DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 04/06/2026.
Claims 1-9 and 11-18 and claim 20 (see “Response to Amendment” below) have been amended and are hereby entered.
Claims 21, and 22 have been added.
Claims 10 and 19 have been canceled.
Claims 1-9, 11-18, and 20-22 are currently pending and have been examined.
This action is made FINAL.
Response to Amendment
The amendment to the claims filed on 04/06/2026 does not comply with the requirements of 37 CFR 1.121(c) because claim 20 has been labeled “New” even though the previous 11/07/2024 claim set included a claim 20. Instead of being a “New” claim, claim 20 of the 04/06/2026 claim set appears to be an amended version of the 11/07/2024 claim 20 without having the requisite markings showing the changed claim language. Amendments to the claims filed on or after July 30, 2003 must comply with 37 CFR 1.121(c) which states:
(c) Claims. Amendments to a claim must be made by rewriting the entire claim with all changes (e.g., additions and deletions) as indicated in this subsection, except when the claim is being canceled. Each amendment document that includes a change to an existing claim, cancellation of an existing claim or addition of a new claim, must include a complete listing of all claims ever presented, including the text of all pending and withdrawn claims, in the application. The claim listing, including the text of the claims, in the amendment document will serve to replace all prior versions of the claims, in the application. In the claim listing, the status of every claim must be indicated after its claim number by using one of the following identifiers in a parenthetical expression: (Original), (Currently amended), (Canceled), (Withdrawn), (Previously presented), (New), and (Not entered)…
(2) When claim text with markings is required. All claims being currently amended in an amendment paper shall be presented in the claim listing, indicate a status of “currently amended,” and be submitted with markings to indicate the changes that have been made relative to the immediate prior version of the claims. The text of any added subject matter must be shown by underlining the added text. The text of any deleted matter must be shown by strike-through except that double brackets placed before and after the deleted characters may be used to show deletion of five or fewer consecutive characters. The text of any deleted subject matter must be shown by being placed within double brackets if strike-through cannot be easily perceived. Only claims having the status of “currently amended,” or “withdrawn” if also being amended, shall include markings. If a withdrawn claim is currently amended, its status in the claim listing may be identified as “withdrawn—currently amended.”
(3) When claim text in clean version is required. The text of all pending claims not being currently amended shall be presented in the claim listing in clean version, i.e., without any markings in the presentation of text. The presentation of a clean version of any claim having the status of “original,” “withdrawn” or “previously presented” will constitute an assertion that it has not been changed relative to the immediate prior version, except to omit markings that may have been present in the immediate prior version of the claims of the status of “withdrawn” or “previously presented.” Any claim added by amendment must be indicated with the status of “new” and presented in clean version, i.e., without any underlining.
Since the reply filed on 04/06/2026 appears to be bona fide, and the “New” claim 20 has been amended to recite analogous features as properly amended claims 1 and 11, Examiner is nonetheless proceeding with examination on the merits in the interest of compact prosecution. Examiner also notes here that page 10 of Applicant’s Remarks mentions a claim 23 that does not appear in the 04/06/2026 claim set. While examination on the merits has been performed below, Examiner respectfully requests Applicant remedy the claim listing in any future responses to clarify the record.
Response to Arguments
Applicant’s arguments, see page 2, filed 04/06/2026, with respect to the specification objection have been fully considered and are persuasive. The specification objection has been withdrawn.
While not specifically argued by Applicant, Examiner notes that the cancelation of claims 10 and 19 render the previous objections to these claims moot. However, Applicant’s amendments to the claims have necessitated new claim objections that are discussed below.
Applicant’s arguments, see pages 10 and 11-12, filed 04/06/2026, with respect to the 35 U.S.C. 101 rejections of claims 1-20 and the eligibility of new claim 22 have been fully considered but are not persuasive. The 35 U.S.C. 101 rejections of claims 10 and 19 have been rendered moot by the cancellation of the claims, but the 35 U.S.C. 101 rejections of claims 1-9, 11-18 have been maintained. New claims 20 (see “Response to Amendment” above regarding claim 20), 21, and 22 stand rejected under 35 U.S.C. 101.
On page 10, Applicant argues that the amendments to claim 1 reciting the training and execution of a machine learning (ML) model based on sensor data and mapping data, and transmitting a message over a computer network result in claim 1 no longer reciting a mental process and in fact no abstract ideas at all. Examiner respectfully disagrees. While Examiner agrees that Applicant’s to claim 1 result in the claims no longer reciting a Mental Process, the claims still recite the Certain Methods of Organizing Human Activity recited in original claim 1 and indicated in the eligibility rejections of the Non-Final Office Action. Specifically, amended claim 1 still recites the fundamental economic practice of managing risk as well as managing personal behavior by observing patterns in a human driver’s behavior and suggesting steps for the human driver to mitigate the risks of their driving behavior. See MPEP 2106.04(a)(2) II.A. for the mitigation of risk falling under fundamental economic practices of Certain Methods of Human Activity. See MPEP 2106.04(a)(2)II.C. for the managing of human behavior including teaching (i.e. suggesting/instructing behavioral modifications to mitigate a driver’s risk) falling under Certain Methods of Organizing Human Activity. Accordingly, the amended independent claims still recite an abstract idea at Step 2A Prong One; Applicant’s arguments are not persuasive.
While steps 2A Prong Two and 2B are not specifically argued by Applicant in Remarks, Examiner notes here that additional elements of the amended independent claims do not integrate the recited judicial exception into a practical application nor recite significantly more than the recited judicial exception. As discussed in greater detail below, the recited computing components of the amended independent claims (i.e. one or more processors, memory/medium storing instructions, transmitting a message over a computer network to a mobile computing device), in combination, amount to no more than mere instructions to apply the judicial exception. Particularly, these recited components, in combination, are merely being used as tools to perform the recited judicial exception (see MPEP 2106.05(f)(2)). The additional elements of training and executing a machine learning model, at the level of disclosure in the claims, amounts to no more than generally linking the judicial exception to the field of machine learning. See MPEP 2106.05(h). Specifically, the independent claims take the judicial exception of determining patterns of risky driving behavior of a particular driver and providing suggested modifications to lower a particular driver’s risk and limit the exception to a particular field. However, as MPEP 2106.05(h) states, limiting a judicial exception to a particular field does not meaningfully limit the claim nor amount to significantly more than the judicial exception. Even considering all of the additional elements in combination, the additional elements do not amount to more than applying the judicial exception using generic computing components and generally linking the claimed judicial exception to the field of machine learning. Accordingly the additional elements do not integrate the claimed judicial exception into a practical application. MPEP 2106.05(f) and 2106.05(h) further state that applying an exception using generic computing components and generally linking a judicial exception to a field of use also do not amount to significantly more than the judicial exception. Accordingly, the amended independent claims are not eligible. The various dependent claims still stand rejected under 35 U.S.C. 101 for the reasoning discussed in the rejections below.
Regarding claim 22, argued on pages 11-12 of Remarks, Applicant argues that claim 22 is patent eligible because the claim amounts to subject matter that is not well-understood, routine, or conventional in the telematics industry. Particularly, Applicant argues that most insurance companies/third parties lack sufficient amounts of claim data to train a machine learning model because insurance companies are unwilling to share proprietary claims data, limiting the size of the training data set. Applicant argues that its position within the marketplace allows them to aggregate claim data from multiple parties to build a more robust model on a larger set of training data, and that this data access and decision to train the model on insurance claims data are unconventional in the industry and provide an inventive concept. Examiner respectfully disagrees.
First Examiner notes that claim 22 recites “wherein the training dataset includes insurance claims data”. As drafted, claim 22 does not require the insurance claims data to come from multiple different parties. The broadest reasonable interpretation of claim 22 covers insurance claims data from a single party, as no requirement is stated that the claims originate from multiple different insurers. Therefore, even if the ability to access claims data from multiple insurers were to be considered patent eligible, which Examiner does not for reasons below, Applicant’s arguments would still be unpersuasive regarding claim 22 because the claim does not reflect the alleged unconventional feature.
However, even if claim 22 were to require that the claims data used to train the model originated with different insurers, Applicant’s arguments would still be unpersuasive. Examiner notes that at step 2B, “an ‘inventive concept’ is furnished by an element or combination of elements that is recited in the claim in addition to (beyond) the judicial exception, and is sufficient to ensure that the claim as a whole amounts to significantly more than the judicial exception itself” (MPEP 2106.05, emphasis added). Furthermore, MPEP 2106.05 states “An inventive concept "cannot be furnished by the unpatentable law of nature (or natural phenomenon or abstract idea) itself." Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1376, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016). In the context of claim 22, Applicant’s arguments that Applicant’s “unique position within the marketplace” allow for an unconventional solution to providing training data are not arguments over an additional element or combination of elements. While Applicant’s market position and agreements/understandings with a plurality of insurers may allow Applicant access to more claims data on which to train a model, the acquisition of this additional claims data is reflective of a business improvement, not an unconventional additional element(s). In other words, the access of multiple insurers claim data reflects an improvement to the commercial interaction/abstract idea of the claims, and does not reflect an additional element or set of additional elements that would amount to significantly more than the abstract idea itself. MPEP 2106.04 I. states “The Supreme Court’s decisions make it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions”. Thus, even if using multiple insurers claims data is not a normal practice in the industry, securing business agreements/relationships that allow access to such data still falls under the judicial exception of the claims and does not amount to significantly more at Step 2B. Therefore, Applicant’s arguments regarding claim 22’s eligibility are not persuasive.
Applicant’s arguments, see pages 10-11, filed 04/06/2026, with respect to the prior art rejections of claims 1-20 have been fully considered but are moot. Specifically, while the amended independent claims are no longer rejected under 35 U.S.C. 102(a)(1) as anticipated by Furukawa (U.S. Pre-Grant Publication No. 2020/0286183, hereafter known as Furukawa), the independent claims are now rejected under 35 U.S.C. 103 as obvious over Sedlik (U.S. Pre-Grant Publication No. 2017/0076395, hereafter known as Sedlik) in view of Furukawa as necessitated by Applicant’s amendment. The dependent claims and new claims stand rejected under 35 U.S.C. 103 as necessitated by Applicant’s amendment. While the art rejections of claims 10 and 19 have been rendered moot by the cancellation of the claims, claims 1-9, 11-18 and 20-22 stand rejected under 35 U.S.C. 103 for the reasoning discussed in the rejections below.
Claim Objections
Claims 5, 8, 9, 15, and 18 are objected to because of the following informalities:
Claims 5 and 15 recite “…wherein the the one or more individualized patterns of risky behavior…” when it appears they both should recite “…wherein the [[the]] one or more individualized patterns of risky behavior…” to correct an apparent typographical error
Claims 8 and 18 recite “…generating, based on the other driver behavior data and the the one or more individualized patterns…” when it appears they both should recite “…generating, based on the other driver behavior data and the [[the]] one or more individualized patterns…” to correct an apparent typographical error
Claim 9 recites “…wherein the the one or more individualized patterns…” when it appears it should recite “…wherein the [[the]] one or more individualized patterns…” to correct an apparent typographical error
Appropriate correction is required.
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 1-9, 11-18, and 20-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite determining a risk for a driver and providing suggestion to mitigate the driver’s risk.
As an initial matter, claims 1-9 fall into at least the process category of statutory subject matter. Claims 11-18 fall into at least the manufacture category of statutory subject matter. Finally, claims 20-22 fall into at least the machine category of statutory subject matter. Therefore, all claims fall into at least one of the statutory categories. Eligibility analysis proceeds to Step 2A.
Claim 1 recites the concept of determining a risk for a driver and providing suggestion to mitigate the driver’s risk which is a certain method of organizing human activity including fundamental economic practices and managing personal behavior. A method, comprising: training a model based on a training dataset indicating risky driving behaviors to produce a trained model; executing the trained model based on an input comprising sensor data and mapping data associated with one or more trips by a particular driver, wherein the model is configured to generate an output comprising one or more individualized patterns of risky behavior for the particular driver based on the input, wherein the one or more individualized patterns of risky behavior include one or more recurrent risky driving behaviors exhibited by the particular driver, and wherein the model is further configured to compare the one or more individualized patterns of risky behavior for the particular driver to population- level driving behavior data to identify behaviors unique to the particular driver or endemic among a population; and transmitting, to the particular driver, one or more feedback messages tailored to the particular driver based on the output of the trained model, wherein the one or more feedback messages include one or more explanations of the one or more individualized patterns of risky behavior and one or more suggestions to mitigate the one or more of the individualized patterns of risky behavior all, as a whole, fall under the categories of fundamental economic practices and managing personal behavior. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of the method being “computer-implemented”, one or more processors, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a machine learning (ML) model and training a ML model are recited at a high level of generality such that it amounts to no more than generally linking the judicial exception to the field of machine learning. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 elements of the method being “computer-implemented”, one or more processors, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a machine learning (ML) model and training a ML model are no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking a judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 2-3 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 4 further limits the abstract idea of claim 1 while introducing the additional elements of vehicle-based sensors and Internet-of-Things (IoT) sensors. The claim does not integrate the abstract idea into a practical application because the elements of vehicle-based sensors and Internet-of-Things (IoT) sensors are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding these new additional elements into the additional elements from claim 1 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 5 further limits the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above, claim 5 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 6 further limits the abstract idea of claim 2 without adding any new additional elements. Therefore, by the analysis of claim 2 above, claim 6 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claims 7-9 further limit the abstract idea of claim 1 without adding any new additional elements. Therefore, by the analysis of claim 1 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 11 recites the concept of determining a risk for a driver and providing suggestion to mitigate the driver’s risk which is a certain method of organizing human activity including fundamental economic practices and managing personal behavior. Perform one or more operations, comprising: training a model based on a training dataset indicating risky driving behaviors to produce a trained model; executing the trained model based on an input comprising sensor data and mapping data associated with one or more trips by a particular driver, wherein the model is configured to generate an output comprising one or more individualized patterns of risky behavior for the particular driver based on the input, wherein the one or more individualized patterns of risky behavior include one or more recurrent risky driving behaviors exhibited by the particular driver, and wherein the model is further configured to compare the one or more individualized patterns of risky behavior for the particular driver to population-level driving behavior data to identify behaviors unique to the particular driver or endemic among a population; and transmitting, to the particular driver, one or more feedback messages tailored to the particular driver based on the output of the trained model, wherein the one or more feedback messages include one or more explanations of the one or more individualized patterns of risky behavior and one or more suggestions to mitigate the one or more of the individualized patterns of risky behavior all, as a whole, fall under the categories of fundamental economic practices and managing personal behavior. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a non-transitory, computer-readable medium storing one or more instructions executable by a computer system, a computer system, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a machine learning (ML) model and training a ML model are recited at a high level of generality such that it amounts to no more than generally linking the judicial exception to the field of machine learning. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 elements of a non-transitory, computer-readable medium storing one or more instructions executable by a computer system, a computer system, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device amounts to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a machine learning (ML) model and training a ML model are no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking a judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claims 12-13 further limit the abstract idea of claim 11 without adding any new additional elements. Therefore, by the analysis of claim 11 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 14 further limits the abstract idea of claim 12 while introducing the additional elements of vehicle-based sensors and Internet-of-Things (IoT) sensors. The claim does not integrate the abstract idea into a practical application because the elements of vehicle-based sensors and Internet-of-Things (IoT) sensors are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding this new additional element into the additional element from claim 12 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 15 further limits the abstract idea of claim 11 without adding any new additional elements. Therefore, by the analysis of claim 11 above, claim 15 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim 16 further limits the abstract idea of claim 12 without adding any new additional elements. Therefore, by the analysis of claim 12 above, claim 16 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claims 17-18 further limit the abstract idea of claim 11 without adding any new additional elements. Therefore, by the analysis of claim 11 above these claims, individually and as an ordered combination, do not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claims are not patent eligible.
Claim 20 recites the concept of determining a risk for a driver and providing suggestion to mitigate the driver’s risk which is a certain method of organizing human activity including fundamental economic practices and managing personal behavior. Perform operations including: training a model based on a training dataset indicating risky driving behaviors to produce a trained model; executing the trained model based on an input comprising sensor data and mapping data associated with one or more trips by a particular driver, wherein the model is configured to generate an output comprising one or more individualized patterns of risky behavior for the particular driver based on the input, wherein the one or more individualized patterns of risky behavior include one or more recurrent risky driving behaviors exhibited by the particular driver, and wherein the model is further configured to compare the one or more individualized patterns of risky behavior for the particular driver to population-level driving behavior data to identify behaviors unique to the particular driver or endemic among a population; and transmitting, to the particular driver, one or more feedback messages tailored to the particular driver based on the output of the trained model, wherein the one or more feedback messages include one or more explanations of the one or more individualized patterns of risky behavior and one or more suggestions to mitigate the one or more of the individualized patterns of risky behavior all, as a whole, fall under the categories of fundamental economic practices and managing personal behavior. The claim falls into the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. Mere recitation of generic computer components does not remove the claim from this grouping. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a system, one or more processors, one or more memories storing program code that is executable by the one or more processors for causing the one or more processors to perform operations, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device. The recited additional elements are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. The additional elements of a machine learning (ML) model and training a ML model are recited at a high level of generality such that it amounts to no more than generally linking the judicial exception to the field of machine learning. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
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 elements of a system, one or more processors, one or more memories storing program code that is executable by the one or more processors for causing the one or more processors to perform operations, a computer network, a mobile computing device, and transmitting messages over the computer network to the mobile computing device amount to no more than mere instructions to apply the exception using generic computer components. Also as discussed above, the additional elements of a machine learning (ML) model and training a ML model are no more than generally linking the judicial exception to the field of machine learning. The combination of these additional elements is also no more than mere instructions to apply the exception using generic computer components and generally linking the judicial exception to the field of machine learning. Mere instructions to apply an exception using generic computer components and generally linking a judicial exception to a field of use cannot provide an inventive concept. The claim is not patent eligible.
Claim 21 further limits the abstract idea of claim 20 while introducing the additional elements of vehicle-based sensors and Internet-of-Things (IoT) sensors. The claim does not integrate the abstract idea into a practical application because the elements of vehicle-based sensors and Internet-of-Things (IoT) sensors are recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components. Adding these new additional elements into the additional elements from claim 20 still amounts to no more than mere instructions to apply the exception using generic computer components. The claim also does not amount to significantly more than the abstract idea because mere instructions to apply an exception using generic computer components cannot provide an inventive concept. The claim is not patent eligible.
Claim 22 further limits the abstract idea of claim 20 without adding any new additional elements. Therefore, by the analysis of claim 20 above, claim 22 does not integrate the abstract idea into a practical application nor amount to significantly more than the abstract idea. The claim is not patent eligible.
Claim Rejections - 35 USC § 103
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 1-8, 11-18, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Sedlik (U.S. Pre-Grant Publication No. 2017/0076395, hereafter known as Sedlik) in view of Furukawa (U.S. Pre-Grant Publication No. 2020/0286183, hereafter known as Furukawa).
Regarding claim 1, Sedlik teaches:
A computer-implemented method, comprising (see Fig. 3 and [0036]-[0037] for the overall method being performed on a vehicle device. See Fig. 4 and [0038]-[0039] for the overall method being performed by a server. See Fig. 5 and [0040]-[0041] for another method performed by a vehicle device. See [0044]-[0045] for the variations discussed in the following sections being implemented in combination)
training, by one or more processors, a machine learning (ML) model based on a training dataset indicating risky driving behaviors to produce a trained ML model (see Fig. 8 and [0063] "FIG. 8 is an illustration of an example scenario 800 featuring a sixth variation of this third aspect, wherein the risk rating 212 of the user 110 is assigned using an artificial neural network 806 that has been trained to identify a risk rating 212 based upon a number of sources of information. In this example scenario 800, the artificial neural network 806 may have been trained, e.g., by providing to the artificial neural network 806 an extensive list of information about a prototypical set of individuals and an appropriate risk rating 212 to be assigned to each such individual" for training an artificial neural network (which reads on a ML model) based on training dataset indicating driving behaviors of individual and how risky each of the driver's behaviors is. See [0036], [0038], and [0040] for the vehicle device and server comprising processors that implement the operations)
executing, by the one or more processors, the trained ML model based on an input comprising sensor data and mapping data associated with one or more trips by a particular driver, wherein the ML model is configured to generate an output comprising one or more individualized patterns of risky behavior for the particular driver based on the input, wherein the one or more individualized patterns of risky behavior include one or more recurrent risky driving behaviors exhibited by the particular driver (see [0063] "Once trained to assign such risk ratings 212 within an acceptable margin of error, the artificial neural network 806 may then be applied to assign a risk rating 212 to the user 110 based on a set of varied information, such as the driving behaviors 210 of the user 110; the driving contexts 104 in which the user 110 chooses to operate the vehicle; a user driving history 802 of the user 110; and actuarial tables 804 that indicate the significance of various individual personality traits, such as health history, to the risk rating 212 of the user 110" for the trained ML model receiving driving behavior context data as inputs to assign risk ratings. See [0049] and [0053] for receiving telemetry and sensor data as driving behavior and [0039] and [0050] for the driving context comprising vehicle causeway types (road types), locations, and the route of the vehicle as mapping data. Also see [0028] for detecting user tendencies. For outputting of recurrent risky patterns of driving behavior, see [0058] for comparing a user's speed and braking rates with safe levels and [0066] "Moreover the user 110 may appreciate an identification of driving behaviors 210 that may be particularly risky or unusual, such as a tendency to brake unnecessarily hard" for outputting that a particular driver has a risky pattern of braking unnecessarily hard. See [0061]-[0062] for the outputting of speeding tickets/driving speed as a risky behavior for a particular driver. See [0036], [0038], and [0040] citations above for processors performing the functions)
and wherein the ML model is further configured to compare the one or more individualized patterns of risky behavior for the particular driver to population- level driving behavior data to identify behaviors unique to the particular driver or endemic among a population (see [0059] "the risk rating 212 of the user 110 may be identified by comparing the driving behavior 210 of the user 110 with a second driving behavior of at least one other user 110, such as an average user who is a similar age and/or has a similar amount of driving experience as the user 110...the driving behavior 210 of the user 110 may be compared with other comparatively inexperienced drivers, and may therefore assign to the user 110 a risk rating 212 that is relative to other drivers of approximately the same level of experience" for comparing the driving behavior of a particular with the behavior of other comparable users to determine the risk of the particular driver. See [0066] for the comparison of the driving behavior of the user compared to other users and identification or unusual/risky behaviors)
and transmitting, by the one or more processors (see [0062] "in addition to assigning a risk rating 212 to the user 110, a vehicle device 202 may reveal the risk rating 212 to the user 110. The vehicle device 202 may also explain to the use the basis for the risk rating 212, e.g., the driving factors 114 that resulted in a conclusion of a particular driving behavior 210 for the user 110 (e.g., an assessment that the user 110 is a safe driver, an aggressive driver, or an overcautious driver)" for providing a tailored explanation for the particular user's rating and [0062] "Moreover, the vehicle device 202 may present to the user 110 at least one suggestion for an adjustment of the driving behavior 210 that is likely to improve the risk rating 212 of the driving behavior 210 of the user 110. For example, the vehicle device 202 may indicate to the user 110 that a 5% reduction in average speed, particularly in some driving contexts 104 such as rainy weather, may significantly reduce the risk rating 212 of the user 110" for the provision of suggestions to modify driving behavior to reduce driving risk. See [0037], [0039], and [0041] for the transmitting of a driving profile to a user)
As discussed above, Sedlik teaches that the driver profile, a driver’s risky tendencies, and suggestions to change particular behaviors to lower driver risk are “transmitted to the user”. Sedlik further teaches the computing devices in the invention comprising network interfaces in [0078] and the computing devices being mobile phones in [0073]. While this description of Sedlik heavily implies that the processors performing the method transmit the information to a user device over a network, Sedlik does not explicitly teach the driver feedback being transmitted to the driver over a computer network to a mobile computing device of the driver. Furukawa teaches transmitting explanations of risky driver behaviors and suggestions to mitigate risky driver behaviors over a computer network to a driver’s mobile device (see Fig. 2 and [0093] for a network connecting a server and mobile devices carried by drivers. See [0095] "the mobile device 91 receives and presents a display image constituted by a user interface/user experience (UI/UX) image regarding...evaluation results according to a driving state" and [0098]-[0100] for the server sending driver evaluation for a display to a mobile device of the driver. Examiner notes that the elements 31, 32, and 33 of Furukawa in Fig. 1 and [0086]-[0090] provide an explanation of driver behaviors compared to a standard, how different risky behaviors contribute to a driver’s overall evaluation, and a suggestion for how to improve driver behavior to mitigate risk).
It would have been obvious to one of ordinary skill in the art before the effectively filed date of the claimed invention to transmit the explanation of risky driver behavior and suggestion to mitigate risky driver behavior to a user’s mobile device via a computer network as in Furukawa in the system executing the method of Sedlik. As in Furukawa, it is within the capabilities of one of ordinary skill in the art to incorporate transmitting the explanation of risky driver behavior and suggestion to mitigate risky driver behavior to a user’s mobile device via a computer network into Sedlik's invention with the predictable result of communicating the driver profile and risky behavior analysis to the driver as needed in Sedlik.
Specifically, as Sedlik already teaches computing devices including mobile phones (and also considers mobile phone use by the driver in the risk calculations as taught by [0054]) and that the computing devices in the invention comprise network interfaces, one of ordinary skill in the art would have found it obvious to utilize these taught features of the Sedlik computing devices to accomplish Sedlik’s goal of transmitting the driver profile, explanations, and suggestions, to a user via a user’s mobile device over a computer network as taught in Furukawa.
Regarding claim 2, the combination of Sedlik and Furukawa teaches all of the limitations of claim 1 above. Sedlik further teaches:
identifying a traffic infraction associated with the one or more individualized patterns of risky behavior for the particular driver (see [0058] “the risk rating 212 of the user 110 may be determined by comparing the driving factors 114 with standardized driving factors, such as comparing an average driving speed of the user 110 with posted speed limits” and [0061] “the selection of a risk rating 212 for a user 110 may also be based upon a user driving history of the user 110. As a first such example, even if the user driving behavior 210 indicates that the user 110 is typically a cautious driver, a user driving history indicating a significant number of accidents or speeding tickets may entail the assignment of a higher-risk rating 212. Conversely, even if the user driving behavior 210 indicates that the user 110 is typically an aggressive driver, a user driving history indicating an absence of accidents or speeding tickets over an extended duration may indicate that the user 110 may also be a focused driver, and therefore suggest the selection of a lower-risk rating 212” for a traffic infraction of speeding/speeding tickets associated with a risky behavior of speeding)
Regarding claim 3, the combination of Sedlik and Furukawa teaches all of the limitations of claim 2 above. Sedlik further teaches:
identifying a type of road associated with the one or more individualized patterns of risky behavior for the particular driver (see [0050] “the techniques presented herein may be used to evaluate the driving behaviors 210 of the user 110 while operating the vehicle 108 in a variety of driving contexts 106, including…a vehicle causeway type context (e.g., an unpaved local road, a residential side street, a main roadway, or a highway)” and [0058] for determining ratings based on particular contexts for the identifying a type of road associated with driving behavior and comparing behavior on a road type with other drivers’ behavior in a road type to determine risk rating)
Regarding claim 4, the combination of Sedlik and Furukawa teaches all of the limitations of claim 1 above. Sedlik further teaches:
wherein the sensor data includes measurements from vehicle-based sensors, a mobile computing device, or Internet-of-Things (IoT) sensors (see [0028] “The vehicle device 202 may monitor driving factors 114 detected during operation of the vehicle 108 by the user 110, and may evaluate the driving factors 114 to identify a driving behavior 210 of the user 110, such as the user's tendency to drive a to particular speed, accelerate at a particular rate, and/or maintain a braking distance with respect to another vehicle 108, in general and/or in particular driving contexts 104” and [0039], [0054], [0055] for vehicle-based devices detecting speed, acceleration, braking, temperature, distance to other vehicles. Examiner notes that these vehicle-based sensors read on the claim as a whole because vehicle-based sensors, mobile computing device, and IoT sensors are listed in the alternative)
Regarding claim 5, the combination of Sedlik and Furukawa teaches all of the limitations of claim 1 above. Sedlik further teaches:
wherein the the one or more individualized patterns of risky behavior for the particular driver include a mobile-computing-device-based distraction, sudden braking, sudden acceleration, sudden deacceleration, or traveling at a high speed (see [0066] “Moreover the user 110 may appreciate an identification of driving behaviors 210 that may be particularly risky or unusual, such as a tendency to brake unnecessarily hard” for the sudden braking/deceleration, [0058] for comparing traveling speed to speed limits and [0061] for determining significant numbers of speeding violations, and [0054] for the detection of the use of a mobile communication device in the vehicle. As the various factors are listed in the alternative in claim 5, only one is needed to teach the claim)
Regarding claim 6, the combination of Sedlik and Furukawa teaches all of the limitations of claim 2 above. Sedlik further teaches:
wherein the traffic infraction includes failing to stop at a stop sign, failing to observe a traffic signal, failing to observe a traffic sign, speeding through a reduced speed zone, performing an illegal U-turn, improper parking or stopping, or driving a wrong direction on a road (see [0058] “the risk rating 212 of the user 110 may be determined by comparing the driving factors 114 with standardized driving factors, such as comparing an average driving speed of the user 110 with posted speed limits” and [0061] “the selection of a risk rating 212 for a user 110 may also be based upon a user driving history of the user 110. As a first such example, even if the user driving behavior 210 indicates that the user 110 is typically a cautious driver, a user driving history indicating a significant number of accidents or speeding tickets may entail the assignment of a higher-risk rating 212” for detected traffic violations including speeding through a zone with a speed limit, which Examiner is interpreting to read on speeding through a reduced speed zone)
Regarding claim 7, the combination of Sedlik and Furukawa teaches all of the limitations of claim 1 above. Sedlik further teaches:
analyzing, to generate other driver behavior data, driver behavior associated with one or more other drivers (see [0059] “the risk rating 212 of the user 110 may be identified by comparing the driving behavior 210 of the user 110 with a second driving behavior of at least one other user 110, such as an average user who is a similar age and/or has a similar amount of driving experience as the user 110… the driving behavior 210 of the user 110 may be compared with other comparatively inexperienced drivers, and may therefore assign to the user 110 a risk rating 212 that is relative to other drivers of approximately the same level of experience” and [0028] for generating other driver behavior in order to compare the particular driver’s behavior with other driver behavior)
Regarding claim 8, the combination of Sedlik and Furukawa teaches all of the limitations of claim 7 above. Sedlik further teaches:
generating, based on the other driver behavior data and the the one or more individualized patterns of risky behavior for the particular driver, an individual risk rating for the particular driver (see [0059] “the risk rating 212 of the user 110 may be identified by comparing the driving behavior 210 of the user 110 with a second driving behavior of at least one other user 110, such as an average user who is a similar age and/or has a similar amount of driving experience as the user 110” and [0066] “the user 110 may not be aware of a perception of the user's driving behavior 210 as compared with other drivers or a driving standard (e.g., the user 110 may not be aware that others may perceive the user 110 as an aggressive or overcautious driver)” for determining a risk rating for the particular driver based on a comparison of the particular driver’s behavior and other drivers’ behavior)
Regarding claim 11, Sedlik teaches:
A non-transitory, computer-readable medium storing one or more instructions executable by a computer system to perform one or more operations, comprising (see [0042] “Still another embodiment involves a computer-readable medium comprising processor-executable instructions configured to apply the techniques presented herein. Such computer-readable media may include, e.g., computer-readable storage media involving a tangible device, such as a memory semiconductor… and which encodes a set of computer-readable instructions that, when executed by a processor of a device, cause the device to implement the techniques presented herein” and [0043] “An example computer-readable medium that may be devised in these ways is illustrated in FIG. 6, wherein the implementation 500 comprises a computer-readable medium 602 (e.g., a CD-R, DVD-R, or a platter of a hard disk drive), on which is encoded computer-readable data 604. This computer-readable data 604 in turn comprises a set of computer instructions 606 configured to operate according to the principles set forth herein”)
Regarding the remaining limitations of claim 11, see the rejection of claim 1 above.
Regarding claim 12, the combination of Sedlik and Furukawa teaches all of the limitations of claim 11 above. Regarding the limitations introduced in claim 12, see the rejection of claim 2 above.
Regarding claim 13, the combination of Sedlik and Furukawa teaches all of the limitations of claim 12 above. Regarding the limitations introduced in claim 13, see the rejection of claim 3 above.
Regarding claim 14, the combination of Sedlik and Furukawa teaches all of the limitations of claim 12 above. Regarding the limitations introduced in claim 14, see the rejection of claim 4 above.
Regarding claim 15, the combination of Sedlik and Furukawa teaches all of the limitations of claim 11 above. Regarding the limitations introduced in claim 15, see the rejection of claim 5 above.
Regarding claim 16, the combination of Sedlik and Furukawa teaches all of the limitations of claim 12 above. Regarding the limitations introduced in claim 16, see the rejection of claim 6 above.
Regarding claim 17, the combination of Sedlik and Furukawa teaches all of the limitations of claim 11 above. Regarding the limitations introduced in claim 17, see the rejection of claim 7 above.
Regarding claim 18, the combination of Sedlik and Furukawa teaches all of the limitations of claim 17 above. Regarding the limitations introduced in claim 18, see the rejection of claim 8 above.
Regarding claim 20, Sedlik teaches:
A system comprising: one or more processors (see Fig. 10 and [0075] “FIG. 10 illustrates an example of a system 1000 comprising a computing device 1002 configured to implement one or more embodiments provided herein. In one configuration, computing device 1002 includes at least one processing unit 1006 and memory 1008”)
and one or more memories storing program code that is executable by the one or more processors for causing the one or more processors to perform operations including (see [0076] “device 1002 may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in FIG. 10 by storage 1010. In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage 1010. Storage 1010 may also store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in memory 1008 for execution by processing unit 1006”. Also see [0042]-[0043])
Regarding the remaining limitations of claim 20, see the rejection of claim 1 above.
Regarding claim 21, the combination of Sedlik and Furukawa teaches all of the limitations of claim 20 above. Sedlik further teaches:
wherein the sensor data includes measurements from vehicle-based sensors or Internet-of-Things (IoT) sensors (see [0028] “The vehicle device 202 may monitor driving factors 114 detected during operation of the vehicle 108 by the user 110, and may evaluate the driving factors 114 to identify a driving behavior 210 of the user 110, such as the user's tendency to drive a to particular speed, accelerate at a particular rate, and/or maintain a braking distance with respect to another vehicle 108, in general and/or in particular driving contexts 104” and [0039], [0054], [0055] for vehicle-based devices detecting speed, acceleration, braking, temperature, distance to other vehicles. Examiner notes that these vehicle-based sensors read on the claim as a whole because vehicle-based sensors or IoT sensors are listed in the alternative)
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Sedlik in view of Furukawa and Volos et al. (U.S. Patent No. 10,300,922; hereafter known as Volos).
Regarding claim 9, the combination of Sedlik and Furukawa teaches all of the limitations of claim 7 above. Sedlik further teaches:
wherein the the one or more individualized patterns of risky behavior for the particular driver and the other driver behavior data is based on road geometry (see [0050] “the techniques presented herein may be used to evaluate the driving behaviors 210 of the user 110 while operating the vehicle 108 in a variety of driving contexts 106, including…a vehicle causeway type context (e.g., an unpaved local road, a residential side street, a main roadway, or a highway)” for determining driver behaviors on which risk ratings are based (and Examiner notes that Sedlik teaches comparison of driver behaviors in the claim 7 citations above) on the context including a type of roadway. Examiner notes that roadway type is explicitly considered “roadway geometry” in Applicant’s specification [0025] in which whether a road is a highway or not is an example of road geometry)
While Sedlik further teaches the “locations through which the user navigates the vehicle” ([0041]) being part of the context data used to determine driver behavior, the combination of Sedlik and Furukawa does not explicitly teach the one or more patterns of risky behavior for the drivers based on the specific roads traveled. Volos further teaches:
wherein the the one or more individualized patterns of risky behavior for the particular driver and the other driver behavior data is based on road geometry and specific roads traveled (see Col. 12 lines 6-9 "The location database 180 stores location information, such as type of road, whether the location includes a turn, an on ramp and/or an off ramp, inclination of the road, speed limit, etc." for location information including road incline, which is interpreted as road geometry. See at least Col. 12 lines 33-40 "The anomaly scores generated by the driving behavior comparator module 112 indicate whether the driver of the target vehicle is behaving... (ii) similar or different than other drivers, when driving in a same location and under similar driving conditions, such as traffic conditions, weather conditions, road conditions, etc" for the scores being assigned for each particular location/ particular road being traveled by the particular driver and other drivers for comparison. See Figs. 21 and 22 and Col. 18 lines 35-54 for particular locations including a specific point on a specific road)
One of ordinary skill in the art would have recognized that applying the known technique of incorporating road geometry and specific roads into the generation of driver behavior data and other driver behavior data of Volos to the combination of Sedlik and Furukawa would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Volos to the teaching of the combination of Sedlik and Furukawa would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such incorporating road geometry and specific roads into the generation of driver behavior data and other driver behavior data. Further, applying incorporating road geometry and specific roads into the generation of driver behavior data and other driver behavior data to the combination of Sedlik and Furukawa would have been recognized by one of ordinary skill in the art as resulting in an improved system that would allow more accurate risk assessment of drivers. As Volos Col. 17 lines 48-63 state, particular locations may be dangerous and cause the average driver to perform maneuvers that appear dangerous. By taking into account road geometry and the specific roads used when determining driver risk, the combined system would be able to more accurately differentiate between drivers that are risky and risky locations themselves that force/precipitate risky behavior from otherwise average/safe drivers. Therefore, one of ordinary skill in the art would have recognized that a more accurate assignment of risk would be achieved with the combination of Sedlik, Furukawa, and Volos than with only the combination of Sedlik and Furukawa.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Sedlik in view of Furukawa and Duan et al. (U.S. Pre-Grant Publication No. 2019/0147538, hereafter known as Duan).
Regarding claim 22, the combination of Sedlik and Furukawa teaches all of the limitations of claim 20 above. While Sedlik [0061] teaches that driver history like accidents and speeding tickets is taken into account when assigning a risk rating to a driver and that extensive information about drivers is provided as part of the training dataset for the model, the combination of Sedlik and Furukawa does not explicitly teach that the training dataset for the model comprising insurance claims data. Duan teaches:
wherein the training dataset includes insurance claims data (see [0034] “Then, at 210, cognitive risk identification program 110A, 110B uses the historical claims data and customer profile to train a risk scoring engine. The cognitive risk identification program 110A, 110B correlates the customer profile features with historical claims data to train a risk scoring engine to predict the likelihood for that driver to bring loss in the future… The machine learning abilities of the system must be applied to a mathematical model to train the system initially; historical claim data and driving profiles of a large number of drivers may be applied to train the model” for training a risk model with claims data and driving profile data. In combination with the Sedlik training, driver information used to train the model would include both driver behavior and driver claims data)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate training the machine learning model determining driver risk using insurance claims data and driving behavior data of Duan into the trained neural network identifying driver risk in the combination of Sedlik and Furukawa. As Duan states in [0013] “In the field of digital risk assessment as it pertains to insurance, there are multiple types of data that insurance companies can use to assess risk. Claim data is the most prolific; claim data includes the information submitted along with insurance claims, such as make and model of the car, nature of the incident, damages incurred, conditions at the time of the incident, et cetera… However, insurance companies have found it difficult to synthesize both the claim data and the limited telematics data in a fashion that lets insurance companies draw useful inferences of risk. As such, it may be advantageous to, among other things, implement a system that allows an insurance company to leverage their large amounts of claim data and limited telematics data through a statistical approach that gives the normalization of a claim and provides the relative risk level and a boundary of confidence regarding that risk level”. Therefore, one of ordinary skill in the art would have recognized that incorporating the technique of Duan training a risk assessment model using claims data and driver behavior data would allow the combined system to advantageously leverage a large amount of claims data to determine driver risks. Furthermore, Sedlik’s consideration of accident and ticket information in risk calculations would lead one of ordinary skill in the art to recognize that the further inclusion of claims data in determining the risks of a driver would have had predictable results.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Gross et al. (U.S. Patent No. 12,002,305) teaches generating usage based insurance policy associated with a driver and generating recommendations for a driver to drive more safely
Craig et al. (U.S. Patent No. 10,740,990) teaches comparing patterns of driver behavior with behavior of other drivers who drive along the same route in the same conditions
Verma et al. (U.S. Pre-Grant Publication No. 2024/0208522) teaches assigning drivers to groups based on risk and alerting drivers to behaviors that could be improved to lower their risk category
Lattanzi et al. (“Machine Learning Techniques to Identify Unsafe Driving Behavior by Means of In-Vehicle Sensor Data”, published 2021) teaches using in-vehicle sensor data like speed, brake pedal pressure, and steering wheel angle with neural networks to identify dangerous driving behavior
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL C MORONEY whose telephone number is (571)272-4403. The examiner can normally be reached Mon-Fri 8:30-5:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber can be reached at (571) 270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/M.C.M./Examiner, Art Unit 3628
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626
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