Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant' s amendment and response filed 6/17/2026 has been entered and made record. This application contains 19 pending claims.
Claims 1 and 11 have been amended.
Response to Arguments
Applicant’s arguments filed 6/17/2026 regarding claims rejections under 35 U.S.C. 103 in claims 1-2, 4-12, and 14-21 have been fully considered and are persuasive. Therefore, the 103 claims rejections in claims 1-2, 4-12, and 14-21 have been withdrawn.
Applicant’s arguments filed 6/17/2026 regarding claims rejections under 35 U.S.C. 101 in claim 1-2, 4-12, and 14-21 have been fully considered but they are not persuasive.
The applicant argues on pages 10-11 of the remark filed on 6/17/2026 that “… Specifically, the claim recites a tangible object that is not abstract, namely a system that includes two sensors and a controller. Further, while there are mathematical components to the claimed configuration of the controller, it is not directed to a mathematical concept. … The present claims are analogous: applying a trained model to sensor-derived time-series data does not set forth a mathematical relationship; it recites a technological operation performed by a configured system. … Further, there is no mental process recited in claim 1 that could reasonably be performed in the human mind. Under Step 2A, Prong One, a claim recites a mental process only when it sets forth limitations that can practically be performed in the human mind (including with pen and paper). … Claim 1 requires, inter alia, a controller configured to, in response to the one of the first and second sensor signals exceeding a threshold value, generate a time series dataset and analyze the time series dataset to determine an event using a machine learning model trained with corresponding training data. These limitations cannot practically be performed in the human mind. A human cannot generate structured time-series datasets from sampled data, and apply a trained model, whose parameters were derived from prior training, to determine an event based on such time series datasets. … .”
The Examiner respectfully disagrees applicant’s argument. The steps of “compare one of the first sensor signal and the second sensor signal with a threshold value”; and “when the one of the first sensor signal and the second sensor signal exceeds the threshold value; generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals” are mathematical concepts, therefore, they are considered to be an abstract idea. The step of “determine an event in the cabin of the vehicle by analyzing both the first and second time series datasets using a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events” is a combination of a mathematical concept and a mental processes, therefore, it is considered to be abstract idea. A human mind can observe and evaluate of collected information of the first and second time series datasets using a mathematical concept, and make determination, judgment and have opinion about an event in the cabin of the vehicle based on the evaluation. Thus, the claims are directed to an abstract idea.
The applicant argues on pages 10-11 of the remark filed that “… Stated another way, since the claim recites a trained neural network without referencing specific mathematical calculations by name, the claim is nearer to published USPTO SME example 39, which is not directed to an abstract idea. … In SME Example 39 (neural network training), the Office explained that a limitation such as "training the neural network" does not recite a mathematical concept merely because neural networks rely on mathematical operations.1 The present claims are analogous: applying a trained model to sensor-derived time-series data does not set forth a mathematical relationship; it recites a technological operation performed by a configured system.”
The Examiner respectfully disagrees applicant’s argument. The claims in Example 39 are dissimilar to the instant claims. Example 39 describes training a Neural Network, however, the instant claims 1, 11, and 21 do not recite training a neural network or a machine learning model. The machine learning model in Claims 1, 11, and 21 is already trained, and the claims limitations only describe utilizing an already trained. The limitation of “determine an event in the cabin of the vehicle by analyzing both the first and second time series datasets using a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events” is a combination of a mathematical concept and a mental processes, therefore, the claims limitations are directed to an abstract idea.
The applicant argues on pages 12-13 of the remark filed that “… Even assuming arguendo that claim 1 were considered to recite a judicial exception, the claim integrates that alleged exception into a practical application and reflects a concrete technological improvement in the field of in-cabin environmental event detection. … In particular, the recitation of generating the time series datasets in response to detecting a sensor value exceeding a threshold value reflects a specific engineering solution to a practical problem identified in the specification: continuous, high-resolution monitoring of particulate matter and gas/VOC concentrations can impose computational and power burdens, and may generate excessive noise or false positives if processed indiscriminately. … Accordingly, even if the claims were viewed as involving a judicial exception at Step 2A, Prong One, they integrate that alleged abstract idea into a concrete technological application and therefore satisfy Step 2A, Prong Two. Consequently, for this additional reason, claim 1 is directed to patent-eligible subject matter.”
The Examiner respectfully disagrees applicant’s argument. Practical application can be demonstrated by limitations that are sufficient to integrate the judicial exception into a practical application. The additional elements “a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin”; “a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin”; and “a controller operably connected to the gas sensor and the PM sensor” are not sufficient to integrate the abstract idea into a practical application because they only add an insignificant extra-solution activity to the judicial exception. The additional element “receive the first and second sensor signals from the gas sensor and the PM sensor” is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e., receiving sensor signals data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
Therefore, the claims do not contain meaningful additional elements that are indicative of integration of an abstract idea into a practical application. The alleged technological improvement in the field of in-cabin environmental event detection relates to improvement to the abstract idea itself.
The applicant argues on page 12 of the remark filed that “Claim 1 Recites Significantly More than the Alleged Abstract Idea”.
The Examiner respectfully disagrees applicant’s argument. Significantly more can be demonstrated by additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application. However, the claims do not recite them. The limitations of “a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin”; “a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin”; “a controller operably connected to the gas sensor and the PM sensor”, and “receive the first and second sensor signals from the gas sensor and the PM sensor” are routine in monitoring and detection of particulate matter and gas/VOC concentrations in in-cabin environmental event of a vehicle, and are well-understood and conventional. Therefore, the claims do not contain additional elements that are not well-understood and conventional that integrate the abstract idea into a practical application.
Dependent claims 2, 4-10, 12, and 14-20 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application. Therefore, claims 2, 4-10, 12, and 14-20 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-2, 4-12, and 15-21 are proper.
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, 4-12, and 14-21 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
As to claim 1, the claim recites “A system for determining an event in a cabin of a vehicle, the system comprising:
a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin;
a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin;
a controller operably connected to the gas sensor and the PM sensor and configured to:
receive the first and second sensor signals from the gas sensor and the PM sensor;
compare one of the first sensor signal and the second sensor signal with a threshold value; and
when the one of the first sensor signal and the second sensor signal exceeds the threshold value; generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals; and
determine an event in the cabin of the vehicle by analyzing both the first and second time series datasets using a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events.”
Under the Step 1 of the eligibility analysis, we determine whether the claim is directed to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (apparatus for claim 1).
Under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the bold type portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the grouping of subject matter when recited as such in a claim that covers mathematical concepts (mathematical relationships, mathematical formulas or equations, mathematical calculations) and mental processes (concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions).
In claim 1, the steps of “compare one of the first sensor signal and the second sensor signal with a threshold value”; and
“when the one of the first sensor signal and the second sensor signal exceeds the threshold value; generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals” are mathematical concepts, therefore, they are considered to be an abstract idea.
The step of “determine an event in the cabin of the vehicle by analyzing both the first and second time series datasets using a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events” is a combination of a mathematical concept and a mental processes, therefore, it is considered to be abstract idea.
Next, under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application.
In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
The claim comprises the following additional elements:
a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin; a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin; a controller operably connected to the gas sensor and the PM sensor and configured to: receive the first and second sensor signals from the gas sensor and the PM sensor.
The additional elements “a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin”; “a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin”; and “a controller operably connected to the gas sensor and the PM sensor” are not sufficient to integrate the abstract idea into a practical application because they only add an insignificant extra-solution activity to the judicial exception. The additional element “receive the first and second sensor signals from the gas sensor and the PM sensor” represents necessary data gathering and does not integrate the limitation into a practical application. In addition, a generic controller or processor is generally recited and therefore, not qualified as a particular machine.
In conclusion, the above additional elements, considered individually and in combination with the other claims elements do not reflect an improvement to other technology or technical field, do not reflect improvements to the functioning of the computer itself, do not recite a particular machine, do not effect a transformation or reduction of a particular article to a different state or thing, and, therefore, do not integrate the judicial exception into a practical application. Therefore, the claim is directed to a judicial exception and require further analysis under the Step 2B.
The above claim, does not include additional elements that are sufficient to amount to significantly more than the judicial exception because they are generically recited and are well-understood/conventional in a relevant art as evidenced by the prior art of record (Step 2B analysis).
For example, receiving the first and second sensor signals from the gas sensor and the PM sensor is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. receiving sensors signals data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015).
For example, generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin by a particulate matter (PM) sensor is disclosed by “Murphy US 20200238786”, FIG. 1, [0018], [0022], [0030], [0031]; and “Meister US 10776643B1”, Abstract; FIG. 1; Col. 3, Lines 5-43; Col. 4, Lines 60-67;
Col. 5, Lines 3-47.
The claim, therefore, is not patent eligible.
Independents claims 11 and 21 recite subject matter that are similar or analogous to that of claim 1, and therefore, the claims are also patent ineligible.
With regards to the dependent claims, claims 2, 4-10, 12, and 14-20 provide additional features/steps which are considered part of an expanded abstract idea of the independent claims, and do not integrate the abstract ideas into a practical application.
The dependent claims are, therefore, also not patent eligible.
Examiner' s Note
Regarding Claims 1-2, 4-12, 14-21, the most pertinent prior arts are "Murphy US 20200238786", "Jain US 20210241137, "Meister US 10776643B1", "Wensley US 20200207298", and "Ventimiglia US 20210190516".
As to claims 1, 11, and 21, Murphy teaches a gas sensor configured to generate a first sensor signal associated with a quantity of at least one gas or volatile organic compound (VOC) in ambient air of the cabin (Murphy, [0019]; FIG. 1, [0030] and [0032]);
a particulate matter (PM) sensor configured to generate a second sensor signal associated with a quantity of particulate matter in the ambient air of the cabin (Murphy, FIG. 1; [0030], [0031]);
a controller operably connected to the gas sensor and the PM sensor (Murphy, FIG. 1 shows that processing unit 40 is connected to the gas sensor 120 and the cabin or PM sensor 160) and configured to: receive the first and second sensor signals from the gas sensor and the PM sensor (Murphy, [0030], [0031], [0051]);
compare one of the first sensor signal and the second sensor signal with a threshold value (Murphy, [0030] and [0031], [0052]); and
notify an operator of the vehicle of the determined event (Murphy, [0033], [0034], and [0052]).
Jain teaches a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events (Jain, [0009], [0432] and [0447]).
However, the prior arts of record, alone or in combination, do not fairly teach or suggest “when the one of the first sensor signal and the second sensor signal exceeds the threshold value; generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals”;
“determine an event in the cabin of the vehicle by analyzing both the first and second time series datasets using a machine learning model that has been trained with training data corresponding to time series data of PM readings and gas sensor readings of known events”; and
“the notifying of the operator including transmitting to the operator at least part of the first and second time series datasets associated with the determined event” including all limitations as claimed.
Dependent claims 2, 4-10, 12, and 14-20 are also distinguish over the prior art for at least the same reason as claims 1, 11, and 21.
Examiner notes, however, that claims 1-2, 4-12, and 14-21 are rejected under 35 U.S.C. 101, and therefore, not patent eligible.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LAL CE MANG whose telephone number is (571)272-0370. The examiner can normally be reached Monday to Friday- 8:30-12:00, 1:00-5:30 EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine T Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/LAL CE MANG/Primary Examiner, Art Unit 2857
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