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 .
DETAILED ACTION
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 Arguments
Applicant's arguments filed 3/23/2026 have been fully considered but they are not persuasive. Applicant asserts as follows:
As a preliminary matter, Applicant respectfully submits that the claims, as amended, do not recite a mental process because they contain limitations that cannot practically be performed in the human mind. MPEP § 2106.04(a)(2) states that "[c]laims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations." The Federal Circuit in SRIInt'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019), declined to identify the claimed collection and analysis of network data as abstract because "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims."
Similarly, the present claims, as amended, recite operations that cannot practically be performed in the human mind. Specifically, amended claim 1 requires the apparatus to: (1) receive, from one or more sensors of a vehicle in real time, driving data collected for certain drivers; (2) extract, from the received driving data in real time, driving data that is determined as dangerous driving; (3) automatically classify the extracted driving data into a plurality of groups by converting each data point to coordinate values and creating groups by selecting data points having coordinate values within a threshold distance away from each other; (4) calibrate a distribution map associated with the safety operation indexes; (5) compare a first distribution of safety operation indexes with a second distribution of motor vehicle insurance accident compensation data; and (6) based on the comparison result, automatically change one or more values of the threshold distance and the predetermined minimum number of data points such that the first distribution is similar to the second distribution to adjust a range designated as the defensive driving group.
As described in the specification, the driving data includes "one or more of vehicle speed, steering angle, yaw rate, distance to the vehicle in front, lane change signal, road type, driving area, driving time, acceleration, sudden acceleration, sudden deceleration, sudden stop, and sudden lane change" measured by "a camera mounted on the vehicle, LiDAR, RADAR, acceleration sensor, speed sensor, GPS sensor, angular velocity sensor, or the like." Specification, paragraph [0052]. The grouper performs density-based clustering using "the density-based algorithm 'DBSCAN', which is based on the assumption that data belonging to the same group are distributed close to each other." Specification, paragraph [0063]. The corrector then "changes one or more values of the radius and the minimum number of data so that the distribution of the safety operation index shows a similar shape to the distribution of the automobile insurance accident damage data within a set range." Specification, paragraph [0083].
The human mind is not equipped to perform these operations. A human cannot practically receive and process real-time multi-dimensional sensor data from vehicle sensors, perform density- based clustering on coordinate values in multi-dimensional space, calibrate a distribution map, compare statistical distributions of safety operation indexes against insurance accident compensation data distributions, and automatically change clustering parameters to adjust the range designated as the defensive driving group-all in real time during vehicle operation. This is analogous to SRIInt'l, where the court found that "the human mind is not equipped to detect suspicious activity by using network monitors and analyzing network packets as recited by the claims." 930 F.3d at 1304.
The Office alleges the steps of previous claim 1 could be performed "by a human with pen and paper." Office Action, page 9. Without conceding the Office assertion, Applicant respectfully submits that the rejection is moot in view of the present amendment. The USPTO's August 4, 2025 memorandum, "Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101," instructs that "[e]xaminers are reminded not to expand this grouping in a manner that encompasses claim limitations that cannot practically be performed in the human mind." The claims as amended require real-time processing of vehicle sensor data, multi-dimensional density-based clustering, calibration of a distribution map, and automatic parameter changes through a feedback loop comparing distributions to adjust the defensive driving group range operations that are beyond human cognitive capabilities.
Examiner respectfully asserts that the claim(s) are, given their broadest reasonable interpretation, a mental process/abstract idea. In Applicant arguments, above, Applicant appears to assert that the claims as amended cannot be practically performed in the human mind, responsive to which Examiner draws reference to Example 47 Claim 2 of the July 2024 subject matter eligibility guidance, which was found to be ineligible under 35 USC 101. In the Example, steps (a) and (b) recite “receiving, at a computer, continuous training data” and “discretizing, by the computer, the continuous training data to generate input data,” respectively. As well as “training, by the computer, the ANN based on the input data and a selected training algorithm to generate a trained ANN, wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” in step (c). The relevant limitations are analyzed to fall under the extra-solution activity of receiving data in the case of (a), as well as performing mathematical calculations and other mental concepts. Examiner correlates the “continuous” nature of training data recited by Example 47 Claim 2 to the “real-time” nature of the data collected in the present claimed invention. Moreover, Example 47 Claim 2 recites specific algorithms to generate a trained neural network based on the continuous data, (a “backpropagation algorithm and a gradient descent algorithm”) and specifically requires the use of a computer in the claim. All of these features together appear to encompass calculations to a correlated degree to those found in the present claimed invention, and as asserted by the Applicant to render the claim patent-eligible as set forth above, specifically “process[ing] real-time multi-dimensional sensor data from vehicle sensors, perform[ing] density- based clustering on coordinate values in multi-dimensional space, calibrat[ing] a distribution map, compar[ing] statistical distributions of safety operation indexes against insurance accident compensation data distributions, and automatically chang[ing] clustering parameters to adjust the range designated as the defensive driving group-all in real time during vehicle operation” however Example 47 Claim 2 was found to be ineligible as set forth above. While the mathematical operations are not identical, Examiner respectfully asserts that the relevant operations are sufficiently similar in complexity between those recited by Example 47 Claim 2 and the present claimed invention such that the present claimed invention is not distinguished over Example 47 Claim 2.
With respect to Applicant Arguments regarding SRIInt'l, Examiner respectfully asserts that the claim(s) in the referenced case are directed to a fundamentally computer technology, namely the detection of “suspicious activity by using network monitors and analyzing network packets” [MPEP 2106.04]. The present claimed invention, in contrast, recites grouping driving data into groups, including a defensive driving group, which is an operation that is not so fundamentally tied to a computer technology, and performable outside of such. Thus, Applicant arguments are not persuasive.
B. The Claims Integrate Any Alleged Abstract Idea into a Practical Application
Even assuming arguendo that the claims recite an abstract idea, which Applicant does not concede, the claims as a whole integrate any such abstract idea into a practical application. MPEP § 2106.04(d) explains that "[a] claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception."
The December 5, 2025 USPTO memorandum regarding Ex Parte Desjardins emphasizes that claims must be evaluated "as a whole" and that examiners should be "careful to avoid oversimplifying the claims" by looking at them generally and failing to account for the specific requirements of the claims. The memorandum further instructs that "[w]hen evaluating a claim as a whole, examiners should not dismiss additional elements as mere 'generic computer components' without considering whether such elements confer a technological improvement to a technical problem."
The present claims provide a specific technological improvement to vehicle safety assessment systems. The specification I'entifies a specific technical problem: "even if a driver with a high safety operation index is driving defensively most of the time, if the driver needs to perform an evasive maneuver due to a dangerous driving behavior of another vehicle, the driver's safety operation index may be classified as dangerous driving and the driver's safety operation index may be adjusted downward." Specification, paragraph [0008]. The specification further explains that "it can be difficult to reflect the driver's driving intentions in the calculation of the safety operation index using only conventional numerical driving data, and it can be difficult to improve the accuracy and reliability of the safety operation index accordingly." Specification, paragraph [0010].
The present claims solve this technical problem through a specific technological solution: automatically classifying driving data using density-based clustering, calibrating a distribution map based on the updated safety operation indexes, comparing the resulting safety operation index distribution with actual insurance accident compensation data, and automatically changing the clustering parameters (threshold distance and minimum number of data points) to adjust the range designated as the defensive driving group. This feedback mechanism ensures that defensive driving is not misclassified as dangerous driving by validating the classification results against real-world data.
This is analogous to the eligible claims in USPTO Subject Matter Eligibility Example 40 (Adaptive Monitoring of Network Traffic Data), where the claim was found eligible because "the claim as a whole is directed to a particular improvement in collecting traffic data" and "provides a specific improvement over prior systems, resulting in improved network monitoring." Similarly, the present claims provide a specific improvement to vehicle safety assessment systems by automatically calibrating classification parameters based on external validation data.
The claims are also analogous to the eligible claims in McRO, Inc. v. Bandai Namco Games Am. Inc., 837 F.3d 1299 (Fed. Cir. 2016), where the Federal Circuit found that claims describing "a specific way (use of particular rules to set morph weights and transitions through phonemes) to solve the problem of producing accurate and realistic lip synchronization and facial expressions in animated characters" were not directed to an abstract idea. Here, the claims describe a specific way (using density-based clustering with automatic parameter adjustment based on insurance data comparison) to solve the problem of accurately classifying defensive driving versus dangerous driving.
Examiner respectfully disagrees that the claims, given their broadest reasonable interpretation, integrate the abstract idea into a practical application. With reference to the December 5, 2025 USPTO memorandum regarding Ex Parte Desjardins, and the relevant Applicant arguments set forth above, Examiner respectfully asserts that even evaluating the claim as a whole, the claim does not represent a “specific technological solution to a specific technological field” as asserted by the Applicant in Arguments, above. While further discussion is set forth below with respect to Section C of Applicant Arguments, the adjustment of clustering parameters, under the broadest reasonable interpretation of the claim, merely encompasses adjusting the parameters of an abstract set of rules defining how data should be grouped without integrating the grouping of data into a specific technology or significantly more than the grouping itself as required by Ex Parte Desjardins.
With respect to USPTO Subject Matter Eligibility Example 40, Examiner respectfully asserts that the claims are not analogous. In Example 40, claim 1, which was found to be eligible, the claims at issue were found to be directed to a practical application by collecting additional network traffic data when monitored information is greater than a predefined threshold, which was found to be directed to an improvement in collecting network traffic data itself. Example 40 Claim 2, however, was found to be ineligible, as the claim recites merely collecting network traffic data and comparing said data to a threshold, without significantly more. Examiner respectfully asserts that the present claimed invention is not integrated into a specific technology in the manner of Example 40 Claim 1, appearing to be closer in scope to Example 40 Claim 2, merely reciting grouping data and refining the parameters of the grouping, rather than effectuating some change or marked transformation into the technology itself.
With respect to Applicant Arguments referencing “McRO, Inc. v. Bandai Namco Games,” similarly to Example 40 Claim 1, the claim(s) at issue in McRO were found to be directed to a computer functionality, instead of an abstract idea. Similarly to as set forth above, Examiner respectfully asserts that the present claimed invention is directed to an abstract idea, implemented using computers, rather than an improvement to computer functionality itself. Thus, Applicant arguments are not persuasive.
C. The Claims Provide an Inventive Concept
Even if the amended claims were directed to an abstract idea, which Applicant does not concede, the claims amount to significantly more than any such abstract idea. The claims recite a non-conventional combination of elements: (1) real-time vehicle sensor data processing; (2) density- based clustering with coordinate conversion; (3) calibration of a distribution map based on updated safety operation indexes; (4) comparison of safety index distributions with insurance accident compensation data distributions; and (5) automatic parameter changes based on the comparison to adjust the range designated as the defensive driving group. This combination is not well-understood, routine, or conventional activity.
The Ex Parte Desjardins memorandum explains that claims may reflect "specific technological improvements" when they address a technical problem through a particular technological solution. Here, the claims address the technical problem of defensive driving misclassification through the specific technological solution of automatic parameter calibration using external validation data. The specification explains that "the corrector 144 resets the parameter values through the configurator 124 to change the radius R and the minimum number of data from 5 to 7 in order to expand the range designated as defensive driving." Specification, paragraph [0084]. This automatic parameter adjustment through a feedback loop represents a technological improvement that goes beyond mere instructions to apply an abstract idea on a generic computer.
Examiner respectfully asserts that the claimed invention does not, under the broadest reasonable interpretation of the claim(s), appear to encompass the addressing of a technical problem through a technical solution as set forth in the Ex Parte Desjardins memorandum. The claim(s) appear to recite an apparatus, taking in sensor data regarding vehicle operation, classifying said data into groups, and adjusting a defensive driving group’s parameter range. This appears, under the broadest reasonable interpretation of the claim, to be a manipulation of data to process and classify the data, rather than an improvement to a specific technology or technological field. The technological problem, as asserted by the arguments above, appears to be indicated to be “defensive driving misclassification” remedied by “automatic parameter calibration using external validation data,” however Examiner respectfully asserts that this is not sufficiently similar to the type of technological problems ‘improved’ through a technological solution as indicated by the Ex Parte Desjardins memorandum. Ex Parte Desjardins identified, inter alia, the improvement to be one relevant to machine learning technology by explaining how the machine learning model is trained to learn new tasks while protecting knowledge about previous tasks to overcome the problem of “catastrophic forgetting,” allowing the system to reduce use of storage capacity and the enablement of reduced complexity in the system, altering how the machine learning model itself would function in operation. Conversely, the present claims do not appear to be so fundamentally ingrained with a specific technology or technological field, rather indicating a specific sequence of steps to group vehicle use data and calibrate the parameters of said grouping. While Applicant asserts in arguments that the “automatic parameter adjustment through a feedback loop represents a technological improvement that goes beyond mere instructions to apply an abstract idea on a generic computer,” Examiner respectfully asserts that the parameter adjustment and feedback loop recited by the claims encompasses the abstract idea of performing mathematical operations to group data and calibrate the grouping [albeit limited to vehicle safety data], rather than improving some technology in an inextricable or fundamental way, such as appears to be addressed by Ex Parte Desjardins. With respect to the specificity of improving “defensive driving misclassification,” Examiner respectfully asserts that this appears to merely limit the grouping system to a particular field of use, which does not render the claim(s) patent-eligible as set forth in MPEP 2106.05(h), particularly with respect to example vi, referencing “Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016).” Thus, Applicant arguments are not persuasive.
For at least these reasons, Applicant respectfully submits that the present claims recite patent- eligible subject matter in compliance with 35 U.S.C. § 101 and that the rejection under 35 U.S.C. § 101 be withdrawn.
For at least the reasons set forth above, as well as those set forth below with respect to the specific rejection(s), Examiner respectfully maintains the rejection(s) of claims 1, 2, 4, 5, 7, 8, 10 – 13, 15, & 16 under 35 USC 101.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 2, 4, 5, 7, 8, 10 – 13, 15, & 16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2) and 2106.05(a) thru (d) for explanations.
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to an apparatus for extracting and classifying driving data (i.e. a machine). Therefore, claim 1 is within at least one of the four statutory categories. Similarly, Claim 7 is directed to a method for extracting and classifying driving data (i.e., a process), and is similarly within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
An apparatus comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
receive, from one or more sensors of a vehicle in real time, driving data collected for drivers whose safety operation indexes are higher than a threshold value; [mental process/step]
extract, from the received driving data in real time, driving data that is determined as dangerous driving; [mental process/step]
automatically classify, based on predetermined criteria, the extracted driving data into a plurality of groups;
designate a group, of the plurality of groups, having a size larger than remaining groups of the plurality of groups, as a defensive driving group; and [mental process/step]
update the safety operation indexes for the drivers based on at least a portion, of the extracted driving data, corresponding to the remaining groups; [mental process/step]
calibrate, based on the updated safety operation indexes, a distribution map associated with the safety operation indexes, [mental process/step]
compare a first distribution of the safety operation indexes of the drivers with a second distribution of motor vehicle insurance accident compensation data; and [mental process/step]
based on a result of the comparison of the first distribution with the second distribution, automatically change one or more values of a threshold distance and a predetermined minimum number of data points such that the first distribution is similar, within a predetermined range, to the second distribution to adjust a range designated as the defensive driving group, [mental process/step]
wherein, to classify the extracted driving data, the instructions, when executed by the one or more processors, cause the apparatus to:
convert each data point of the extracted driving data to coordinate values; and [mental process/step]
create each group, of the plurality of groups, by selecting at least the predetermined minimum number of data points, of the extracted driving data, having coordinate values within a threshold distance away from each other. [mental process/step]
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “for drivers whose safety operation indexes…” in the context of the claim encompasses a mental process of comparing the safety operation index of a driver to a threshold value to determine if data should be extracted or not, which is a simple judgment under its broadest reasonable interpretation. Similarly, “that is determined as…” in the context of the claim also encompasses a mental process of making a simple judgement based on data to determine data to extract, in this instance looking at the data collected to determine if it resembles dangerous driving or not, which is also a mental process under its broadest reasonable interpretation. Further, “automatically classify…” in the context of this claim encompasses a person applying predetermined criteria to extracted data and forming a simple judgement as to how said data should be grouped, which is an abstract idea of making a judgement under its broadest reasonable interpretation. Similarly, “designate…” in the context of the claim encompasses a user forming another simple grouping judgement based on the data and overall group size, specifically designating a group based on assessed defensive driving. The limitation “update…” in the context of the claim encompasses forming a judgment as to an adjustment that should be made in safety operation indexes based on extracted data that is not part of the defensive driving group, which is an abstract idea of making a judgment based on data under its broadest reasonable interpretation, alternatively involving the performance of mathematical calculations, which represents an abstract idea.
Additionally, “calibrate…” in the context of the claim, similarly encompasses adjusting data based on the updated safety operation indexes, the data specifically being embodied as a distribution map, which is a mathematical process/abstract idea under its broadest reasonable interpretation. The limitation “compare a first…” in the context of the claim encompasses a mental process of comparing two data sets to one another, which is a simple judgement made based on collected data, said judgement being used in the subsequent limitation “based on a result… automatically change…,” which given the broadest reasonable interpretation of the claim encompasses modifying data such that the data sets are similar to one another, which is a mathematical evaluation based on collected data. The limitation “convert…” in the context of the claim encompasses a modification of data into a form suitable for mathematical evaluation in the subsequent step, and is therefore a recitation of forming a simple judgement as to how data should be represented in mathematical form, which is an abstract idea of mathematical calculation under its broadest reasonable interpretation. Finally, “create each group… by selecting…” in the context of the claim encompasses comparing the positions of generated coordinates with one another to form a simple judgement as to which points are sufficiently close to one another to group.
Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
An apparatus comprising:
one or more processors; and memory storing instructions that, when executed by the one or more processors, cause the apparatus to: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
receive, from one or more sensors of a vehicle in real time, driving data collected [pre-solution activity (data gathering), 2106.05(g) using generic sensors, generic link to technical field, 2106.05(h)] for drivers whose safety operation indexes are higher than a threshold value;
extract, from the received driving data, driving data in real time [pre-solution activity (data gathering), 2106.05(g), generic link to technical field, 2106.05(h)] that is determined as dangerous driving;
automatically classify, based on predetermined criteria, the extracted driving data into a plurality of groups;
designate a group, of the plurality of groups, having a size larger than remaining groups of the plurality of groups, as a defensive driving group; and
update the safety operation indexes for the drivers based on at least a portion, of the extracted driving data, corresponding to the remaining groups
calibrate, based on the updated safety operation indexes, a distribution map associated with the safety operation indexes,
compare a first distribution of the safety operation indexes of the drivers with a second distribution of motor vehicle insurance accident compensation data; and
based on a result of the comparison of the first distribution with the second distribution, automatically change one or more values of a threshold distance and a predetermined minimum number of data points such that the first distribution is similar, within a predetermined range, to the second distribution to adjust a range designated as the defensive driving group,
wherein, to classify the extracted driving data, the instructions, when executed by the one or more processors, cause the apparatus to: [applying the abstract idea using generic computing module, Apply it 2106.05(f)]
convert each data point of the extracted driving data to coordinate values; and
create each group, of the plurality of groups, by selecting at least the predetermined minimum number of data points, of the extracted driving data, having coordinate values within a threshold distance away from each other.
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “one or more processors…,” “receive…,” “extract…” and “wherein, to classify…,” the examiner submits that these limitations are insignificant extra-solution activities that merely use a computer (vehicle controller) to perform the process. In particular, the “receive…” step is recited at a high level of generality, gathering driving data using generically recited sensors, (i.e. as a general means of gathering relevant driving data, using generically recited sensors that generically link to the technical field of vehicles) and amounts to mere data gathering, which is a form of insignificant extra-solution activity. Following the receipt of sensor data, the “extract…” step is also recited at a high level of generality (i.e. as a general means of gathering relevant driving data for use in the classification and grouping steps), and also amounts to mere data gathering from the received data, which is a form of insignificant extra-solution activity. While each of the “receive…” and “extract…” limitations are recited to be implemented in real time, as set forth above with respect to Applicant arguments, such a limitation does not integrate the claim into substantially more than the abstract idea, with reference to the “continuous training data” of Example 47 Claim 2 of the July 2024 USPTO Subject Matter Eligibility Guidelines. Lastly, the “one or more processors…” and “wherein, to classify…” are each recited at a high-level of generality (i.e., as generic processors and memory performing generic computer functions of evaluating, grouping, and updating information) such that each limitation amounts to no more than mere instructions to apply the exception using a generic computer component.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of “one or more processors…” and “wherein, to classify…” amount to nothing more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. And as discussed above, the additional limitations of “receive…” and “extract…,” the examiner submits that these limitations are insignificant extra-solution activities.
Dependent claim(s) 2, 4, 5, 8, 10 – 13, 15, & 16 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and do not integrate the judicial exception into a practical application. Specifically:
Claim 2 recites particular forms of extracted driving data, which merely constrains the insignificant extra-solution activity of data gathering to a specific embodiment, and therefore does not render the claim patent-eligible. Claim 8 recites substantially similar limitations as those found in Claim 2, and is rejected under similar rationale.
Claim 4 recites designating a subset of drivers as a “dangerous” driving group, which is a mental process of evaluating data and forming a simple judgement, and therefore is not patent-eligible. Claim 10 recites substantially similar limitations as those found in Claim 4, and is rejected under similar rationale.
Claim 5 recites adjusting the threshold used to determine if a driver safety index is sufficient to extract data from said driver based on data corresponding to the defensive driving group, which is a mental process of evaluating data and forming a simple judgement under its broadest reasonable interpretation, and therefore is not patent-eligible. Claim 11 recites substantially similar limitations as those found in Claim 5, and is rejected under similar rationale.
Claim 12 recites adjusting the threshold value used to determine if a driver safety index is sufficient to extract data from said driver based on a second portion of data conforming to a normal distribution, which is a mental process of forming a simple judgement/mathematical operation of threshold value adjustment based on collected data, which is an abstract idea, and therefore is not patent-eligible.
Claim 13 recites adjusting the threshold value used to determine if a driver safety index is sufficient to extract data from said driver using a machine learning algorithm and a second portion of extracted driving data corresponding to the defensive driving group, the mere use of which does not render the claim patent-eligible as the use of a machine learning algorithm is a fundamentally mathematical operation, and therefore an abstract idea under its broadest reasonable interpretation, which is not patent-eligible.
Claim 15 recites wherein based on an output threshold value, subsequently received driving data is classified in real-time, which is a mental process of comparing data and forming a simple judgement under its broadest reasonable interpretation. Claim 16 recites substantially similar limitations as those found in Claim 15, and is rejected under similar rationale.
Therefore, dependent claims 2, 4, 5, 8, 10 – 13, 15, & 16 are not patent eligible under the same rationale as provided for in the rejection of Independent Claims 1 & 7.
Therefore, claim(s) 1, 2, 4, 5, 7, 8, 10 – 13, 15, & 16 is/are ineligible under 35 USC §101.
Conclusion
The following prior art made of record but not relied upon is considered pertinent to the Applicant’s disclosure:
Ferguson (US 2022/0250628 A1): Ferguson recites a driving analysis server configured to receive operation data from mobile devices disposed within vehicles, and generating driving patterns for each vehicle, as well as driving patterns for a collective group. Outliers may be determined within driving data groups, corresponding to specific drivers that behave in a greater or less safe manner than the group, based on the prevalence of unsafe driving events detected within the telematics data. Based on such detected events, an outlier’s driving score may be adjusted, however if the unsafe driving event is determined to be due to another vehicle’s unsafe driving, [i.e. if the own vehicle’s unsafe driving is defensive], the own vehicle’s unsafe driving may be ignored, or positively factor into the driving score.
Kong (CN 115204252 A): Kong recites a driving behavior analysis method based on feature extraction from driving data, including radar and trajectory data. Based on the data obtained, driving behaviors are rated into a plurality of categories, including impulsive, steady, and defensive driving, which each include features such as vehicle speed, acceleration, lane change probability, and the like.
Stefan (US 2014/0051041 A1): Stefan recites a system for monitoring driver behavior data for a motor vehicle, including the evaluation of the defensiveness of the driving style of a driver. This may be performed through the evaluation of driving telematics data, such as the values of the frequencies of severe steering wheel and accelerator pedal movements, as well as abrupt speed changes and following distances from other vehicles. Non-driving data may further be collected and used in the analysis, including if regular maintenance work and testing is performed on the vehicle, as well as if vehicle parameters, such as tire pressure, are set to correct values.
Berntorp (US 11,327,492 B2): Berntorp recites a vehicle controller configured to operate a vehicle in accordance with a plurality of driving style options, including defensive driving, normal driving, and aggressive driving. For each driving style, specific settings may be adjusted to make the driving style act more or less in concert with the selected style, such as adjusting the defensive driving style to be less defensive by shifting the style towards more aggression.
Krishnan (US 10,474,916 B2): Krishnan recites a system for training an autonomous vehicle control system, including the recording of events corresponding to human actions, reactions, and responses. Driving may be categorized into a plurality of categories, based on a detection of driving events, including a defensive driving category, which is a driving style in which it is assumed that others will make driving mistakes/hazardous driving by others, and the own vehicle makes a defensive maneuver prior to the event occurring. Data from identified driving data categories may be weighted by said category in developing a training package for an AV, such that different knowledge or response types may be prioritized.
Takahashi (US 9,650,052 B2): Takahashi recites a driving diagnosis method, including the assessment of a degree of dangerous driving for a driver. Driving behavior is assessed using travel history information, and groups are determined for safe drivers and dangerous drivers with associated distributions. For example, safe and dangerous drivers may be categorized based on an accident prevalence, with driving behavior including numerical information regarding vehicle deceleration, to determine a correlation between driving behavior and vehicle safety, with the safety of a currently assessed vehicle being determined based on the distribution data.
Russo (US 11,584,380 B2): Russo recites a system for reducing collisions based on driver risk groups, including by grouping vehicle operators based on shared attributes, such as location, workplace, school, demographic, and the like. Vehicle sensor data from a plurality of drivers may be collected, and analyzed to determine indicia of safe driving behavior, which may be associated with the grouping and provided to a third party. Sensor data may include speed, acceleration, braking, following distance, and seatbelt use data, which may be analyzed to determine an associated score or rating with the driver risk group.
Yu (CN 103268426 A): Yu recites a method for evaluating the safe driving level of a vehicle driver through a safety index, including the evaluation of driving parameters in driving data. Said parameters may include speed, overspeed time, maximum speed percentage, steering wheel angle, and turn signal usage, which may be compared with a standard driving model to determine a corresponding safety level index.
Brinkmann (US 12,002,308 B1): Brinkmann recites a driving analysis server configured to identify potentially high-risk or unsafe driving events by a vehicle, and adjust driver score(s) based on the data analysis and determined driving event cause. Data received in the determination of unsafe events may include data from vehicle sensors, such as rates of acceleration, vehicle speed, braking usage, impact data, and the like, with said vehicle sensor data being supplemented by corresponding image, video, and proximity data. The cause of unsafe driving identified in the data may be evaluated, to include if the driver of the vehicle was at fault or not, with the driver score being adjusted (or not) based on the determination.
Jeon (US 2014/0365086 A1): Jeon recites a system for determining driving habits based on driving data, such as accelerator pedal and vehicle speed data. Driving patterns recognized may include defensive driving patterns, in which a driver may have a gentle acceleration pattern, as compared to a “sporty” driving tendency in which an aggressive driving pattern and rapid acceleration is observed.
Weidner (NPL: Telematic driving profile classification in car insurance pricing): Weidner recites an insurance pricing system based on classifications and categorizations of driving styles, and risk categorization based on such. Driving style evaluation may take place based on analysis of driving data obtained from telematics, including vehicle velocity, acceleration, deceleration, and the like. Among the driving profiles analyzed may include defensive driving profiles, which may include emergency braking profiles as acceleration behavior, though as an emergency condition this is observed as an outlier in the telematics data.
Siami (NPL: A mobile telematics pattern recognition framework for driving behavior extraction): Siami recites a learning technique for classifying driving data collected by a telematics system, in order to determine and categorize driving styles. Driving styles categorized may range from normal to dangerous driving, which may be recognized by identifying patterns in driving data in accordance with clustering algorithms, with the clustering performance for each algorithm tested being assessed against a plurality of known metrics.
Bowne (US 10,410,288 B2): Bowne recites a method for determining a vehicle insurance premium based on collected vehicle operation data, which is transmitted to a remote computer for analysis. A rating engine may be utilized to evaluate use-based information, by weighting said information and placing a greater weight on factors that have greater predictive power on the occurrence of accidents, which is then used to determine a vehicle insurance premium based in part on the collected vehicle operation data.
Rosenbaum (US 11,407,410 B2): Rosenbaum recites a method for estimating the accident risk of an autonomous vehicle, including the monitoring of driving parameters associated with the vehicle, and associating an accident rate value with said observed driving qualities. Observed driving features may include acceleration, speed, and the like of the vehicle.
Deng (CN 110239560 B): Deng recites a supervision system for monitoring and scoring driving habits, including the assessment of following distance and defensive driving habits. Other factors such as speed profile (inclusive of rapid acceleration/deceleration events) are compiled into an overall safe habits score. Scoring may be accomplished by weighting individual feature scores by coefficients according to their importance.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTOPHER RYAN CARDIMINO whose telephone number is (571)272-2759. The examiner can normally be reached M-Th 8:30-5:00.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramya Burgess can be reached at (571)272-6011. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CHRISTOPHER R CARDIMINO/Examiner, Art Unit 3661
/RAMYA P BURGESS/Supervisory Patent Examiner, Art Unit 3661