating details of performance of past loans and insurance policies associated with prior ratings of the IP assets;
retraining the machine learning model utilizing the second training dataset such that a retrained machine learning model is generated that learns, without human intervention, attributes of IP asset data, IP assessment data, and IP valuation data that are more likely or less likely to be associated with issuance of loans, insurance policies, and favorable ratings;
generating, utilizing a retrained machine learning models and the IP data, the assessment data indicating an assessment of multiple metrics associated with the IP assets one of the multiple metrics being a quality of the IP assets associated with the entity, wherein the assessment data includes at least one of claim breadth data indicating a breadth of rights confirmed by a patent claim, geographic reach indicating an applicability or strength of the IP assets in various geographic regions, timing data indicating a duration of coverage of the IP assets, validity data indicating how likely the IP assets are to be invalidated, or exposure data indicating a likelihood that the IP assets or the entity will be associated with an exposure event;
generating, utilizing the assessment data, valuation data indicating a value of the IP assets;
sending, via a first network protocol over a first network, to a second device associated with a lender, an indication that a loan from the lender to the entity is sufficiently secured by the value of the IP assets;
displaying, to a user, a first user interface (UI) accessible by the second device, the first UI configured to display the indication.
sending, via a second network protocol over a second network to the first device, issuance of the loan from the lender to the entity, at least a portion of the terms of the loan determined from the valuation data;
displaying, to a user, a second user interface (UI) accessible by the first device, the second UI configured to display the issuance of the loan.
receiving, via a third network protocol over a third network from a third device associated with an insurer, insurance policy from the insurer where an insurance payout is triggered when the entity defaults on the loan, the loan secured using the IP assets as collateral, at least a portion of the terms of the insurance policy determined from the valuation data;
receiving, via a fourth network protocol over a fourth network to from a fourth device associated with a rating agency a rating of the loan associated with the insurance policy as secured with the IP assets.
displaying, to a user, a third user interface (UI) accessible by the first device or the second device, the third UI configured to display the issuance rating.
wherein the first UI, the second UI and the third UI are dynamically updated based at least in part on new user input data.
querying, during a term of the loan, one or more databases for updated IP data associated with the IP assets, the updated IP data indicating differences between the IP data prior to the loan and the IP data after issuance of the loan; generating, utilizing the trained machine learning models, updated assessment data; generating, utilizing the updated assessment data, updated valuation data indicating an updated value of the IP assets; determining that the updated value of the IP assets is within a threshold amount of the value of the IP assets; and in response to the updated value being within the threshold amount, causing the first secure user interface to display an indication that the value of the IP assets has been maintained.
identifying a second entity in a technology category with which the IP assets are associated;
mapping the second entity to the IP assets based, at least in part, on historical purchase data of the second entity associated within a willingness of the second entity to purchase the IP assets; and
transmitting an indicator metric, based, at least in part, on identifying the second entity and the mapping, to at least one of the first device, the second device, or the third device via their respective networks.
inputting a first training dataset into a machine learning model;
inputting a second training dataset into the machine learning model; and
training, without human intervention, the machine learning model to attribute attributes of the first training dataset and the second training dataset with likelihoods of insurance being issued or defaulted on, the first training dataset and the second training dataset being at least one of IP assessment data, loan data, policy data, or rating data, and the first training dataset and the second training dataset being different types of data.
wherein the first device, the second device, and the third device are associated with different entities, each of the entities being one of a lender, an insurer, a rating agency, or a borrower.
Based on the limitations above, the claims describe a process that covers facilitating issuance of a loan secured by intellectual property asset. Issuance of secured loans is considered to be a commercial interaction, which falls 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 “receiving, from a first device associated with an entity, Intellectual Property (IP) data associated with entity, the IP data corresponding to IP assets including at least a patent owned by the entity, wherein the IP data includes at least one of claims data indicating patent claims of the IP assets, specification data indicating specifications of the IP assets, file wrapper data indicating information found in a file wrapper of the IP assets, products data indicating one or more items or services offered by the entity, assignment data indicating assignment information associated with the IP assets, or litigation data indicating litigation- related information associated with the IP assets” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of receiving the IP data;
the limitation “generating a machine learning model configured to generate assessments of multiple metrics associated with reference IP assets, wherein the machine learning model utilizes predictive analytic techniques including at least one of decision tree learning, association rule learning, support vector machines, clustering, Bayesian networks, reinforcement learning, or representation learning” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of generating the assessment data using the predictive machine learning models and IP data;
the limitation “generating a training dataset including performance metrics associated with use of the machine learning model, the training dataset associated with IP assessment data, loan data, policy data, and rating data” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of generating valuation data of the IP assets utilizing the assessment data;
the limitation “sending, via a first network protocol over a first network, to a second device associated with a lender, an indication that a loan from the lender to the entity is sufficiently secured by the value of the IP assets; causing a first user interface (UI) accessible by the second device to display, without user input, the indication” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of sending an indication that loan is secured to a lender;
the limitation “sending, via a second network protocol over a second network to the first device, issuance of the loan from the lender to the entity, at least a portion of the terms of the loan determined from the valuation data; causing a second user interface (UI) accessible by the first device to display, without user input, the issuance of the loan” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of facilitating communication between the entity and the lender to issue the loan;
the limitation “receiving, via a third network protocol over a third network from a third device associated with an insurer, insurance policy from the insurer where an insurance payout is triggered when the entity defaults on the loan, the loan secured using the IP assets as collateral, at least a portion of the terms of the insurance policy determined from the valuation data” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of receiving the insurance policy from an insurer;
the limitation “receiving, via a fourth network protocol over a fourth network to from a fourth device associated with a rating agency a rating of the loan associated with the insurance policy as secured with the IP assets; and causing a third user interface (UI) accessible by the first device or the second device to display, without user input, the issuance rating” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of receiving a rating of the loan from a rating agency and displaying the rating to the lender or the entity;
the limitation “wherein the first UI, the second UI and the third UI are dynamically updated based at least in part on new user input data” encompasses no more than generically invoking one or more user interfaces to apply the Judicial Exception step of dynamically updating information based at least in part on new user input.
the limitation “querying, during a term of the loan, one or more databases for updated IP data associated with the IP assets, the updated IP data indicating differences between the IP data prior to the loan and the IP data after issuance of the loan; generating, utilizing the one or more predictive machine learning models, updated assessment data; generating, utilizing the updated assessment data, updated valuation data indicating an updated value of the IP assets; determining that the updated value of the IP assets is within a threshold amount of the value of the IP assets; and in response to the updated value being within the threshold amount, causing the first secure user interface to display an indication that the value of the IP assets has been maintained” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of querying databases for updated IP data associated with IP asset, generating updated valuation data and determining whether the valuation has been maintained with a threshold amount;
the limitation “identifying a second entity in a technology category with which the IP assets are associated” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of identifying the second entity;
the limitation “mapping the second entity to the IP assets based, at least in part, on historical purchase data of the second entity associated within a willingness of the second entity to purchase the IP assets” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of mapping the second entity to the IP assets;
the limitation “transmitting an indicator metric, based, at least in part, on identifying the second entity and the mapping, to at least one of the first device, the second device, or the third device via their respective networks” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of sending the indicator metric to lender, entity, insurer or rating agency;
the limitation “inputting a first training dataset into a machine learning model” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of inputting the training dataset into the machine learning model;
the limitation “inputting a second training dataset into the machine learning model” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of inputting the training dataset into the machine learning model;
the limitation “training, without human intervention, the machine learning model to attribute attributes of the first training dataset and the second training dataset with likelihoods of insurance being issued or defaulted on, the first training dataset and the second training dataset being at least one of IP assessment data, loan data, policy data, or rating data, and the first training dataset and the second training dataset being different types of data” encompasses no more than generically invoking one or more processor to apply the Judicial Exception step of training the machine learning model;
the limitation “wherein the first device, the second device, and the third device are associated with different entities, each of the entities being one of a lender, an insurer, a rating agency, or a borrower” encompasses no more than generically invoking one or more devices to apply the Judicial Exception step of acting as a lender, an insurer, a rating agency or a borrower;
wherein the first UI, the second UI and the third UI are dynamically updated based at least in part on new user input data
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 “…network protocol over a … network” are generically recited to perform communication steps such as receiving and transmitting.
The additional element(s) of “… 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 additional element(s) of “generating a machine learning model”, “generating a training data set”, “training the machine learning model using the training dataset such that a trained machine learning model is generated wherein training the machine learning model utilizes an artificial neural network including deep learning to determine associations between explanatory variables and predicted variables from past occurrences and utilizing these variables to predict unknown outcomes”, “generating a second training dataset based on additional feedback”, “retraining the machine learning model utilizing the second training dataset such that a retrained machine learning model is generated that learns, without human intervention” and “wherein training the machine learning model utilizes an artificial neural network” are generically recited to perform analyzing (modeling) steps described only by a result-oriented solution with insufficient technological detail for how the generating and training are accomplished.
The above additional elements are 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 a processor to facilitate issuance of loan to no more than mere instructions to apply the exception using generic computer components. The recited ordered combination of additional elements includes mere instructions to apply the Judicial Exception using generic computing elements. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. 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, 2, 14 and 21-27 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Response to Arguments
Applicant's other arguments filed 05/11/2026 have been fully considered but they are not persuasive.
Regarding the applicant’s argument that the amended claims obviate the 112a rejection, the examiner respectfully disagrees. As shown in the current 112a rejection, the amended features of generating the machine learning model, generating the training dataset and training the machine learning model actually introduced more written description deficiency to the claims. The examiner noted that the claims nominally recite that a “bucket list” of techniques the machine learning model may utilize. However, there is no specific detail discussing how the machine learning model for any one of the techniques result in the generation of the model generating assessments of multiple metrics associated with reference IP assets. The same issue is shared with other rejected features in a way that only nominal “may be utilized” is included without showing how the machine learning model is generated, how the first and second training data are generated, how the unspecified machine learning model is trained or retrained. As such, the argument is not persuasive.
Regarding the applicant’s argument that the amended claims integrate the Judicial Exception into practical application, the examiner respectfully disagrees. As stated in the rejection, the mere invoking of additional elements, such as the machine learning model invoked, to apply the Judicial Exception is Mere Instruction to Apply. Non-meaningful inclusion of computing elements does not integrate the Judicial Exception into practical application. As to the alleged technical solution to a technical problem, it should be noted that IP asset valuation is not a technical problem. As to the alleged improve computer functionality, The examiner noted that the mere inclusion of machine learning model usage / training does not render an otherwise abstract claim patent eligible under 101. In Recentive Analytics, Inc. v. Fox Corp., the Federal Circuit held that "patents that do no more than claim the application of generic machine learning to new data environments, without disclosing improvements to the machine learning models to be applied, are patent ineligible under 101." 2023-2437, slip op. at 18 (Fed. Cir. Apr. 18, 2025). The court specifically rejected the argument that requiring iterative training of a machine learning model creates patent eligibility, noting that "[i]terative training using selected training material and dynamic adjustments based on real-time changes are incident to the very nature of machine learning." Id. at 12. As the patentee in Recentive conceded, "'using a machine learning technique … necessarily includes [an] iterative training step.'" Id. The court further explained that "the requirements that the machine learning model be 'iteratively trained' or dynamically adjusted . . . do not represent a technological improvement" because these features are inherent to the applying of machine learning technology itself. Id. Accordingly, the claimed invention here, which similarly applies conventional machine learning technique[s] to evaluate IP asset, fails to recite patent-eligible subject matter under 101. As such, the argument is not persuasive.
Regarding the applicant’s argument that the claims recite significantly more, the examine respectfully disagrees. Mere Instruction to Apply the Judicial Exception invoking highly generalized computing elements without any technological details of how the desired result would be achieved does not result in practical application or inventive concept. As such, the argument is not persuasive.
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 CHO KWONG whose telephone number is (571)270-7955. The examiner can normally be reached 9am - 5pm EST M-F.
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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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