DETAILED ACTION
Status of the Application
The following is a Final Office Action. In response to Examiner's communication of October 16, 2025, Applicant, on January 15, 2026, amended claims 1, 4, 6, 7, 16, & 18-20 and canceled claims 3, 10, 13, & 14. Claims 1, 2, 4-9, 11, 12, & 15-20 are now pending in this application and have been rejected below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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Response to Amendment
Applicant's amendments are not sufficient to overcome the 35 USC 101 rejections set forth in the previous action. Therefore, these rejections are maintained below.
Applicant's amendments are sufficient to overcome the 35 USC 103 rejections set forth in the previous action. Therefore, these rejections are withdrawn.
Response to Arguments - 35 USC § 101
Applicant’s arguments with respect to the 35 USC 101 rejections have been fully considered, and they are not persuasive.
Applicant argues the claims do not recite an abstract idea under prong 1 of Step 2A because Applicant is claiming much more than just an abstract idea of organizing human behavior, Applicant is not claiming rules to follow to manage the human behavior or rules to follow to manage the human behavior of people by monitoring responses of people during takeoff events as alleged by the Examiner, and rather Applicant is claiming a method which lists specific steps in order to predict takeover events across mobilities, and the method may predict takeover events in a driver assisted micro-mobility vehicle based on data collected on takeover events in car simulations, monitors and records responses during takeover events in a car simulation by monitoring a plurality of sensors in the car simulation for eye movement, physiological readings and body movement of the plurality of participants in the car simulation, the takeover event in the car simulation is monitored by movement of a brake pedal above a predetermined threshold value, monitors and records responses during takeover events in a micro-mobility vehicle simulation by monitoring a plurality of sensors in the micro-mobility vehicle simulation for eye movement, physiological readings and body movement of the plurality of participants in the micro-mobility vehicle simulation, the takeover event in the micro-mobility vehicle simulation is monitored by movement of a throttle lever above a predetermined threshold value, statistical analysis is performed on characteristics extracted from the responses recorded by the sensors in the car simulation during the takeover events and characteristics extracted from responses recorded by the sensors in the micro-mobility vehicle simulation during the takeover to find patterns and deviations between the characteristics extracted, forms takeover predictions on a different types of micro-mobility vehicles using predictive modeling from the characteristics extracted using the responses during the takeover events in the car simulations as training samples and characteristics extracted from responses during the takeover events in the micro-mobility simulations as test samples using a feed forward deep neural network (DNN), integrates transfer learning in the predictive modeling formed by the feed forward DNN forming a takeover prediction model for a micro-mobility vehicle based on the relationships between the characteristics extracted from the car simulation and the characteristics extracted from the micro-mobility vehicle simulation, and signals a driver assisted micro-mobility vehicle of a potential takeover event based on the takeover prediction model and state detection of the driver assisted micro-mobility vehicle. Examiner respectfully disagrees.
Contrary to Applicant’s assertions, as detailed below, under Prong 1 of Step 2A, aside from the generic computer components, such as the sensors and generic off-the-shelf machine learning techniques, implementing the steps referred to by Applicant, each of the steps referred to by Applicant are part of and directed to the recited abstract idea of predicting takeover events based on the responses of people by monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle.
As noted in the previous action, the steps referred to by Applicant recite certain methods of organizing human activity because they manage human behavior and provide instructions or rules to follow to manage the human behavior of people during takeover events based on the responses of people by signaling drivers in response to monitoring responses of people during takeover events including eye and body movements and physiological readings of people, performing normalization of responses by the people, performing statistical analysis to find patterns and deviations in the responses of the people, and predicting takeover by people based on the responses of the people. Further, as noted in the previous action, the steps referred to by Applicant recite a mental process because each of the steps could all be reasonably interpreted as a human making observations of information regarding the responses of people and a state of a driver assisted vehicle, a human performing an evaluation using math and judgment based on the observed information to normalize, perform statistical analysis, determine modalities of identifying takeover predictions in different micro-mobility vehicles, and predict takeover responses manually and/or with a pen and paper, and a human manually and/or with pen and paper outputting the results of the evaluation by signal a driver of a takeover event based on the prediction and an observed state of a driver assisted vehicle. Moreover, as set forth in the previous action, in addition to being mental concepts, the limitations referred to by Applicant include a recitation of a mathematical concept because the steps referred to by Applicant include performing statistical analysis of responses by the people are recitations of mathematical relationships and calculations. Accordingly, the steps referred to by Applicant recite a certain method of organizing human activity, mental processes, and mathematical concepts under Prong 1 of Step 2A.
Pursuant to 2019 Revised Patent Subject Matter Eligibility Guidance, in order to determine whether a claim is directed to an abstract idea, under Step 2A, we first (1) determine whether the claims recite limitations, individually or in combination, that fall within the enumerated subject matter groupings of abstract ideas (mathematical concepts, certain methods of organizing human activity, or mental processes), and (2) determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. Next, if a claim (1) recites an abstract idea and (2) does not integrate that exception into a practical application, in order to determine whether the claim recites an “inventive concept,” under Step 2B, we then determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 56.
Under Prong 1 of Step 2A, claim 20, and similarly claims 1, 2, 4-9, 11, 12, & 15-19, recites “predicting takeover events across mobilities, comprising: monitoring and responses during takeover events in a car simulation from a plurality of participants using … to monitor and record monitoring eye movements, physiological readings, and body movements of the plurality of participants in the car simulation; monitoring and recording responses during takeover events in a micro-mobility simulation from the plurality of participants using … to monitor and record eye movements, physiological readings, and body movements of the plurality of participants in the micro-mobility simulation; performing a Z-normalization on the characteristics extracted from the responses during the takeover events in the car simulations and the characteristics extracted from responses during the takeover events in the micro-mobility simulations; performing statistical analysis characteristics extracted from the responses recorded … during the takeover events in the car simulations and characteristics extracted from responses recorded … during the takeover events in the micro-mobility simulations; … determine sensing modalities for takeover predictions on a different type of micro-mobility vehicle; forming takeover predictions on the different type of micro-mobility vehicle using predictive modeling … from the characteristics extracted from the responses during the takeover events in the car simulations as training samples and characteristics extracted from responses during the takeover events in the micro-mobility simulations as test samples; and … the predictive modeling formed by … forming a takeover prediction model for a micro-mobility vehicle based on the relationship between the characteristics extracted from the car simulation and the characteristics extracted from the micro-mobility vehicle simulation; and signaling a driver … of a potential takeover event based on the takeover prediction model and state detection the driver assisted micro-mobility vehicle.” Claims 1, 2, 4-9, 11, 12, & 15-20, in view of the claim limitations, recite the abstract idea of predicting takeover events based on the responses of people by monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle.
The above limitations, including the steps referred to by Applicant, manage human behavior and provide instructions or rules to follow to manage the human behavior of people during takeover events based on the responses of people by signaling drivers in response to monitoring responses of people during takeover events including eye and body movements and physiological readings of people, performing normalization of responses by the people, performing statistical analysis to find patterns and deviations in the responses of the people, and predicting takeover by people based on the responses of the people; thus, the claims recite certain methods of organizing human activity. In addition, as a whole, in view of the claim limitations, including the steps referred to by Applicant, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle could all be reasonably interpreted as a human making observations of information regarding the responses of people and a state of a driver assisted vehicle, a human performing an evaluation using math and judgment based on the observed information to normalize, perform statistical analysis, determine modalities of identifying takeover predictions in different micro-mobility vehicles, and predict takeover responses manually and/or with a pen and paper, and a human manually and/or with pen and paper signal a driver of a takeover event based on the prediction and an observed state of a driver assisted vehicle; therefore, the claims recite mental processes. Moreover, in addition to being mental concepts, the limitations, including some of the steps referred to by Applicant, performing normalization and statistical analysis of responses by the people are recitations of mathematical relationships and calculations, and thus, the claims recite a mathematical concept. Accordingly, since the claims, including the steps referred to by Applicant, recite a certain method of organizing human activity, mental processes, and mathematical concepts, the claims recite an abstract idea under the first prong of Step 2A.
Applicant argues, under Prong 2, that the claims recite additional elements recited in the claim beyond the judicial exception because the claims disclose a specific steps in order to predict takeover events across mobilities, and the method may predict takeover events in a driver assisted micro-mobility vehicle based on data collected on takeover events in car simulations, monitors and records responses during takeover events in a car simulation by monitoring a plurality of sensors in the car simulation for eye movement, physiological readings and body movement of the plurality of participants in the car simulation, the takeover event in the car simulation is monitored by movement of a brake pedal above a predetermined threshold value, monitors and records responses during takeover events in a micro-mobility vehicle simulation by monitoring a plurality of sensors in the micro-mobility vehicle simulation for eye movement, physiological readings and body movement of the plurality of participants in the micro-mobility vehicle simulation, the takeover event in the micro-mobility vehicle simulation is monitored by movement of a throttle lever above a predetermined threshold value, statistical analysis is performed on characteristics extracted from the responses recorded by the sensors in the car simulation during the takeover events and characteristics extracted from responses recorded by the sensors in the micro-mobility vehicle simulation during the takeover to find patterns and deviations between the characteristics extracted, forms takeover predictions on a different types of micro-mobility vehicles using predictive modeling from the characteristics extracted using the responses during the takeover events in the car simulations as training samples and characteristics extracted from responses during the takeover events in the micro-mobility simulations as test samples using a feed forward deep neural network (DNN), integrates transfer learning in the predictive modeling formed by the feed forward DNN forming a takeover prediction model for a micro-mobility vehicle based on the relationships between the characteristics extracted from the car simulation and the characteristics extracted from the micro-mobility vehicle simulation, and signals a driver assisted micro-mobility vehicle of a potential takeover event based on the takeover prediction model and state detection of the driver assisted micro-mobility vehicle. Examiner respectfully disagrees.
As noted above, under Prong 2 of Step 2A, we determine whether any additional elements beyond the recited abstract idea, individually and as an ordered combination, integrate the judicial exception into a practical application. 84 Fed. Reg. 52, 54-55. However, contrary to Applicant’s assertion, as detailed above under Prong 1 of Step 2A, aside from the generic computer components, such as the sensors and generic off-the-shelf machine learning techniques, implementing the steps referred to by Applicant, each of the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, each of the steps referred to by Applicant are part of and directed to the recited abstract idea of predicting takeover events based on the responses of people by monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle.
As noted in the previous action, the steps referred to by Applicant recite certain methods of organizing human activity because they manage human behavior and provide instructions or rules to follow to manage the human behavior of people during takeover events based on the responses of people by signaling drivers in response to monitoring responses of people during takeover events including eye and body movements and physiological readings of people, performing normalization of responses by the people, performing statistical analysis to find patterns and deviations in the responses of the people, and predicting takeover by people based on the responses of the people. Further, as noted in the previous action, the steps referred to by Applicant recite a mental process because each of the steps could all be reasonably interpreted as a human making observations of information regarding the responses of people and a state of a driver assisted vehicle, a human performing an evaluation using math and judgment based on the observed information to normalize, perform statistical analysis, determine modalities of identifying takeover predictions in different micro-mobility vehicles, and predict takeover responses manually and/or with a pen and paper, and a human manually and/or with pen and paper outputting the results of the evaluation by signal a driver of a takeover event based on the prediction and an observed state of a driver assisted vehicle. Moreover, as set forth in the previous action, in addition to being mental concepts, the limitations referred to by Applicant include a recitation of a mathematical concept because the steps referred to by Applicant include performing statistical analysis of responses by the people are recitations of mathematical relationships and calculations. Accordingly, the steps referred to by Applicant recite a certain method of organizing human activity, mental processes, and mathematical concepts under Prong 1 of Step 2A.
Under Prong 2 of Step 2A, the only additional elements beyond the recited abstract idea include the recitations of “a plurality of sensors in the car simulation,” “a plurality of sensors in the micro-mobility vehicle simulation,” “the sensors in the car simulation,” “the sensors in the micro-mobility vehicle simulation,” “using a feed forward deep neural network (DNN),” “integrating transfer learning,” “by the feed forward DNN,” and “a driver assisted micro-mobility vehicle” in claim 1, and similarly claims 16, 18, and 20; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, these additional elements beyond the recited abstract idea merely generally link the abstract idea to a technical environment and/or field of use, namely the generic environment of a generic computer with generic sensors implementing generic and off-the-shelf machine learning techniques, which is not sufficient to integrate an abstract idea into a practical application. MPEP 2106.05(f). In addition, as a whole, the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to integrate an abstract idea into a practical application. MPEP 2106.05(h).
Applicant argues, under 2B, that the claims certainly amount to significantly more than an abstract idea in and of itself because the other dependent claims go beyond a method to organize human activity as alleged by the Examiner, the claims disclose a specific steps in order to predict takeover events across mobilities, and the method monitors and records responses during takeover events in a car simulation by monitoring a plurality of sensors in the car simulation for eye movement, physiological readings and body movement of the plurality of participants in the car simulation, the takeover event in the car simulation is monitored by movement of a brake pedal above a predetermined threshold value, monitors and records responses during takeover events in a micro-mobility vehicle simulation by monitoring a plurality of sensors in the micro-mobility vehicle simulation for eye movement, physiological readings and body movement of the plurality of participants in the micro-mobility vehicle simulation, the takeover event in the micro-mobility vehicle simulation is monitored by movement of a throttle lever above a predetermined threshold value, statistical analysis is performed on characteristics extracted from the responses recorded by the sensors in the car simulation during the takeover events and characteristics extracted from responses recorded by the sensors in the micro-mobility vehicle simulation during the takeover to find patterns and deviations between the characteristics extracted, forms takeover predictions on a different types of micro-mobility vehicles using predictive modeling from the characteristics extracted using the responses during the takeover events in the car simulations as training samples and characteristics extracted from responses during the takeover events in the micro-mobility simulations as test samples using a feed forward deep neural network (DNN), integrates transfer learning in the predictive modeling formed by the feed forward DNN forming a takeover prediction model for a micro-mobility vehicle based on the relationships between the characteristics extracted from the car simulation and the characteristics extracted from the micro-mobility vehicle simulation, and signals a driver assisted micro-mobility vehicle of a potential takeover event based on the takeover prediction model and state detection of the driver assisted micro-mobility vehicle. Examiner respectfully disagrees.
As noted above, under Prong 2 of Step 2A, we determine whether any of the additional elements beyond the recited abstract idea, individually and in combination, are significantly more than the abstract idea itself. 84 Fed. Reg. 52, 54-55. However, contrary to Applicant’s assertion, as detailed above under Prong 1 of Step 2A, aside from the generic computer components, such as the sensors and generic off-the-shelf machine learning techniques, implementing the steps referred to by Applicant, each of the steps referred to by Applicant are not additional elements beyond the recited abstract idea, but rather, each of the steps referred to by Applicant are part of and directed to the recited abstract idea of predicting takeover events based on the responses of people by monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle.
As noted in the previous action, the steps referred to by Applicant recite certain methods of organizing human activity because they manage human behavior and provide instructions or rules to follow to manage the human behavior of people during takeover events based on the responses of people by signaling drivers in response to monitoring responses of people during takeover events including eye and body movements and physiological readings of people, performing normalization of responses by the people, performing statistical analysis to find patterns and deviations in the responses of the people, and predicting takeover by people based on the responses of the people. Further, as noted in the previous action, the steps referred to by Applicant recite a mental process because each of the steps could all be reasonably interpreted as a human making observations of information regarding the responses of people and a state of a driver assisted vehicle, a human performing an evaluation using math and judgment based on the observed information to normalize, perform statistical analysis, determine modalities of identifying takeover predictions in different micro-mobility vehicles, and predict takeover responses manually and/or with a pen and paper, and a human manually and/or with pen and paper outputting the results of the evaluation by signal a driver of a takeover event based on the prediction and an observed state of a driver assisted vehicle. Moreover, as set forth in the previous action, in addition to being mental concepts, the limitations referred to by Applicant include a recitation of a mathematical concept because the steps referred to by Applicant include performing statistical analysis of responses by the people are recitations of mathematical relationships and calculations. Accordingly, the steps referred to by Applicant recite a certain method of organizing human activity, mental processes, and mathematical concepts under Prong 1 of Step 2A.
Under Step 2B, the only additional elements beyond the recited abstract idea include the recitations of “a plurality of sensors in the car simulation,” “a plurality of sensors in the micro-mobility vehicle simulation,” “the sensors in the car simulation,” “the sensors in the micro-mobility vehicle simulation,” “using a feed forward deep neural network (DNN),” “integrating transfer learning,” “by the feed forward DNN,” and “a driver assisted micro-mobility vehicle” in claim 1, and similarly claims 16, 18, and 20; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, these additional elements beyond the recited abstract idea merely generally link the abstract idea to a technical environment and/or field of use, namely the generic environment of a generic computer with generic sensors implementing generic and off-the-shelf machine learning techniques, which is not sufficient to amount to significantly more than an abstract idea. MPEP 2106.05(h). In addition, as a whole, the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components, which is not sufficient to amount to significantly more than an abstract idea. MPEP 2106.05(f).
Response to Arguments - 35 USC § 103
Applicant’s arguments with respect to the prior art rejections have been fully considered, and they are persuasive; therefore, these rejections have been withdrawn.
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, 4-9, 11, 12, & 15-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 20, and similarly claims 1, 2, 4-9, 11, 12, & 15-19, recites “predicting takeover events across mobilities, comprising: monitoring and responses during takeover events in a car simulation from a plurality of participants using … to monitor and record monitoring eye movements, physiological readings, and body movements of the plurality of participants in the car simulation; monitoring and recording responses during takeover events in a micro-mobility simulation from the plurality of participants using … to monitor and record eye movements, physiological readings, and body movements of the plurality of participants in the micro-mobility simulation; performing a Z-normalization on the characteristics extracted from the responses during the takeover events in the car simulations and the characteristics extracted from responses during the takeover events in the micro-mobility simulations; performing statistical analysis characteristics extracted from the responses recorded … during the takeover events in the car simulations and characteristics extracted from responses recorded … during the takeover events in the micro-mobility simulations; … determine sensing modalities for takeover predictions on a different type of micro-mobility vehicle; forming takeover predictions on the different type of micro-mobility vehicle using predictive modeling … from the characteristics extracted from the responses during the takeover events in the car simulations as training samples and characteristics extracted from responses during the takeover events in the micro-mobility simulations as test samples; and … the predictive modeling formed by … forming a takeover prediction model for a micro-mobility vehicle based on the relationship between the characteristics extracted from the car simulation and the characteristics extracted from the micro-mobility vehicle simulation; and signaling a driver … of a potential takeover event based on the takeover prediction model and state detection the driver assisted micro-mobility vehicle.” Claims 1, 2, 4-9, 11, 12, & 15-20, in view of the claim limitations, recite the abstract idea of predicting takeover events based on the responses of people by monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle.
The above limitations manage human behavior and provide instructions or rules to follow to manage the human behavior of people during takeover events based on the responses of people by signaling drivers in response to monitoring responses of people during takeover events including eye and body movements and physiological readings of people, performing normalization of responses by the people, performing statistical analysis to find patterns and deviations in the responses of the people, and predicting takeover by people based on the responses of the people; thus, the claims recite certain methods of organizing human activity. In addition, as a whole, in view of the claim limitations, but for the computer components and systems performing the claimed functions, the broadest reasonable interpretation of the recited monitoring responses of people during takeover events including eye and body movements and physiological readings of people in car and micro-mobility vehicle simulations, performing normalization of responses by the people, performing statistical analysis on the responses of people during the simulations of takeover events, determining modes of identifying takeover predictions of a different micro-mobility vehicle, predicting takeover predictions of the different micro-mobility vehicle based on the responses of the people in the simulations using predictive modeling, and signaling a driver of a potential takeover event based on the prediction and a state of a driver assisted vehicle could all be reasonably interpreted as a human making observations of information regarding the responses of people and a state of a driver assisted vehicle, a human performing an evaluation using math and judgment based on the observed information to normalize, perform statistical analysis, determine modalities of identifying takeover predictions in different micro-mobility vehicles, and predict takeover responses manually and/or with a pen and paper, and a human manually and/or with pen and paper signal a driver of a takeover event based on the prediction and an observed state of a driver assisted vehicle; therefore, the claims recite mental processes. Moreover, in addition to being mental concepts, the limitations performing normalization and statistical analysis of responses by the people are recitations of mathematical relationships and calculations, and thus, the claims recite a mathematical concept. Further, with respect to the dependent claims, aside from the additional elements beyond the recited abstract idea addressed below under the second prong of Step 2A and 2B, the limitations of dependent claims 2, 4-9, 11, 12, 15, & 17-19 recite similar further abstract limitations to those discussed above that narrow the abstract idea recited in the independent claims because, aside from the computer components and systems performing the claimed functions the limitations of claims recite mental processes that can be practically performed mentally by observing, evaluating, and judging information mentally and/or with a pen and paper and recite a certain method of organizing human activity that manages business interactions and personal human behavior. Accordingly, since the claims recite a certain method of organizing human activity, mental processes, and mathematical concepts, the claims recite an abstract idea under the first prong of Step 2A.
This judicial exception is not integrated into a practical application under the second prong of Step 2A. In particular, the claims recite the additional elements beyond the recited abstract idea of “a plurality of sensors in the car simulation,” “a plurality of sensors in the micro-mobility vehicle simulation,” “the sensors in the car simulation,” “the sensors in the micro-mobility vehicle simulation,” “using a feed forward deep neural network (DNN),” “integrating transfer learning,” “by the feed forward DNN,” and “a driver assisted micro-mobility vehicle” in claim 1, and similarly claims 16, 18, and 20, “performing an ablation study” in claims 2, 17, and 20, “using a feed forward deep neural network (DNN)” in claims 3, 16, and 20, “using a feed forward deep neural network (DNN) using a Tensorflow Keras library” in claim 4, “integrating transfer learning” in claims 5 & 6, and “processor communicatively coupled to a memory device” in claim 16; however, individually and when viewed as an ordered combination, and pursuant to the broadest reasonable interpretation, these additional elements beyond the recited abstract idea merely generally link the abstract idea to a technical environment and/or field of use, namely the generic environment of a generic computer with generic sensors implementing generic and off-the-shelf machine learning techniques. In addition, as a whole, the additional elements are computing elements recited at high level of generality implementing the abstract idea on a computer (i.e. apply it), and thus, are no more than applying the abstract idea with generic computer components. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 4-9, 11, 12, 15, & 17-19 do not integrate the abstract idea into a practical application because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception under Step 2B. As noted above, the aforementioned additional elements beyond the recited abstract idea, as an order combination, are no more than mere instructions to implement the idea using generic computer components (i.e. apply it), and further, generally link the abstract idea to a field of use, which is not sufficient to amount to significantly more than an abstract idea; therefore, the additional elements are not sufficient to amount to significantly more than an abstract idea. Additionally, these recitations as an ordered combination, simply append the abstract idea to recitations of generic computer structure performing generic computer functions that are well-understood, routine, and conventional in the field as evinced by Applicant’s Specification at [0044] (describing the present disclosure may be realized in a combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein). Furthermore, as an ordered combination, these elements amount to generic computer components performing repetitive calculations, receiving or transmitting data over a network, which, as held by the courts, are well-understood, routine, and conventional. See MPEP 2106.05(d); July 2015 Update, p. 7. Moreover, aside from the aforementioned additional elements, the remaining elements of dependent claims 2, 4-9, 11, 12, 15, & 17-19 do not transform the recited abstract idea into a patent eligible invention because these claims merely recite further limitations that provide no more than simply narrowing the recited abstract idea.
Looking at these limitations as an ordered combination adds nothing additional that is sufficient to amount to significantly more than the recited abstract idea because they simply provide instructions to use a generic arrangement of generic computer components and recitations of generic computer structure that perform well-understood, routine, and conventional computer functions that are used to “apply” the recited abstract idea. Thus, the elements of the claims, considered both individually and as an ordered combination, are not sufficient to ensure that the claims as a whole amount to significantly more than the abstract idea itself. Since there are no limitations in these claims that transform the exception into a patent eligible application such that these claims amount to significantly more than the exception itself, claims 1, 2, 4-9, 11, 12, & 15-20 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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.
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CHARLES GUILIANO
Primary Examiner
Art Unit 3623
/CHARLES GUILIANO/Primary Examiner, Art Unit 3623