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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 06/23/2026 has been entered.
Status of the claims
The amendment received on June 23, 2026 has been acknowledged and entered. Claims 1, 7, 8, 14, 15, and 21 are amended. Claims 2, 5, 9, 12, 16, and 19 are cancelled. Claims 26-27 are previously added. Thus, claims 1, 3-4, 6-8, 10-11, 13-15, 17-18, 20-27 are currently pending. Claims 22-25 were previously withdrawn.
Response to Arguments
Applicant’s arguments filed on June 23, 2026 with respect to claims 1, 3-4, 6-8, 10-11, 13-15, 17-18, 20-21, and 26-27 under 35 U.S.C. 101 have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 101
Claims 1, 3-4, 6-8, 10-11, 13-15, 17-18, 20-21, and 26-27 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Specifically, representative Claim 1 recites:
A computer-implemented method, comprising:
determining, by a processor set, different baseline vibration patterns of a bridge for different vehicle categories based on vibration data collected from one or more vehicle-embedded vibration sensors of plural vehicles in the respective vehicle categories;
obtaining, by the processor set, vibration data from at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge;
filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest;
classifying, by the processor set, the vehicle into a respective one of the vehicle categories;
selecting, by the processor set, a respective one of the different baseline vibration patterns based on the respective one of the vehicle categories;
determining, by the processor set, a difference between the filtered vibration data and the respective one of the different baseline vibration patterns; and
in response to the difference exceeding a threshold,
determining, by the processor set, a type of anomaly associated with the bridge, wherein the threshold is determined based on contextual information associated with the bridge, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor affecting the bridge; and
performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the bridge.
The claim limitations in the abstract idea have been highlighted in bold above; the remaining limitations are “additional elements.”
Step 1: under the Step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: Process, machine, manufacture, or composition of matter. The above claim is considered to be in a statutory category (Process).
Step 2A, Prong One: under the Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitations that fall into/recite an abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject matter Eligibility Guidance, it falls into the groupings of subject matter when recited as such in a claim limitation that falls into the grouping of subject matter when recited as such in a claim limitation, that covers mathematical concepts - mathematical relationships, mathematical formulas or equations, mathematical calculations and mental processes – concepts performed in the human mind including an observation, evaluation, judgement, and/or opinion.
For example, the limitations of “determining, by a processor set, different baseline vibration patterns of a bridge for different vehicle categories based on vibration data collected from one or more vehicle-embedded vibration sensors of plural vehicles in the respective vehicle categories (paras. [0077]: step 420, [0082]: step 710 of instant application),” “filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest (para. [0063]: including only data from vehicles in that vehicle category of instant application),” “classifying, by the processor set, the vehicle into a respective one of the vehicle categories (para. [0084]: step 730 of instant application),” and “selecting, by the processor set, a respective one of the different baseline vibration patterns based on the respective one of the vehicle categories (para. [0085]: step 740 of instant application)” are mental processes (i.e., evaluation or judgement).
Further, the limitation of “determining, by the processor set, a difference between the filtered vibration data collected from the vehicle and the respective one of the different baseline vibration patterns (para. [0086]: step 750 of instant application) and “in response to the difference exceeding a threshold (para. [0087]: step 760 of instant application)” is mathematical calculations because determining a difference between the vibration data collected from the vehicle and the respective one of the baseline vibration patterns is indicative of mathematical calculation. Further, the limitation of “determining, by the processor set, a type of anomaly associated with the bridge, wherein the threshold is determined based on contextual information associated with the bridge, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor affecting the bridge (see paras. [0066], [0073]-[0074] of instant application)” is mental processes (evaluation/judgement) regarding a type of anomaly. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mathematical concepts and/or human mind, then it falls within the “Mental Processes” and/or “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Similar limitations comprise the abstract ideas of Claims 8 and 15.
Step 2A, Prong Two: under the Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception. This judicial exception is not integrated into a practical application.
Therefore, the claims are directed to a judicial exception and require further analysis under the Step 2B.
Step 2B:
The above claims comprise the following additional elements:
In Claim 1: a computer-implemented method (preamble); processor; steps of obtain vibration data from at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge and perform a predefined remediation action associated with the bridge; performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the bridge;
In Claim 8: a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media (preamble); steps of obtain vibration data from at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge and perform a predefined remediation action associated with the bridge; performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the bridge; and
In Claim 15: a system (preamble); and a computerized simulation validating system for a full-scale distribution network single phase-to-ground fault test (preamble); steps of obtain vibration data from at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge and perform a predefined remediation action associated with the bridge. performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the bridge.
The additional elements such as a processor, computer-implemented method, a computer program product comprising one or more computer readable storage media having program instructions collectively stored on the one or more computer readable storage media processor, bridge are recited at a high-level of generality (MPEP 2106.05(d)). Further, note that the step of “obtain vibration data from at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge” is insignificant (gathering data) extra-solution activity (MPEP 2106.05(g)). The limitation of “at least one vehicle-embedded vibration sensor of a vehicle crossing the bridge” is merely a description (or definition) about obtained data. The additional element of “performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the bridge” is broadly and generically recited such that it is the equivalent to “apply it”. See MPEP 2106.05(f)) and is also insignificant post-solution activity based on abstract idea (i.e., determining, filtering, classifying, selecting, determining, and determining steps) because performing a predefined remediation action of a plurality of predefined remediation action can be an alert notification (see para. [0074]: step 560). See MPEP 2106.05(g)). Further, the independent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record (Dingli, Du (US 2023/0049919 A1)). For example, Dingli and Du teach at least one vehicle-embedded vibration sensor of a vehicle (paras. [0029], [0065] of Dingli; paras. [0007], [0024], [0027], [0029], [0032]-[0034], [0040] of Du). Therefore, independent claims 1, 8, and 15 are not patent eligible.
Claim 1 does not present tangible or physical elements/components and/or
integration of improvements to be indicative of specific features/structure/acts, for
example, how and or with what to determine the type of anomaly associated with the bridge. Further, an abstract idea itself is just that, abstract, and whether such feature is or is not significant does not preclude it from being considered abstract. An abstract idea by itself, whether it or not it has a benefit, does not reasonably overcome a 101 rejection because it is still an abstract idea. Therefore, the above advantages relate to abstract idea limitations which are not considered. The Improvements in the abstract idea are not qualified as improvements indicating a practical application. The pending claims are not patent eligible since a claim for a new abstract idea is still an abstract idea (see MPEP 2106.05(a).I) and an improvement in the abstract idea itself is not an improvement in technology (see MPEP 2106.05(a).II and MPEP 2106.05(a).II: Examples that the courts have indicated may not be sufficient to show an improvement to technology include: iii. Gathering and analyzing information using conventional techniques and displaying the result, TLI Communications, 823 F.3d at 612-13, 118 USPQ2d at 1747-48)). This is just a processor running mathematics and mental processes. Similar limitations comprise the abstract ideas of Claims 8 and 15. Therefore, the independent claims 1, 8, and 15 are ineligible.
Claims 3, 10, and 17,
The additional elements of “the vehicle is classified based on type, make, and model of the vehicle” is well-understood, routine, and conventional in the relevant based on the prior art of record (para. [0026] of Dingli; page 3, line 20 of Fu (CN 115221327A)). Therefore, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because these additional elements/steps are well-understood, routine, and conventional in the relevant based on the prior art of record.
Regarding claims 4, 6-8, 11, 13-15, 18, 20-21, and 26-27,
All features recited in these claims are abstract ideas, as all features found in these claims are directed towards mathematical calculations or mathematical calculation/mental process steps. The explanation for the rejection of Claims 4, 6-8, 11, 13-15, 18, 20-21, and 26-27 therefore are incorporated herein and applied to Claims 1, 8, and 15. These claims therefore stand rejected for similar reasons as explained in above Claims 1, 8, and 15.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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, 3-4, 6-8, 10-11, 13-15, 17-18, 20-21, and 26 are rejected under 35 U.S.C. 103 as being unpatentable over Dingli et al. (US 2021/0134088 A1, hereinafter referred to as “Dingli”) in view of Bohannan et al. (US 4,956,999, hereinafter referred to as “Bohannan”), Codevilla et al. (US 2020/0159231 A1, hereinafter referred to as “Codevilla”), and Soon-Shiong (US 2019/0050536 A1, hereinafter referred to as “Soon-Shiong”).
Regarding claim 1, Dingli teaches a computer-implemented method (para. [0006]: non-transitory computer readable media; para. [0008]: a computer program product), comprising:
determining, by a processor set (para. [0065]: one or more hardware processors), different baseline vibration patterns of a road segment (para. [0023]: a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc); Fig. 1 and para. [0029]: a vibrational signature for a vehicle and a baseline vibrational signature for a road segment along which the vehicle is travelling may be compared to determine if the vehicle is exhibiting anomalous vibrational characteristics that are outside of the range of expected vibrational behavior of the vehicle on that particular road segment, note that the above feature of “a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc.)” in para. [0023] and “a vibrational signature for a vehicle and a baseline vibrational signature for a road segment” and “particular road segment” in para. [0029] reads on “different baseline vibration patterns of a road segment) for vehicle categories (para. [0023]: a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc); para. [0029] the real-time sensor data is indicative of vibrational characteristics of the vehicle as it travels along a particular road segment, note that the above feature of “a vehicle (e.g., an autonomous vehicle, a driverless vehicle),” in para. [0023] and “vibrational characteristics of the vehicle as it travels along a particular road segment” in para. [0029] reads on “vehicle categories”) based on vibration data collected from one or more vehicle-embedded vibration sensors of plural vehicles (Fig. 2, 206A-C ) in the respective vehicle categories (para. [0023]: see above; para. [0029]: see above);
obtaining, by the processor set (para. [0065]: one or more hardware processors), vibration data from at least one vehicle-embedded vibration sensor of a vehicle (Fig. 2, 206A-C) crossing the road segment (para. [0029]: a vibrational signature for a vehicle and a baseline vibrational signature for a road segment along which the vehicle is travelling may be compared to determine if the vehicle is exhibiting anomalous vibrational characteristics that are outside of the range of expected vibrational behavior of the vehicle on that particular road segment, note that the above feature of “a vibrational signature for a vehicle and a baseline vibrational signature for a road segment along which the vehicle is travelling” and “particular road segment” reads on “vibration data from a vehicle crossing the bridge” and “bridge,” respectively);
classifying, by the processor set (para. [0065]: one or more hardware processors), the vehicle into a respective one of the vehicle categories (para. [0023]: a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc); para. [0029]: the real-time sensor data is indicative of vibrational characteristics of the vehicle as it travels along a particular road segment, note that since Dingli teaches vehicle categories in para. [0023] and the real-time sensor data is indicative of vibrational characteristics of the vehicle in para. [0029], classifying the vehicle into a respective one of the vehicle categories is inherent functional property of such result);
selecting, by the processor set (para. [0065]: one or more hardware processors), a respective one of the different baseline vibration patterns (para. [0029]: a baseline vibrational signature for a road segment; para. [0038]: The vibrational data 114 may include multiple baseline vibrational signatures, each of which corresponds to a respective road segment represented in the map data 112, note that the above feature of “multiple baseline vibrational signatures, each of which corresponds to a respective road segment” reads on “a respective one of the baseline vibration patterns”) based on the respective one of the vehicle categories (para. [0029]: the real-time sensor data is indicative of vibrational characteristics of the vehicle as it travels along a particular road segment);
determining, by the processor set (para. [0065]: one or more hardware processors), a difference between the vibration data collected from the vehicle and the respective one of the different baseline vibration patterns (para. [0029]: a vibrational signature for a vehicle and a baseline vibrational signature for a road segment along which the vehicle is travelling may be compared to determine if the vehicle is exhibiting anomalous vibrational characteristics that are outside of the range of expected vibrational behavior of the vehicle on that particular road segment, note that the above feature of “anomalous vibrational characteristics” reads on “a difference between the vibration data collected from the vehicle and the respective one of the baseline vibration patterns”); and
in response to the difference exceeding a predefined threshold (para. [0029]: anomalous vibrational characteristics; para. [0030]: vibrational signature for the vehicle deviates from the baseline vibrational signature for the road segment by more than a threshold amount),
performing, by the processor set, a predefined remediation action of a plurality of predefined remediation actions based on the type of anomaly, wherein the plurality of predefined remediation actions include automatically restricting access to the road segment (para. [0016]: it is determined that the vibrational signature for the vehicle deviates from the baseline vibrational signature for the road segment by more than a threshold amount—and thus that the vehicle is exhibiting anomalous vibrational behavior in relation to the particular road segment it is traveling on—an automated vibrational anomaly alert may be generated. Various responses may then be initiated in response to the vibrational anomaly alert. For instance, in example embodiments, an on-board processing unit of an AV may utilize a GPS device, and optionally third-party data such as traffic data, weather data, or the like, to identify a safe location to halt the vehicle, note that the above feature of “various responses in response to the vibrational anomaly alert“ and “an automated vibrational anomaly alert” reads on “the plurality of predefined remediation actions include automatically restricting access”).
Dingli does not specifically teach vibration patterns of a bridge for different vehicle and bridge; filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest; determining, by the processor set, a type of anomaly associated with the bridge, wherein the threshold is determined based on contextual information associated with the bridge, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor affecting the bridge, filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest, and determining, by the processor set, a type of anomaly, wherein the threshold is determined based on contextual information, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor.
However, Bohannan teaches the vibration patterns (col. 9, lines 21-22; the vibrations associated with the motion of the structural member being monitored) of a bridge for different vehicle (col. 7, lines 41-47: in the context of structural members that are bridge span support beams, the transient loads may be vehicles traveling the longitudinal length of the span, for example, cars, trucks, and busses of different sizes and weights) and bridge (col. 7, lines 41-47: bridge).
Dingli and Bohannan are both considered to be analogous to the claimed invention because they are in the same filed of monitoring the status of vehicle. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the vibration patterns of a bridge for different vehicle such as is described in Bohannan into Dingli, in order to monitor structural members subjected to transient loads over time to identify and/or measure changes in the structural integrity of the structural member (Bohannan, col. 5, lines 16-20).
Dingli and Bohannan do not specifically teach filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest.
However, Codevilla teaches filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest (para. [0018]: Sensors 108 may include, by way of non-limiting example: a vibration sensor; para. [0028]: Filtering component 116 may be configured to filter the contextual information spatially… contextual information may be filtered on a higher level such that some contextual information is ignored and some contextual information is acknowledged; para. [0030]: filtering component 116 may be configured to filter the contextual information spatially such that specific contextual information of the contextual information is determined, note that the above feature of “a vibration sensor” in para. [0018], “filter the contextual information spatially… contextual information may be filtered on a higher level such that some contextual information is ignored and some contextual information is acknowledged” in para. [0028], and “specific contextual information of the contextual information is determined” in para. [0030] reads on “filtering, by the processor set, the vibration data obtained from the vehicle to eliminate vibration data according to a context including a predetermined factor of disinterest”).
Dingli and Codevilla are both considered to be analogous to the claimed invention because they are in the same filed of controlling a vehicle with context. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the filtering the vibration data such as is described in Codevilla into Dingli, in order to generate output signals conveying contextual information and vehicle information. The contextual information may characterize a contextual environment surrounding a vehicle. The vehicle information may characterize vehicle operations of the vehicle (Codevilla, para. [0004]).
Dingli, Bohannan, and Codevilla do not specifically teach determining, by the processor set, a type of anomaly, wherein the threshold is determined based on contextual information, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor.
However, Soon-Shiong teaches determining, by the processor set (para. [0032]: one or more processing unit), a type of anomaly, wherein the threshold is determined based on contextual information, the contextual information being obtained from one or more data sources and including at least one of environmental factor or location factor (para. [0014]: the cross-validation engine uses one or more resources that are related to the anomaly, typically using a secondary parameter that is not directly linked to the anomaly (e.g., contextual data or patient history or second independent patient test; para. [0047]: it can be desirable to couple analytical module 145 to subscription module 155 so that expert subscribers can access and refine the algorithms, modify the thresholds used to identify anomalies, or for like reasons; para. [0049]: one or more of the expert subscribers to fine-tune the abnormality determination by adjusting this threshold associated with the qualifier, note that the above feature of “modify the thresholds used to identify anomalies” in para. [0047], “fine-tune the abnormality determination by adjusting this threshold associated with the qualifier” in para. [0049], and “the anomaly, typically using a secondary parameter that is not directly linked to the anomaly (e.g., contextual data)” read on “the threshold is determined based on contextual information, the contextual information being obtained from one or more data sources and including at least one of environmental factor or local factor”).
Dingli and Soon-Shiong are both considered to be pertinent to the claimed invention because they are in the same filed of computational analysis of data. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the type of anomaly such as is described in Soon-Shiong into Dingli, in order to improve on the pace of discovering new practical information based on large amounts of datasets collected, where anomalies from the datasets are automatically identified, flagged, and validated by a cross-validation engine (Soon-Shiong, para. [0011]).
Regarding claim 2, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1, in addition, Dingli teaches that the vibration data is collected by at least one sensor installed on the vehicle (para. [0026]: sensor data captured by various on-board vehicle sensors may be indicative of vibrational characteristics of a vehicle as it travels along a road surface).
Regarding claim 3, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1. Dingli, Codevilla, and Soon-Shiong do not specifically teach that the vehicle is classified based on type, make, and model of the vehicle.
However, Bohannan teaches the vehicle is classified based on type, make, and model of the vehicle (col. 7, lines 41-47: in the context of structural members that are bridge span support beams, the transient loads may be vehicles traveling the longitudinal length of the span, for example, cars, trucks, and busses of different sizes and weights, note that the above feature of “cars, trucks, and busses of different sizes and weights” in col. 7, lines 41-47 reads on “ “the vehicle is classified based on type, make, and model of the vehicle”).
Dingli and Bohannan are both considered to be analogous to the claimed invention because they are in the same filed of monitoring structural members. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the vehicle such as is described in Bohannan into Dingli, in order to monitor structural members subjected to transient loads over time to identify and/or measure changes in the structural integrity of the structural member (Bohannan, col. 5, lines 16-20).
Regarding claim 4, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1, in addition, Dingli teaches that the different baseline vibration patterns are determined using vibration data from plural vehicles passing over the road segment prior to obtaining the vibration data of the vehicle crossing the bridge (para. [0023]: a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc); para. [0028]: the baseline vibrational signature for a road segment may be indicative of a cumulative or an average level of vibration caused by defects, impediments, obstructions, and/or overall road surface quality of the road segment, as reflected by the vibrational characteristics of multiple vehicles traversing the road segment, note that the above feature of “a vehicle (e.g., an autonomous vehicle, a driverless vehicle, etc)” in para. [0023] and “the baseline vibrational signature for a road segment” in para. [0028] reads on “different baseline vibration patterns are determined using vibration data from plural vehicles passing over the road segment prior to obtaining the vibration data of the vehicle crossing the bridge”).
Dingli, Codevilla, and Soon-Shiong do not specifically teach the bridge.
However, Bohannan teaches the bridge (col. 7, lines 41-47: in the context of structural members that are bridge span support beams, the transient loads may be vehicles traveling the longitudinal length of the span, for example, cars, trucks, and busses of different sizes and weights).
Dingli and Bohannan are both considered to be analogous to the claimed invention because they are in the same filed of monitoring structural members. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the bridge such as is described in Bohannan into Dingli, in order to monitor structural members subjected to transient loads over time to identify and/or measure changes in the structural integrity of the structural member (Bohannan, col. 5, lines 16-20).
Regarding claim 5, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 4, in addition, Dingli teaches further comprising filtering the vibration data from the plural vehicles based on context (para. [0041]: the context module 214 may receive the respective vibrational data 208A, 208B, 208C from each of the plurality of vehicles 206A, 206B, 206C via the network(s) 210, which may include any combination of public and/or private networks; para. [0042] aggregating the vibrational data 208A, 208B, 208C may include, without limitation, sensor data fusion, data interpolation, data redundancy elimination, or any other form of data aggregation or manipulation, note that the above feature of “data redundancy elimination, or any other form of data aggregation or manipulation” reads on “filtering the vibration data from the plural vehicles based on context”).
Regarding claim 6, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1, in addition, Dingli teaches that the predefined remediation action is selected from a set of plural different predefined remediation actions using a decision tree and based on an amount of the difference (para. [0030]: it is determined that the vibrational signature for the vehicle deviates from the baseline vibrational signature for the road segment by more than a threshold amount—and thus that the vehicle is exhibiting anomalous vibrational behavior in relation to the particular road segment it is traveling on—an automated vibrational anomaly alert may be generated, various responses may then be initiated in response to the vibrational anomaly alert, note that the above feature of “an automated vibrational anomaly alert may be generated, various responses may then be initiated in response to the vibrational anomaly alert” reads on “the predefined remediation action is selected from a set of plural different predefined remediation actions using a decision tree and based on an amount of the difference”).
Regarding claim 7, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1, in addition, Dingli teaches that the threshold is based on environmental impacts or location impacts associated with the road segment (para. [0030]: it is determined that the vibrational signature for the vehicle deviates from the baseline vibrational signature for the road segment by more than a threshold amount—and thus that the vehicle is exhibiting anomalous vibrational behavior in relation to the particular road segment it is traveling on, note that the above feature of “exhibiting anomalous vibrational behavior in relation to the particular road segment it is traveling on” reads on “environmental impacts or location impacts associated with the road segment”).
Dingli, Codevilla, and Soon-Shiong do not specifically teach the bridge.
However, Bohannan teaches the bridge (col. 7, lines 41-47: in the context of structural members that are bridge span support beams, the transient loads may be vehicles traveling the longitudinal length of the span, for example, cars, trucks, and busses of different sizes and weights).
Dingli and Bohannan are both considered to be analogous to the claimed invention because they are in the same filed of monitoring structural members. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the bridge such as is described in Bohannan into Dingli, in order to monitor structural members subjected to transient loads over time to identify and/or measure changes in the structural integrity of the structural member (Bohannan, col. 5, lines 16-20).
Regarding claim 26, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1. Dingli, Bohannan, and Soon-Shiong do not specifically teach that the predetermined factor of disinterest is selected from the group consisting of weather information and presence of another vehicle on the roadway.
However, Codevilla teaches the predetermined factor of disinterest (para. [0028]: some contextual information is ignored and some contextual information is acknowledged; para. [0030]: filtering component 116 may be configured to filter the contextual information spatially such that specific contextual information of the contextual information is determined) is selected from the group consisting of weather information (para. [0017]: Ambient conditions may include rain, hail, snow, fog, and/or other naturally occurring conditions) and presence of another vehicle (Fig. 5, 502 ) on the roadway (Fig. 5, 522).
Dingli and Codevilla are both considered to be analogous to the claimed invention because they are in the same filed of controlling a vehicle with context. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the predetermined factor of disinterest such as is described in Codevilla into Dingli, in order to generate output signals conveying contextual information and vehicle information. The contextual information may characterize a contextual environment surrounding a vehicle. The vehicle information may characterize vehicle operations of the vehicle (Codevilla, para. [0004]).
Dingli, Codevilla, and Soon-Shiong do not specifically teach that the roadway is bridge.
However, Bohannan teaches that the roadway is bridge (col. 7, lines 41-47: in the context of structural members that are bridge span support beams, the transient loads may be vehicles traveling the longitudinal length of the span, for example, cars, trucks, and busses of different sizes and weights).
Dingli and Bohannan are both considered to be analogous to the claimed invention because they are in the same filed of monitoring structural members. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the bridge such as is described in Bohannan into Dingli, in order to monitor structural members subjected to transient loads over time to identify and/or measure changes in the structural integrity of the structural member (Bohannan, col. 5, lines 16-20).
Regarding claim 27, Dingli in view of Bohannan, Codevilla, and Soon-Shiong teaches all the limitation of claim 1. Dingli, Bohannan, and Soon-Shiong do not specifically teach filtering the vibration data comprises filtering noisy data.
However, Codevilla teaches that filtering the vibration data comprises filtering noisy data (para. [0018]: Sensors 108 may include, by way of non-limiting example: a vibration sensor; para. [0028]: Filtering component 116 may be configured to filter the contextual information spatially… contextual information may be filtered on a higher level such that some contextual information is ignored and some contextual information is acknowledged; para. [0030]: filtering component 116 may be configured to filter the contextual information spatially such that specific contextual information of the contextual information is determined, note that the above feature of “a vibration sensor” in para. [0018], “filter the contextual information spatially… contextual information may be filtered on a higher level such that some contextual information is ignored and some contextual information is acknowledged” in para. [0028], and “specific contextual information of the contextual information is determined” in para. [0030] reads on “filtering the vibration data comprises filtering noisy data” because other information except specific information (or ignored information) is noise data).
Dingli and Codevilla are both considered to be analogous to the claimed invention because they are in the same filed of controlling a vehicle with context. Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the filtering the vibration data such as is described in Codevilla into Dingli, in order to generate output signals conveying contextual information and vehicle information. The contextual information may characterize a contextual environment surrounding a vehicle. The vehicle information may characterize vehicle operations of the vehicle (Codevilla, para. [0004]).
Regarding claim 8, it is a computer program product or computer readable storage media type claim having similar limitations as for claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding claim 10, it is dependent claim on claim 8 and has similar limitations as of claim 3 above. Therefore, it is rejected under the same rationale as of claim 3 above.
Regarding claim 11, it is dependent claim on claim 8 and has similar limitations as of claim 4 above. Therefore, it is rejected under the same rationale as of claim 4 above.
Regarding claim 13, it is dependent claim on claim 8 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rationale as of claim 6 above.
Regarding claim 14, it is dependent claim on claim 8 and has similar limitations as of claim 7 above. Therefore, it is rejected under the same rationale as of claim 7 above.
Regarding claim 15, it is a system type claim having similar limitations as for claim 1 above. Therefore, it is rejected under the same rationale as of claim 1 above.
Regarding claim 17, it is dependent claim on claim 15 and has similar limitations as of claim 3 above. Therefore, it is rejected under the same rationale as of claim 3 above.
Regarding claim 18, it is dependent claim on claim 15 and has similar limitations as of claim 4 above. Therefore, it is rejected under the same rationale as of claim 4 above.
Regarding claim 20, it is dependent claim on claim 15 and has similar limitations as of claim 6 above. Therefore, it is rejected under the same rationale as of claim 6 above.
Regarding claim 21, it is dependent claim on claim 15 and has similar limitations as of claim 7 above. Therefore, it is rejected under the same rationale as of claim 7 above.
Conclusion
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/SANGKYUNG LEE/Examiner, Art Unit 2858
/LEE E RODAK/Supervisory Patent Examiner, Art Unit 2858
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