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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 program instructions are adapted to cause a computing system, when executing the program instructions, disclosed by Kearns to perform the method according to claim 1 to cause the system to perform the method according to claim 1 for the benefit of providing a means that would allow for efficiently performing complex calculations and mathematical processes and for rapid processing of data.
Claim(s) 24 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) in further view of Koivisto et al. WO 9524616 A1 (hereinafter Koivisto).
Regarding claim 24, combination of Karabacak and Kearns discloses the materials as discussed above for claim 21. Karabacak further teaches that the model is implemented as a neural network (see para. 0010).
However the combination of Karabacak and Kearns do not expressly or explicitly discloses the neural network comprising at least one of (i) a feedback loop, or (ii) a hidden layer (emphasis added).
Koivisto discloses a load measurement system directed to measuring loads in structures and particularly for measuring the weight of a vehicle (see abstract) and further discloses a neural network being trained with test loads to process the measurement signals from strain gauge detectors, wherein the load weight is defined on the basis of the signals obtained from the strain gauge detectors, wherein the neural network comprises at least one of (i) a feedback loop, or (ii) a hidden layer (see , wherein a neural network comprises a hidden layer and is trained with loads using a feedback by using real wheel weights; see page 7, ll. 10-28, page 13, ll. 29-38 and page 14, ll. 1-11).
Therefore, it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed given the teachings of Koivisto, to configure the system of Karabacak as modified by Kearns with a neural network comprising at least one of (i) a feedback loop or (ii) a hidden layer for the benefit of providing an enhanced processing system that would enable the network to perform feature extraction for identifying and separating out relevant information from input data necessary for making predictions or decision and for allowing for large number of measurement signals to be processed without limiting the number of measuring points and/or desired measurement signals on the condition that the measuring system is provided with sufficient data processing capacity, particularly calculatory capacity (see Koivisto P. 3 lines 26-P. 4 line 2).
Claim(s) 31 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) in further view of Carnegie et al. CA2693531A1 (hereinafter Carnegie).
Regarding claim 31, the combination of Karabacak and Kearns discloses the materials as discussed above for claim 25.
However, it does not expressly or explicitly discloses that obtaining a plurality of measured strain values from the plurality of strain gauges comprises: compressing, in a roadside processing unit, a plurality of measured strain values using a wavelet compression technique to obtain compressed data; transmitting the compressed data from the roadside processing unit to a processing center; and decompressing, by the processing center, the compressed data.
Carnegie discloses a system further for measuring weight applied to a hook comprises a strain gauge (see ¶0006) and a method for data processing including transforming measurement data by using wavelet transform and compressing data for facilitating computation, transmission and storage of data since data compression use fewer data points to represent the whole dataset (see abstract, ¶0009, 0011, 0032-0033, 0037-0038, 0056). Carnegie further discloses decompressing the transmitted compressed data in order to reconstruct the signals at a later stage (see abstract, ¶0033, 0037-0039, 0056, 0066, 0075).
Therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was effectively filed to apply the teachings of a wavelet compression and decompression techniques disclosed by Carnegie into the system of Karabacak as modified by Kearns in order to compress, by a roadside processing unit, the plurality of measured strain values using a wavelet compression technique to obtain compressed data; transmitting the compressed data from the roadside processing unit to a processing center; and decompressing, by the processing center, the compressed data for the benefit of use fewer data points to represent the whole dataset and decompressing it in order to provide a means for reconstructing signals for use at a later stage. Therefore, the system would be enhanced since the compression and decompression of such data would also allow for improved data analysis.
Reasons to overcome the Prior Art
Regarding claim 32 and 33, currently rejected under 35 USC 101, the closest prior art of record either in singularly or in combination do not expressly or explicitly discloses the subject matter disclosed by the claims.
Regarding claim 32 the closest prior art of record either in singularly or in combination fails to anticipate or render obvious the limitations of “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” in combination with the limitations set forth by the claim including the limitations set forth by the base claim and any intervening claims without the use of impermissible hindsight.
Regarding claim 33 the closest prior art of record either in singularly or in combination fails to anticipate or render obvious the limitations of “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” in combination with the limitations set forth by the claim including the limitations set forth by the base claim and any intervening claims, without the use of impermissible hindsight.
Response to Arguments
Applicant's arguments filed 10/27/2025 with respect to newly added claims 21-36 have been fully considered but they are not persuasive.
With respect to rejections under 35 USC 101 made to claims 21-36 applicant argues that the claimed invention of claims 21, 25 and 34 is not merely an abstract idea but describes a specific technical solution to a technical problem: accurately determining load values exerted by a vehicle on a trafficway, that the invention addresses the challenge of obtaining precise load data from strain measurements, which is crucial for traffic management and infrastructure monitoring and submits that this is a practical application that provides improvement to existing technology, specifically in the field of traffic and infrastructure monitoring systems, by enabling greater accuracy due to the model as defined in the independent claims (in last paragraph of page 9 through third line of page 10 of the remarks). Applicant further submits that the model is a concrete technical component, directly reflecting the physical characteristics and behavior of the trafficway under load and that it moves beyond purely abstract mathematical concept by explicitly linking the physical load (output) to measure strain (input) within real-world structure, and its adjustability through model parameters allows for adaptation to specific trafficway conditions, thereby enhancing accuracy (see second paragraph of page 10 of the remarks); and that the claimed invention provides such an improvement by enabling more accurate and reliable determination of loads on trafficways, which enhances the functionality of traffic monitoring and infrastructure management systems, which is a practical, real-world benefit that integrates any underlying mathematical concepts into a concrete technical application.
In response, the examiner disagrees and submits that the claimed language do not reflect the alleged improvement nor amounts to an improvement on the operation of the computer and that the alleged improvements mentioned is generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) and as such is not indicative of a practical application of abstract idea.
The alleged improvement is part of the abstract idea as it is geared described concepts related to mathematical calculations for determining load value exerted by at least one wheel of the vehicle traveling along a trafficway, from strain measurements gathered and generally linking the abstract idea to a field of use and does not amount to significantly more than an abstract idea itself, the additional argued limitation is mere data gathering recited at a high level of generality, and because the data for performing the algorithm must be necessarily obtained and because the results of the algorithm are merely output as part of insignificant post-solution activity and are not used in anu particular matter as to integrate the abstract idea into a practical application.
Examiner further submits that the argued concrete technical component directly reflecting the physical characteristics and behavior of the trafficway under load and that it moves beyond purely abstract mathematical concept by explicitly linking the physical load (output) to measure strain (input) within real-world structure, and its adjustability through model parameters allows for adaptation to specific trafficway conditions, is part of the data gathering process acquiring a strain measurement in order to determine a load exerted by the tires of a vehicle on a surface of a traffic way and does not amount to significantly more than the abstract idea itself and because the argued limitation recited by the claim merely ties in the abstract idea to a field of use and do not add significantly more to the abstract idea and only pertains as to where the data comes from in performing the abstract idea which is 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 claim 21 is rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
With respect to applicant arguments that the steps of “conducting a training phase” and “conducting a production phase” where a processor is configured to perform explicit steps are phases that are not generic data gathering output steps, nor are they mental processes, that the phases are structured concrete computational processes performed by a processor to configure and then operate a technical model to achieve a tangible, real-world result: accurate load determination for a physical trafficway; and that the configuration of the processor to implement this specific, physically-grounded model and execute these distinct training and production phases transforms a general-purpose computer into a specialized apparatus for this technical application, which goes beyond merely “applying” and abstract idea using a generic computer (see third and fourth paragraph of page 10 of the remarks).
In response the examiner disagrees and submits that the claimed “training phase” and “production phase” are part of the abstract idea and as such are concepts related to mathematical algorithms/concepts for determining a load exerted by the at least one wheel of the vehicle traveling along a trafficway.
Although applicant have argued that a processor is defined to operate in a training phase and a production phase as well as operations carried out by the processor in these modes, the recitation of a processor for performing the claimed functions amounts to a general purpose computer to implement the abstract idea and as such it is not indicative of integration of the abstract idea into a practical application since as such it merely amounts to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
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” claimed amounts to the recitation of a general purpose computer used to apply the abstract idea (claims 21, 25, 34). Contrary to applicant assertions, the processor claimed is not a specialized apparatus, but a generic computer used to implement the abstract idea, that fails to add inventive concept to the claims since the claim nor the original disclosure of the invention specifies distinctive and unique characteristics of the processor nor the claims include limitations that amount to an improvement of the processor such that it can be considered significantly more than the abstract idea itself. Examiner further submits that 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.
Regarding new claims 22-24, 26-33, 35 and 36 are dependent on new claim 21 or new claim 25 applicant argues that the claims inherit the non-abstract nature and technical improvements of the independent claims.
In response the examiner disagrees for similar reasons discussed above with respect to claim 21 and submits that the claimed language do not reflect the alleged improvement nor amounts to an improvement on the operation of the computer and that the alleged improvements mentioned is generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h) and as such is not indicative of a practical application of abstract idea.
The alleged improvement is part of the abstract idea as it is geared described concepts related to mathematical calculations for determining load value exerted by at least one wheel of the vehicle traveling along a trafficway, from strain measurements gathered and generally linking the abstract idea to a field of use and does not amount to significantly more than an abstract idea itself, the additional argued limitation is mere data gathering recited at a high level of generality, and because the data for performing the algorithm must be necessarily obtained and because the results of the algorithm are merely output as part of insignificant post-solution activity and are not used in anu particular matter as to integrate the abstract idea into a practical application.
Examiner further submits that the argued concrete technical component directly reflecting the physical characteristics and behavior of the trafficway under load and that it moves beyond purely abstract mathematical concept by explicitly linking the physical load (output) to measure strain (input) within real-world structure, and its adjustability through model parameters allows for adaptation to specific trafficway conditions, is part of the data gathering process acquiring a strain measurement in order to determine a load exerted by the tires of a vehicle on a surface of a traffic way and does not amount to significantly more than the abstract idea itself and because the argued limitation recited by the claim merely ties in the abstract idea to a field of use and do not add significantly more to the abstract idea and only pertains as to where the data comes from in performing the abstract idea which is 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.
Although applicant have argued that a processor is defined to operate in a training phase and a production phase as well as operations carried out by the processor in these modes, the recitation of a processor for performing the claimed functions amounts to a general purpose computer to implement the abstract idea and as such it is not indicative of integration of the abstract idea into a practical application since as such it merely amounts to mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f).
Therefore claims 22-36 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more.
With respect to claim 24, applicant argues that the use of a neural network is not merely an abstract algorithm but is defined within the context of the specific, physical trafficway model for determining load from strain, thereby contributing to the practical application and technical improvement.
In response the examiner disagrees and submits that the judicial exception is not integrated into a practical application in claims 24 because the abstract idea is not performed by using any particular device and because the “neural network”, recited in claims 24 amounts to the recitation of a general purpose computer used to apply the abstract idea; which 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); which is mere data gathering recited at a high level of generality and generally linking the abstract idea to a field of use or technological environment in which the judicial exception is performed and the results are not used in any particular matter as to integrate the abstract idea in a practical application.
Therefore claims 21-36 are rejected under 35 USC 101 as being directed to an abstract idea without significantly more for the reasons discussed above with respect to 35 USC 101 rejections.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
With respect to the newly added claims 21-36 applicant submits that the combination of Karabacak and Kearns fails to disclose, teach or suggest the specific “model of the trafficway” as recited in new claim 21, and submits that the models of Karabacak do not describe a physical model of the trafficway itself that represents its physical properties and defines a direct load-to-strain relationship in a parameterized and adjustable manner, independent of the temporal signal patterns and submits that the processing modules that derive signal features related to mechanical behavior or static load, not a direct load-to-strain model of the trafficway’s physical properties.
In response the examiner disagrees and submits that as acknowledged by applicant the model of Karabacak does describe the viscoelastic state of the trafficway, therefore it describes a physical model of the trafficway, which does describes a direct load-to-strain relationship (see para. 0026-0027, 0034, 0074-0075, 0085-0089, 0101, 0122). In Karabacak strain values measured are employed in order to determine a load value, therefore these values have a direct load-to-strain relationship, which is adjustable as it depends on the changes in strain in order to determine the load value.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., direct load-to-strain model) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that Kearns is nonanalogous art (see last paragraph of page 12 through fourth line of page 13 of the remarks), it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Karabacak and Kearns are analogous arts as both references relates to strain measurements on a surface in response to loads applied to the structure (para. 0042, 0056, 0065-0069).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references (see second paragraph of page 13 of the remarks), the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
The examiner submits that, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
Furthermore, it is noted that Kearns was brought into the combination as disclosing the features of 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.
Kearns further discloses a plurality of loads are applied to a plurality of points on a structure, which deforms and obtaining strain data for the structure when the structure has deformed due to the load applied (see claims 11 and 12, ¶0065-0069, 0087, 0090) therefore a load-to-strain deformation relationship/model is disclosed.
Therefore the combination of Karabacak and Kearns discloses the argued features of the claimed invention as presented on claims 21-36 of instant application.
With respect to new claim 25 applicant submits that Karabacak directly infers vehicle weight or weight class from the characteristics of temporal signal patterns (Karabacak, at paragraphs 0087 and 0091) or uses curve-fitting to derive signal features to weight (Karabacak, at paragraph 0139) and argues that Karabacak does not employ a model of the trafficway that represents its physical properties and defines a load-to-strain relationship adjustable by model parameters to determined load values, and subsequently, vehicle weight. Applicant further argues that Kearns as previously established focuses on deformation and not on load and therefore the core distinguishing features of the specific “model of the trafficway” and its use in determined weight are not disclosed by the prior art (see last paragraph of page 13 of the remarks).
In response, the examiner disagrees and submits that as discussed with respect to claim 21, that as acknowledged by applicant the model of Karabacak does describe the viscoelastic state of the trafficway, therefore it describes a physical model of the trafficway, which does describes a direct load-to-strain relationship (see para. 0026-0027, 0034, 0074-0075, 0085-0089, 0101, 0122). In Karabacak strain values measured are employed in order to determine a load value, therefore these values have a direct load-to-strain relationship, which is adjustable as it depends on the changes in strain in order to determine the load value and weight of the vehicle (see abstract, ¶0033-0034, 0074-0075, 0140).
Regarding new claim 34, applicant argues that the non-obviousness of the system of claim 34 follows from the non-obviousness of the method it is configured to perform and submits that as argued for new claim 21, the prior art specifically Karabacak and Kearns, fails to disclose or suggest a “model of trafficway” that represents its physical properties and defines adjustable relationship between load and strain and submits that since the method of determining load values using such a novel an non-obvious trafficway model and its associated training and production phases (as recited in new claim 21) it is not taught or suggested by the prior art, a system specifically configured to implement and execute this non-obvious method (as claimed in claim 34) is likewise non-obvious (see first paragraph of page 14 of the remarks).
In response the examiner disagrees for similar reasons discussed above with respect to claim 21, and submits that as acknowledged by applicant the model of Karabacak does describe the viscoelastic state of the trafficway, therefore it describes a physical model of the trafficway, which does describes a direct load-to-strain relationship (see para. 0026-0027, 0034, 0074-0075, 0085-0089, 0101, 0122). In Karabacak strain values measured are employed in order to determine a load value, therefore these values have a direct load-to-strain relationship, which is adjustable as it depends on the changes in strain in order to determine the load value.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., direct load-to-strain model) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
In response to applicant's argument that Kearns is nonanalogous art, it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Karabacak and Kearns are analogous arts as both references relates to strain measurements on a surface in response to loads applied to the structure (para. 0042, 0056, 0065-0069).
In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references, the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007).
The examiner further submits that, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
Furthermore it is noted that Kearns was brought into the combination as disclosing the features of 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.
Kearns further discloses a plurality of loads are applied to a plurality of points on a structure, which deforms and obtaining strain data for the structure when the structure has deformed due to the load applied (see claims 11 and 12, ¶0065-0069, 0087, 0090) therefore a load-to-strain deformation relationship/model is disclosed.
Therefore the combination of Karabacak and Kearns discloses the argued features of the claimed invention as presented on claims 21-36 of instant application.
With respect to applicant arguments regarding new claims 22-24, 26-33 and 35-36, applicant argues that since the independent claims are non-obvious over the cited prior art, these dependent claims which inherit that non-obviousness and introduce further specific and inventive features not taught or suggested by Karabacak, Kearns, Koivisto, or Carnegie are likewise non-obvious (see first paragraph of page 15 of the remarks).
In response, the examiner disagrees for similar reasons as those discussed with respect to their respective independent claims 21, and 25 as discussed above.
With respect In response to applicant’s argument that there is no teaching, suggestion, or motivation to combine the references (see first paragraph of page 15 of the remarks), the examiner recognizes that obviousness may be established by combining or modifying the teachings of the prior art to produce the claimed invention where there is some teaching, suggestion, or motivation to do so found either in the references themselves or in the knowledge generally available to one of ordinary skill in the art. See In re Fine, 837 F.2d 1071, 5 USPQ2d 1596 (Fed. Cir. 1988), In re Jones, 958 F.2d 347, 21 USPQ2d 1941 (Fed. Cir. 1992), and KSR International Co. v. Teleflex, Inc., 550 U.S. 398, 82 USPQ2d 1385 (2007). In this case, In this case, Karabacak and Kearns are analogous arts as both references relates to strain measurements on a surface in response to loads applied to the structure (para. 0042, 0056, 0065-0069).
The examiner further submits that, a recitation of the intended use of the claimed invention must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim.
Furthermore it is noted that Kearns was brought into the combination as disclosing the features of 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.
Kearns further discloses a plurality of loads are applied to a plurality of points on a structure, which deforms and obtaining strain data for the structure when the structure has deformed due to the load applied (see claims 11 and 12, ¶0065-0069, 0087, 0090) therefore a load-to-strain deformation relationship/model is disclosed.
Therefore the combination of Karabacak and Kearns discloses the argued features of the claimed invention as presented on claims 21-36 of instant application.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YARITZA H PEREZ BERMUDEZ whose telephone number is (571)270-1520. The examiner can normally be reached Monday-Friday.
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/YARITZA H. PEREZ BERMUDEZ/
Examiner
Art Unit 2857
/SHELBY A TURNER/Supervisory Patent Examiner, Art Unit 2857
Prosecution Insights