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 .
Response to Amendment
Applicant’s “Amendment” filed on 03/06/2026 has been considered.
Claims 1, 11, and 21 are amended. Claims 1-7, 9-17, 19-27, and 29-30 remain pending in this application and an action on the merits follow.
Applicant’s response by virtue of amendment to claims has not overcome the Examiner’s rejection under 35 USC § 101.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/30/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
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 1-7, 9-17, 19-27, and 29-30 are rejected under 35 USC 101. The claimed invention is directed to non-statutory subject matter because claims 1, 11, and 21 are directed to an abstract idea without significantly more. Claims 2-7, 9-10, 12-17, 19-20, 22-27, and 29-30 fail to remedy these deficiencies.
The claims 1, 11, and 21 recite receiving an initial claim, processing to identify one or more defects, performing pattern recognition using a trained artificial intelligence system, notifying the submitter of the identified defects…by opening a notification window, and enabling the submitter to address the identified defects includes generating a plurality of available options, enabling the submitter to choose options, processing a selection provided by the submitter, submitting the vetted claim, receiving a rejection, and automatically amended the vetted claim includes: identifying a claim processing bot, processing the vetted claim by analyzing past submissions of claims, generating one or more rejection patterns, and correcting the one or more rejections using the one or more rejection patterns generated.
The Claims 1, 11, and 21 recite receiving, processing, performing pattern recognition, notifying by opening a notification window, and enabling includes generating a plurality of options, enabling the submitter to choose options, processing a selection provided by the submitter, submitting, receiving, and automatically amending steps includes identifying, processing, generating, and correcting as drafted, are processes that under broadest reasonable interpretation, cover performance of managing personal behavior and utilizing mathematical algorithms/modeling, but for the recitation of generic computer components. That is, other than reciting “a computer system including a processor and a memory, a handheld electronic device, a display, a claim processing bot repository, and a claim server processor”, nothing in the claim element precludes the steps from practically being performed by organizing human activity and utilizing mathematical algorithms/modeling. For example, but for the “a computer system including a processor and a memory, a handheld electronic device, a display, a claim processing bot repository, and a claim server processor” in the context of these claims encompasses a person/entity/claim service manually receives an initial claim from a submitter, processes the initial claim to identify one or more defects, performs pattern recognition using a trained artificial intelligence system, notifies the submitter of the identified defects thru a notification window, enables the submitter to choose the options generated and process based on the selection made by the submitter to correct/address the identified defects, submits a claim, receives a rejection, and automatically amends the claims using the AI system includes identifying a processing bot/software/application, processes the claims using the AI system of the processing bot, analyzes past submissions using the AI system, generates one or more rejections patterns using the AI system, and corrects the one or more rejections using the AI system. The ability of a graphical user interface to receive selections and output data is generic. Training a machine learning model or a AI system and apply this trained model/system is generic data process. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing personal behavior and utilizing mathematical algorithms/modeling but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” and the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of receiving, processing, performing, notifying, enabling, generating, selection, processing the selection, submitting, receiving, amending, identifying, processing, analyzing, generating, and correcting steps in a computer environment. The claimed computer components such as the computer system, the processor, the memory, the handheld electronic device, the display, the claim processing bot repository, and the claim server processor are recited at a high level of generality and are merely invoked as tools to perform receiving, processing, performing, notifying, enabling, generating, selection, processing the selection, submitting, receiving, amending, identifying, processing, analyzing, generating, and correcting steps. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claims 1, 11, and 21 are directed to an abstract idea.
The claims 1, 11, and 21 do 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 elements of using the computer system, the processor, the memory, the handheld electronic device, the display, the claim processing bot repository, and the claim server processor to perform receiving, processing, performing, notifying, enabling, generating, selection, processing the selection, submitting, receiving, amending, identifying, processing, analyzing, generating, and correcting steps amount 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. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 1, 11, and 21 are not patent eligible.
Claims 2-5, 12-15, and 22-25, disclose insignificant helpful content to further describe content, such as the initial claim concerns one or more of: a covered maintenance claim for the vehicle; an over-the-counter parts claim for the vehicle; a transportation damage claim for the vehicle; a pre-delivery inspection claim for the vehicle; a warranty claim for the vehicle; and a recall for the vehicle, the submitter includes one or more of: an authorized service center associated with the vehicle; a dealership associated with the vehicle; a repair shop servicing the vehicle; and a company associated with the vehicle, the vehicle is one of: a private vehicle; a commercial vehicle; a watersport vehicle; a heavy equipment vehicle; an aircraft; and a fleet vehicle, and the fleet vehicle includes one or more of: a corporate vehicle; a rideshare vehicle; and a rental vehicle, which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 2-5, 12-15, and 22-25 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 2-5, 12-15, and 22-25 further limit the abstract idea but not make it less abstract. Thus, the claims 2-5, 12-15, and 22-25 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 2-5, 12-15, and 22-25. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 2-5, 12-15, and 22-25 are not patent eligible.
Claims 6-7, 16-17, and 26-27 disclose insignificant helpful content to further describe content, such as the claim service processor is an original equipment manufacturer of the vehicle or a third-party warranty provider for the vehicle, which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 6-7, 16-17, and 26-27 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 6-7, 16-17, and 26-27 further limit the abstract idea but not make it less abstract. Thus, the claims 6-7, 16-17, and 26-27 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 6-7, 16-17, and 26-27. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 6-7, 16-17, and 26-27 are not patent eligible.
Claims 9, 19, and 29 disclose insignificant helpful content to further describe content, such as different corrective actions, which are merely descriptive content to further limit the abstract idea but not make it less abstract. Thus, the claims 9, 19, and 29 are directed to an abstract idea.
This judicial exception is not integrated into a practical application because descriptive content in claims 9, 19, and 29 further limit the abstract idea but not make it less abstract. Thus, the claims 9, 19, and 29 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 9, 19, and 29. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 9, 19, and 29 are not patent eligible.
The Claims 10, 20, and 30 recite submitting step as drafted, is processes that under broadest reasonable interpretation, cover performance of managing personal behavior. That is, nothing in the claim element precludes the steps from practically being performed by organizing human activity. For example, in the context of these claims encompasses a person manually submits the vetted claim. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation by managing personal behavior but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
This judicial exception is not integrated into a practical application because the claims as a whole merely describe how to generally “apply” the concept of submitting in a computer environment. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. The claims 10, 20, and 30 are directed to an abstract idea.
There are no additional claim element limitations recited in the claims 10, 20, and 30. Therefore, the claim does not amount to significantly more than the recited abstract idea (Step 2B: NO). The claims 10, 20, and 30 are not patent eligible.
Claims 11-20 are rejected under 35 USC 101 because the machine readable medium does not specifically exclude signals and needs to be amended to specify that the medium is ‘non-transitory’.
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.
Claims 1-7, 9-17, 19-27, and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Application Publication No. 2013/0297353 to Strange et al., in view of Canadian Patent Application Publication No. CA 3,052,163 to Raw et al., and further in view of U.S. Patent No. 12,131,387 to Leners.
With regard to claims 1, 11, and 21, Strange discloses a computing system including a processor and memory configured to perform operations comprising:
receiving an initial claim for a vehicle from a submitter, wherein the initial claim is destined for a claim service processor (Fig. 2, paragraph 9, An application on the mobile device coordinates the initiation and submission of an insurance claim by capturing images of documents and other information needed for the insurance claim using the image capture capability, then processing the images to extract content which is transmitted to an insurance company for processing of the claim. );
processing the initial claim to identify one or more defects within the initial claim, thus defining identified defects (paragraphs 109-110 and 115, Mobile device and remote server can be configured to perform various processing on a mobile image to correct various defects in the image quality. A document image taken using a mobile device might have one or more of the defects discussed above. These defects or others may cause low accuracy when processing the image, for example, when processing one or more of the fields on a document. Accordingly, in some embodiments, systems and methods using a mobile device to create images of documents can include the ability to identify poor quality images.);
notifying the submitter of the identified defects (paragraphs 84, 97, and 115, If the quality of an image is determined to be poor, a user may be prompted to take another image. Notifications sent to the user--through the application or by email, phone, text, etc), wherein notifying the submitter of the identified defects includes interfacing with an application installed on a handheld electronic device by opening a notification window on a display of the handheld electronic device (paragraphs 85, 97, and 384, The user may receive audio or visual alerts or notifications from the application indicating the status of the claim, such as when a claim needs additional information or when the claim has been paid. Notifications sent to the user--through the application or by email, phone, text, etc--may direct the user to additional interfaces on the mobile application which ask supplemental questions or request additional images or content from the captured images. Any number of mechanisms for informing the user about an upcoming payment may be used according to embodiments of the present invention (including, but not limited to: e-mail, popup windows, SMS messages, "push"/PAP messaging, calendar alerts, scheduled printing, and phone messages/voicemail).);
enabling the submitter to address the identified defects, thus defining a vetted claim (paragraphs 84, and 97, In one embodiment, the insurance company may transmit messages to the user at the mobile device to indicate that the claim has been received, verify that the claim information is complete, or indicate that additional information is needed. Notifications sent to the user--through the application or by email, phone, text, etc--may direct the user to additional interfaces on the mobile application which ask supplemental questions or request additional images or content from the captured images); and
submitting the vetted claim for a vehicle from the submitter to the claim service processor (abstract, paragraph 9, Documents such as an automobile insurance card (AIC), driver's license, vehicle identification number (VIN), license plate, police report, damage estimate and repair invoice may all be captured and processed by image processing techniques in order to extract relevant content. An application on a mobile device provides for the initiation and submission of an insurance claim by capturing information and images of documents using an image capture capability, then processing the images to extract content which is transmitted to an insurance company for processing of the claim); and
identifying a claim processing bot from a claim processing bot repository for processing the vetted claim based upon, at least in part, a type of claim and a type of vehicle (paragraph 86, if the user selects "File a Claim," as indicated by the arrow in FIG. 3B, a Claim Type interface 306 appears, as shown in FIG. 3C, which asks the user to select the type of claim they wish to submit, such as "Automobile," "Auto Glass," "Home" or "Other." once the claim type is selected as "Automobile," a series of accident-related questions 308 is displayed to determine if the user is at the scene of the accident and to ensure that emergency responders have been contacted if needed. If the user indicates that they are at the scene of the accident, any location-based services on the mobile device may be utilized to aid in preparing and submitting the claim information, as will be described further below. In FIGS. 3G and 3H, the user is prompted to gather claim information 310, capture and send an estimate 312, capture and send an invoice 314, or select from additional options 316. ).
However, Strange does not disclose processing the initial claim includes performing pattern recognition on the initial claim using a trained artificial intelligence (AI) system that is trained to identify the one or more defects within the initial claim by comparing the initial claim to training data comprising one or more known issues from a claim; wherein enabling the submitter to address the identified defects includes: generating a plurality of options to address the identified defects using the trained AI system; enabling the submitter to choose between the plurality of available choices; processing a selection provided by the submitter to address the identified defects by replacing the identified defects with the selection provided by the submitted; receiving a rejection of the vetted claim from the claim service processor, thus defining a rejected claim that identifies one or more specific rejections; and automatically amending the vetted claim to address the one or more specific rejections on behalf of the submitter, thus defining an amended claim, wherein automatically amending the vetted claim to address the one or more specific rejections includes processing the vetted claim using machine learning /artificial intelligence of the claim processing bot by: analyzing past submissions of claims to the claim service processor as input to the machine learning /artificial intelligence of the claim processing bot, generating one or more rejection patterns associated with the vetted claim using the machine learning /artificial intelligence of the claim processing bot, and correcting the one or more rejections using the one or more rejection patterns generated using the machine learning / artificial intelligence of the claim processing bot.
However, Raw teaches processing the initial claim includes performing pattern recognition on the initial claim using a trained artificial intelligence (AI) system that is trained to identify the one or more defects within the initial claim by comparing the initial claim to training data comprising one or more known issues from a claim (In some embodiments, machine learning unit 124 is configured to receive one or more training sets (e.g., training parameters of a model) and use same to train one or more classification models, for example, to recognize patterns (e.g., self variation, cross variation). For example, a payor who is in the process of submitting an electronic bill payment may receive a notification via an interface application where machine learning unit 124 or payment management unit 126 detects an error, where the payor will dismiss the proposed correction (e.g., classification) or accept the proposed correction and modify the bill payment. In some embodiments, machine learning unit 124 is configured to train one or more classification models using bill payment data stored in an electronic data warehouse, for example, using a test data set and a validation data set to enable the models to detect incorrect and correct information, paragraphs 43-45), wherein enabling the submitter to address the identified defects includes: generating a plurality of options to address the identified defects using the trained AI system; enabling the submitter to choose between the plurality of available choices; and processing a selection provided by the submitter to address the identified defects by replacing the identified defects with the selection provided by the submitted (payment recovery platform 100, in some embodiments, is configured to simplify the selection options and curate the options for display based on what the other payees are paying in that area. In the case of a Utility Company X payment, for example, payment recovery platform 100 can display choices based on the payments made by other clients and flag any anomalies to the user. For example, if Jane's neighbors make payments to Utility Company X .Math. Nova Scotia and she attempts to pay Utility Company X .Math. PEI, we can identify this as a potential error and ask Jane to
confirm the payment before it is processed by a client system interconnected with payment recovery platform 100. If the user selects Nova Scotia by accident, payment management unit 126 can be configured to alert them that Nova Scotia may not be the bill they are trying to pay and recommend the correct one based on a prediction from machine learning unit 124 or a correct result determined by payment management unit. If during clustering/processing of the data, payment recovery platform 100 detects an error (e.g., a bill payment error), the platform 100 can be configured to send a notification (e.g., SMS Message, JSON object through network) to a user (e.g., engaged at interface application 130) to fix the payment immediately. the processors can prevent the execution of the transaction data processing task (i.e. can prevent adding of the payee) until this potentially flagged error is fixed or input is received acknowledging or confirming selection of this payee, paragraphs 95-97, 111, and 137).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a filing insurance claims system of Strange to include, processing the initial claim includes performing pattern recognition on the initial claim using a trained artificial intelligence (AI) system that is trained to identify the one or more defects within the initial claim by comparing the initial claim to training data comprising one or more known issues from a claim; wherein enabling the submitter to address the identified defects includes: generating a plurality of options to address the identified defects using the trained AI system; enabling the submitter to choose between the plurality of available choices; and processing a selection provided by the submitter to address the identified defects by replacing the identified defects with the selection provided by the submitted, as taught in Raw, in order to provide a technical system for identifying and/or intercepting potentially erroneous electronic transaction data processing tasks (Raw, paragraph 5).
However, Leners teaches receiving a rejection of the vetted claim from the claim service processor, thus defining a rejected claim that identifies one or more specific rejections (an indication that a prescription submitted to an insurance provider associated with a patient for payment has been rejected by the insurance provider may be received (block 202), Fig. 2); and automatically amending the vetted claim to address the one or more specific rejections on behalf of the submitter, thus defining an amended claim, wherein automatically amending the vetted claim to address the one or more specific rejections includes processing the vetted claim using machine learning /artificial intelligence of the claim processing bot by: analyzing past submissions of claims to the claim service processor as input to the machine learning /artificial intelligence of the claim processing bot (an exemplary patient insurance correction system may operate to use pattern recognition techniques to analyze prescriber instructions in order to automatically update patient insurance information in response to a rejection by an insurance provider., col. 3, lines 33-40), generating one or more rejection patterns associated with the vetted claim using the machine learning /artificial intelligence of the claim processing bot (Insurer rejections may be analyzed to determine whether a patient insurance lookup search should be initiated, e.g., based on factors such as whether resolving the patient information will likely result in a resolution of the rejection, whether the medication would be covered by insurance, whether the amount that would be paid by the insurer would be worth the cost of the patient insurance lookup search, etc. col. 3, lines 48-58), and correcting the one or more rejections using the one or more rejection patterns generated using the machine learning / artificial intelligence of the claim processing bot (Based on the rejection message and/or the rejection code, a determination (block 204) may be made that the rejection is related to incorrect insurance information associated with the patient (e.g., expired or otherwise outdated insurance information associated with the patient). In other words, a determination may be made that the rejection is likely to be successfully resolved by correcting and/or updating insurance information associated with the patient. For example, certain rejection codes from some insurance providers only appear when insurance information associated with the patient is incorrect in some way, e.g., a code for “group number no longer valid,” a code for “invalid group number,” a code for “plan terminated,” a code for “patient not found,” etc. Moreover, in some examples, machine learning techniques may be used to identify common words or phrases found in historical rejection messages associated with incorrect patient insurance information. Systems and methods for automatically updating patient insurance information in response to a rejection by an insurance provider are provided. Corrected insurance information associated with the patient may be obtained using the patient insurance lookup system, and the prescription may be re-submitted for payment using the corrected insurance information for the patient. Fig. 2, col. 8, lines 20-col. 9, line 7, abstract).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify a filing insurance claims system of Strange to include, receiving a rejection of the vetted claim from the claim service processor, thus defining a rejected claim that identifies one or more specific rejections; and automatically amending the vetted claim to address the one or more specific rejections on behalf of the submitter, thus defining an amended claim, wherein automatically amending the vetted claim to address the one or more specific rejections includes processing the vetted claim using machine learning /artificial intelligence of the claim processing bot by: analyzing past submissions of claims to the claim service processor as input to the machine learning /artificial intelligence of the claim processing bot, generating one or more rejection patterns associated with the vetted claim using the machine learning /artificial intelligence of the claim processing bot, and correcting the one or more rejections using the one or more rejection patterns generated using the machine learning / artificial intelligence of the claim processing bot, as taught in Leners, in order to automatically update patient insurance information in response to a rejection by an insurance provider (Leners, col. 1, lines 27-28).
With regard to claims 2, 12, and 22, Strange discloses the initial claim concerns one or more of: a covered maintenance claim for the vehicle; an over-the-counter parts claim for the vehicle; a transportation damage claim for the vehicle; a pre-delivery inspection claim for the vehicle; a warranty claim for the vehicle; and a recall for the vehicle (paragraph 5, a car accident).
With regard to claims 3, 13, and 23, Strange discloses the submitter includes one or more of: an authorized service center associated with the vehicle; a dealership associated with the vehicle; a repair shop servicing the vehicle; and a company associated with the vehicle (Fig. 2, paragraphs 82, an application on the mobile device is launched by the user).
With regard to claims 4, 14, and 24, Strange discloses the vehicle is one of: a private vehicle; a commercial vehicle; a watersport vehicle; a heavy equipment vehicle; an aircraft; and a fleet vehicle (paragraph 5, It’s obvious that a car accident can be related to different types of vehicles).
With regard to claims 5, 15, and 25, Strange discloses the fleet vehicle includes one or more of: a corporate vehicle; a rideshare vehicle; and a rental vehicle (paragraph 5, It’s obvious that a car accident can be related to different types of vehicles).
With regard to claims 6, 16, and 26, Strange discloses the claim service processor is an original equipment manufacturer of the vehicle (paragraph 74, When a claim has been processed by the insurance company, the user may be reimbursed via the mobile application. Examiner notes that a claim submission to a specific type of claim server party/entity is a well-known technique and not an inventive step).
With regard to claims 7, 17, and 27, Strange discloses the claim service processor is a third-party warranty provider for the vehicle (paragraph 74, Examiner notes that a claim submission to a specific type of claim server party/entity is a well-known technique and not an inventive step).
With regard to claims 9, 19, and 29, Strange discloses enabling the submitter to address the identified defects, thus defining a vetted claim includes one or more of: enabling the submitter to submit one or more required photographs; enabling the submitter to correct one or more pieces of inaccurate information; enabling the submitter to choose between multiple available choices; and enabling the submitter to provide one or more pieces of missing information (paragraph 97).
With regard to claims 10, 20, and 30, Strange discloses submitting the vetted claim to the claim service processor (Fig. 2, paragraph 84, the completed claim request is transmitted to the insurance server 112 for processing by an insurance company, which will then process the claim.).
Response to Arguments
Applicants' arguments filed on 03/06/2026 have been fully considered but they are not fully persuasive especially in light of the previously reference used in the rejections.
Applicants remark that “the combination of references does not disclose identifying a claim processing bot from a claim processing bot repository for processing the vetted claim based upon, at least in part, a type of claim and a type of vehicle, and processing the vetted claim using machine learning /artificial intelligence of the claim processing bot by: analyzing past submissions of claims to the claim service processor as input to the machine learning /artificial intelligence of the claim processing bot, generating one or more rejection patterns associated with the vetted claim using the machine learning /artificial intelligence of the claim processing bot, and correcting the one or more rejections using the one or more rejection patterns generated using the machine learning / artificial intelligence of the claim processing bot”.
Examiner directs Applicants' attention to the office action above.
Applicants remark that “Applicant respectfully submits that amended independent claims 1, 11, 21 recite at least an inventive concept. As such, Applicant respectfully asserts that claims 1-7, 9-17, 19-27, and 29-30 are directed to patentable subject matter under 35 U.S.C. § 101.”
Examiner directs Applicants' attention to the office action above.
Conclusion
Please refer to form 892 for cited references.
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 extension fee 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 from the examiner should be directed to Ariel Yu whose telephone number is 571-270-3312. The examiner can normally be reached on Monday-Friday 9:00am-5:00pm EST.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Obeid Fahd A can be reached on 571-270-3324. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ARIEL J YU/Primary Examiner, Art Unit 3627
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