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 .
Claims 1-9 and 11-14 are presented for examination.
Claim 1 has been amended. Claims 1-9 and 11-14 are rejected.
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Response to Arguments
Applicant’s amendments and arguments, see page 7-11, filed 03/10/2026, with respect to the rejection of claims 1-3, 5-9, and 11-14 under 35 U.S.C. 101 have been fully considered but they are not persuasive. The 35 U.S.C. 101 rejection of claims 1-3, 5-9, and 11-14 is maintained.
The rejection under 35 U.S.C. § 101 is maintained because, even after the amendments, the claims remain directed to the abstract idea of collecting road/tire information, analyzing that information using measured road roughness and tire-specific load characteristics, and determining a recommended maximum vehicle load. Although amended claim 1 now recites sensors configured to measure tire deformation or road surface irregularities while a vehicle travels along a route, those sensors are recited generically and merely provide input data for the claimed analysis. The claims do not recite a specific improvement to the sensors, tire structure, vehicle control system, computer operation, or any particular technical algorithm for measuring or processing the data. Nor do the claims require physically controlling the vehicle, adjusting the vehicle load, changing tire pressure, modifying route navigation, or otherwise transforming a tangible article. Instead, the claims end with determining a recommended load value. Accordingly, the amendments do not integrate the abstract idea into a practical application and do not add significantly more than the abstract idea itself.
Applicants’ amendments and arguments, see page 11-15, filed 03/10/2026, with respect to the rejection(s) of claim(s) 1-9 and 11-14 under 35 USC § 103, are sufficient to overcome the previous rejections. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made further in view of Singh (US 20140257629 A1).
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “A control unit” in claim 12. See specification (page 8 lines 30-35).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
Claim 1-3, 5-9, and 11-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1. A method for determining a recommended maximum load for a vehicle to be operated along a predefined route, comprising:
acquiring, by one or more sensors configured to measure at least one of tire deformation or road surface irregularities, while a vehicle travels along said predefined route, road topography data for said predefined route, the road topography data containing information about the topography of the road including road roughness along said predefined route,
determining, based on measured road roughness of the acquired road topography data and tire specific load characteristics, a respective maximum allowable tire load for each individual tire of the vehicle for said predefined route, and determining, based on the determined maximum allowable tire loads, a recommended maximum load for the vehicle to be operated along said predefined route.
Claims 2-3 and 5-9 recite methods, claim 11 recites a non-transitory computer-readable medium, and claims 12-14 recite a control unit / vehicle. These claims fall within statutory categories.
Step 1: Statutory Category: – Yes
The claims recite a method, a non-transitory computer-readable medium, and a control unit, which falls under the statutory category of processes under 35 U.S.C. § 101. See MPEP 2106.03.
Step 2A Prong One Evaluation: Judicial Exception – Yes – Mental Processes
In Step 2A, Prong one of the 2019 Patent Eligibility Guidance (PEG), a claim is analyzed to determine whether it recites subject matter that falls within one of the following groups of abstract ideas: a) mathematical concepts, b) mental processes, and/or c) certain methods of organizing human activity.
The Office submits that the foregoing bolded limitation(s) constitute judicial exceptions in terms of “mental processes” because, under their broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using pen and paper.” See MPEP 2106.04(a)(2)(III).
The claims recite determining, based on the road roughness of the acquired road topography data, a respective maximum allowable tire load for each individual tire of the vehicle for said predefined route, and determining, based on the determined maximum allowable tire loads, a recommended maximum load for the vehicle for said predefined route. Analyzing and evaluating information using mathematical relationships or models (e.g., determining maximum allowable tire load based on road roughness, deformation, wear data, or tire identification data; determining a recommended maximum vehicle load based on per-tire allowable loads); and outputting a result or recommendation (e.g., a recommended maximum load for a vehicle for a predefined route). These steps constitute mental processes and mathematical concepts that could be performed by a human using pen and paper or a generic computer, such as comparing data values, applying known relationships between variables (road roughness, tire load, wear), and producing a recommendation. Accordingly, the claims recite abstract ideas.
Step 2A Prong Two Evaluation: Practical Application: -No
In Step 2A, Prong two of the 2019 PEG, a claim is evaluated to determine whether it integrates the recited judicial exception into a practical application. As noted in MPEP 2106.04(d), it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the judicial exception. The courts have indicated that additional elements such as merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitation(s) recite additional elements that do not integrate the recited judicial exception into a practical application.
The claims do not integrate the abstract idea into a practical application. Collecting information (e.g., acquiring road topography data, road roughness data, tire deformation data, tire wear data, tire identification data). Although the claims recite vehicles, tire sensors, control units, cameras, detectors, or computer-readable media, these elements are generic and conventional components used only as tools to gather data or execute calculations; and do not impose any meaningful limits on the abstract idea itself. The claims do not recite a specific improvement to the functioning of a computer, processor, sensor, a specific improvement to tire hardware, sensing architecture, or vehicle control mechanisms. Instead, the claims merely apply the abstract idea of evaluating road and tire data to determine allowable loads in a vehicle context, which is an environmental limitation and does not amount to a practical application.
Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B Evaluation: Inventive Concept: -No
In Step 2B of the 2019 PEG, the claim(s) are evaluated to determine whether they amount to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using a generic computer component and a control unit. Claims 11-14 merely implement the abstract idea using: a computer-readable medium, a control unit, or a vehicle including sensors and processors, all of which are well-understood, routine, and conventional components used in their ordinary capacity to collect data, perform calculations, and store or output results. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception on a generic computer cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B, MPEP 2106.05(f). Claims 11-14 recite a non-transitory computer-readable medium, a control unit, and a vehicle configured to perform the method of claim 1, merely implement the same abstract idea using generic computer and vehicle components operating in their conventional manner. Implementing an abstract idea on a computer-readable medium or within a generic control unit or vehicle does not render the abstract idea patent-eligible. Thus, the claims are ineligible.
Dependent Claims
Dependent claims 2, 3, and 5-9 merely add further limitations directed to additional sources or types of data (e.g., tire deformation data, tire wear data, tire identification data, optical or wave-based sensors, or vehicle-to-vehicle data acquisition) and/or specify that the abstract idea is implemented using generic sensing or data-acquisition techniques. Such limitations do not meaningfully limit the scope of the abstract idea recited in independent claim 1, nor do they effect a transformation of the abstract idea into a practical application. Instead, these dependent claims merely recite insignificant extra-solution activity or field-of-use limitations, and therefore do not add “significantly more” than the abstract idea itself.
Therefore, claims 1-3, 5-9, and 11-14 are ineligible under 35 USC § 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, 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 1-7 and 11-14 are rejected under § 103 as being unpatentable over Tang (US 20160290815 A1), in view of Garbelli (US 20200041297 A1), and further in view of Singh (US 20140257629 A1).
Regarding Claim 1, Tang discloses a method for determining a recommended maximum load for a vehicle to be operated along a predefined route (See Fig. 1), comprising:
acquiring road … data for said predefined route, the road … data containing information about the … data of the road along said predefined route [0030] “The road condition information may include weather information, maximum load limits on possible drive routes (i.e., predefined route) that may be planned for the vehicle, and maximum height limits on the possible drive routes. A maximum load limit on a possible drive route or a maximum height limit on the possible drive route may be obtained by analyzing and screening road sections on the possible drive route according to a maximum load limit or a maximum height limit on each road section.
determining, based on the acquired road … data, a respective maximum allowable tire load for each individual tire of the vehicle for said predefined route [0043] “a current load of the vehicle is computed according to the shape change information or the tire pressure information (i.e., a respective maximum allowable tire load), and it is judged whether the current load of the vehicle exceeds the maximum load limit on a drive route planned for the vehicle. If the current load of the vehicle exceeds the maximum load limit on the planned drive route, an alarm is sent to a user and an alternative safe route is planned.” [0033] “After planning the drive route for the vehicle, the terminal obtains a maximum load limit on the planned drive route based on maximum load limits of bridges and road surfaces on each section of the planned drive route, computes a current load of the vehicle according to the shape change information or the tire pressure information, and determines whether the current load of the vehicle exceeds the maximum load limit on the planned drive route.” [0031] “For example, one camera may be fixed above each tire of the vehicle, and the camera may record pictures of the tire periodically and then save the recorded pictures in a preset memory device.” [0043] “a current load of the vehicle is computed according to the shape change information or the tire pressure information, and it is judged whether the current load of the vehicle exceeds the maximum load limit on a drive route planned for the vehicle. If the current load of the vehicle exceeds the maximum load limit on the planned drive route, an alarm is sent to a user, and an alternative safe route is planned.”
determining, based on the determined maximum allowable tire loads, a recommended maximum load for the vehicle for said predefined route [0043] “a current load of the vehicle is computed according to the shape change information or the tire pressure information, and it is judged whether the current load of the vehicle exceeds the maximum load limit on a drive route planned for the vehicle. If the current load of the vehicle exceeds the maximum load limit on the planned drive route, an alarm is sent to a user and an alternative safe route is planned.”
Tang does not explicitly disclose acquiring road topography data including measured road roughness by sensors configured to measure tire deformation or road surface irregularities or determining a respective maximum allowable tire load based on measured road roughness and tire-specific load characteristics.
However, Garbelli tire level load estimation and route topography characteristics wherein acquiring road topography data for said predefined route, the road topography data containing information about the topography of the road along said predefined route [0166] “A length PL of the tire contact area could be then estimated and the tire pressure and the length PL can be then used to estimate the load exerted by the vehicle on the tire.” [0170] “the vehicle weight can be estimated as the sum of the estimated load exerted by the vehicle on each of the four tires.” [0173] “The route characteristics taken into consideration by the vehicle control system (12) in assessing which is the best route to take can include travel length of each possible route and additional important parameters related to the route, such as gradients and downhill slopes (i.e., topography data) encountered along a route”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tang to include the features of Garbelli to have the system acquire road topography data for said predefined route, the road topography data containing information about the topography of the road along said predefined route.
A person that is skilled in the art would have been motivated to combine Tang and Garbelli teachings improve safety and operation of the system [0174] “the vehicle control system (12) can thus suggest the vehicle driver which, among two or more alternative routes available for reaching a destination from an origin, is the better route (block 709 in FIG. 7), for reasons of safety: for example, when the weight is greater than a predetermined value, the vehicle control system will recommend the route with less uphill’s/downhills and/or the degree of windings or curves.”
Tang and Garbelli do not appear to explicitly disclose “the road topography data containing information about the topography of the road including road roughness along said predefined route, determining, based on the measured road roughness of the acquired road topography data and tire specific load characteristics”
However, Singh teaches the missing roughness-based sensor and tire-specific load features wherein the road topography data containing information about the topography of the road including road roughness along said predefined route, determining, based on the measured road roughness of the acquired road topography data and tire specific load characteristics [0080] teaches that each tire is “equipped with a sensor package 14,” including “a vibration sensor mounted for measuring tire deformation during tire operation” and [0081] teaches that “the piezo-sensor within module 14 generates a signal indicative of tire deformation within a rolling tire footprint” and [0082] teaches that “road roughness affects tire deformation” and that the adaptive filter “takes into account road Surface roughness in the load estimation procedure.” [0083] also teaches that the algorithm “utilizes a tire mounted sensor to estimate both tire load and road roughness.” [0084] also teaches that the algorithm “takes into account road roughness during load estimation.” [0088] teaches that “a tire-specific look-up table provides tire loading based on inputs of Footprint Length, Tire Identification, and Tire Inflation Pressure.” [0145] teaches estimating road profile height and using it “to estimate the tire load variation caused due to road undulations.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Tang and Garbelli to include the sensor-based tire deformation, road roughness estimation, and tire-specific load estimation teachings of Singh in order to improve the accuracy and safety of route-dependent load determinations under real-world driving conditions. Tang already teaches determining whether a vehicle load is permissible for a planned route based on route load limits. Garbelli teaches estimating the load exerted on individual tires and estimating vehicle weight from those individual tire loads. Singh teaches that road roughness affects tire deformation and tire load estimation and that road roughness should be considered during load estimation to more closely reflect real-world driving conditions.
A person that is skilled in the art would have been motivated to combine Tang, Garbelli, and Singh motivated to include Singh road-roughness-adaptive, tire-specific load estimation technique in Tang and Garbelli’s vehicle route/load system to improve safety, load accuracy, tire performance, and route-specific vehicle operation Singh [0084] expressly states that the algorithm “takes into account road roughness during load estimation and, as such, more closely reflects real-world driving conditions.”
Regarding Claim 2, The combination of Tang, Garbelli, and Singh teaches the method according to claim 1, wherein the step of acquiring road topography data comprises:
Tang discloses causing said vehicle or another vehicle to travel along said predefined route [0030] “maximum load limits on possible drive routes (i.e., predefined route) that may be planned for the vehicle, and maximum height limits on the possible drive routes.”
acquiring tire deformation data representative of … of the tires of said vehicle or said another vehicle occurring when said vehicle or said another vehicle travels along said predefined route [0031] “while planning a drive route for a vehicle, the terminal first obtains the shape change information (i.e., tire deformation data) and tire pressure information of one or more tires of the vehicle as well as the real-time road condition information. The shape change information of the tires of the vehicle may be collected via one or more cameras preset on the vehicle at fixed locations. For example, one camera may be fixed above each tire of the vehicle, and the camera may record pictures of the tire periodically, and then save the recorded pictures in a preset memory device.”
determining the road … data based on the acquired tire … data [0030] “the status information of the tires of the vehicle may include shape change information or tire pressure information of the tires of the vehicle. The shape change information may be the height change information or volume change information of the tires of the vehicle, etc. The road condition information may include weather information, maximum load limits on possible drive routes that may be planned for the vehicle, and maximum height limits on the possible drive routes. A maximum load limit on a possible drive route or a maximum height limit on the possible drive route may be obtained by analyzing and screening road sections on the possible drive route according to a maximum load limit or a maximum height limit on each road section.” A person that is skilled in the art would understand that tire deformation (shape change and vertical mode response) reflects, in part, the underlying road profile (including roughness) as the vehicle travels the route, and thus would find it obvious to infer road topography/roughness characteristics from such deformation and vibration data.
Tang and Garbelli do not appear to explicitly disclose “including tire deformation in the data to”
However, Singh teaches equivalent teachings wherein including tire deformation in the data to [0081] teaches “The piezo-sensor within module 14 generates a signal indicative of tire deformation within a rolling tire footprint.” [0082] teaches “road roughness affects tire deformation” [0083] “The use of an algorithm is proposed that utilizes a tire mounted sensor, preferably a piezo-sensor, to estimate both tire load and road roughness.” [0087] “the raw signal from piezo-sensor of module 14, in addition to being used in an initial raw footprint length estimation 54, is also used in a roughness estimation algorithm 52 shown.” [0087] further teaches “The Adaptive Kalman Filter 56 uses Filter parameters which are tuned as a function of the road Surface condition by Roughness Estimation Algorithm 52.” [0148] teaches “road roughness may be considered as micro-road profile changes and road profile change may be considered as a macro-road roughness variation.” Therefore, Singh teaches using tire deformation data from a tire-mounted sensor to determine road roughness / road profile data, which corresponds to determining road topography data based on acquired tire deformation data.
It would have been obvious to a person of ordinary skill in the art before the effective filling date to combine Tang, Garbelli, and Singh to modify Tang’s system, which already acquires tire shape-change information during route planning, to use Singh tire deformation based road roughness / road profile estimation so that the road topography data for the predefined route is determined from the acquired tire deformation data.
A person that is skilled in the art would have been motivated to combine Tang, Garbelli, and Singh motivated to include Singh road-roughness-adaptive, tire-specific load estimation technique in Tang and Garbelli’s vehicle route/load system to improve safety, load accuracy, tire performance, and route-specific vehicle operation Singh [0084] expressly states that the algorithm “takes into account road roughness during load estimation and, as such, more closely reflects real-world driving conditions.”
Regarding Claim 3, The combination of Tang, Garbelli, and Singh teaches the method according to claim 2, Tang discloses wherein the tires of said vehicle or said another vehicle are provided with tire sensors configured to register deformation of the individual tires when said vehicle or said another vehicle travels along said predefined route wherein said step of acquiring tire deformation data comprises: acquiring tire deformation data by means of said tire sensors when said vehicle or said another vehicle travels along said route [0031] “The tire pressure information of the tires of the vehicle may be collected by a preset tire pressure measurement device (i.e., tire sensors), and after collecting, the collected data may be saved (i.e., registering) in the preset memory device or manually input to the preset memory device by the user.” [0032] “After obtaining the shape change information and the tire pressure information of the tires of the vehicle as well as the real-time road condition information, the terminal may perform the route navigation according to the obtained information and a preset navigation strategy, and plan a safe drive route for the vehicle.”
Regarding Claim 4, The combination of Tang, Garbelli, and Singh teaches the method according to claim 3, comprising, prior to said step of acquiring road topography data for said predefined route: Tang discloses mounting, on each tire of said vehicle or said another vehicle, a plurality of said tire sensors such that they are distributed in the … of the tire [0031] “one camera (i.e., sensor) may be fixed (i.e., mounted) above each tire of the vehicle, and the camera may record pictures of the tire periodically, and then save the recorded pictures in a preset memory device. The terminal may obtain the shape change information of the tire by reading the pictures from the memory device and comparing the pictures to obtain the shape change information such as the height difference change or the volume change.”
or integrating, during manufacturing of each tire of said vehicle or said another vehicle, a plurality of said tire sensors in the rubber of the tire such that they become distributed in the … of the completed tire. [0031] “The tire pressure information of the tires of the vehicle may be collected by a preset tire pressure measurement device, and after collecting, the collected data may be saved in the preset memory device or manually input to the preset memory device by the user.”
Tang does not disclose claim elements regarding the tire sensor such that they are distributed in the circumferential direction of the tire. Therefore, Tang does not appear to explicitly disclose “such that they are distributed in the circumferential direction of the tire.”
However, Garbelli teaches equivalent teachings wherein such that they are distributed in the circumferential direction of the tire [0023] “the length of the tire contact area represents on average only a small portion (3% to 15%) of the tire circumference, so that the tire monitoring unit (i.e., tire sensor) remains in correspondence of the tire contact area for a very small amount of time over a tire roundtrip.” The sensor is located in the contact area which is a small area on the tire circumference direction. A person that is skilled in the art would understand that tire sensors are installed on the circumferential direction of the completed tire during manufacturing.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tang to include the features of Garbelli to have the system include tire’s sensors such that they are distributed in the circumferential direction of the tire.
A person that is skilled in the art would have been motivated to combine Tang and Garbelli teachings improve safety and operation of the system [0174] “the vehicle control system (12) can thus suggest the vehicle driver which, among two or more alternative routes available for reaching a destination from an origin, is the better route (block 709 in FIG. 7), for reasons of safety: for example, when the weight is greater than a predetermined value, the vehicle control system will recommend the route with less uphill’s/downhills and/or the degree of windings or curves.”
Regarding Claim 5, The combination of Tang, Garbelli, and Singh teaches the method according to claim 3, Tang discloses wherein the tire sensors are configured to register tire deformation in a plurality of directions, such as the longitudinal, lateral and vertical directions of the respective tire. [0031] “The shape change information of the tires of the vehicle may be collected via one or more cameras preset on the vehicle at fixed locations. For example, one camera may be fixed above each tire of the vehicle, and the camera may record pictures of the tire periodically, and then save the recorded pictures in a preset memory device. The terminal may obtain the shape change information of the tire by reading the pictures from the memory device and comparing the pictures to obtain the shape change information such as the height difference change or the volume change. The tire pressure information of the tires of the vehicle may be collected by a preset tire pressure measurement device, and after collecting, the collected data may be saved in the preset memory device or manually input to the preset memory device by the user.” The system collects tire deformation data by cameras mounted on top of each tire which is understood to a person that is skilled in the art to monitor all directions on the tire.
Regarding Claim 6, The combination of Tang, Garbelli, and Singh teaches the method according to claim 3, Tang discloses wherein said step of determining a respective maximum allowable tire load comprises: determining the respective maximum allowable tire load for each individual tire based on said acquired tire deformation data. [0033] “the terminal obtains a maximum load limit on the planned drive route based on maximum load limits of bridges and road surfaces on each section of the planned drive route, computes a current load of the vehicle according to the shape change information (i.e., acquired tire deformation data) or the tire pressure information, and determines whether the current load of the vehicle exceeds the maximum load limit on the planned drive route. If the current load of the vehicle exceeds the maximum load limit on the planned drive route, the planned drive route may be determined as a dangerous road, and in this case, an alarm may be sent to the user, and an alternative safe route may be planned to avoid the dangerous road.”
Regarding Claim 7, The combination of Tang, Garbelli, and Singh teaches the method according to claim 1, Tang discloses wherein the step of acquiring road topography data comprises: causing said vehicle or another vehicle to travel along said predefined route “maximum load limits on possible drive routes (i.e., predefined route) that may be planned for the vehicle, and maximum height limits on the possible drive routes.”
wherein said vehicle is equipped with an optical camera or with a detector comprising a wave emitter and a wave receiver for receiving a reflected wave, wherein the detector is suitably one of a Lidar, radar or ultrasonic detectors, scanning the road by means of the optical camera or detector when said vehicle or said another vehicle travels along said route. [0075] “the navigation device 800 may include one or more of following components: a processing component 801, a memory 802, a power component 803, a multimedia component 804, an audio component 805, an input/output (I/O) interface 806, a sensor component 807, and a communication component 808.” [0079] “the multimedia component 804 includes a front camera and/or a rear camera.” [0082] “The sensor component 807 includes one or more sensors to provide status assessments of various aspects of the device 800. For instance, the sensor component 807 may detect an open/closed status of the device 800 and relative positioning of components (e.g., the display and the keypad of the device 800). The sensor component 807 may also detect a change in position of the device 800 or of a component in the device 800, a presence or absence of user contact with the device 800, an orientation or an acceleration/deceleration of the device 800, and a change in temperature of the device 800. The sensor component 807 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor component 807 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications.” The system uses sensors and optical cameras which are mounted on front and rear of the vehicle.
Regarding Claim 11, The claim recites a non-transitory computer readable medium [0086] “instructions in the storage medium are executed by the processor of a terminal” and the parallel limitations in claim 1, respectively for the reasons discussed above. Therefore, claim 11 is rejected using the same rational reasoning.
Regarding Claim 12, Tang discloses a control unit for determining a recommended load for a vehicle to be operated along a predefined route, the control unit being configured to perform the steps of the method according to claim 1. [0076] “The processing component 801 typically controls overall operations of the device 800, such as the operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing component 801 may include one or more processors 809 to execute instructions to perform all or part of the steps in the above-described methods.
Regarding Claim 13, The combination of Tang, Garbelli, and Singh teaches a vehicle comprising: Tang discloses a control unit according to claim 12, tire sensors configured to register deformation of the individual tires when the vehicle is in motion and configured to generate said tire deformation data, wherein the control unit is configured to acquire the generated tire deformation data [0031] “while planning a drive route for a vehicle, the terminal first obtains the shape change information (i.e., tire deformation data) and tire pressure information of one or more tires of the vehicle as well as the real-time road condition information. The shape change information of the tires of the vehicle may be collected via one or more cameras preset on the vehicle at fixed locations. For example, one camera may be fixed above each tire of the vehicle, and the camera may record pictures of the tire periodically, and then save the recorded pictures in a preset memory device.”
wherein each tire of the vehicle is provided with a plurality of said tire sensors such that they are distributed in the circumferential direction of the tire. [0072] “The processor is configured to obtain status information of tires of a vehicle; obtain real-time road condition information; and perform a route navigation for the vehicle according to the status information, the road condition information and a preset navigation strategy.”
Regarding Claim 14, The combination of Tang, Garbelli, and Singh teaches the method according to claim 1, Tang discloses wherein said vehicle for which a recommended load is to be determined is a first vehicle, the method comprises using a second vehicle configured for acquiring said road topography data based on the generated tire deformation data. [0031] “while planning a drive route for a vehicle, the terminal first obtains the shape change information (i.e., tire deformation data which indicates road topography change) and tire pressure information of one or more tires of the vehicle as well as the real-time road condition information. The shape change information of the tires of the vehicle may be collected via one or more cameras preset on the vehicle at fixed locations. For example, one camera may be fixed above each tire of the vehicle, and the camera may record pictures of the tire periodically, and then save the recorded pictures in a preset memory device.” [0083] “The communication component 808 is configured to facilitate wired or wireless communication between the device 800 and other devices. The device 800 can access a wireless network based on a communication standard, such as WIFI, 2G; or 3G; or a combination thereof. In one exemplary embodiment, the communication component 808 receives a broadcast signal or broadcast associated information from an external broadcast management system via a broadcast channel.” The system communicates and shares information with other devices (i.e., vehicles).
Claim 8 is rejected under § 103 as being unpatentable over Tang (US 20160290815 A1), in view of Garbelli (US 20200041297 A1), and further in view of Singh (US 20140366618 A1).
Regarding Claim 8, The combination of Tang, Garbelli, and Singh teaches the method according to claim 1, Tang discloses wherein said step of determining a respective maximum allowable tire load comprises: accessing stored tire wear data, in which expected wear of an individual tire is represented as a function of forces applied to the individual tire over time [0038] “a tire wear degree may be obtained, and a distance along which the vehicle may drive safely may be determined according to the tire wear degree. If the tire wear degree is high, and the distance along which the vehicle may drive safely is less than the distance to destination along the planned drive route, an alarm may be sent to the user, and a closer route to the destination may be planned or a drive route to the nearest maintenance station may be planned.” [0042] The Applicant surprisingly found that such statistical approach leads to a very precise estimation of the length of the tire contact area, and/or of other parameters related to tires, such as the load exerted by the vehicle on the tires. From this, the actual vehicle weight can be accurately estimated. [0077] “The memory 802 is configured to store various types of data to support the operation of the device 800. Examples of such data include instructions for any applications or methods operated on the device 800, contact data, phonebook data, messages, pictures, video, etc.”
Tang and Garbelli do not appear to explicitly disclose “determining the respective maximum allowable tire load for each individual tire based on said stored tire wear data”
However, Singh teaches equivalent teachings wherein including road roughness level in the data to determine a maximum tire load [0064] Referring to FIG. 1, a tire tread wear estimation system 10 is shown based on spectral analysis of the tire vertical vibration signal. Such a system is useful in advising a vehicle owner on when to change tires and may be used to provide a driver with information on the interrelation between the state of tire tread wear and other factors such as road condition. Tire properties generally change as a function of tire wear. Accordingly, an estimate of tire tread wear level may be used as one input for tire state estimation. [0069] “other factors were determined which influence tire vertical mode frequency. Those other factors include tire load, rolling speed and road roughness level (smooth versus rough or very rough). Inflation pressure affects the vertical stiffness of a tire; tread depth affects belt mass (m); vertical load affects impact force; rotational velocity affects impact force and stiffness; and road roughness affects input excitation. The graphs 34, 36, 38, 40 shown respectively in FIGS. 3A, 3B, 4A, 4B show experimentally how different operating conditions influence the resonance frequencies of the tire. Cleated wheel tests were performed on a fixed spindle machine. Cleat inputs are known to introduce torsional and vertical excitations in a tire while the spindle machine controls tire load and rolling speed. The road roughness effects are captured by using cleats of different sizes and the inflation pressure was manually changed prior to each test. The wear dependencies were captured by using tires with different levels of non-skid depth.” A person that is skilled in the art would understand that road roughness level is another route/road parameter that significantly affects input excitation, tire vertical mode, and thereby forces and wear.
It would have been obvious to a person of ordinary skill in the art before the effective filling date to combine Tang, Garbelli, and Singh to make the system determining the respective maximum allowable tire load for each individual tire based on said stored tire wear data.
A person that is skilled in the art would have been motivated to combine Tang, Garbelli, and Singh to improve safety and overall life of the tire to reduce wear and tear based on road conditions Singh [0003] Other factors such as tire wear state are important considerations for vehicle operation and safety. It is accordingly further desirable to measure tire wear state and communicate wear state to vehicle systems such as braking and stability control systems in conjunction with the measured tire parameters of pressure and temperature.
Claim 9 is rejected under § 103 as being unpatentable over Tang (US 20160290815 A1), in view of Tanno (US 20170308749 A1), and further in view of Singh (US 20140366618 A1.)
Regarding Claim 9, The combination of Tang, Garbelli, and Singh teaches the method according to claim 1, Tang discloses wherein said step of determining a respective maximum allowable tire load comprises: accessing tire identification data, wherein the tire identification data comprises information about the tires of said vehicle, such as type of tire, manufacturer, model of the tire, and/or dimensions of the tire. [0030] “The road condition information (i.e., road topography data) may include weather information, maximum load limits on possible drive routes (i.e., predefined route) that may be planned for the vehicle, and maximum height limits on the possible drive routes.
Tang fails to teach the claim limitations elements “regarding tire identification data.”
However, Tanno teaches equivalent teachings wherein accessing tire identification data, wherein the tire identification data comprises information about the tires of said vehicle, such as type of tire, manufacturer, model of the tire, and/or dimensions of the tire [0030] “In the first aspect of the present technology, the unique information may include at least one of a tire manufacturing date, a maximum tire traveling speed, a tire dimension, a tire type, or a tire performance.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Tang to include the features of Tanno to have the system accessing tire identification data.
A person that is skilled in the art would have been motivated to combine Tang and Tanno teachings improve safety and operation of the system [0028] “As a result, a safety state of the pneumatic tire, including the wear state of the tread portion, is understood by the following vehicle. When the wear state of the tread portion is great, the vehicle itself on which the pneumatic tire is mounted has a greater possibility of having a longer braking distance, or slipping.”
Tang and Tanno do not appear to explicitly disclose “determining the respective maximum allowable tire load based on said tire identification data and said road topography data”
However, Singh teaches equivalent teachings wherein determining the respective maximum allowable tire load based on said tire identification data and said road topography data [0103] “From the foregoing, it will be appreciated the subject tread wear estimation system utilizes a novel algorithm to estimate the tire wear state. Tire wear state is recursively estimated by using a RLS algorithm formulated based on a polynomial model which captures the dependencies between the tire wear state, inflation pressure and the tire vertical mode frequency. The model inputs for the RLS algorithm include tire inflation pressure, tire ID (required for using the correct tire specific model coefficients) and the tire vertical mode frequency. The tire inflation pressure and tire ID information is available from a tire attached TPMS module.” [0064] Referring to FIG. 1, a tire tread wear estimation system 10 is shown based on spectral analysis of the tire vertical vibration signal. Such a system is useful in advising a vehicle owner on when to change tires and may be used to provide a driver with information on the interrelation between the state of tire tread wear and other factors such as road condition. Tire properties generally change as a function of tire wear. Accordingly, an estimate of tire tread wear level may be used as one input for tire state estimation. [0069] “other factors were determined which influence tire vertical mode frequency. Those other factors include tire load, rolling speed and road roughness level (smooth versus rough or very rough). Inflation pressure affects the vertical stiffness of a tire; tread depth affects belt mass (m); vertical load affects impact force; rotational velocity affects impact force and stiffness; and road roughness affects input excitation. The graphs 34, 36, 38, 40 shown respectively in FIGS. 3A, 3B, 4A, 4B show experimentally how different operating conditions influence the resonance frequencies of the tire. Cleated wheel tests were performed on a fixed spindle machine. Cleat inputs are known to introduce torsional and vertical excitations in a tire while the spindle machine controls tire load and rolling speed. The road roughness effects are captured by using cleats of different sizes and the inflation pressure was manually changed prior to each test. The wear dependencies were captured by using tires with different levels of non-skid depth.” A person that is skilled in the art would understand that road roughness level is another route/road parameter that significantly affects input excitation, tire vertical mode, and thereby forces and wear.
It would have been obvious to a person of ordinary skill in the art before the effective filling date to combine Tang, Garbelli, and Singh to access tire identification data for each tire (type, dimensions, performance limits), and to use this data together with road topography/roughness data (from Tang and Singh) to compute the maximum allowable tire load consistent with that tire’s specifications and behavior under the given route conditions. That is, a particular tire model may support different allowable loads depending on its construction, size, and performance limits; combining this knowledge with route topography/roughness data to determine allowable loads is a predictable implementation.
A person that is skilled in the art would have been motivated to combine Tang, Garbelli, and Singh to improve safety and overall life of the tire to reduce wear and tear based on road conditions [Singh 0003] Other factors such as tire wear state are important considerations for vehicle operation and safety. It is accordingly further desirable to measure tire wear state and communicate wear state to vehicle systems such as braking and stability control systems in conjunction with the measured tire parameters of pressure and temperature.
Conclusion
The following prior art is considered pertinent to the claimed invention but is not relied
upon in the rejection. Morinaga (US 7546764 B2) teaches measuring tire deformation using tire sensors to estimate tire dynamic state quantities such as load, lateral force, or longitudinal force, but does not teach determining a route-specific recommended maximum vehicle load based on measured road roughness and tire-specific maximum allowable tire loads. Giustino (US 6550320 B1) teaches predicting tire forces using tire deformation sensors and preprogrammed equations but does not teach acquiring road topography data including road roughness along a predefined route or deriving a recommended maximum vehicle load from per-tire maximum allowable loads. Singh (US 9840118 B2) teaches tire sensor-based road surface roughness classification.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/HUSSAM ALDEEN ALZATEEMEH/Examiner, Art Unit 3662
/CHRISTOPHER GEORGE FEES/Primary Examiner, Art Unit 3662
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