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 2/26/2025 has been entered and made record. This application contains 19 pending claims.
Claims 1, 4-6, 9, 11, 14-17, and 19 have been amended.
Claims 3 and 13 have been cancelled.
Claim 21 has been added.
Response to Arguments
Applicant’s arguments filed 2/26/2026 regarding claims rejections under 35 U.S.C. 101 in claim 1-20 have been fully considered but they are not persuasive.
The applicant argues on pages 9-11 of the remark filed on 2/26/2026 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. … Further, there is no mental process recited in claim 1 that could reasonably be performed in the human mind. … 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 “the one of the first sensor signal and the second sensor signal exceeding the threshold value” 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 page 10 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, than to example 47. (2025 Memo at 3.) 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. 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 and 11 do not recite training a neural network or a machine learning model. The machine learning model in Claims 1 and 11 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. … As in McRO, the claims here do not merely automate what a human could subjectively do. Rather, they impose specific constraints on the manner in which sensor data is collected and processed, namely, threshold-triggered time-series generation and model-based determination of an event. The improvement is to the technological process itself: the claimed method enhances the operation of in-cabin sensing systems by intelligently gating time-series acquisition, reducing computational overhead, and improving event discrimination based on temporally aligned, multi- sensor data streams. … 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”; “a controller operably connected to the gas sensor and the PM sensor” and “generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals” 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 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 improvement in the technological process of enhancing the operation of in-cabin sensing systems by intelligently gating time-series acquisition, reducing computational overhead, and improving event discrimination based on temporally aligned, multi- sensor data streams 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 limitation 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”, “receive the first and second sensor signals from the gas sensor and the PM sensor” and “generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals” 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 2-10, and 12-20 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 2-10, and 12-20 are also patent ineligible.
Hence, the Examiner submits that the rejections of Claims 1-20 are proper.
Applicant’s arguments filed 2/26/2026 regarding claims rejections under 35 U.S.C. 103 in claim 1-20 have been fully considered but they are not persuasive.
The applicant argues on pages 14-16 of the remark filed on 2/26/2026 that “In the Office Action, in the rejection of claim 3, the Office alleged that Murphy teaches generating time series datasets and determining the event in the cabin because the environmental data is processed and analyzed at step 406. (Office Action at page 12.) While Murphy uses a threshold to determine if the pollutants exceed a certain value, it does not condition the generation of the time series datasets based on exceeding a threshold. (See, e.g., Murphy paragraphs [0033]-[0034] and [0052].) Thus, Murphy does not teach or suggest "in response to the one of the first sensor signal and the second sensor signal exceeding 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," as recited in amended claim 1.”
The Examiner respectfully disagrees applicant’s argument. Murphy generate the first sensor signal and the second sensor signal (Murphy, [0019]; FIG. 1, [0030] and [0032]). FIG. 1 shows that processing unit 40 is connected to the gas sensor 120 and the cabin or PM sensor 160, and receive the first and second sensor signals from the gas sensor and the PM sensor. The environmental data (e.g. in-cabin and external environmental data) is processed and analyzed, at 406, and the levels of particular pollutants or contaminants may be compared to one or more standards to determine whether the levels meet or exceed the thresholds, and a characterization of the environment is also determined. If the NO2 level exceeds a particular threshold, the environmental quality may be deemed “poor”. If the NO, level is between certain thresholds, the environmental quality may be deemed “moderate”. If the NO2 level is below a threshold, the environmental quality may be considered “ good ”. The environmental data is provided. The levels of particular contaminates in the environmental data is displayed, the environmental quality may be displayed and/or the levels or quality of regions may be mapped, and the environmental data may also be provided to vehicles, such as vehicle 200, for use in determining mitigation actions (Murphy, [0052], [0057], [0060], [0061], [0062]). Murphy can generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals. Murphy also generates the environmental data in response to
the NO2 level pr the levels of particular pollutants or contaminants exceeds a particular threshold. Therefore, Murphy teaches generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals, in response to the one of the first sensor signal and the second sensor signal exceeding the threshold value.
The applicant argues on pages 15-16 of the remark filed that “ … The Examiner alleged that it would have been obvious to modify Murphy to train the machine learning model with the Jain features. The Applicant respectfully disagrees, since the configuration of the Jain reference is so different from the Murphy reference that the variables used in Jain are not applicable to Murphy. Specifically, the Office's combination of Jain with Murphy lacks a rational basis. Jain teaches a machine learning system that determines if a person will reach a desired level of readiness to perform a task or achieve an objective. (Jain paragraphs [0003] and [0432].) Murphy is concerned with detecting environmental air quality external to the vehicle and sharing this information between vehicles. (Murphy paragraphs [0042]-[0046], [0050]-[0053].) In one embodiment, this can include detecting in-cabin environmental quality to correct the external source readings based on occurrences within the cabin, for example someone smoking in the vehicle. (Murphy paragraph [0076].) The Office has made no connection, however, between the variables used in the Jain system and variables that would be useful in Murphy. Indeed, one of ordinary skill in the art would not reasonably have used Jain's variables for determining readiness of a person to perform a task to be applicable to or useful in the Murphy vehicle environmental determination. … The purported rationale for modifying Murphy's environmental detection to use the same variables Jain uses to determine readiness of a human is not logically sound, and thus lacks the rational underpinning required to support the legal conclusion of obviousness. Consequently, for this initial reason, Murphy and Jain cannot be combined so as to establish a prima facie case of obviousness with respect to claim 1.
The Examiner respectfully disagrees applicant’s argument. Jain teaches that environmental data in the car is obtained and the environment surrounding an individual inside and outside the contextual location of the potential subjects such as environmental data in the car, home, and office. The environmental data includes environmental data in the car, air quality data, carbon dioxide, hydrogen, carbon monoxide or possibly smoke, oxygen, ozone data, measuring temperature and gases or particles such as formaldehyde, alcohol vapor, benzene, Liquefied Petroleum Gas (LPG), butane, propane, weather data, and water-quality data in the environment surrounding an individual inside and outside the contextual location of the potential subjects such as vehicle, home, and office (Jain, [0006], [0009], [0447]). The variables used in the Jain system are similar to variables used in Murphy, and thus, the variables used in the Jain system would be useful in Murphy. Therefore, one of ordinary skill in the art would use Jain's variables for determining readiness of a person to perform a task to be applicable to or useful in the Murphy vehicle environmental determination.
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;
in response to the one of the first sensor signal and the second sensor signal exceeding 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 (process for claim 11, and apparatus for claims 1 and 21).
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
“the one of the first sensor signal and the second sensor signal exceeding the threshold value” 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; and generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals.
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”; “a controller operably connected to the gas sensor and the PM sensor” and “generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals” 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 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.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 6-9, 11-12, 16-19, and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy et al. (US 20200238786, hereinafter Murphy) in view of Jain et al. (US 20210241137, hereinafter Jain).
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 ([0019]; FIG. 1, [0030] and [0032] disclose gas sensor or volatile organic compound (VOC) 110 senses one or more of NO2, CO, NO, O3, SO2, CO2, VOCs, CH4; and sensor platform 102A may drawing in air and transporting air to sensors 110 , 120 and 130 for testing (i.e., sensor 110 in FIG. 1 can be assigned as gas or volatile organic compound (VOC) that detects and generates a first sensor signal - emphasis added by Examiner));
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 (FIG. 1; [0030] discloses sensors 110, 120 and 130 may be particulate matter (PM) sensors (i.e., sensor 130 in FIG. 1 can be assigned as particulate matter (PM) sensor - emphasis added by Examiner); [0031] discloses cabin sensor 160 senses particulate matter; and senses exhalations from passengers (e.g. CO2), components of smoke from passengers' cigarettes, outgassing from components of the vehicle such as the dashboard, and other contaminants that might be present in the vehicle's cabin (i.e., cabin sensor 160 in FIG. 1 can be assigned as particulate matter (PM) sensor that detect and generate a second sensor signal - emphasis added by Examiner));
a controller operably connected to the gas sensor and the PM sensor (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 ([0030] discloses gas sensor or volatile organic compound (VOC) 110 senses one or more of air, VOCs, NO2, CO, NO, O3, SO2, CO2, CH4; and sensor 120 may be particulate matter (PM) sensor; [0031] discloses cabin sensor or PM sensor 160 senses components of smoke from passengers' cigarettes; [0051]);
compare one of the first sensor signal and the second sensor signal with a threshold value ([0030] and [0031] disclose gas sensor or volatile organic compound (VOC) 110 senses one or more of air, VOCs, NO2, CO, NO, O3, SO2, CO2, CH4; and cabin sensor or PM sensor 160 senses components of smoke from passengers' cigarettes.
[0052] discloses the environmental data (e.g. in-cabin and external environmental data) is processed and analyzed, at 406, and the levels of particular pollutants or contaminants may be compared to one or more standards to determine whether the levels meet or exceed the thresholds, and a characterization of the environment is also determined (i.e., generating the first time series dataset by sensor 110 and second time series dataset by sensor 120 - emphasis added by Examiner));
in response to the one of the first sensor signal and the second sensor signal exceeding the threshold value ([0052] discloses the environmental data (e.g. in-cabin and external environmental data) is processed and analyzed, at 406, and the levels of particular pollutants or contaminants may be compared to one or more standards to determine whether the levels meet or exceed the thresholds, and a characterization of the environment is also determined. For example, if the NO2 level exceeds a particular threshold, the environmental quality may be deemed “poor” (i.e., the first time series dataset and second time series dataset are compared to the threshold values, and the sensors signals exceeding the threshold values - emphasis added by Examiner)), generate a first time series dataset of the first sensor signals and a corresponding second time series dataset of the second sensor signals ([0033], [0034], and [0052] disclose data from sensors 110, 120 and 130 incorporates time; and data from in-cabin sensor(s) 160 incorporates time; and in-cabin environmental data from one or more
are provided. if the NO2 level exceeds a particular threshold, the environmental quality may be deemed “poor”, and the levels of particular contaminates in the environmental data may be displayed, and/or the levels or quality of regions may be
mapped. The environmental data may also be provided to vehicles, such as vehicle 200 (i.e., generate a first time series dataset and a corresponding second time series dataset of the second sensor signals - emphasis added by Examiner), for use in determining mitigation actions); 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 ([0042] discloses Environment manager 210 may measure, receive data (i.e., the received data would include data from gas or volatile organic compound (VOC) sensor 110 - emphasis added by Examiner) and/or control aspects of the in-cabin environment of vehicle 200, and processing of in-cabin and/or external environmental data from in-cabin sensor(s) 260 and external sensor platform 202, and evaluate data to determine the in-cabin and/or external environmental quality in and around vehicle 200; [0052] discloses the environmental data (e.g. in-cabin and external environmental data) is processed and analyzed, at 406, and machine learning, computational intelligence and/or other data processing tools may be used. The specific levels of particular pollutants or contaminants are determined at 406, and for example, the parts per million of a specific size of PM may be determined and a characterization of the environment such as whether the NO2 level exceeds a particular threshold is also determined at 406);
notify an operator of the vehicle of the determined event, 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 ([0033], [0034], and [0052] disclose data from sensors 110, 120 and 130 incorporates time; and data from in-cabin sensor(s) 160 incorporates time; and in-cabin environmental data from one or more
are provided. if the NO2 level exceeds a particular threshold, the environmental quality may be deemed “poor”, and the levels of particular contaminates in the environmental data may be displayed, and/or the levels or quality of regions may be
mapped. The environmental data may also be provided to vehicles, such as vehicle 200, for use in determining mitigation actions (i.e., notify an operator of the vehicle of the determined event, that includes transmitting to the operator at least part of the first and second time series datasets associated with the determined event - emphasis added by Examiner)).
Murphy does not explicitly teach 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 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 ([0009] discloses time-series data; [0432] and [0447] disclose the data collected and used by the computer system 110 to train models and identify and select actions to improve readiness, etc., include environmental data (e.g., air quality data, ozone data, temperature and gases or particles such as alcohol vapor, carbon dioxide ( Molecular Formula: C02), hydrogen (Molecular Formula: H2), carbon monoxide or possibly smoke (Molecular Formula: CO) (i.e., PM readings of time-series data - emphasis added by Examiner); and oxygen (Molecular Formula: O2) in the environment surrounding an individual inside and outside the contextual location of the potential subjects such as vehicle, home, and office including data).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Jane into Murphy for the purpose of collecting and analyzing environmental data such as air quality, temperature, gases or particles, and alcohol vapor inside a vehicle or home or office using trained machine learning models. This combination would improve in accurately determining environmental condition inside vehicle or home or office so that necessary mitigation action can be implemented.
As to claims 2 and 12, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy does not explicitly teach wherein the machine learning model is an artificial neural network.
Jane teaches wherein the machine learning model is an artificial neural network ([0010] and [0219] disclose the one or more models can be machine learning
models, a neural networks or classifiers; and artificial intelligence tools).
It would have been obvious to one of ordinary skill in the art before the
effective filing date of the claimed invention to incorporate Jane into Murphy for the purpose of collecting and analyzing environmental data such as air quality, temperature, gases or particles, and alcohol vapor inside a vehicle or home or office using trained machine learning or artificial neural network models. This combination would improve in accurately determining environmental condition inside vehicle or home or office so that necessary mitigation action can be implemented.
As to claims 6 and 16, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy teaches wherein: the controller includes at least one local processor disposed in the vehicle and at least one remote processor disposed remote from the vehicle and in wireless communication with the at least one local processor (FIG. 1 shows processing unit 140 or one local processor is disposed in the vehicle and processor(s) 154 is disposed remote from the vehicle and in wireless communication through data network 108 with the local processor 140; [0029], [0039]),
the at least one local processor is configured to compare the one of the first sensor signal and the second sensor signal with the threshold value ([0052] discloses data processing tools may be used to analyze the environmental data at 406, and the levels of particular pollutants or contaminants may be compared to one or more standards to determine whether the levels meet or exceed the threshold (i.e., the levels of particular pollutants or contaminants are compared standards or threshold values, and thus, the processing tool or processor would be able to compare the contaminant or the first sensor signal and the particular matter or the second sensor signal can also be - emphasis added by Examiner), and
the at least one remote processor is configured to determine the event in the cabin of the vehicle (FIG. 1 and [0039] disclose server 150 consists of processor(s) 154 and other components as shown in FIG. 1, and measurements data are sent to server 150. The server processes the sensor data, and determines the in-cabin and external environmental quality, and provides mitigation actions, if any).
As to claims 7 and 17, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy teaches after determining the event, transmit data corresponding to the determined event to a remote server (FIG. 1 and [0041] disclose “External and/or in-cabin environmental data may be sent to a server, such as server 150”).
As to claims 8 and 18, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy teaches wherein the controller is further configured to notify an operator of the vehicle of the determined event ([0039] and [0042] disclose environment manager 210 utilizes the processing unit, performs some or all of processing of in-cabin and/or external environmental data; and information related to environmental data may also be presented to the user).
As to claims 9 and 19, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 8 and 18, respectively.
Murphy teaches wherein the notifying of the operator includes transmitting to the operator at least part of the first and second time series datasets associated with the determined event ([0033] discloses “data from sensors 110, 120 and 130 incorporates time.”; [0044] discloses environment manager 210 displays a warning regarding high in-cabin CO2 levels (i.e., first time series datasets associated with the determined event - emphasis added by Examiner) and a directive to ventilate the cabin on display 220, and indicate regions and/or roads having high levels of pollution, such as NO, or PM (i.e., second time series datasets associated with the determined event - emphasis added by Examiner), on a map depicted on display 220. Environment manager also utilize display 220 to provide information to the user(s) of vehicle 200).
Claims 4-5 and 14-15 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy and Jain, in view of Meister et al. (US 10776643B1, hereinafter Meister).
As to claims 4 and 14, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
The combination of Murphy and Jane does not explicitly teach determine the threshold value based on a baseline value determined from a third time series dataset of the one of the first sensor signal and the second sensor signal that represents background values.
Meister teaches determine the threshold value based on a baseline value determined from a third time series dataset of the one of the first sensor signal and the second sensor signal that represents background values (Col. 7, Lines 9-25 discloses the threshold is additionally or alternatively established dynamically based upon a signal (i.e., determine the threshold value based on value from a third time series dataset - emphasis added by Examiner) from one or more of the airborne particulate matter concentration sensors 106/108. A constant output from the airborne particulate matter concentration sensors 106/108 over a predetermined period of time eliminates data from smoke events (i.e., a baseline value determined from a third time series dataset of the one of the first sensor signal and the second sensor signal that represents background values, and the threshold value can be determined based on a baseline value from a third time series dataset - emphasis added by Examiner); Col. 8, Lines 58-62 discloses an airborne particulate matter concentration sensor may detect an increased baseline of airborne particulate matter after a smoking event due to the sensitivity of the airborne particulate matter concentration sensor).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Meister into Murphy in view of Jane for the purpose of detecting airborne particulate matter concentration detection in vehicles in order to identify driver and/or stranger who is responsible for a particulate matter or smoking event in the vehicle. This combination would improve in effectively identifying driver and/or stranger who is responsible for the particulate matter or smoking event in the vehicle so that cost of fumigating the vehicle can be passed on to individual responsible for the particulate matter or smoking event.
As to claims 5 and 15, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy teaches at least one external sensor configured as an external PM sensor or an external gas sensor that is configured to generate a third sensor signal ([0020] and [0038] disclose external environmental data may include measurements of PM, NO2, CO, NO, O3, SO2, CO2, CH4, VOCs; and external data captured by sensors 110, 120 and 130 (i.e., sensor 130 can be assigned as a third external gas sensor, and external data or a third sensor signal is captured and generated by the sensor 130 - emphasis added by Examiner), wherein the controller is further configured to determine the threshold value (Claim 16 discloses the processor provides the mitigation action based on the predetermined data being utilized).
The combination of Murphy and Jane does not explicitly teach determine the threshold value based on the third sensor signal.
Meister teaches at least one external sensor configured as an external PM sensor or an external gas sensor that is configured to generate a third sensor signal, wherein the controller is further configured to determine the threshold value based on the third sensor signal (Col. 3, Lines 38-39 discloses the controller to obtain the third signal, to establish an airborne particulate matter concentration threshold based upon the 40 obtained third signal; Col. 6, Lines 60-63 discloses the threshold is additionally or alternatively established dynamically based upon the signal from the airborne particulate matter concentration sensor 110 (i.e., PM sensor 110 can be assigned as a third external gas sensor, and external data or a third sensor signal is captured and generated by the PM sensor 130 - emphasis added by Examiner)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Meister into Murphy in view of Jane for the purpose of detecting airborne particulate matter concentration detection in vehicles in order to identify driver and/or stranger who is responsible for a particulate matter or smoking event in the vehicle. This combination would improve in effectively identifying driver and/or stranger who is responsible for the particulate matter or smoking event in the vehicle so that cost of fumigating the vehicle can be passed on to individual responsible for the particulate matter or smoking event.
Claims 10 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Murphy and Jain, in view of Wensley et al. (US 20200207298, hereinafter Wensley).
As to claims 10 and 20, the combination of Murphy and Jane teaches the claimed limitations as discussed in claims 1 and 11, respectively.
Murphy does not explicitly teach wherein the controller is further configured to update the training data to include the first and second datasets in response to determining the event.
Jane teaches wherein the controller is further configured to update the training data to include the first and second datasets in response to determining the event ([0061] and [0077] disclose classify the cleanliness attribute of the cabin 102 (block
316) based on the chemical compounds in the air of the cabin 102 (the stream of attributes A) (i.e., the first datasets in response to determining the event - emphasis added by Examiner); and the possible classifications for the cleanliness of the cabin 102 may comprise dirty, clean, stain on seat detected, food detected, or smoke smell detected (i.e., the second datasets in response to determining the event - emphasis added by Examiner). The pre-processors of the pre-processing assembly 120 may use models that incorporate various predetermined thresholds, predetermined ranges, and/or trained neural networks to determine streams of attributes that are provided to the sensor fusion module 122, and these parameters can be adjusted (i.e., updated - emphasis added by Examiner) or tuned based on training data and/or ground truth).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate Wensley into Murphy in view of Jane for the purpose of determining changes in a scene inside a cabin of a vehicle based on changes in the scene estimation before and after usage in order to intelligently sense the vehicle interior to detect certain events of interest. This combination would improve in identifying if and when cleaning or other maintenance is needed , and/or identifying an emergency situation in which emergency services such as police or ambulance need to be called.
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.
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/LAL CE MANG/Examiner, Art Unit 2857