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 .
Response to Amendment
Claims 1-8 and 16-20 are amended, claim 15 is cancelled, and claims 22-23 are new. Claims 1-14 and 16-23 are pending.
Response to Arguments
Applicant's arguments filed 1/28/2026 have been fully considered and are partially persuasive.
Regarding the rejections of claims 1-21 under 101, Examiner finds Applicant’s arguments on pages 8-11 that the amendments overcome the rejections persuasive. In particular, Examiner finds that the elements added by amendment to independent claims 1 and 16 result in additional elements that integrate the judicial exception into a practical application such that amended claims 1, 16, and all claims depending therefrom are not directed to the judicial exception and are therefore patent eligible under 101.
The rejections of claims 1, 16 and all claims depending therefrom under 101 are therefore withdrawn.
Regarding the rejections of independent claims 1 and 16 under 103, Examiner finds that the amendments to claims 1 and 16 and particularly the feature “wherein the tire monitoring device is configured to delete from the local memory values indicative of the tire parameter obtained during the first time period in conjunction with the storage of the performance coefficient” overcomes the previous grounds of rejection combining Bill (US 2021/0101423 A1) in view of Shaw (US 2006/0082451 A1) under 103. In view of further search and analysis, claims 1 and 16 are rejected under 103 as unpatentable over Bill in view of Shaw and in further view of Cibaud (US 2017/0359194 A1).
Regarding Applicant’s particular arguments for why the rejections are overcome, the Examiner respectfully disagrees with some of the arguments for the following reasons.
On pages 12-13 of the response, Applicant notes that the Non-Final Office Action cites Bill as teaching a “performance characteristic” (e.g., [0056]) and contends that the “trends” disclosed by Bill do not constitute an intermediate value (“performance characteristic” as recited in the claims) and are not used with more recently obtained values of tire pressures to determine a characteristic (“performance characteristic” as recited in the claims) of a tire.
The Examiner submits that in disclosing ongoing collection of sensor data used for determining trends “over time” of reductions in tire pressure, Bill teaches both a performance coefficient (a point in the trend at any given time indicating a level of tire pressure) and the performance characteristic determined based on the performance coefficient and additional sensor data (a future point in the trend that forms an overall, extended trend including the previous trend point and subsequent trend information).
On page 12 of the response, Applicant contends that Shaw does not disclose a performance coefficient of a tire that is used with more recently obtained values of a tire parameter to determine a performance characteristic. Applicant further asserts that Shaw fails to provide a motivation or rationale for using a performance coefficient with the system/method disclosed by Bill.
The Examiner notes that Bill, not Shaw, is/was not applied as teaching the “performance coefficient,” the “performance characteristic” and the relation therebetween. Shaw is applied in the rejections as teaching a performance coefficient corresponding to performance of a tire is stored in local memory and the motivation provided in the rejection is therefore related to this localized storage feature.
On page 13 of the response, Applicant characterizes the motivation to combine Shaw’s teachings as set forth in the rejections of the independent claims, in a more generalized manner than as actually set forth in the rejections. Applicant contends that the proposed motivation would not have reasonably caused a person of ordinary skill to modify Bill using Shaw “to form the claimed invention. The purported motivations do not provide a rationale for using a performance coefficient as an intermediate value with recently obtained values of a tire parameter to determine a performance characteristic of a tire on an aircraft.”
Examiner notes that Shaw is applied as teaching the local storage of an analogous “performance coefficient” and Applicant’s arguments do not address whether the motivation set forth in the rejections is effective in conveying a motivation/rationale to combine this aspect of Shaw with the teachings of Bill.
On page 14 of the response and regarding claims 5 and 19, Applicant contends, without specific support, that the combination of Bill and Shaw do not teach or suggest a tire performance characteristic that is based on a performance coefficient and the recently obtained values of a tire parameter.
Examiner submits that, as set forth in the current grounds of rejection of claims 1 and 5, Bill itself teaches a tire performance characteristic that is based on a performance coefficient and more recently obtain values (obtained during the “second time period”) of a tire parameter.
On page 14 and regarding claims 7 and 8, Applicant notes that amended claim 8 combines the different durations of the first and second time periods (25 day for determining values used for determining performance coefficient and 10 days for determining values used with performance coefficient to determine performance characteristic). Applicant contends that Bill’s teaching of a 25 day collection period is contrary to the requirement in claim 8 that the period should be no longer than 10 days and that the period described by Bill relates to collecting values that is continuously rolling with older data being overwritten. Applicant asserts that collecting values on rolling basis during which old values are overwritten by new values is a different approach than the claimed invention, which is to characterize historical values with the performance coefficient to determine a tire performance characteristic.
Examiner notes that Bill teaches a variety of different possible measurement collection intervals in association with corresponding storage and deletion of the measured/stored data in [0032]. Bill’s teaching in this regard is applied in the rejections of claims 7 and 8 as disclosing measurement collection periods that may encompass the 25 day and 10 day periods recited in claims 7 and 8. Examiner submits that Bill’s teaching of using a 25 day or 10 day collection interval is not contrary to a teaching of a maximum 10 day collection interval and furthermore notes that the “rolling” collection/deletion relates to deleting an oldest set of potentially multiple stored sets of the measurement data collected over any of the possible collection intervals as disclosed in [0032]. Examiner submits that Bill’s teachings of collection of measurement data over any of a variety of possible time periods (with collection intervals within each period of, for example, 3 hours as disclosed in [0052]-[0053]) and continuous monitoring of such data for determining pressure reduction trends is not contrary to the claimed invention in terms of characterizing historical values with the performance coefficient to determine a tire performance characteristic.
On page 15, and in further regarding claims 7 and 8, Applicant contends that the 25 day or longer (presumably with the addition of “no more than 10 days”) period is longer than the periods discussed by Bill.
Examiner submits that the individual collection periods disclosed by Bill exceed 25 days, Bill ([0031]-[0032] “one or more days” “at least 120 hours”) teaches storing the respective data over multiple consecutive such periods, such that the effective collection periods would extend beyond 25 days.
On page 15, and in further regarding claims 7 and 8, Applicant contends that the combined use of periods having significantly different time spans (at least 25 days and no more than 10 days) over which collected data is used to determine the performance coefficient and performance characteristic is not disclosed by the combination of Bill and Shaw.
Examiner acknowledges that Bill does not explicitly teach the specific combination of time intervals in which a first interval for determining an intermediate “performance coefficient” (trend in deflation at a particular time point) is at least 25 days and a second interval for determining a subsequent “performance characteristic” is no more than 10 days. However, as noted in the grounds for rejecting claim 1, the “performance characteristic” as disclosed by Bill may be a time-based trend that per [0055]-[0056] is “monitored” “over time” suggesting that a performance characteristic may be determined at any of a given number of periods that per [0032] may constitute one or more days on a rolling (continuous) basis in accordance within any of a range of measurement time intervals that may span from “no more than 10 days” to “at least 25 days, and in which a corresponding “performance coefficient” that is entailed within the “performance characteristic” as explained in the grounds for rejecting claim 1, is similarly determined in a continuous manner and therefore may be determined in accordance with (including within) any of a range of measurement time intervals that may span from “no more than 10 days” to “at least 25 days.”
It would have been obvious to one of ordinary skill in the art before the effective filing date, in view of the combined teachings of Bill of a variety of possible time periods including “no more than 10 days” and “at least 25 days” designated for collecting sensor measurement data, and given that the determination of rate of pressure reduction is performed in a continuous manner (over time), to have configured the system such that a performance coefficient is determined for 25 day period and a performance characteristic is determined based on the performance coefficient and subsequent data collected over no more than 10 days. For example, in accordance with Bill’s disclosure a performance coefficient (trend in pressure reduction) may be determined over a sensor measurement/collection period of 25 day and subsequent data may be collected over an interval of ten days (e.g., within a subsequent 25 day sensor measurement/collection period) and utilized per the continuous trend monitoring described in [0020] and [0055]-[0056] to determine, during the monitoring, a performance characteristic based on the newer data over 10 days and also encompassing the previously determined trend data over 25 days.
On page 15 of the response and regarding claims 6 and 20, Applicant contends that Chong does not teach or suggest a tire monitoring device with a local memory storing a performance coefficient and more recently obtained values of tire parameters and that such features are not disclosed by Chong, Shaw, and Bill.
Examiner notes that Chong is only applied for supporting the motivation to combine Shaw’s teachings with Bill’s for achieving the results of strategically implemented distributed processing such as flexible access to data that may be stored at multiple locations and may be unavailable at any of the locations at any given time. Furthermore, Examiner notes that the combination of Bill and Shaw teaches the combined elements of claims 6 and 20 as set forth in the current grounds for rejecting claims 6 and 20 under 103.
Claim Objections
Claim 16 is objected to because of the following informalities:
In claim 16 line 10, “on conjunction” should read “in conjunction.”
Appropriate correction is required.
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.
Claim 23 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
In claim 23 lines 3-4, “the determining of the tire performance characteristic is determined by the tire monitoring device and stored in the local memory” is not sufficiently comprehensible in terms of what is “stored in local memory.” For purposes of examination, and based on apparent intent from the overall claim language, “the determining of the tire performance characteristic is determined by the tire monitoring device and stored in the local memory” is interpreted as “the determining of the tire performance characteristic is determined by the tire monitoring device based on data stored in the local memory.”
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 1-5, 7-14, 16-19, and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over Bill (US 2021/0101423 A1) in view of Shaw (US 2006/0082451 A1), and in further view of Cibaud (US 2017/0359194 A1).
As to claim 1, Bill teaches “[a] tire performance monitoring system (Abstract; FIG. 3 smart sensor device 122 and device 140) comprising:
a tire monitoring device (FIG. 3 smart sensor device 122 configured to monitor wheel 112) configured to obtain a plurality of values indicative of a tire parameter of a tire over a first time period (FIG. 3 smart sensor device 122 includes pressure sensor 124, temperature sensor 126, and accelerometer 132; [0011]-[0012] pressure/temperature readings performed for tire; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records multiple sensor readings over time (ongoing time periods); [0031]-[0032] method may be performed over periods spanning multiple flights/multiple days; [0055]-[0056] trends in tire pressure monitoring over time) and a second time period subsequent to the first time period ([0011]-[0012] control unit (e.g., CPU 128 in FIG. 3) records in memory unit (e.g., memory 131 in FIG. 3) pressure and temperature readings; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records multiple sensor readings over time (over ongoing time periods); [0030]; [0032] data collected on a rolling basis in which older data (first period) is deleted as new data (second period) is stored; [0055]-[0056] trends in tire pressure monitored over time (inherently entails multiple time periods)), the tire monitoring device comprising a local memory (FIG. 3 smart sensor device 122 includes memory 131 that is local to the sensor device and wheel 112 (and aircraft to which wheel is attached)) configured to store” “the plurality of values obtained during the second time period ([0032] data collected on a rolling basis in which older data (first period) is deleted as new data (second period) is stored; [0056] monitoring data recorded on the “memory unit” and may be downloaded from the “sensor unit” (i.e., the memory unit is part of the sensor unit)); and
a processing system (FIG. 3 CPU 128 in combination with memory 131 (local processing system) and device 140 (remote processing system that per [0056] may be a handheld device for processing data (e.g., evaluating and comparing)) configured to determine a tire performance characteristic based on the plurality of values and” [a] “performance coefficient ([0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] are collected over time) to determine a trend in pressure reduction over time. Examiner notes that the pressure reduction over time constitutes a performance coefficient (broadest reasonable interpretation of “performance coefficient corresponding to performance of the tire” in view of Applicant’s specification, which does not disclose actual application as a “coefficient,” appears to entail any relational numeric value such as, for example, a deflation rate that may be used in some manner as a coefficient). The trend in pressure reduction may also be a “tire performance characteristic” based on Applicant’s specification. As such, the “tire performance characteristic” is determined by determining the tire performance coefficient, which is determined based on the plurality of values, such that the tire performance characteristic is determined based on the plurality of values and the performance coefficient. In a distinct but related aspect, assuming an interpretation requiring that the tire performance characteristic be determined by multiple tire parameter values in addition to a given tire performance coefficient, the Examiner notes that the determining/monitoring of a trend in pressure reduction over time (effectively a curve) is performed as an ongoing process such that a most current interval in the trend is based on the previous portion of the trend and new tire parameter values.),
wherein the tire monitoring device is mounted to a wheel of an aircraft and the wheel includes the tire (FIG. 2 smart sensor device 122 mounted to wheel 112 (per FIG. 1 wheel of an aircraft) that includes tire 116),
wherein the performance coefficient is representative of the values of the tire parameter obtained during the first time period ([0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] and [0055]-[0056] are collected/monitored over time) to determine a trend in pressure reduction over time), and
wherein the tire monitoring device is configured to delete from the local memory values indicative of the tire parameter obtained during the first time period ([0032] data collected on a rolling basis in which older data (first period) is deleted as new data (second period) is stored; [0056] monitoring data recorded on the “memory unit” and may be downloaded from the “sensor unit” (i.e., the memory unit is part of the sensor unit)).”
Regarding a local memory “configured to store a performance coefficient corresponding to performance of the tire during the first period,” Bill teaches a local memory storing tire performance information and therefore capable of storing a tire performance coefficient (FIG. 3 smart sensor device 122 includes processor/memory (CPU 128/memory 131) configured for storing data such as measured values pressure/temperature/motion in association with (per [0011]-[0012]) times of collection, such that memory 131 is capable of storing a performance coefficient corresponding to performance of the tire; [0020] memory may store reference pressure; [0024]and [0030]-[0032] sensor device memory stores data related to multiple readings over time).
Shaw discloses a system/method for implementing a tire monitoring system (Abstract; FIG. 1 tire monitoring system 10 including wheel module 11 and corresponding receiver 12. Examiner notes this wirelessly connected system is similar to Bill’s disclosed “local” system in which for sensing connectivity reasons wireless communications are used between the sensors and processing components ([0022])) in which a performance coefficient corresponding to performance of a tire is stored in local memory ([0011] discloses that each of wheel module 11 and receiver 12 processes sensor data (e.g., wheel module 11 (via process controller 22) gathers temperature and pressure data at a given interval and identifies, based on pressure and temperature data, wheel motion and also whether pressure reaches or drops below threshold, and processing circuitry 28 detects air leaks, which inherently requires local memory (beyond the ROM expressly described) for each of wheel module 11 and receiver 12. FIG. 2, FIG. 3 block 38, and [0027]-[0028] depicting and describing determined conditions including decreasing tire pressure including trending over time (performance coefficient corresponding to a performance of a tire) and associated P/T ratio determined by the system (either or both wheel module 11 and receiver 12). Examiner notes that the generation of and further processing of decreasing pressure, which can only be determined by measurements over time, inherently entails storage of such pressure decrease; [0043] describing processing of pressure and temperature values over time (curve as depicted in FIG. 4) include P/T ratios, which, in addition to deflation rate, are also entailed within a broadest reasonable interpretation of tire performance coefficient because it conveys a relation over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of locally generating and storing performance values that are computed based on measured tire parameter values (e.g., pressure and temperature) to the system taught by Bill in which a tire processing system computes and stores performance coefficient values (e.g., trend over time of pressure reduction) using a system that includes a local system capable of storing such values, such that in combination the system includes a local memory “configured to store a performance coefficient corresponding to performance of the tire during the first time period.”
The motivation would have been to leverage processing and storage capabilities that are increasingly implemented with a local tire monitoring system/sub-system to locally generate and store such information that may be useful for determining tire operational condition and/or in determining further information relating to tire monitoring (e.g., a tire leakage condition) and/or in determining local monitoring device operating mode (e.g., collection rate) based on such information as suggested by Shaw and also to provide remote access to the various locally stored information as disclosed by Bill.
Regarding deleting from the local memory values indicative of the tire parameter obtained during the first time period “in conjunction with the storage of the performance coefficient,” Bill teaches the generalized concept of deleting older data in association with storage of newer data on a rolling basis ([0032]), but neither Bill nor Shaw expressly teach associating the deletion of a performance coefficient representative of a first collected set of tire performance data with the storage of a newer set of tire performance data.
Cibaud discloses a system/method for collecting and sharing monitoring sensor data (Abstract) that includes generating predictive model that is representative of measurement data collected over a period of time ([0072] compute predictive model that is representative of the data received over the period concerned) and deleting the data from which the model was computed in conjunction with generating (inherently entails storage of) the model ([0076] delete the data from which the model was computed in order to avoid unnecessarily occupying storage space on the server that generated the model. Examiner notes that the deletion is causally related to the generation and inherent storage of the model).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Cibaud’s teaching of deleting sensor measurement data in conjunction with storing data representative of the measurement data to the system taught by Bill as modified by Shaw, which teaches periodically deleted older sensor data as new data is obtained, such that in combination the tire monitoring device is configured to delete from the local memory values indicative of the tire parameter obtained during the first time period “in conjunction with the storage of the performance coefficient.”
The motivation would have been to conserve the capacity of limited data storage resources as disclosed by Cibaud.
As to claim 2, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the tire performance monitoring system comprises a remote memory remote from the tire monitoring device (Bill: FIG. 3 device 140 that per [0014] may be a separate “hand-held” device that receives and enable user access to stored data (inherently requires its own memory to receive and provide the information); Abstract data from sensor device uploaded to device 140; [0037] disclosing an additional remote memory “further computer” such as a central server that forms part of the overall system), and the processing system is configured to determine,” “based on the plurality of values obtained during the second time period and the performance coefficient stored in the local memory (as combined in the grounds for rejecting claim 1 Bill and Shaw teach or otherwise render obvious storing the performance coefficient (trend of pressure loss and/or P/T over ongoing time periods) in local memory), the tire performance characteristic (as combined in the grounds for rejecting claim 1 the combination of Bill and Shaw teaches determining the tire performance characteristic (overall trend in pressure reduction over time, as updated by most/more recent tire parameter values) based on the locally stored performance coefficient (e.g., portion of trend in pressure reduction over time) and the plurality of values (pressure/temperature readings used to determine the pressure reduction over time). In this manner, the combination of Bill and Shaw teaches storing the performance coefficient in local memory, with the tire performance characteristic being an equivalent or updated equivalent of performance coefficient, such that the tire performance characteristic is based on the performance coefficient and the plurality of values from which the performance coefficient is determined. As such, the tire performance characteristic is determined is based on information in local memory.).”
Bill does not expressly describe scenarios in which the remote memory (e.g., remote memory in device 140 (FIG. 1) and/or remote memory in another computer device such as a central server ([0037]) is inaccessible. However, the claim does not require an affirmative determination of remote memory inaccessibility and/or even a causal relation between remote memory inaccessibility and use of the data stored in local memory for determining the tire performance characteristic, such that a broadest reasonable interpretation of claim 2 entails a simple coincidence.
Therefore, and given that it is inherently possible for Bill’s disclosed remote memory to be inaccessible at any given time (e.g., due to failure or powering off of remote device 140 and/or another remote computer device such as a server), it would have been obvious to one of ordinary skill in art before the effective filing data to have determined, based on the plurality of values and the performance coefficient stored in the local memory, the tire performance characteristic in a circumstance in which the remote memory happens to be inaccessible.
The motivation would have been to leverage the local storage capability of the sensor device to enable determination of tire performance data based on a variety of different but related data that are centrally located in the local memory.
Examiner notes that a broadest reasonable interpretation of “based on the plurality of values and the performance coefficient stored in the local memory” only requires the performance coefficient to be stored in local memory.
As to claim 3, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 2, wherein the processing system is configured to determine, where the remote memory is accessible (Bill: FIG. 3 depicting device 140 communicatively coupled (doubled ended arrow) with smart sensor device 122) and based on the performance coefficient stored in the remote memory (Bill: [0020] remote device (handheld) processing trend in pressure over time (requires at least temporary storage of pressure trend in remote device); [0055]-[0056] data transmitted from device 140 (accessible) to determine/process trend data) and the plurality of values obtained during the second time period (trend in pressure over time (trend of pressure loss and/or P/T over ongoing time periods) necessarily determined in accordance with multiple pressure values), the tire performance characteristic (the tire performance characteristic entails the overall trend in pressure reduction over time as updated from a portion of the trend in pressure reduction constituting the performance coefficient)).”
As to claim 4, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 3, wherein the processing system is configured to determine the tire performance characteristic (the trend in pressure reduction disclosed by Bill (performance coefficient) is also the tire performance characteristic) based on the plurality of values obtained during the second period of time (Bill: [0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] are collected over ongoing time periods) to determine a trend in pressure reduction over time.) and the performance coefficient stored in the remote memory (Bill: [0020] remote device (handheld) processing trend in pressure over time (requires at least temporary storage of pressure trend in remote device); [0055]-[0056] data transmitted from device 140 to determine/process trend data).”
Examiner notes that a broadest reasonable interpretation of “determine the tire performance characteristic based on the plurality of values and the performance coefficient stored in the remote memory” only requires the performance coefficient to be stored in remote memory.
As to claim 5, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the tire monitoring device comprises the processing system (Bill: FIG. 3 CPU 128 in combination with memory 131 (local processing system) and device 140 (remote processing system that per [0056] may be a handheld device for processing data (e.g., evaluating and comparing)) to determine the tire performance characteristic based on the plurality of values obtained during the second period of time and the performance coefficient stored in the local memory (Bill: [0020], [0055]-[0056] determining tire performance characteristic (trend in reduction of pressure over ongoing time period) based on tire parameter values and performance coefficient (portion of the overall trend)); As combined in the grounds for rejecting claim 1 Bill and Shaw teach or otherwise render obvious storing the performance coefficient (trend of pressure reduction and/or P/T over ongoing time period) in local memory).”
As to claim 7, the combination of Bill and Shaw teaches “[t]he tire performance monitoring system according to Claim 1” but neither Bill nor Shaw expressly discloses a particular period or limit of a number of days over which values of a tire parameter are collected for determining the performance coefficient and therefore neither expressly teaches “the first time period is at least 25 days.”
Bill teaches that the method may be performed over a period of multiple days including data collection intervals of multiple days ([0032] rolling data collected over an interval of at least 120 hours which includes an interval of 25 days (600 hours) or longer). As set forth in the grounds for rejecting claim 1, Bill further teaches that the performance coefficient/performance characteristic determinations (rate of pressure reduction) based on the measurements are performed in an ongoing, continuous manner ([0020] and [0055]-[0056] pressure reduction monitored as a trend over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have combined Bill’s teaching of 600 hours (25 days) as a rolling time interval for data collection with Bill’s teaching of determining/monitoring a trend in pressure reduction such that the determined trend in pressure reduction is based on data collected over at least 25 days.
The motivation would have been to use a sufficient time period for accurately tracking variations on the measured data such as may be manifest in temporal trends as suggested by Bill.
As to claim 8, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 7,” and Bill further teaches that the second time period may be “no more than 10 days (Bill: [0032] data collected over an interval of at least 120 hours which includes an interval of 5 days (less than 10 days)).”
Bill does not explicitly teach the specific combination of time intervals in which a first interval for determining an intermediate “performance coefficient” (trend in deflation at a particular time point) is at least 25 days and a second interval for determining a subsequent “performance characteristic” is no more than 10 days. However, as noted in the grounds for rejecting claim 1, the “performance characteristic” as disclosed by Bill may be a time-based trend that per [0055]-[0056] is “monitored” “over time” suggesting that a performance characteristic may be determined at any of a given number of periods that per [0032] may constitute one or more days on a rolling (continuous) basis in accordance within any of a range of measurement time periods that may span from “no more than 10 days” to “at least 25 days, and in which a corresponding “performance coefficient” that is entailed within the “performance characteristic” as explained in the grounds for rejecting claim 1, is similarly determined in a continuous manner and therefore may be determined in accordance with (including within) any of a range of measurement time intervals that may span from “no more than 10 days” to “at least 25 days.”
It would have been obvious to one of ordinary skill in the art before the effective filing date, in view of the combined teachings of Bill of a variety of possible time periods including “no more than 10 days” and “at least 25 days” designated for collecting sensor measurement data and with collection intervals within each period of, for example, 3 hours as disclosed in [0052]-[0053], and given that the determination of trend in pressure reduction is performed in a continuous manner (over time), to have configured the system such that a performance coefficient is determined for 25 day period and a performance characteristic is determined based on the performance coefficient and subsequent data collected over no more than 10 days.
The motivation would have been to use sufficient time period for accurately tracking variations on the measured data over an extended period (25 days) that is set in accordance with perceived trend analysis requirements that can be added to an additional trend analysis (10 days) to provide an updating consistent with ongoing temporal tracking of the performance characteristic, and would incidentally occur in accordance with Bill’s disclosed method in which pressure reduction trends are “monitored” over time (monitoring in a continuous, ongoing manner).
As to claim 9, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the processing system is further configured to:
determine, based on the determined tire performance characteristic, an updated performance coefficient (per the grounds for rejecting claim 1, the trend in pressure reduction disclosed by Bill is determined over time and may constitute a performance coefficient as well as the tire performance characteristic based on the performance coefficient. In this manner, a tire performance characteristic may be a current pressure reduction trend that when being update serves as a performance coefficient that is updated by a next (newest) set of tire parameter values. In which case, the new tire performance characteristic becomes a new (updated) performance coefficient.); and
store the updated performance coefficient in the local memory (as set forth in the grounds for rejecting claim 1, the combination of Bill and Shaw teaches or otherwise renders obvious storing the performance coefficient (trend in pressure reduction over time is inherently an updated metric) in local memory).
As to claim 10, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the processing system is configured to provide a notification to a user based on the tire performance characteristic (Bill: [0020] and [0056] warning message by separate electronic device relating to trend in pressure reduction over time; [0035] determining whether to inflate tire based on data received by separate device).”
As to claim 11, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the processing system is configured to determine, based on the tire performance characteristic, a maintenance action to be performed on the tire (Bill: [0035] determining whether to inflate tire (in context related to determining trend in reduction of pressure over time per [0020]) based on data received by separate device; [0056]).”
As to claim 12, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 11, wherein the processing system is configured to cause, based on the tire performance characteristic, the maintenance action to be performed on the tire (Bill: [0035] determining whether to inflate tire (in context related to determining trend in reduction of pressure over time per [0020]) based on data received by separate device; [0056] tire may be inflated inferentially based on (caused by) pressure reduction trend output by system).”
As to claim 13, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the tire parameter comprises one or more of a tire pressure and a tire temperature (Bill: FIG. 3 smart sensor device 122 including pressure sensor 124 and temperature sensor 126; [0011]-[0012]).”
As to claim 14, the combination of Bill, Shaw, and Cibaud teaches “[t]he tire performance monitoring system according to Claim 1, wherein the tire performance characteristic comprises one or more of a rate of deflation of the tire (Bill: [0020] trend in pressure reduction over time; [0056]), a predicted future inflation point of the tire, a pressure leakage rate of the tire, and a predicted time for the tire to cool to a predefined temperature.”
As to claim 16, Bill teaches “[a] method of determining a tire performance characteristic of a tire (Abstract describing method for determining pressure/temperature of a tire over time; [0020] and [0056] method determines trends in pressure reduction over time) on an aircraft (FIGS. 1 and 2 smart sensor device 122 on tire 116 of an aircraft), the method comprising:
obtaining values indicative of a tire parameter of the tire over a first time period (FIG. 3 smart sensor device 122 includes pressure sensor 124, temperature sensor 126, and accelerometer 132; [0011]-[0012] pressure/temperature readings performed for tire; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records multiple sensor readings over time; [0031]-[0032] method may be performed over periods spanning multiple flights/multiple days) using a tire monitoring device (FIG. 3 smart sensor device 122 configured to monitor wheel 112) mounted to a wheel including the tire (FIG. 2 smart sensor device 122 mounted to wheel 112 (per FIG. 1 wheel of an aircraft) that includes tire 116),
determining a performance coefficient which is representative of the values of the tire parameter over the first time period ([0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] are collected over time) to determine a trend in pressure reduction over time. Examiner notes that the pressure reduction over time constitutes a performance coefficient (broadest reasonable interpretation of “performance coefficient corresponding to performance of the tire” in view of Applicant’s specification, which does not disclose actual application as a “coefficient,” appears to entail any relational numeric value such as, for example, a deflation rate that may be used in some manner as a coefficient). The trend in pressure reduction may also be a “tire performance characteristic” based on Applicant’s specification. As such, the “tire performance characteristic” is determined by determining the tire performance coefficient, which is determined based on the plurality of values, such that the tire performance characteristic is determined based on the plurality of values and the performance coefficient. In a distinct but related aspect, assuming an interpretation requiring that the tire performance characteristic be determined by multiple tire parameter values in addition to a given tire performance coefficient, the Examiner notes that the determining/monitoring of a trend in pressure reduction over time (a curve) is performed as an ongoing process such that a most current interval in the trend is based on the previous portion of the trend and new tire parameter values.);
storing, in a local memory of the tire monitoring device (FIG. 3 smart sensor device 122 configured to monitor wheel 112 and including memory 131),”
“deleting from the local memory the values indicative of the tire parameter obtained during the first time period ([0032] data collected on a rolling basis in which older data (first period) is deleted as new data (second period) is stored; [0056] monitoring data recorded on the “memory unit” and may be downloaded from the “sensor unit” (i.e., the memory unit is part of the sensor unit))”
“obtaining values indicative of the tire parameter of the tire over a second time period, which is after the first time period ([0011]-[0012] control unit (e.g., CPU 128 in FIG. 3) records in memory unit (e.g., memory 131 in FIG. 3) pressure and temperature readings; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records multiple sensor readings over time (over ongoing time periods); [0030]; [0032] data collected on a rolling basis in which older data (first period) is deleted as new data (second period) is stored); and
determining the tire performance characteristic using a processing system (FIG. 3 device 140 (remote processing system that per [0056] may be a handheld device for processing data (e.g., evaluating and comparing)) and based on the plurality of values obtained during the second time period and the performance coefficient ([0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] are collected over ongoing time periods) to determine a trend in pressure reduction over time. Examiner notes that the pressure reduction over time constitutes a performance coefficient (broadest reasonable interpretation of “performance coefficient corresponding to performance of the tire” in view of Applicant’s specification, which does not disclose actual application as a “coefficient,” appears to entail any relational numeric value such as, for example, a deflation rate that may be used in some manner as a coefficient). The trend in pressure reduction may also be a “tire performance characteristic” based on Applicant’s specification. As such, the “tire performance characteristic” is determined by determining the tire performance coefficient, which is determined based on the plurality of values, such that the tire performance characteristic is determined based on the plurality of values and the performance coefficient. In a distinct but related aspect, assuming an interpretation requiring that the tire performance characteristic be determined by multiple tire parameter values in addition to a given tire performance coefficient, the Examiner notes that the determination/monitor of a trend in pressure reduction over time (a curve) is performed as an ongoing process such that a most current interval in the trend is based on the previous portion of the trend and new tire parameter values.).
Regarding storing, in a local memory of the tire monitoring device, “the performance coefficient corresponding to performance of the tire,” Bill teaches a local memory storing tire performance information and therefore capable of storing a tire performance coefficient (FIG. 3 smart sensor device 122 includes processor/memory (CPU 128/memory 131) configured for storing data such as measured values pressure/temperature/motion in association with (per [0011]-[0012]) times of collection, such that memory 131 is capable of storing a performance coefficient corresponding to performance of the tire; [0020] memory may store reference pressure; [0024]and [0030]-[0032] sensor device memory stores data related to multiple readings over time).
Furthermore, Shaw discloses a system/method for implementing a tire monitoring system (Abstract; FIG. 1 tire monitoring system 10 including wheel module 11 and corresponding receiver 12. Examiner notes this wirelessly connected system is similar to Bill’s disclosed “local” system in which for sensing connectivity reasons wireless communications are used between the sensors and processing components ([0022])) in which a performance coefficient corresponding to performance of a tire is stored in local memory ([0011] discloses that each of wheel module 11 and receiver 12 processes sensor data (e.g., wheel module 11 (via process controller 22) gathers temperature and pressure data at a given interval and identifies, based on pressure and temperature data, wheel motion and also whether pressure reaches or drops below threshold, and processing circuitry 28 detects air leaks, which inherently requires local memory (beyond the ROM expressly described) for each of wheel module 11 and receiver 12. FIG. 2, FIG. 3 block 38, and [0027]-[0028] depicting and describing determined conditions including decreasing tire pressure including trending over time (performance coefficient corresponding to a performance of a tire) and associated P/T ratio determined by the system (either or both wheel module 11 and receiver 12). Examiner notes that the generation of and further processing of decreasing pressure, which can only be determined by measurements over time, inherently entails storage of such pressure decrease; [0043] describing processing of pressure and temperature values over time (curve as depicted in FIG. 4) include P/T ratios, which, in addition to deflation rate, are also entailed within a broadest reasonable interpretation of tire performance coefficient because it conveys a relation over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of locally generating and storing performance values that are computed based on measured tire parameter values (e.g., pressure and temperature) to the method taught by Bill in which a tire processing system computes and stores performance coefficient values (e.g., trend over time of pressure reduction) using a system that includes a local system capable of storing such values, such that in combination the system includes storing, in a local memory of the tire monitoring device, “the performance coefficient corresponding to performance of the tire.”
The motivation would have been to leverage processing and storage capabilities that are increasingly implemented with a local tire monitoring system/sub-system to locally generate and store such information that may be useful for determining tire operational condition and/or in determining further information relating to tire monitoring (e.g., a tire leakage condition) and/or in determining local monitoring device operating mode (e.g., collection rate) based on such information as suggested by Shaw and also to provide remote access to the various locally stored information as disclosed by Bill.
Regarding deleting from the local memory values indicative of the tire parameter obtained during the first time period “in conjunction with storing the performance coefficient,” Bill teaches the generalized concept of deleting older data in association with storage of newer data on a rolling basis ([0032]), but neither Bill nor Shaw expressly teach associating the deletion of a performance coefficient representative of a first collected set of tire performance data with the storage of a newer set of tire performance data.
Cibaud discloses a system/method for collecting and sharing monitoring sensor data (Abstract) that includes generating predictive model that is representative of measurement data collected over a period of time ([0072] compute predictive model that is representative of the data received over the period concerned) and deleting the data from which the model was computed in conjunction with generating (inherently entails storage of) the model ([0076] delete the data from which the model was computed in order to avoid unnecessarily occupying storage space on the server that generated the model. Examiner notes that the deletion is causally related to the generation and inherent storage of the model).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Cibaud’s teaching of deleting sensor measurement data in conjunction with storing data representative of the measurement data to the method taught by Bill as modified by Shaw, which teaches periodically deleted older sensor data as new data is obtained, such that in combination the method includes deleting from the local memory the values indicative of the tire parameter obtained during the first time period “in conjunction with storing the performance coefficient.”
The motivation would have been to conserve the capacity of limited data storage resources as disclosed by Cibaud.
As to claim 17, the combination of Bill, Shaw, and Cibaud teaches “[t]he method according to Claim 16, wherein the method further comprises:
storing the performance coefficient in a remote memory remote from the tire monitoring device (Bill: [0020] remote device (handheld) processing trend in pressure over time (requires at least temporary storage of pressure trend in remote device); [0055]-[0056] data transmitted from device 140 to determine/process trend data); and”
“determining by the tire monitoring device the tire performance characteristic based on the values obtained during the second time period and the performance coefficient (as combined in the grounds for rejecting claim 1 the combination of Bill and Shaw teaches determining the tire performance characteristic (overall trend in pressure reduction over ongoing time periods, as updated by most/more recent tire parameter values) based on the locally stored performance coefficient (e.g., portion of trend in pressure reduction over time) and the plurality of values (pressure/temperature readings used to determine the pressure reduction over time). In this manner, the combination of Bill and Shaw teaches storing the performance coefficient in local memory, with the tire performance characteristic being an equivalent or updated equivalent of performance coefficient, such that the tire performance characteristic is based on the performance coefficient and the plurality of values from which the performance coefficient is determined. As such, the tire performance characteristic is determined is based on information in local memory.) stored in the local memory (as combined in the grounds for rejecting claim 16 Bill and Shaw teach or otherwise render obvious storing the performance coefficient (trend of pressure loss and/or P/T over time) in local memory).”
Bill does not expressly describe scenarios in which the remote memory (e.g., remote memory in device 140 (FIG. 1) and/or remote memory in another computer device such as a central server ([0037]) is inaccessible. However, the claim does not require an affirmative determination of remote memory inaccessibility and/or even a causal relation between remote memory inaccessibility and use of the data stored in local memory for determining the tire performance characteristic, such that a broadest reasonable interpretation of claim 2 entails a simple coincidence.
Therefore, and given that it is inherently possible for Bill’s disclosed remote memory to be inaccessible at any given time (e.g., due to failure or powering off of remote device 140 and/or another remote computer device such as a server), it would have been obvious to one of ordinary skill in art before the effective filing data to have determined, based on the plurality of values and the performance coefficient stored in the local memory, the tire performance characteristic in a circumstance in which the remote memory happens to be inaccessible.
The motivation would have been to leverage the local storage capability of the sensor device to enable determination of tire performance data based on a variety of different but related data that is centrally located in the local memory.
Examiner notes that a broadest reasonable interpretation of “based on the plurality of values and the performance coefficient stored in the local memory” only requires the performance coefficient to be stored in local memory.
As to claim 18, the combination of Bill, Shaw, and Cibaud teaches “[t]he method according to Claim 17, wherein the method further comprises:
where the remote memory is accessible (Bill: FIG. 3 depicting device 140 communicatively coupled (doubled ended arrow) with smart sensor device 122), determining the tire performance characteristic (the tire performance characteristic entails the overall trend in pressure reduction over time (disclosed by Bill) as updated from a portion of the trend in pressure reduction constituting the performance coefficient)) based on the performance coefficient stored in the remote memory (Bill: [0020] remote device (handheld) processing trend in pressure over time (requires at least temporary storage of pressure trend in remote device); [0055]-[0056] data transmitted from device 140 (accessible) to determine/process trend data) and the values obtained during the second time period (trend in pressure over ongoing time periods necessarily determined in accordance with multiple pressure values).”
As to claim 19, the combination of Bill, Shaw, and Cibaud teaches “[t]he method according to Claim 16, wherein the method further comprises:
determining when the tire performance characteristic takes place at the tire monitoring device (Bill: [0020] trend in pressure reduction over time determined using data obtain from local memory that per [0011]-[0012] includes pressure/temperature readings performed by local sensors (e.g., temperature/pressure sensors within smart sensor device 122 in FIG. 3) that are associated with respective collection times. The collection of the time-associated pressure/temperature data in association with times of collection by the tire monitoring device therefore entails an effective determination of when the reduction of pressure over time occurs; in another aspect [0020] discloses when another related tire performance characteristic may occur (tire will soon be underinflated) may be recognized by “ground crew” (proximate to tire monitoring device)).”
As to claim 21, the combination of Bill, Shaw, and Cibaud teaches “[t]he method according to Claim 16, further comprising:
determining, based on the tire performance characteristic, a maintenance action to be performed on the tire (Bill: [0035] determining whether to inflate tire (in context related to determining trend in reduction of pressure over time per [0020]) based on data received by separate device; [0056]), and
performing the maintenance action on the tire (Bill: [0056] tire may be inflated inferentially based on pressure reduction trend output by system).”
As to claim 22, the combination of Bill, Shaw and Cibaud teaches “[t]he tire performance monitoring system of claim 1, wherein the values of the tire parameter obtained by the tire monitoring device during the first period are stored in the local memory (Bill: FIG. 3 smart sensor device 122 includes pressure sensor 124, temperature sensor 126, and accelerometer 132; [0011]-[0012] pressure/temperature readings performed for tire; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records in local memory multiple sensor readings over time (ongoing time periods); [0031]-[0032] method may be performed over periods spanning multiple flights/multiple days), and wherein the processing system is included in the tire monitoring system (Bill: FIG. 3 CPU 128 included in smart sensor device 122) and” [a] “processing system is configured to determine the performance coefficient based on the values of the tire parameter obtained during the first period and stored in the local memory ([0020] and [0056] handheld device downloads data from the memory of the sensor device to determine trend in pressure reduction (requires processor in handheld device)).”
Bill does not expressly teach that the processor local to/onboard the mounted tire sensor device (e.g., CPU 128) is used to determine the performance coefficient.
Shaw further discloses that the local processor within the wheel module (FIG. 1 process controller 22) may monitor the tire pressure level data to ascertain tire pressure reduction ([0011] process controller 22 monitors tire pressure level indicated by pressure sensor 16 to determine when pressure falls below a threshold and sends an alarm to remote information processing circuitry 28 for further processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of using the local processor within the monitoring device to determine detect trends in tire pressure to the system taught by Bill as modified by Shaw and Cibaud, which teaches using processing means for determining the performance coefficient in terms of a trend in reduction of tire pressure, such that in combination Bill’s local processor (e.g., CPU 128) is used to determine the performance coefficient based on the values of the tire parameter obtained during the first period and stored in the local memory.
The motivation would have been to leverage local processing capability to ascertain information related to the raw sensor measurements that is relevant to tire monitoring in an efficient manner in terms of having local access to the raw sensor measurements as suggested by Shaw.
As to claim 23, as best understood in view of the grounds for rejecting claim 23 under 112(b), the combination of Bill, Shaw, and Cibaud teaches “[t]he method of claim 16, wherein the determining of the performance coefficient is by a processing system (Bill: [0020] and [0056] handheld device downloads data from the memory of the sensor device to determine trend in pressure reduction (requires processor in handheld device))” “and the determining of the tire performance characteristic” [based on data] “stored in the local memory ([0056] device 140 configured to process data from memory (pressure, temperature, acceleration that per [0024] are collected in memory of the local device over ongoing time periods and per [0014]-[0015], and [0056] downloaded to handheld device) to determine a trend in pressure reduction over time).”
Bill does not expressly teach that the performance coefficient is determined by a processor local to/onboard the mounted tire sensor device (e.g., CPU 128) or that the related “performance characteristic” (updated trend in reduction in tire pressure) is also performed locally by the tire monitoring device.
Shaw further discloses that the local processor within the wheel module (FIG. 1 process controller 22) may monitor the tire pressure level data to ascertain tire pressure reduction ([0011] process controller 22 monitors tire pressure level indicated by pressure sensor 16 to determine when pressure falls below a threshold and sends an alarm to remote information processing circuitry 28 for further processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of using the local processor within the monitoring device to determine detect trends in tire pressure to the system taught by Bill as modified by Shaw and Cibaud, which teaches using processing means for determining the performance coefficient and the performance characteristic in terms of a trend in reduction of tire pressure, such that in combination Bill’s local processor (e.g., CPU 128) is used to determine the performance coefficient (trend in pressure reduction) and the performance characteristic (updated trend in pressure reduction) by the local processor/tire monitoring device.
The motivation would have been to leverage local processing capability to ascertain information related to the raw sensor measurements that is relevant to tire monitoring in an efficient manner in terms of having local access to the raw sensor measurements as suggested by Shaw.
Claims 6 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bill in view of Shaw and Cibaud as applied to claims 1 and 16 above, and further in view of Chong (US 2017/0136834 A1).
As to claim 6, the combination of Bill and Shaw teaches “[t]he tire performance monitoring system according to Claim 1, wherein the tire performance monitoring system comprises a remote device (Bill: FIG. 3 device 140; [0037] other computer such as a central server) configured to obtain the plurality of values of the tire parameter obtained during the second period of time” “from the local memory (Bill: [0015], [0020], and [0034] remote device obtains sensor readings from local memory), and the remote device is configured to determine, based on the plurality of values obtained during the second period of time”, “the tire performance characteristic (Bill: ([0020] trends in pressure reduction over ongoing time periods determined in some manner by combined processing of local memory unit and remote device (the series of pressure measurements that per [0011] and [0012] are associated with respective times enables such determination); [0055]-[0056] process of collecting sensor readings over time includes monitoring trends in tire pressure and the data may be transmitted to handheld device 140 and used to monitor (determine) time pressures over time (e.g., trend in pressure reduction)).”
Bill does not appear to expressly disclose that the performance coefficient (e.g., trend in pressure reduction) is determined by the smart sensor device 122 ([0020] and [0055] generally indicate that the trend is determined) such that Bill does not expressly teach that the data retrieved by the remote device (e.g., either or both device 140 or other computer referenced in [0037]) from the sensor device includes a performance coefficient which is used to determine the tire performance characteristic.
As explained in the grounds for rejecting claim 1, Shaw teaches using a localized sensor device (FIG. 1 tire pressure monitoring system 10 including wheel module 11 and receiver 12) to compute and locally store a tire performance coefficient ([0011] discloses that each of wheel module 11 and receiver 12 processes sensor data (e.g., wheel module 11 (via process controller 22) gathers temperature and pressure data at a given interval and identifies, based on pressure and temperature data, wheel motion and also whether pressure reaches or drops below threshold, and processing circuitry 28 detects air leaks, which inherently requires local memory (beyond the ROM expressly described) for each of wheel module 11 and receiver 12. FIG. 2, FIG. 3 block 38, and [0027]-[0028] depicting and describing determined conditions including decreasing tire pressure including trending over time (performance coefficient corresponding to a performance of a tire) and associated P/T ratio determined by the system (either or both wheel module 11 and receiver 12). Examiner notes that the generation of and further processing of decreasing pressure, which can only be determined by measurements over time, inherently entails storage of such pressure decrease; [0043] describing processing of pressure and temperature values over time (curve as depicted in FIG. 4) include P/T ratios, which, in addition to deflation rate, are also entailed within a broadest reasonable interpretation of tire performance coefficient because it conveys a relation over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of using a local system for determining and locally storing a tire performance coefficient to the system taught by Bill, in which both the local device and the remote device include processing and storage capabilities and in which the remote device retrieves tire performance data including sensor measurements and additional information such as reference pressures (claim 1, [0020]) that are used in conjunction with the measurement data to determine tire performance, such that in combination the system is configured to uses the processing and storage capabilities of the local device (e.g.., CPU 128 and memory 131 in FIG. 1) to compute some or all of the pressure reduction trend information locally, such that the data retrieved by the device 140 and/or other computer system referenced in [0037] to determine a tire performance characteristic (e.g., a most current overall trend in pressure reduction) includes a performance coefficient (e.g., a portion of the pressure reduction trend) as well as multiple tire parameter values (e.g., most recent pressure/temperature readings that may be used to update the pressure reduction trend data).
Such a combination would amount to implementing known processing and storage techniques to implement an overall distributed processing (sharing of processing functions) and data sharing environment to achieve predictable results of strategically implemented distributed processing such as flexible access to data that may be stored at multiple locations and may be unavailable at any of the locations at any given time as disclosed by Chong (Abstract; [0016], [0062]).
As to claim 20, the combination of Bill and Shaw teaches “[t]he method according to Claim 16, wherein the method further comprises:
storing the values of the tire parameter obtained during the second time period in the local memory (Bill: [0011]-[0012] control unit (e.g., CPU 128 in FIG. 3) records in memory unit (e.g., memory 131 in FIG. 3) pressure and temperature readings; [0024] device includes controller (e.g., CPU 128 in FIG. 3) that records multiple sensor readings over ongoing time periods (i.e., sensor readings stored locally); [0030]; [0056]);
retrieving, using a remote device (Bill: FIG. 3 device 140; [0037] other computer such as a central server), the values of the tire parameter obtained during the second time period” “from the local memory (Bill: [0015], [0020], and [0034] remote device obtains sensor readings from local memory); and
determining the tire performance characteristic using the remote device and based on the values obtained during the second period” “retrieved from the local memory (Bill: ([0020] trends in pressure reduction over time determined in some manner by combined processing of local memory unit and remote device (the series of pressure measurements that per [0011] and [0012] are associated with respective times enables such determination); [0055]-[0056] process of collecting sensor readings over ongoing time periods includes monitoring trends in tire pressure and the data may be transmitted to handheld device 140 and used to monitor (determine) time pressures over time (e.g., trend in pressure reduction)).”
Bill does not appear to expressly disclose that the performance coefficient (e.g., trend in pressure reduction) is determined by the smart sensor device 122 ([0020] and [0055] generally indicate that the trend is determined) such that Bill does not expressly teach that the data retrieved by the remote device (e.g., either or both device 140 or other computer referenced in [0037]) from the sensor device includes a performance coefficient which is used to determine the tire performance characteristic.
As explained in the grounds for rejecting claim 16, Shaw teaches using a localized sensor device (FIG. 1 tire pressure monitoring system 10 including wheel module 11 and receiver 12) to compute and locally store a tire performance coefficient ([0011] discloses that each of wheel module 11 and receiver 12 processes sensor data (e.g., wheel module 11 (via process controller 22) gathers temperature and pressure data at a given interval and identifies, based on pressure and temperature data, wheel motion and also whether pressure reaches or drops below threshold, and processing circuitry 28 detects air leaks, which inherently requires local memory (beyond the ROM expressly described) for each of wheel module 11 and receiver 12. FIG. 2, FIG. 3 block 38, and [0027]-[0028] depicting and describing determined conditions including decreasing tire pressure including trending over time (performance coefficient corresponding to a performance of a tire) and associated P/T ratio determined by the system (either or both wheel module 11 and receiver 12). Examiner notes that the generation of and further processing of decreasing pressure, which can only be determined by measurements over time, inherently entails storage of such pressure decrease; [0043] describing processing of pressure and temperature values over time (curve as depicted in FIG. 4) include P/T ratios, which, in addition to deflation rate, are also entailed within a broadest reasonable interpretation of tire performance coefficient because it conveys a relation over time).
It would have been obvious to one of ordinary skill in the art before the effective filing date, to have applied Shaw’s teaching of using a local system for determining and locally storing a tire performance coefficient to the method taught by Bill, in which both the local device and the remote device include processing and storage capabilities and in which the remote device retrieves tire performance data including sensor measurements and additional information such as reference pressures (claim 1, [0020]) that are used in conjunction with the measurement data to determine tire performance, such that in combination the system is configured to uses the processing and storage capabilities of the local device (e.g.., CPU 128 and memory 131 in FIG. 1) to compute some or all of the pressure reduction trend information locally, such that the data retrieved by the device 140 and/or other computer system referenced in [0037] to determine a tire performance characteristic (e.g., a most current overall trend in pressure reduction) includes a performance coefficient (e.g., a portion of the pressure reduction trend) as well as multiple tire parameter values (e.g., most recent pressure/temperature readings that may be used to update the pressure reduction trend data).
Such a combination would amount to implementing known processing and storage techniques to implement an overall distributed processing (sharing of processing functions) and data sharing environment to achieve predictable results of strategically implemented distributed processing such as flexible access to data that may be stored at multiple locations and may be unavailable at any of the locations at any given time as disclosed by Chong (Abstract; [0016], [0062]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW W BACA whose telephone number is (571)272-2507. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Schechter can be reached at (571) 272-2302. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW W. BACA/Examiner, Art Unit 2857
/ANDREW SCHECHTER/Supervisory Patent Examiner, Art Unit 2857