DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
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.
Claim Status
This action is in response to application filed on January 16, 2025. Claims 1-20 are pending for examination.
Claim Objections
Claim 20 is objected to because of the following informalities: Claim 20 recites “wherein and the second indication” which is supposed to be “wherein the second indication”.
Appropriate correction is required.
CLAIM INTERPRETATION
Examiner interprets Claim 14 limitation “one or more of: a microcontroller; a battery; a transponder coil; a sensor interface; a pressure sensor; a wheel phase angle sensor; a transmitter; and an antenna,” as “ one or more of a microcontroller; one or more of a battery; one or more of a transponder coil; one or more of a sensor interface; one or more of a pressure sensor; one or more of a wheel phase angle sensor; one or more of a transmitter; and one or more of an antenna,”. The plain English meaning of the phrase “one or more of X and Y” is “one or more of X and one or more of Y”.
Examiner interprets Claim 8 limitation “result in one or more operations, the operations comprising: receiving …, determining …, determining …, forming …, displaying …, calculating …; and calculating …” as “result in one or more receiving …, one or more determining …, one or more determining …, one or more forming …, one or more displaying …, one or more calculating …; and one or more calculating …”. The plain English meaning of the phrase “one or more of X and Y” is “one or more of X and one or more of Y”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 8-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 8 recites “the TPMS” in the second last line without proper antecedent basis in the claims.
Claims 9-13 are also rejected since they depend from the rejected claim 8.
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 1-20 are rejected under 35 U.S.C. § 101 because the claimed subject matter is directed to a judicial exception (an abstract idea) without significantly more.
Claim 1 recites receiving sensor-derived tire temperature and pressure history, comparing relative temperatures and pressure decreases across tires, forming a predictive analysis, and displaying a recommendation for preventive maintenance. At a high level, these steps recite collecting information, performing comparative and predictive analysis, and presenting results—i.e., data-collection, data-analysis, and recommendation steps.
These steps constitute a judicial exception: they are evaluative/mathematical concepts and mental processes (observations, comparisons, inferences, and recommendations) that can be performed by humans or by generic data processing. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Mayo v. Prometheus, 566 U.S. 66 (2012); MPEP § 2106.04.
The claim’s additional elements—generic “receiving” of sensor data, application of an undefined “algorithm on a system level,” forming a “predictive analysis,” and displaying a recommendation—are recited at high-level generality. As claimed, these elements amount to conventional data acquisition, routine analysis, and display functions implemented on generic computers or monitoring systems. Absent further limitations, these do not integrate the abstract idea into a technical application. See Electric Power Group v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018).
The claim does not recite a specific technical improvement to sensing, signal processing, model structure/training, hardware architecture, or control-action that would provide “significantly more” than the abstract idea. Therefore, the claim, considered as a whole, does not supply an inventive concept sufficient to render it patent-eligible.
Regarding the dependent claim 2, the additional steps are merely calculating crude mileage using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 2 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 2 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 3, the additional steps are merely determining wear or tread depth using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 3 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 3 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 4, the additional steps are merely comparing using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 4 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 4 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 5, the additional steps are merely calculating battery life using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 5 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 5 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claims 6-7, the additional steps are merely indications and determining indications using collected data and therefore, can be also interpreted as nothing more than an idea of collecting, analyzing and displaying intangible data. For the reasons above, claims 6-7 are directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claims 6-7 are not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Claim 8 recites receiving sensor-derived tire temperature and pressure history, comparing relative temperatures and pressure decreases across tires, forming a predictive analysis, displaying a recommendation for preventive maintenance, and calculating a crude milage and a battery life. At a high level, these steps recite collecting information, performing comparative and predictive analysis, and presenting results—i.e., data-collection, data-analysis, and recommendation steps.
These steps constitute a judicial exception: they are evaluative/mathematical concepts and mental processes (observations, comparisons, inferences, and recommendations) that can be performed by humans or by generic data processing. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Mayo v. Prometheus, 566 U.S. 66 (2012); MPEP § 2106.04.
The claim’s additional elements— generic “a non-transitory computer readable storage medium having stored thereon instructions, which when executed by a processor result in one or more operations”, generic “receiving” of sensor data, application of an undefined “algorithm on a system level,” forming a “predictive analysis,” and displaying a recommendation—are recited at high-level generality. As claimed, these elements amount to conventional data acquisition, routine analysis, and display functions implemented on generic computers or monitoring systems. Absent further limitations, these do not integrate the abstract idea into a technical application. See Electric Power Group v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018).
The claim does not recite a specific technical improvement to sensing, signal processing, model structure/training, hardware architecture, or control-action that would provide “significantly more” than the abstract idea. Therefore, the claim, considered as a whole, does not supply an inventive concept sufficient to render it patent-eligible.
Regarding the dependent claim 9, the additional steps are merely determining wear or tread depth using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 9 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 9 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 10, the additional steps are merely comparing using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 10 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim10 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claims 11-13, the additional steps are merely indications and determining indications using collected data and therefore, can be also interpreted as nothing more than an idea of collecting, analyzing and displaying intangible data. For the reasons above, claims 11-13 are directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claims 11-13 are not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Claim 14 recites receiving sensor-derived tire temperature and pressure history, comparing relative temperatures and pressure decreases across tires, forming a predictive analysis, and displaying a recommendation for preventive maintenance. At a high level, these steps recite collecting information, performing comparative and predictive analysis, and presenting results—i.e., data-collection, data-analysis, and recommendation steps.
These steps constitute a judicial exception: they are evaluative/mathematical concepts and mental processes (observations, comparisons, inferences, and recommendations) that can be performed by humans or by generic data processing. See Alice Corp. v. CLS Bank Int’l, 573 U.S. 208 (2014); Mayo v. Prometheus, 566 U.S. 66 (2012); MPEP § 2106.04.
The claim’s additional elements—"a receiver; a plurality of antilock brake system (ABS) sensors; an Electronic Control Unit (ECU); at least one wheel unit includes one or more of: a microcontroller; a battery; a transponder coil; a sensor interface; a pressure sensor; a wheel phase angle sensor; a transmitter; and an antenna” are conventional/generic elements in the vehicle. Generic “receiving” of sensor data, forming a “predictive analysis,” and displaying a recommendation—are recited at high-level generality. As claimed, these elements amount to conventional data acquisition, routine analysis, and display functions implemented on generic computers or monitoring systems. Absent further limitations, these do not integrate the abstract idea into a technical application. See Electric Power Group v. Alstom S.A., 830 F.3d 1350 (Fed. Cir. 2016); SAP Am., Inc. v. InvestPic, LLC, 898 F.3d 1161 (Fed. Cir. 2018).
The claim does not recite a specific technical improvement to sensing, signal processing, model structure/training, hardware architecture, or control-action that would provide “significantly more” than the abstract idea. Therefore, the claim, considered as a whole, does not supply an inventive concept sufficient to render it patent-eligible.
Regarding the dependent claim 15, the additional steps are merely calculating crude mileage using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 151 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 15 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 16, the additional steps are merely determining wear or tread depth using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 16 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 16 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 17, the additional steps are merely comparing using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 17 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 17 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claim 18, the additional steps are merely calculating battery life using collected data and therefore, can be also interpreted as nothing more than an idea of collecting and analyzing intangible data. For the reasons above, claim 18 is directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claim 18 is not directed to patent-eligible subject matter under 35 U.S.C. § 101.
Regarding the dependent claims 19-20, the additional steps are merely indications and determining indications using collected data and therefore, can be also interpreted as nothing more than an idea of collecting, analyzing and displaying intangible data. For the reasons above, claims 19-20 are directed to an abstract idea and does not recite additional elements that amount to significantly more than the abstract idea. Therefore, claims 19-20 are not directed to patent-eligible subject matter under 35 U.S.C. § 101.
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 of this title, 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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 2, 4, 6, 7, 14, 15, 17, 19 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Stenneth et al. (Stenneth: US20230173856) in view of Tuttle (US 7075421).
Regarding claim 1, Stenneth disclose a computer-implemented method in a tire pressure monitoring system (“TPMS”), comprising:
receiving temperature and history over multiple events of an individual tire (Par 38, The machine learning model may be trained based on historical data including a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures may include: (1) sensor data associated with a tire pressure change; (2) travel data of a vehicle impacted by the tire pressure change; (3) vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change; (4) or a combination thereof. The sensor data may indicate one or more tire pressure levels of one or more tires of the vehicle over a predetermined period. … The sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; …. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments; );
determining a first indication by using temperature (Par 29, one or more sensors of the dTPMS may also measure and alert temperature levels of the tire. And Par 38-Par 39);
determining a second indication by reviewing decrease in pressure of a wheel of the individual tire over time and comparing the decreased pressure of the wheel with other wheels of the various tires on a vehicle using an algorithm on a system level (Par 29, TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface and Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure);
forming predictive analysis based on the first indication and second indication to recommend further manual investigation of a vehicle usability as an aid in a preventive maintenance of the vehicle (Par 34, the one or more service 117 may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors 107 may be transferred to the UE 101, the prediction platform 123, the database 125, or other entities communicatively coupled to the communication network 121 through the service platform 115. The services 117 may also be other third-party services and include mapping services, navigation services, travel planning services, weather-based services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information-based services, etc. In one embodiment, the services platform 115 uses the output data generated by of the prediction platform 123 to provide services such as navigation, mapping, other location-based services and Par [0039] Each of the plurality of tire pressure signatures may further include correlation data that correlate the sensor data, the travel data, the vehicle attribute data, or the combination thereof to ground truth data indicating a cause of the tire pressure change associated with one or more tires of the vehicle. The ground truth data may be recorded based on an actual observation of a cause of the tire pressure change. For example, a mechanic may review the state of a tire and record the ground truth data. ); and
displaying a recommendation for the preventive maintenance of the vehicle to a user on a user electronic device (Par 42, after the prediction platform 123 renders a prediction for a cause of a tire pressure change, a wheel of the vehicle 105 may be inspected by a service personnel (e.g., a mechanic) to validate the actual cause of the tire pressure change. If the inspection validates the prediction, the service personnel may provide an input via the UE 101 to indicate that the prediction is true. And Par 63; the calculation module 303 may determine it is unsafe for a vehicle to travel once the tire pressure thereof is below 25 percent of the maximum tire pressure capacity. In one embodiment, after detecting a tire pressure change, the calculation module 303 may identify a destination (e.g., the nearest auto repair shop, home, dealership, etc.) and use the machine learning model to estimate a route to the destination that yields a minimum amount of tire pressure loss. And Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source).
Stenneth does not explicitly disclose the first indication is by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario.
However, the preceding limitation is known in the art of tire monitoring systems. Tuttle teaches determining a first indication by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario (col. 6 lines 1-15; when the vehicle is in motion, the high rim temperature correlates inversely with tire health. The temperature of a single tire can also be related and compared to that of other tires on the same vehicle or to a similar class of tires under similar conditions of vehicle speed and external ambient temperature. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire. As previously mentioned, the increased area of road contact R with road surface 6 of an under-inflated tire 4 causes an increase in friction as the vehicle moves, causing an increase in temperature in the tire tread, causing the air 7 inside the tire to heat up, which then raises the temperature of tire rim 5 and Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring. See also Col. 7 lines 55-60; each individual TMS could monitor internal tire temperature and/or pressure via a hole in the rim that accesses the inner pneumatic chamber of the tire ).
Therefore, it would have been obvious to the one of the ordinary skill in the art at the time of the invention was made to combine the teachings of Tuttle in order to improve safety and lengthening tire life (Tuttle: abstract).
Regarding Claim 2, the combination of Stenneth and Tuttle teaches the method of claim 1, further comprising calculating a crude mileage by coupling the history over multiple events of the individual tire with data from a length of service of the individual tire, number or roll events and a duration (Stenneth: Par [0040] and Par [0058]; sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; (4) an amount of time elapsed starting from a time point of the latest instalment of a tire to the start of the predetermined period; (5) a total amount of distance travelled by the vehicle; (6) a total amount of distance travelled by the vehicle since the latest instalment of a tire; or (7) a combination thereof. In one embodiment, the detection module 301 may acquire certain sensor data, such as a speed of the vehicle, from one or more detection entities 113 that is proximate to the vehicle at the predetermined period. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segments; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments and Par 23).
Regarding Claim 4, the combination of Stenneth and Tuttle teaches the method of claim 1, wherein the relative temperature between the various tires is compared in partnership with at least a tire identification or other derived tire pairing technique (Tuttle: Col. 6 lines 40-55: Each TMS would also contain a unique manufacturing number identification to be used with the on-board data processing unit (OBDPU) in identifying each unique TMS to a particular tire on the vehicle. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire.).
Regarding Claim 6, the combination of Stenneth and Tuttle teaches the method of claim 1, wherein the first indication is a precursory indication mechanism in the prediction of the tire blow out scenario (Tuttle: Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring.).
Regarding Claim 7, the combination of Stenneth and Tuttle teaches the method of claim 1, wherein the second indication is a puncture indication (Stenneth: Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source) and the second indication is determined by partnering the first indication with an application for reviewing decrease in pressure of the wheel of the individual tire over time and comparing the decreased pressure of the wheel with the other wheels of the various tires on the vehicle (Stenneth: Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure and Par 62, the calculation module 303 may normalize sensor data acquired during the revolution period to identify a tire pressure signature corresponding to the derived pattern and/or rate of change. For example, tire pressure levels acquired during the revolution period may be normalized based on ambient temperature levels, road pavement temperature levels, atmospheric temperature levels, or a combination thereof.).
Regarding Claim 14, Stenneth teaches a tire pressure monitoring system (“TPMS”) comprising:
a receiver (Par [0031] The on-board communications platform 109 includes wired or wireless network interfaces to enable communication with external networks. );
a plurality of antilock brake system (ABS) sensors (Par 23, ABS wheel speed sensors);
an Electronic Control Unit (ECU) including a processor and a storage, wherein the ECU is coupled to the plurality of ABS sensors (Fig. 1, elements 109, 123 and Par 30, the on-board computing platform 109 may aggregate sensor data generated by at least one of the sensors 107 and transmit the sensor data via the on-board communications platform 111. The on-board computing platform 109 may receive control signals for performing one or more of the functions from the prediction platform 123, the UE 101, the services platform 115, one or more of the content providers 119a-119n, or a combination thereof via the on-board communication platform 111. The on-board computing platform 109 includes at least one processor or controller and memory); and
a plurality of wheel units (Par 29, TPMS/dTPMS), wherein at least one wheel unit includes one or more of:
a microcontroller (Par 29; microcontroller); a battery (Par 29; battery);
a transponder coil (Par 29, NFC);
a sensor interface (Par 29; The TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface); a pressure sensor (Par 29; pressure sensor);
a wheel phase angle sensor (Par 29, measure air pressures using software-based systems, which by evaluating existing sensor signals like wheel speeds, ,,, iTPMS systems is based on the principle that under-inflated tires have a slightly smaller diameter (and hence higher angular velocity) than a correctly inflated one. These differences are measurable through the wheel speed sensors of ABS/ESC systems.);
a transmitter; and an antenna ( Par 29, an oscillator, a radio frequency transmitter, a low frequency receiver. … Bluetooth, Wi-Fi, NFC),
where the microcontroller is coupled to the sensor interface and the sensor interface is coupled to the wheel phase angle sensor (Par 23; TPMS integrates Antilock Braking System's (ABS) wheel speed sensors. If a tire pressure is at a “low” level, the wheel speed sensors determine a roll of a tire at a different wheel speed than other tires. Such information is detected by the on-board computing platform, and in response, the on-board computing platform triggers the dashboard indicator light),
wherein the processor is configured to:
receive temperature and history over multiple events of an individual tire via the receiver (Par 38, The machine learning model may be trained based on historical data including a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures may include: (1) sensor data associated with a tire pressure change; (2) travel data of a vehicle impacted by the tire pressure change; (3) vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change; (4) or a combination thereof. The sensor data may indicate one or more tire pressure levels of one or more tires of the vehicle over a predetermined period. … The sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; …. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments;);
determine a first indication by using temperature (Par 29, one or more sensors of the dTPMS may also measure and alert temperature levels of the tire. And Par 38-Par 39);
determine a second indication by reviewing decrease in pressure of a wheel of the individual tire over time and comparing the decreased pressure of the wheel with other wheels of the various tires on a vehicle (Par 29, TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface and Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure);
form predictive analysis based on the first indication and second indication to recommend further a manual investigation of a vehicle usability as an aid in a preventive maintenance of the vehicle; (Par 34, the one or more service 117 may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors 107 may be transferred to the UE 101, the prediction platform 123, the database 125, or other entities communicatively coupled to the communication network 121 through the service platform 115. The services 117 may also be other third-party services and include mapping services, navigation services, travel planning services, weather-based services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information-based services, etc. In one embodiment, the services platform 115 uses the output data generated by of the prediction platform 123 to provide services such as navigation, mapping, other location-based services and Par [0039] Each of the plurality of tire pressure signatures may further include correlation data that correlate the sensor data, the travel data, the vehicle attribute data, or the combination thereof to ground truth data indicating a cause of the tire pressure change associated with one or more tires of the vehicle. The ground truth data may be recorded based on an actual observation of a cause of the tire pressure change. For example, a mechanic may review the state of a tire and record the ground truth data);
and display the recommendation for the preventive maintenance of the vehicle to a user electronic device (Par 42, after the prediction platform 123 renders a prediction for a cause of a tire pressure change, a wheel of the vehicle 105 may be inspected by a service personnel (e.g., a mechanic) to validate the actual cause of the tire pressure change. If the inspection validates the prediction, the service personnel may provide an input via the UE 101 to indicate that the prediction is true. And Par 63; the calculation module 303 may determine it is unsafe for a vehicle to travel once the tire pressure thereof is below 25 percent of the maximum tire pressure capacity. In one embodiment, after detecting a tire pressure change, the calculation module 303 may identify a destination (e.g., the nearest auto repair shop, home, dealership, etc.) and use the machine learning model to estimate a route to the destination that yields a minimum amount of tire pressure loss. And Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source).
Stenneth does not explicitly disclose the first indication is by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario.
However, the preceding limitation is known in the art of tire monitoring systems. Tuttle teaches determining a first indication by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario (col. 6 lines 1-15; when the vehicle is in motion, the high rim temperature correlates inversely with tire health. The temperature of a single tire can also be related and compared to that of other tires on the same vehicle or to a similar class of tires under similar conditions of vehicle speed and external ambient temperature. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire. As previously mentioned, the increased area of road contact R with road surface 6 of an under-inflated tire 4 causes an increase in friction as the vehicle moves, causing an increase in temperature in the tire tread, causing the air 7 inside the tire to heat up, which then raises the temperature of tire rim 5 and Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring. See also Col. 7 lines 55-60; each individual TMS could monitor internal tire temperature and/or pressure via a hole in the rim that accesses the inner pneumatic chamber of the tire ).
Therefore, it would have been obvious to the one of the ordinary skill in the art at the time of the invention was made to combine the teachings of Tuttle in order to improve safety and lengthening tire life (Tuttle: abstract).
Regarding Claim 15, the combination of Stenneth and Tuttle teaches the system of claim 14, wherein the processor is further configured to calculate a crude mileage by coupling the history over multiple events of the individual tire with data from a length of service of the individual tire, number or roll events and a duration (Stenneth: Par [0040] and Par [0058]; sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; (4) an amount of time elapsed starting from a time point of the latest instalment of a tire to the start of the predetermined period; (5) a total amount of distance travelled by the vehicle; (6) a total amount of distance travelled by the vehicle since the latest instalment of a tire; or (7) a combination thereof. In one embodiment, the detection module 301 may acquire certain sensor data, such as a speed of the vehicle, from one or more detection entities 113 that is proximate to the vehicle at the predetermined period. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segments; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments and Par 23).
Regarding Claim 17, the combination of Stenneth and Tuttle teaches the system of claim 14, wherein the relative temperature between the various tires is compared in partnership with at least a tire identification or other derived tire pairing technique (Tuttle: Col. 6 lines 40-55: Each TMS would also contain a unique manufacturing number identification to be used with the on-board data processing unit (OBDPU) in identifying each unique TMS to a particular tire on the vehicle. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire).
Regarding Claim 19, the combination of Stenneth and Tuttle teaches the system of claim 14, wherein the first indication is a precursory indication mechanism in the prediction of the tire blow out scenario and the second indication is a puncture indication (Tuttle: Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring).
Regarding Claim 20, the combination of Stenneth and Tuttle teaches the system of claim 14, wherein and the second indication is determined by partnering the first indication with an application for reviewing decrease in pressure of the wheel of the individual tire over time and comparing the decreased pressure of the wheel with the other wheels of the various tires on the vehicle (Stenneth: Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure and Par 62, the calculation module 303 may normalize sensor data acquired during the revolution period to identify a tire pressure signature corresponding to the derived pattern and/or rate of change. For example, tire pressure levels acquired during the revolution period may be normalized based on ambient temperature levels, road pavement temperature levels, atmospheric temperature levels, or a combination thereof).
Claims 3 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Stenneth in view of Tuttle further in view of Brey (US 20060208902 A1).
Regarding Claim 3, the combination of Stenneth and Tuttle teaches the method of claim 2, but does not explicitly disclose wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire.
However, Brey teaches a method for monitoring tread wear of a tire (abstract) and further teaches wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire (Par 16, if the RFID tags at the same corresponding tread depths in each tire are not all destroyed within a predetermined mileage range the controller can direct the user interface to warn a user of the vehicle that the vehicle is experiencing uneven tire wear).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brey in order to detect uneven wear between tires (Brey: Par 3).
Regarding Claim 16, the combination of Stenneth and Tuttle teaches the system of claim 15, but does not explicitly wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire.
However, Brey teaches a method for monitoring tread wear of a tire (abstract) and further teaches wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire (Par 16, if the RFID tags at the same corresponding tread depths in each tire are not all destroyed within a predetermined mileage range the controller can direct the user interface to warn a user of the vehicle that the vehicle is experiencing uneven tire wear).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brey in order to detect uneven wear between tires (Brey: Par 3).
Claims 5, 8, 10-13 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Stenneth in view of Tuttle further in view of Uehara (JP2007223463A).
Regarding Claim 5, the combination of Stenneth and Tuttle teaches the method of claim 2, but does not explicitly disclose further comprising calculating a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage.
Stenneth teaches TPMS includes a battery (Par 29).
However, Uehara teaches a tire condition detection device that detects tire conditions such as tire pressure and temperature (Par 1) and further teaches calculating a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage (Par [0015] The controller 25 further performs a calculation for predicting the life of the battery 15 in association with the temperature of the tire from the number of transmissions of the detection signal transmitted from the tire-side unit 1 to the in-vehicle unit 3, and functions as a prediction unit that predicts the battery life. And Par 23. And Par 26, If the apparatus is of a type in which detection signal data is transmitted from the tire-side unit 1 according to the travel distance of the vehicle, the travel distance of the vehicle may be used as the operation data. It is noted that when the vehicle is travelling, the tires/wheels are rolling i.e. roll event).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Uehara in order to avoid the troublesome work of removing the tire from the rim to replace the battery (Uehara: Par 23).
Regarding Claim 8, Stenneth teaches a non-transitory computer readable storage medium having stored thereon instructions (Par 5, a non-transitory computer-readable storage medium having computer program code instructions ), which when executed by a processor result in one or more operations, the operations comprising:
receiving temperature and history over multiple events of an individual tire (Par 38, The machine learning model may be trained based on historical data including a plurality of tire pressure signatures. Each of the plurality of tire pressure signatures may include: (1) sensor data associated with a tire pressure change; (2) travel data of a vehicle impacted by the tire pressure change; (3) vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change; (4) or a combination thereof. The sensor data may indicate one or more tire pressure levels of one or more tires of the vehicle over a predetermined period. … The sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; …. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments;);
determining a first indication by using temperature (Par 29, one or more sensors of the dTPMS may also measure and alert temperature levels of the tire. And Par 38-Par 39);
determining a second indication by reviewing decrease in pressure of a wheel of the individual tire over time and comparing the decreased pressure of the wheel with other wheels of the various tires on a vehicle using an algorithm on a system level (Par 29, TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface and Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure);
forming predictive analysis based on the first indication and second indication to recommend further manual investigation of a vehicle usability as an aid in a preventive maintenance of the vehicle (Par 34, the one or more service 117 may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors 107 may be transferred to the UE 101, the prediction platform 123, the database 125, or other entities communicatively coupled to the communication network 121 through the service platform 115. The services 117 may also be other third-party services and include mapping services, navigation services, travel planning services, weather-based services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information-based services, etc. In one embodiment, the services platform 115 uses the output data generated by of the prediction platform 123 to provide services such as navigation, mapping, other location-based services and Par [0039] Each of the plurality of tire pressure signatures may further include correlation data that correlate the sensor data, the travel data, the vehicle attribute data, or the combination thereof to ground truth data indicating a cause of the tire pressure change associated with one or more tires of the vehicle. The ground truth data may be recorded based on an actual observation of a cause of the tire pressure change. For example, a mechanic may review the state of a tire and record the ground truth data);
displaying a recommendation for the preventive maintenance of the vehicle to a user on a user electronic device (Par 42, after the prediction platform 123 renders a prediction for a cause of a tire pressure change, a wheel of the vehicle 105 may be inspected by a service personnel (e.g., a mechanic) to validate the actual cause of the tire pressure change. If the inspection validates the prediction, the service personnel may provide an input via the UE 101 to indicate that the prediction is true. And Par 63; the calculation module 303 may determine it is unsafe for a vehicle to travel once the tire pressure thereof is below 25 percent of the maximum tire pressure capacity. In one embodiment, after detecting a tire pressure change, the calculation module 303 may identify a destination (e.g., the nearest auto repair shop, home, dealership, etc.) and use the machine learning model to estimate a route to the destination that yields a minimum amount of tire pressure loss. And Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source);
calculating a crude mileage by coupling the history over multiple events of the individual tire with data from a length of service of the individual tire, number or roll events and a duration (Par [0040] and Par [0058]; sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period; (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; (4) an amount of time elapsed starting from a time point of the latest instalment of a tire to the start of the predetermined period; (5) a total amount of distance travelled by the vehicle; (6) a total amount of distance travelled by the vehicle since the latest instalment of a tire; or (7) a combination thereof. In one embodiment, the detection module 301 may acquire certain sensor data, such as a speed of the vehicle, from one or more detection entities 113 that is proximate to the vehicle at the predetermined period. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segments; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments and Par 23).
Stenneth does not explicitly disclose the first indication is by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario.
However, the preceding limitation is known in the art of tire monitoring systems. Tuttle teaches determining a first indication by comparing a relative temperature between various tires during an operation to predict a tire blow out scenario (col. 6 lines 1-15; when the vehicle is in motion, the high rim temperature correlates inversely with tire health. The temperature of a single tire can also be related and compared to that of other tires on the same vehicle or to a similar class of tires under similar conditions of vehicle speed and external ambient temperature. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire. As previously mentioned, the increased area of road contact R with road surface 6 of an under-inflated tire 4 causes an increase in friction as the vehicle moves, causing an increase in temperature in the tire tread, causing the air 7 inside the tire to heat up, which then raises the temperature of tire rim 5 and Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring. See also Col. 7 lines 55-60; each individual TMS could monitor internal tire temperature and/or pressure via a hole in the rim that accesses the inner pneumatic chamber of the tire ).
Therefore, it would have been obvious to the one of the ordinary skill in the art at the time of the invention was made to combine the teachings of Tuttle in order to improve safety and lengthening tire life (Tuttle: abstract).
Stenneth does not explicitly disclose calculating a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage.
However, Uehara teaches a tire condition detection device that detects tire conditions such as tire pressure and temperature (Par 1) and further teaches calculating a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage (Par [0015] The controller 25 further performs a calculation for predicting the life of the battery 15 in association with the temperature of the tire from the number of transmissions of the detection signal transmitted from the tire-side unit 1 to the in-vehicle unit 3, and functions as a prediction unit that predicts the battery life. And Par 23. And Par 26, If the apparatus is of a type in which detection signal data is transmitted from the tire-side unit 1 according to the travel distance of the vehicle, the travel distance of the vehicle may be used as the operation data. It is noted that when the vehicle is travelling, the tires/wheels are rolling i.e. roll event).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Uehara in order to avoid the troublesome work of removing the tire from the rim to replace the battery (Uehara: Par 23).
Regarding Claim 10, the combination of Stenneth, Tuttle and Uehara teaches the non-transitory computer readable storage medium of claim 8, wherein the relative temperature between the various tires are compared in partnership with at least a tire identification or other derived tire pairing technique (Tuttle: Col. 6 lines 40-55: Each TMS would also contain a unique manufacturing number identification to be used with the on-board data processing unit (OBDPU) in identifying each unique TMS to a particular tire on the vehicle. And Col. 9 lines 40-55; The temperature of metal wheel rim 5 of a single tire 4 can be related and compared to that of other tires on the same vehicle, to a similar class of tires under similar conditions of vehicle speed and external ambient temperature, or to a history of the speed/temperature log of that tire).
Regarding Claim 11, the combination of Stenneth, Tuttle and Uehara teaches the non-transitory computer readable storage medium of claim 8, wherein the first indication is a precursory indication mechanism in the prediction of the tire blow out scenario (Tuttle: Col. 10 lines 45-55; analyze and compare individual tire data to groups of tire data. For example, if one TMS 1 is sending temperature data (and its unique identity) that is significantly higher than other TMS 1 data, action would be required. … various warning or alarm conditions would be displayed to the operator and could be sent to an external system in the cases of finding a trend, warning or emergency condition. And Abstract; notifying or warning the vehicle operator of early detection of imminent tire failure and performance degradation; thus improving safety by preventing blowouts from occurring).
Regarding Claim 12, the combination of Stenneth, Tuttle and Uehara teaches the non-transitory computer readable storage medium of claim 8, wherein the second indication is a puncture indication (Stenneth: Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source).
Regarding Claim 13, the combination of Stenneth, Tuttle and Uehara teaches the non-transitory computer readable storage medium of claim 8, wherein the second indication is a puncture indication (Stenneth: Par 66, The calculation module 303 predicts the cause of the tire pressure change, and the presentation module 307 generates a display 421 that indicates the cause. In the illustrated example, the display 421 states “NAIL OR METAL PENETRATING THE TIRE AT THIS LOCATION.” In one embodiment, the first visual representation 400 may further indicate coordinates of an exact location of the source) and the second indication is determined by partnering the first indication with an application for reviewing decrease in pressure of the wheel of the individual tire over time and comparing the decreased pressure of the wheel with the other wheels of the various tires on the vehicle (Stenneth: Par 60; detection module 301 may periodically receive from the vehicle 105 sensor data indicating a current tire pressure level of a wheel of the vehicle 105, and the detection module 301 may determine that the vehicle 105 is impacted by the tire pressure change based on the received sensor data. When the detection module 301 receives the data indicate the tire pressure change, detection module 301 may acquire sensor data associated with the vehicle 105, vehicle attribute data associated with the vehicle 105, and travel data associated with the vehicle 105 from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), and Par 63, the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure. the calculation module 303 uses the machine learning model to predict an amount of tire pressure loss associated one or more road segments that the vehicle 105 is predicted to traverse. In such embodiment, the machine learning model refers to past instances in which a similar vehicle with a similar tire type has traversed the one or more road segments (or similar road segments) and lost tire pressure and Par 62, the calculation module 303 may normalize sensor data acquired during the revolution period to identify a tire pressure signature corresponding to the derived pattern and/or rate of change. For example, tire pressure levels acquired during the revolution period may be normalized based on ambient temperature levels, road pavement temperature levels, atmospheric temperature levels, or a combination thereof).
Regarding Claim 18, the combination of Stenneth and Tuttle teaches the system of claim 15, but does not explicitly discloses wherein the processor is further configured to calculate a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage.
Stenneth teaches TPMS includes a battery (Par 29).
However, Uehara teaches a tire condition detection device that detects tire conditions such as tire pressure and temperature (Par 1) and further teaches calculating a battery life for a remaining capacity of a battery of the TPMS by using the data from the roll events and the crude mileage (Par [0015] The controller 25 further performs a calculation for predicting the life of the battery 15 in association with the temperature of the tire from the number of transmissions of the detection signal transmitted from the tire-side unit 1 to the in-vehicle unit 3, and functions as a prediction unit that predicts the battery life. And Par 23. And Par 26, If the apparatus is of a type in which detection signal data is transmitted from the tire-side unit 1 according to the travel distance of the vehicle, the travel distance of the vehicle may be used as the operation data. It is noted that when the vehicle is travelling, the tires/wheels are rolling i.e. roll event).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Uehara in order to avoid the troublesome work of removing the tire from the rim to replace the battery (Uehara: Par 23).
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Stenneth in view of Tuttle and Uehara further in view of Brey (US 20060208902 A1).
Regarding Claim 9, the combination of Stenneth and Tuttle teaches the non-transitory computer readable storage medium of claim 8, but does not explicitly disclose wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire.
However, Brey teaches a method for monitoring tread wear of a tire (abstract) and further teaches wherein the calculation of the crude mileage determines an early identification of wear or tread depth nearing end of life of the individual tire (Par 16, if the RFID tags at the same corresponding tread depths in each tire are not all destroyed within a predetermined mileage range the controller can direct the user interface to warn a user of the vehicle that the vehicle is experiencing uneven tire wear).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of Brey in order to detect uneven wear between tires (Brey: Par 3).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Prior arts cited for the record but not used in Office Action, are listed in attached PTO-892.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Nay Tun whose telephone number is (571)270-7939. The examiner can normally be reached on Mon-Thurs from 9:00-5:00. If attempts to reach the examiner by telephone are unsuccessful, the examiner's Supervisor, Steven Lim can be reached on (571) 270-1210. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
/Nay Tun/Primary Examiner, Art Unit 2688
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