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 .
This action is responsive to communication filed on 10/27/2025. Claims 21-36 are pending. Claims 1-20 have been cancelled. Claims 21-36 are new. Entry of this amendment is accepted and made of record.
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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 10/27/2025 has been entered.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claim 23 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 23 is improper dependent claim as it is a dependent claim claiming dependency on itself. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
For examination on the merits dependent claim 23 is interpreted to be dependent from dependent claim 22.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 21-36 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The current 35 USC 101 analysis is based on the current guidance (2019
Revised Patent Subject Matter Eligibility Guidance, “2019 PEG’). The patent subject
matter eligibility analysis is threefold. First, via step 1, determine that the claim belongs
to a valid statutory class. Second, via step 2A, identify that an abstract idea is claimed in
prong one and if so, identify whether additional elements are recited that integrate the
abstract idea into a practical application in prong two. Finally, in step 2B, determine
whether the claims contain something significantly more than the abstract idea.
With respect to Step 1, applied to the present application, the claims belong to
one of the statutory classes of a process (method claims 21-33, and 35-36), and system (claim 34) with substantially similar claim language as the method of claims 21 and 25.
Step 2A of the 2019 Guidance is divided into two prongs. Prong 1 requires the
examiner to determine if the claims recite an abstract idea, and further requires that the
abstract idea belong to one of three enumerated groupings: mathematical concepts,
mental processes, and certain methods of organizing human activity.
With respect to step 2A, prong one, the claims recite an abstract idea.
The claim(s) 21, 25 and 34 recite(s) concepts related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion in which given load values and given strain values measured by the at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter; and in which the processor applies at least one measured strain value from the least one strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter, and obtains from the model the at least one load exerted by the at least one wheel of the vehicle traveling along a trafficway (claims 21 and 25); determine the weight of the vehicle based on the at least one load obtained from the model (claim 25); defining a relationship between the at least one load and the at least one strain value, the relationship being adjustable through at least one model parameter, … in which given load values and given strain values measured by the at least one strain gauge in response to the given load values have been applied to the model of the trafficway to determine the at least one value for the at least one model parameter, wherein the processor is configured to apply at least one measured strain value from the least one strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter, and to obtain from the model the at least one load exerted by the at least one wheel of the vehicle traveling along a trafficway (claim 34).
The concepts discussed above can be considered to describe mental processes, namely concepts performed in the human mind or with pen and paper, and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers. Although, the claim does not spell out any particular equation or formula being used, the lack of specific equations for individual steps merely points out that the claim would monopolize all possible calculations in performing the steps. These steps recited by the claims, therefore amount to a series of mental or mathematical steps, making these limitations amount to an abstract idea at Prong 1 of the 101 analysis.
Prong 2, of Step 2A of the 2019 Guidance requires the examiner to determine if the claims recite additional element(s) or a combination of additional elements which integrate the abstract idea into a practical application. This requires additional element(s) in the claim to apply, rely on, or use the abstract idea in a manner that imposes a meaningful limit on the abstract idea, such that the claim is more than a drafting effort designed to monopolize the abstract idea.
This judicial exception is not integrated into a practical application because the abstract idea is not performed by using any particular device and because the “processor” amounts to the recitation of a general purpose computer used to apply the abstract idea; the “strain gauge…; measured strain values”, “strain values measured by at least one strain gauge in the surface layer of the trafficway…” recited by the claims, is mere gathering recited at high level of generality and the results of the algorithm are merely output as part of insignificant post-solution activity and are not used in any particular matter as to integrate the abstract idea in a practical application.
Various considerations are used to determine whether the additional elements
are sufficient to integrate the abstract idea into a practical application. The claim does
not recite a specific machine. The claim does not effect a real-world transformation or
reduction of any particular article to a different state or thing. The claim does not
contain additional elements which describe the functioning of a computer, or which
describe a particular technology or technical field, which is being improved by the use of
the abstract idea. (This is understood in the sense of the claimed invention from
Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing
process including a rubber-molding press, a timer, a temperature sensor adjacent the
mold cavity, and the steps of closing and opening the press, in which the recited use of
a mathematical calculation served to improve that particular technology by providing a
better estimate of the time when curing was complete. Here, the claim does not recite
carrying out any comparable technological process.) Instead the additional elements in
the claim appear to merely be generic computing elements and insignificant extra-
solution activity - merely gathering the relevant data necessary which is the input for the
mental process/math in the abstract idea, and then outputting a result of the abstract
idea. Based on these considerations, the additional elements in the claim do not appear
to integrate the abstract idea into a practical application. Instead, the claim would tend
to monopolize the abstract idea itself, across a wide variety of different practical
applications in the general field-of-use.
Step 2B of the 2019 Guidance requires the examiner to determine whether the
additional elements cause the claim to amount to significantly more than the abstract
idea itself. The considerations in this case are essentially the same as the
considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion
that the claim does not amount to significantly more than the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements amounts the recitation of a general purpose computer “processor” and to mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself.
Therefore, claims 21, 25 and 34 are rejected under 35 USC 101 as directed to an abstract idea without significantly more.
Dependent claims 22-24, 26-33 and 35-36, when each is analyzed as a whole, are similarly held to be patent ineligible under 35 U.S.C. 101. The claims only recite further limitations which are part of the abstract idea discussed previously, and do not recite any additional elements which are sufficient to integrate the abstract idea into a practical application or to make the claims amount to significantly more than the abstract idea. The limitations merely add further details as to the type of data being received/input and used with the mental process and/or math steps recited in the independent claims, and also further calculations and math, so they are properly viewed as part of the recited abstract idea at Prong 1.
Claims 22-24, 26-33 and 35-36 further expands on the abstract idea by appending additional steps which can be considered to describe concepts performed in the human mind or with pen and paper, and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers, which includes data characterization.
Dependent claims 22-24, 26-33 and 35-36merely expands on the abstract idea by reciting additional concepts related to mathematical algorithms/concepts, and mental processes and concepts performed in the human mind e.g. observation, evaluation, judgment, opinion, i.e. modifying at least one of the plurality of model parameters (claim 22-23); the model includes a scaling function, which is adjustable through the at least one model parameter (claim 26), the at least one load obtained from the model is based on a plurality of measured strain values that is assumed to correspond to strain caused by a single wheel (claim 29); determines the weight of the vehicle on the basis of a plurality of respective loads obtained from the model, whereby the respective loads are assumed to be exerted by respective wheels of a single vehicle (claim 30); compressing, a plurality of measured strain values using a wavelet compression technique to obtain compressed data and decompressing, the compressed data (claim 31); wherein the wavelet compression technique uses a basis wavelet that represents a shape of deformation of the surface layer of the trafficway caused by a load exerted by a wheel of a vehicle (claim 32); applying a wavelet transformation to a some or all of the plurality of measured strain values to obtain a plurality of coefficients; and losslessly encoding some or all of the obtained plurality of coefficients, or quantized versions thereof (claim 33).
Note that the use of a neural network recited in claim 24 was treated as an additional element that fails to integrate the abstract idea into a practical application because it amounts to a general processing computer to implement the abstract idea.
The concepts discussed above can be considered to describe mental processes, namely concepts performed in the human mind or with pen and paper, and/or mathematical concepts, namely a series of calculations leading to one or more numerical results or answers. Although, the claim does not spell out any particular equation or formula being used, the lack of specific equations for individual steps merely points out that the claim would monopolize all possible calculations in performing the steps. These steps recited by the claims, therefore amount to a series of mental or mathematical steps, making these limitations amount to an abstract idea at Prong 1 of the 101 analysis.
Prong 2, of Step 2A of the 2019 Guidance requires the examiner to determine if the claims recite additional element(s) or a combination of additional elements which integrate the abstract idea into a practical application. This requires additional element(s) in the claim to apply, rely on, or use the abstract idea in a manner that imposes a meaningful limit on the abstract idea, such that the claim is more than a drafting effort designed to monopolize the abstract idea.
This judicial exception is not integrated into a practical application because the abstract idea is not performed by using any particular device and because, the “processor” amounts to a general purpose computer to implement the abstract idea and the model implemented as a neural network, (claim 4), amounts to the implementation of the abstract idea on a generic computer and also merely indicates a field of use or technological environment in which the judicial exception is performed, this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h); and computing system (claim 19) which amounts to the recitation of a general purpose computer used to apply the abstract idea; and because the “strain gauges… optical fiber strain gauges” measuring strain values (claims 11, 12 and 15), “…transmitting the compressed data…” (claim 15), memory and non-transitory machine-readable media storing program instructions (claims 19 and 20), is mere gathering recited at high level of generality and the results of the algorithm are merely output as part of insignificant post-solution activity and are not used in any particular matter as to integrate the abstract idea in a practical application.
Various considerations are used to determine whether the additional elements
are sufficient to integrate the abstract idea into a practical application. The claim does
not recite a specific machine. The claim does not effect a real-world transformation or
reduction of any particular article to a different state or thing. The claim does not
contain additional elements which describe the functioning of a computer, or which
describe a particular technology or technical field, which is being improved by the use of
the abstract idea. (This is understood in the sense of the claimed invention from
Diamond v Diehr, in which the claim as a whole recited a complete rubber-curing
process including a rubber-molding press, a timer, a temperature sensor adjacent the
mold cavity, and the steps of closing and opening the press, in which the recited use of
a mathematical calculation served to improve that particular technology by providing a
better estimate of the time when curing was complete. Here, the claim does not recite
carrying out any comparable technological process.) Instead the additional elements in
the claim appear to merely be generic computing elements and insignificant extra-
solution activity - merely gathering the relevant data necessary which is the input for the
mental process/math in the abstract idea, and then outputting a result of the abstract
idea. Based on these considerations, the additional elements in the claim do not appear
to integrate the abstract idea into a practical application. Instead, the claim would tend
to monopolize the abstract idea itself, across a wide variety of different practical
applications in the general field-of-use.
Step 2B of the 2019 Guidance requires the examiner to determine whether the
additional elements cause the claim to amount to significantly more than the abstract
idea itself. The considerations in this case are essentially the same as the
considerations for Prong 2 of Step 2a, and the same analysis leads to the conclusion
that the claim does not amount to significantly more than the abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the only additional elements amounts to mere data gathering/output recited at a high level of generality and insignificant extra-solution activity that when further analyzed under Step 2B is found to be well-understood, routine and conventional activities as evidenced by MPEP 2106.05(d)(II); and because the data of performing the algorithm must necessarily be “obtained” and the use of a general purpose computer to implement the abstract idea for performing the algorithm does not amount to significantly more than the recitation of the abstract idea itself.
Therefore, claims 21-36 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
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 (i.e., changing from AIA to pre-AIA ) 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.
Claim(s) 21-23, 25-30, and 34-36 is/are rejected under 35 U.S.C. 103 as being unpatentable over Karabacak et al. US20190137305A1 (hereinafter Karabacak) in view of Kearns et al. US2013/0238532A1 (hereinafter Kearns).
Regarding claim 21, Karabacak discloses a method for determining at least one load (see abstract, ¶0033-0034, 0074-0075, 0140, wherein a preprocessor is disclosed) exerted by at least one wheel of a vehicle traveling along a trafficway (see abstract, ¶0074-0075), comprising:
providing, to a processor (see abstract, ¶0033-0034, 0074-0075, 0140, wherein a preprocessor is disclosed), a model of the trafficway representing the properties of the trafficway, in particular the way in which the at least one load acting on a surface layer of the trafficway leads to deformations (see Fig. 1, 1a, Fig. 10a-b, Fig. 20; ¶0001-0002, 0012, 0018-0019, 0021-0024, 0026, 0027, 0029, 0033-0034, 0074-0075, 0082, wherein a model is disclosed for analyzing optical signals from optic strain-sensor elements in order to gain more reliable an accurate data from the tyre loads on the road, and that relates to properties of conveyance elements and or its kinetic state to a shape of the temporal pattern is disclosed see ¶0085-0089; furthermore a trained neural network is disclosed , see ¶0101-0103) and thereby to at least one strain value in a plane in which at least one strain gauge is located (see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”, and wherein optic strain-sensor elements is disclosed, see abstract, ¶0007, 0014, 0016, 0022-0025 ), the model defining a relationship between the at least one load and the at least one strain value, the relationship being adjustable through at least one model parameter (see Fig. 20, ¶0024-0027, 0029, 0033-0034, wherein a conversion module converts an optic response signal into a detection signal having a magnitude indicative for a detected strain of the at least one optic sensor element, which corresponds to mechanical load of vehicles; ¶0074-0075, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor, therefore the relationship being adjustable through at least one model parameter is implied);
conducting a training phase in which given strain values measured by the at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter (see ¶0101-0103, wherein a neural network is trained by spatial signal pattern where x is the position of each optic strain sensor element and wherein interrogator can be configured to compare temporal pattern of the detected optical signal with respective stored temporal pattern to provide an output signal indicative for the stored temporal pattern that best matches the detected optical signal as a signal feature indicative of the shape of the temporal pattern of the detected optical signal of strain sensor, see ¶0140, wherein signal features related to load of traffic infrastructure are derived) and
obtains from the model the at least one load exerted by the at least one wheel of the vehicle traveling along a trafficway (see Fig. 20, ¶0026-0027, ¶0034, ¶0074-75, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor; see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”).
However, Karabacak do not expressly or explicitly discloses conducting a training phase in which given load values and given strain values are applied to the model to determine at least one value for the at least one model parameter (emphasis added) and conducting a production phase in which the processor applies at least one measured strain value from the at least one strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter.
Kearns discloses a monitoring system for identifying deformation of a structure and conducting a training phase in which given load values and given strain values are applied to the model to determine values for the plurality of model parameters (emphasis added) (see abstract; ¶0035, 0042, 0056, 0065-0069, wherein a training is performed, wherein training deformation/load data and strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model and wherein the heuristic models trained to generate estimated deformation/load data with a desired level of accuracy based on input strain data) and conducting a production phase in which the processor applies at least one measured strain value from the at least one strain gauge to the model with the at least one value that has been determined for the at least one model parameter, (see abstract, ¶0035, 0065-0069, wherein parameters are adjusted such that heuristic model is capable of generating estimated deformation data based on input strain data and the heuristic model is trained using plurality of training cases and an iterative process [production mode], and wherein historical environmental and test environmental data may be used for training of heuristic model, see ¶0071, 0073, 0075, 0083-0084, 0087; 0054-0055, 0076, wherein a computer system and a processor unit is disclosed).
Therefore, it would have been obvious to one of ordinary skilled in the before the effective filing date of the claimed invention given the teachings of Kearns discussed above to configure the system of Karabacak such that a training phase in in which given load values and given strain values measured by at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter and a production phase in which the processor applies at least one measured strain value form the at least strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter for the benefit of providing a means for generating estimated deformation/load data with a desired level of accuracy based on input strain data and enhancing the system by increasing an accuracy of output data generated.
Regarding claim 22, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 20. Karabacak further teaches adaptively modifying the at least one value for the at least one parameter of the trafficway (see abstract, ¶0023, 0086-890101, 0103, 0125, 0139, wherein curve-fitting algorithms are applied, i.e. to derive the set of signal features by using a model that relates properties of said conveyance element and or its kinetic state to a shape of the temporal pattern and only store the essential signal features to be able to replicate the measurements as a function of these essential signal features).
Regarding claim 23, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 22. Karabacak further teaches that wherein the operation adaptively modifying the at least one of the plurality of model parameters uses information about the weight of vehicles obtained from external sources (see abstract, ¶0023, 0086-0089, 0090, 0101, 0103, 0125, 0139, wherein the pressure exerted by the tire as indicated by the height of the peak of the temporal signal pattern indicate a weight class of the vehicle, and wherein he weight distribution may be inferred from the relative heights of the peaks, wherein the position reconstruction unit receive an estimation of the vehicles weight based on other sensor readings and alternatively can be estimated from maximum amplitude).
Regarding claim 25, Karabacak discloses a method for determining a weight of a vehicle in motion on a trafficway (see abstract, ¶0033-0034, 0074-0075, 0140) the method comprising:
providing, to a processor (see abstract, ¶0033-0034, 0074-0075, 0140, wherein a preprocessor is disclosed), a model of the trafficway representing the properties of the trafficway, in particular the way in which the at least one load exerted by at least one wheel of the vehicle on a surface layer of the trafficway leads to deformations (see Fig. 1, 1a, Fig. 10a-b, Fig. 20; ¶0001-0002, 0012, 0018-0019, 0021-0024, 0026, 0027, 0029, 0033-0034, 0074-0075, 0082, wherein a model is disclosed for analyzing optical signals from optic strain-sensor elements in order to gain more reliable an accurate data from the tyre loads on the road, and that relates to properties of conveyance elements and or its kinetic state to a shape of the temporal pattern is disclosed; see ¶0085-0089; furthermore a trained neural network is disclosed, see ¶0101-0103) and thereby to at least one strain value in a plane in which at least one strain gauge is located (see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”, and wherein optic strain-sensor elements is disclosed, see abstract, ¶0007, 0014, 0016, 0022-0025 ), the model defining a relationship between the at least one load and the at least one strain value, the relationship being adjustable through at least one model parameter (see Fig. 20, ¶0024-0027, 0029, 0033-0034, wherein a conversion module converts an optic response signal into a detection signal having a magnitude indicative for a detected strain of the at least one optic sensor element, which corresponds to mechanical load of vehicles; ¶0074-0075, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor, therefore the relationship being adjustable through at least one model parameter is implied);
conducting a training phase in which given strain values measured by the at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter (see ¶0101-0103, wherein a neural network is trained by spatial signal pattern where x is the position of each optic strain sensor element and wherein interrogator can be configured to compare temporal pattern of the detected optical signal with respective stored temporal pattern to provide an output signal indicative for the stored temporal pattern that best matches the detected optical signal as a signal feature indicative of the shape of the temporal pattern of the detected optical signal of strain sensor, see ¶0140, wherein signal features related to load of traffic infrastructure are derived) and
obtains from the model the at least one load exerted by the at least one wheel of the vehicle traveling along a trafficway (see Fig. 20, ¶0026-0027, ¶0034, ¶0074-75, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor; see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”) and determines the weight of the vehicle based on the at least one load obtained from the model (see abstract, ¶0026-0027, 0033-0034, 0074-0075, 0101, 0140, wherein a temporal signal pattern Δλ(t) for an optic strain sensor element is disclosed and wherein the height Δλmax is indicative of the pressure/load value exerted on the road surface by the tire; see abstract, ¶0086-0087, 0089-0091, 0125, 0139-0140, wherein the height Δλmax is indicative of pressure/load exerted and wherein a weight of the vehicle may be inferred from the relative heights of the peaks).
However, Karabacak do not expressly or explicitly discloses conducting a training phase in which given load values and given strain values are applied to the model to determine at least one value for the at least one model parameter (emphasis added) and conducting a production phase in which the processor applies at least one measured strain value from the at least one strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter.
Kearns discloses a monitoring system for identifying deformation of a structure and conducting a training phase in which given load values and given strain values are applied to the model to determine values for the plurality of model parameters (emphasis added) (see abstract; ¶0035, 0042, 0065-0069, wherein a training is performed, wherein training deformation/load data and strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model and wherein the heuristic models trained to generate estimated deformation/load data with a desired level of accuracy based on input strain data) and conducting a production phase in which the processor applies at least one measured strain value from the at least one strain gauge to the model with the at least one value that has been determined for the at least one model parameter, (see abstract, ¶0035, 0065-0069, wherein parameters are adjusted such that heuristic model is capable of generating estimated deformation data based on input strain data and the heuristic model is trained using plurality of training cases and an iterative process [production mode], and wherein historical environmental and test environmental data may be used for training of heuristic model, see ¶0071, 0073, 0075, 0083-0084, 0087; 0054-0055, 0076, wherein a computer system and a processor unit is disclosed).
Therefore, it would have been obvious to one of ordinary skilled in the before the effective filing date of the claimed invention given the teachings of Kearns discussed above to configure the system of Karabacak such that a training phase in in which given load values and given strain values measured by at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter and a production phase in which the processor applies at least one measured strain value form the at least strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter, for the benefit of providing a means for generating estimated deformation/load data with a desired level of accuracy based on input strain data and enhancing the system by increasing an accuracy of output data generated.
Regarding claim 26, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 25. Karabacak further teaches calculation rule is implemented by a scaling function which is adjustable through the at least one model parameter (¶0018, 0031, 0032, 0138-0139, 0146, 0148, wherein correction unit is disclosed for processing the response optic signal of each optic strain-sensor element and to compensate for mutual differences in geometrical configuration in the respective neighborhoods of optic strain-sensor elements; alternatively a compensation module uses an estimated value for temperature T of an optic train sensor element and performs a compensation of a response optic signal received from the optic-strain sensor element).
Regarding claim 27, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 25. Karabacak further discloses that the at least one strain gauges comprises a plurality of optical fiber strain gauges (see abstract, ¶0007, 0075; wherein “optic strain-sensor elements 22”, e.g. Fiber Bragg Gratings [FBG] are disclosed).
Regarding claim 28, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 25. Karabacak further discloses at least some of the plurality of strain gauges are arranged in at least one linear arrangement traverse or oblique to a direction of traffic on the trafficway (see Fig. 1-1A, 5 wherein strain gauges are shown, see ¶104, wherein fiber optic sensors 20a, 20b, 20c, 20d that are embedded in a road with traffic carrying surface 51. The road has a longitudinal direction indicated by arrow 52. The four fiber optic sensors 20a, 20b, 20c, 20d extend transverse to this direction).
Regarding claim 29, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 25. Karabacak further discloses that the at least one load obtained from the model is based on a plurality of measured strain values that is assumed to correspond to strain caused by a single wheel (Fig. 8, ¶0074-0075, ¶0089-0091, ¶0111, wherein the shape of the temporal pattern associated with a single tire of a vehicle traversing the traffic infrastructure is disclosed; see FIG. 4b. FIG. 4b schematically shows a temporal signal pattern Δλ(t) for an optic strain-sensor element for the full time-window wherein the vehicle traverses the traffic carrying surface 51 above it. The complete temporal pattern in this case includes 5 peaks, one for each tire of the vehicle on its side above that optic strain-sensor element, and wherein a plurality optic strain sensor elements may be combined to derive information as illustrated in Fig. 4c, see ¶0096).
Regarding claim 30, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 29. Karabacak further discloses determines the weight of the vehicle on the basis of a plurality of respective loads obtained from the model, whereby the respective loads are assumed to be exerted by respective wheel of a single vehicle (Fig. 8, ¶0074-0075, ¶0089-0091, ¶0111; see FIG. 4b. FIG. 4b schematically shows a temporal signal pattern Δλ(t) for an optic strain-sensor element for the full time-window wherein the vehicle traverses the traffic carrying surface 51 above it. The complete temporal pattern in this case includes 5 peaks, one for each tire of the vehicle on its side above that optic strain-sensor element, and wherein a plurality optic strain sensor elements may be combined to derive information as illustrated in Fig. 4c, see ¶0096; see Figs 10a-b, 11, 12a-b, 13a-c, wherein a color coded two-dimensional plot shows a response measured for the left most fiber optic sensor 20a of Fig. 5 as a function of time and of position in the lateral direction, here the optic strain sensor elements located at a lateral position and wherein a response measured for the rightmost fiber optic sensor 20d are disclosed, see ¶0115-0119).
Regarding claim 34, Karabacak discloses a system for determining at least one load exerted by at least one wheel of a vehicle traveling along a trafficway (see abstract, ¶0033-0034, 0074-0075, 0140), the system comprising:
at least one strain gauge arranged to measure at least one strain value in a surface layer of the trafficway (see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”, and wherein optic strain-sensor elements is disclosed, see abstract, ¶0007, 0014, 0016, 0022-0025; see Fig. 1-1A, 5 wherein strain gauges are shown, see ¶104, wherein fiber optic sensors 20a, 20b, 20c, 20d that are embedded in a road with traffic carrying surface 51);
a processor (see abstract, 0074-0075, 0140, wherein a preprocessor is disclosed) that is provided with a model of the trafficway representing the properties of the trafficway, in particular the way in which the at least one load acting on a surface layer of the trafficway leads to deformations (see Fig. 1, 1a, Fig. 10a-b, Fig. 20; ¶0001-0002, 0012, 0018-0019, 0021-0024, 0026, 0027, 0029, 0033-0034, 0074-0075, 0082, wherein a model is disclosed for analyzing optical signals from optic strain-sensor elements in order to gain more reliable an accurate data from the tyre loads on the road, and that relates to properties of conveyance elements and or its kinetic state to a shape of the temporal pattern is disclosed see ¶0085-0089; furthermore a trained neural network is disclosed , see ¶0101-0103) and thereby to at least one strain value in a plane in which the at least one strain gauge is located (see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”, and wherein optic strain-sensor elements is disclosed, see abstract, ¶0007, 0014, 0016, 0022-0025 ), the model defining a relationship between the at least one load and the at least one strain value, the relationship being adjustable through at least one model parameter (see Fig. 20, ¶0024-0027, 0029, 0033-0034, wherein a conversion module converts an optic response signal into a detection signal having a magnitude indicative for a detected strain of the at least one optic sensor element, which corresponds to mechanical load of vehicles; ¶0074-0075, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor, therefore the relationship being adjustable through at least one model parameter is implied);; and
a memory containing at least one value for the at least one model parameter (see para. 0075, 0102-0103, wherein a memory is implied) that has been determined in a training phase in which given strain values measured by the at least one strain gauge in response to the given load values have been applied to the model of the trafficway to determine the at least one value for the at least one model parameter (see ¶0101-0103, wherein a neural network is trained by spatial signal pattern where x is the position of each optic strain sensor element and wherein interrogator can be configured to compare temporal pattern of the detected optical signal with respective stored temporal pattern to provide an output signal indicative for the stored temporal pattern that best matches the detected optical signal as a signal feature indicative of the shape of the temporal pattern of the detected optical signal of strain sensor, see ¶0140, wherein signal features related to load of traffic infrastructure are derived),
wherein the processor is configured to apply at least one measured strain value from the least one strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter (¶101-0103, wherein a neural network is trained by spatial signal pattern where x is the position of each optic strain sensor element and wherein interrogator can be configured to compare temporal pattern to provide an output signal indicative for the stored temporal pattern that best matches the detected optical signal as a signal feature indicative of the shape of the temporal pattern of the detected optical signal of strain sensor, see ¶0140, wherein signal features related to load of traffic infrastructure are derived), and
to obtain from the model the at least one load exerted by the at least one wheel of the vehicle traveling along a trafficway (see Fig. 20, ¶0026-0027, ¶0034, ¶0074-75, ¶0101, 0122 wherein interrogator 10 comprises processing module I31 which processes subcomponents to derive signal features related to mechanical behavior of traffic infrastructure and processing module I41 which process second subcomponents to derive signal features related to static load of traffic infrastructure e.g. caused by parked vehicles or waiting at a traffic light, vehicle moving over traffic carrying surface, wherein interrogator 10 comprises correction unit I8, which process response optic signal from strain sensor; see figs. 1, 1A, 10A-B, 20; see ¶0026, ¶0075 “tyre load on the road”).
However, Karabacak do not expressly or explicitly discloses a training phase in which given load values and given strain values are applied to the model to determine at least one value for the at least one model parameter (emphasis added) and although a memory is implied (¶075, 0102-0103), however a memory is not explicitly taught by Karabacak.
Kearns discloses a data processing system 1500 used to implement computer system 122 and or computer system 202, wherein the data processing system comprises processor unit 1504 to execute instructions for software, that may be a number of processors, and memory 1506, and persistent storage 1508 (see ¶0151-0154, 0157-0159, 0165) and a monitoring system for identifying deformation of a structure and conducting a training phase in which given load values and given strain values are applied to the model to determine values for the plurality of model parameters (emphasis added) (see abstract; ¶0035, 0042, 0056, 0065-0069, wherein a training is performed, wherein training deformation/load data and strain data are configured for use by a heuristic model to increase an accuracy of output data generated by the heuristic model and wherein the heuristic models trained to generate estimated deformation/load data with a desired level of accuracy based on input strain data) and further teaches a processor applying at least one measured strain value from the at least one strain gauge to the model with the at least one value that has been determined for the at least one model parameter, (see abstract, ¶0035, 0042, 0056, 0065-0069, wherein parameters are adjusted such that heuristic model is capable of generating estimated deformation data based on input strain data and the heuristic model is trained using plurality of training cases and an iterative process [production mode], and wherein historical environmental and test environmental data may be used for training of heuristic model, see ¶0071, 0073, 0075, 0083-0084, 0087; 0054-0055, 0076, wherein a computer system and a processor unit is disclosed).
Therefore, it would have been obvious to one of ordinary skilled in the before the effective filing date of the claimed invention given the teachings of Kearns discussed above to configure the system of Karabacak with such that a training phase in in which given load values and given strain values measured by at least one strain gauge in response to the given load values are applied to the model of the trafficway to determine at least one value for the at least one model parameter and applying at least one measured strain value form the at least strain gauge to the model of the trafficway with the at least one value that has been determined for the at least one model parameter, for the benefit of providing a means for generating estimated deformation/load data with a desired level of accuracy based on input strain data and enhancing the system by increasing an accuracy of output data generated and to provide a memory for the benefit of providing a means for saving data and to provide a means that would allow for data saved therein to accessed for data analysis functions as needed.
Regarding claims 35 and 36, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 1. Although Karabacak discloses a plurality of processors (see ¶0075 , preprocessor 80 and ¶124, processing system 62, wherein processing a response optical signal of an optic strain sensor elements is disclosed, see ¶0023-0024) and further discloses that temporal patterns and essential signal features are stored (see ¶0102-0103, therefore the memory is implied).
However it does not explicitly discloses a non-transitory machine-readable medium having program instructions stored thereon, wherein the program instructions are adapted to cause a computing system, when executing the program instructions, to perform the method according to claims 21 and 25 respectively.
Kearns discloses a data processing system 1500 used to implement computer system 122 and or computer system 202, wherein the data processing system comprises processor unit 1504 to execute instructions for software, that may be a number of processors, and memory 1506, wherein the memory is non-volatile for storing computer usable program code (see ¶0151-0154, 0157-159, 0165).
Therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed to configure the system of Karabacak with a machine-readable medium having program instructions stored thereon, wherein the progra