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 .
Status of Application
Claims 1-19 are pending. Claims 1, 10, and 11 are the independent claims. Claims 1, 2, 6, 10, 11, 12, 14, and 18 have been amended. This office action is in response to the Amendments received on 01/07/2026.
Response to Arguments
With respect to Applicant’s remarks filed on 01/07/2026, “Applicant Arguments/Remarks Made in an Amendment” have been fully considered. Applicant’s remarks will be addressed in sequential order as they were presented.
In response to the amended claims files on 01/04/2026, the objections to claims 2,6, and 14 and the rejection of claims 1-9, 10, and 11-13 under 35 U.S.C § 112(b) have been withdrawn.
Applicant's arguments according to the Applicant’s Remarks, see page 8 “35 U.S.C § 101”, with respect to claims 1-9, 10, and 11-19, have been fully considered but they are not persuasive. Applicant has amended claims 1, 10 and 11 to encompass the execution of the previously recited limitations by use of machine parts like “by use of an interface” and “by use of a processor”. However, the amended claim fails to overcome the rejection under 35 U.S.C § 101, because it merely recites generic computer components (e.g. interface, processor) to perform an abstract idea, which does not amount to significantly more than the exception itself. As detailed in MPEP 2106.04(a)(2)(III)(C) and the court cases cited therein, the recitation of an abstract idea applied to a computer does not prohibit the idea from being performed mentally. Furthermore, the claim as recited is directed to obtaining sensor values and control values that can be used in performing the paving procedures and the physical performance of the paving procedure itself is not positively recited. Therefore, the claim is directed to a mental process—a category of abstract idea that can be performed in the human mind or by using pen and paper. Accordingly, the applicant argument is respectfully not persuasive and the rejection of the claims under 35 U.S.C § 101 is maintained.
Moreover, applicant’s argument with respect to claims 1, 10, and 11, have been considered, but they are not fully persuasive. However, the new ground of rejection has been applied necessitated by the amendment that more explicitly teach the limitations as currently recited. Furthermore, on page 11 of the present Remarks, applicant explains that the amended claim 1 is directed to evaluating quality of generated pavement by diagnosing a quality situation based on machine internal data namely sensor values and actuator control values, however, the claim as recited does not encompass that the sensor values and actuator control values being machine internal data and instead sensor values in the claim as written, is interpreted as directly being received from one or more sensors. It is the office stance that the combination of Mansell with the newly applied art according to the Final Office Action below, teach the claimed limitations as currently recited (See the rejections of claims 1, 10, and 11 below).
Office Note: Due to applicant’s amendments, further claim rejections appear on the record as stated in the below Office Action.
It is the Office’ stance that all of applicant arguments have been considered.
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 1-9, 10 and 11-19, are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Statutory Category – Yes
Claims 1, 10 and 11 are respectively directed to a method, a manufacture and a machine. Therefore, the claims fall within at least one of the four statutory categories. See MPEP 2106.03
Step 2A Prong I evaluation: Judicial Exception – Yes – Mental processes
Regarding Prong I of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes.
In this case independent claims 1, 10 and 11 are directed to an abstract idea without significantly more.
Claim 1 recites:
A method for evaluating of a quality of a generated pavement, generated by use of a road finishing machine, comprising: receiving -by use of an interface sensor values from one or more sensors of a leveling system of the road finishing machine and/or a layer thickness measurement system of the road finishing machine and one or more actuator control values for controlling a screed of the road finishing machine performing the paving procedure; determining -by use of a processor- a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation; and outputting - by use of a human machine interface and/or a wireless communication link to a mobile device - an operator message to an operator, the operator message being indicative of the specific quality situation.
The Office submits that the foregoing bolded limitations constitute judicial exceptions in terms of “mental processes” because under its broadest reasonable interpretation, the limitations can be “performed in the human mind, or by a human using a pen and paper”. See MPEP 2106.04(a)(2)(III). For example, the limitations “evaluating of a quality of a generated pavement”, “receiving sensor values”, and “determining a specific sensor value combination within the sensor values taking into account the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation; and outputting an operator message dependent on the specific quality situation” in the context of this claim encompasses processes that can be performed in human mind it falls under mental process that is a category of abstract idea. Accordingly, the claim recites at least one abstract idea.
Step2A Prong II evaluation: Practical Application – No
Regarding Prong II of the Step 2A analysis in the 2019 PEG, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract idea into a practical application. As noted in the 2019 PEG, it must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
The Office submits that the foregoing underlined limitations recite additional elements that do not integrate the recited judicial exception into a practical application. The claim recites the additional element of “by use of a road finishing machine”, “from one or more sensors of a leveling system of the road finishing machine and/or a layer thickness measurement system of the road finishing machine and one or more actuator control values for controlling the road finishing machine, especially the paving procedure or a screed of the road finishing machine” that is recited at a high level of generality and merely to apply and automate the steps of evaluating of a quality of a generated pavement. Terms “by use of an interface” and “by use of a processor” as recited in the claim are just additional elements without significantly more, because they merely recites generic computer components (e.g. interface, processor) to perform an abstract idea, which does not amount to significantly more than the exception itself. As detailed in MPEP 2106.04(a)(2)(III)(C) and the court cases cited therein, the recitation of an abstract idea applied to a computer does not prohibit the idea from being performed mentally. Therefore, the claim does not use the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception. The additional limitation is no more than mere instructions to apply the exception using a computer. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea.
Step 2B evaluation: Inventive Concept – No
In Step 2B of the 2019 PEG, the claim(s) is to be evaluated as to whether the claim, as a whole, amounts to significantly more than the recited exception, i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05.
Claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of “by use of a road finishing machine”, “from one or more sensors of a leveling system of the road finishing machine and/or a layer thickness measurement system of the road finishing machine and one or more actuator control values for controlling a screed of the road finishing machine performing the paving procedure” amounts to nothing more than applying the exception using a generic computer component. Furthermore, the physical performance of the paving procedure itself is not positively recited. Generally applying an exception using a generic computer component cannot provide an inventive concept. And as discussed above, the Office submits that these limitations are insignificant extra-solution activities.
Claims 10 and 11 recites similar limitations, respectively, for a storage medium and a paving machine that comprise the same abstract of claim 1. Therefore, claims 10 and 11 are also patent ineligible for the same reasons stated in the above for claim 1 rejection.
Dependent claims 2-9 and 12-19 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of the dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application. Claims 2-3, and 5-7 recites more description about the limitations of a specific sensor value combination and its pattern in determining the specific quality situation and evaluating the pavement which falls under mental processes that can be done by human with use of pen and paper and do not impose any meaningful limits on practicing the abstract idea. With respect to claims 4, 8-9, the steps as recited amounts to nothing more than applying the exception using a well‐understood, routine, and conventional method which are claimed in a merely generic manner. Therefore, dependent claims 2-9 and similarly 12-19 (dependent on claim 11) are not patent eligible under the same rationale as provided for the rejection of claim 1.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-8, 10-11, 13-19 are rejected under 35 U.S.C. 103 as being unpatentable over Mansell, US 20210295486 A1, hereinafter “Mansell”, in view of Steffen et al., US 20230002981, hereinafter “Steffen”.
Regarding claim 1, Mansell discloses a method for evaluating of a quality of a generated pavement, generated by use of a road finishing machine (Abstract, “a method […] identifying a defect associated with the paved surface”, [0001], “paving machine”, [0006], [0022]), comprising: receiving -by use of interface- sensor values from one or more sensors of a leveling system of the road finishing machine and/or a layer thickness measurement system of the road finishing machine (Abstract, [0007]-[0008], “receive, from the sensing device, sensor data indicative of a paved surface, and identify, based at least in part on the sensor data, a defect associated with the paved surface, the defect having a characteristic that is characterized by a value.”, [0024], [0031]-[0032], “control interface”, [0038], “values characterizing such example characteristics include size values (e.g., length, width, height, depth, and the like of the suspected defect)”, __ According to the cited reference, the sensor data indicative of a paved surface/pavement, is indicative of the characteristic values of the paved surface (defected paved surface) including size of the paved surface such as height and depth. Furthermore, according to Fig. 5, element 508, and paragraphs [0052]- [0054] of the cited reference, feed sensor reads on thickness measurement system as it monitors the level of the auger/paving material by measuring the height (thickness) of the material.__) and one or more actuator control values for controlling a screed of the road finishing machine performing the paving machine ([0007], [0023], [0023], “The paving machine 100 further includes a tow arm 116 which couples a height adjustable screed portion” , “The tow arm 116 is actuated by a hydraulic actuator, an electric actuator (not shown), and/or any other type of actuator “, “[…] the screed portion 118 can receive commands from the mat defect ID component 214 to adjust settings”);
Mansell doesn’t clearly teach determining - by use of processor- a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation; and outputting – by use of a human interface and/or a wireless communication link to a mobile device- an operator message to an operator, the operator message being indicative of the specific quality situation.
Steffen teaches determining - by use of a processor - a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation (Abstract, [0006]-[0008], [0050], [0051], “he monitoring component 106 is configured to receive the sensor data 220 and compare the sensor data 220 to the criteria 224 for use in determining whether a defect is predicted and/or the cause of defect.”, [0076], “the monitoring component 106 may compare the information received from the controller 144 of the paving machine 100 (at 402) to determine whether the information satisfies (e.g., matches) the criteria 224. In some instances, this involves the monitoring component 106 processing, in parallel or sequentially, the sensor data 220 and comparing the sensor data 220 to the criteria 224 associated with each of the paving mat defects. As discussed herein, such processing may be performed in real-time for providing substantially instantaneous feedback for informing the operation of potential defects in the paving mat 108.”, __Accordingly to the disclosure of Steffen and the cited paragraphs, the data from monitoring value in parallel with sensor data regarding the criteria 224 (which is associated with respective effects in the paving amt), is used to predict the potential defects in the paving mat__, [0032], [0038], [0039], “components of the paving machine 100 (e.g., hydraulic actuators, lines, pumps, etc.),”, [0047], “The sensor data 220, as discussed herein, is usable to determine defects in the paving mat 108. That is, the sensor data 220 generated by the various sensor(s) of the paving machine 100, or components thereof, is usable to determine defects in the paving mat 108. For example, the sensor data 220 may indicate a status of the paving machine 100 (e.g., configurations), or a status of components of the paving machine 100, operational parameters of the paving machine 100 (e.g., material feed), and/or worksite conditions (e.g., temperature).”, [0057], “The monitoring component 106 is therefore configured to receive the sensor data 220 from the controller 144 and map the sensor data 220 (or information associated therewith) to the criteria 224 in the defect database 226 to determine whether a defect is predicted, as well as the type or cause of the defect. […] This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108 and/or a productivity of the paving machine 100.”, [0060]-[0061], See also Table 1,__Note: according to paragraph [0057], sensor data from the controller is used in combination with the defect database to predict the defect and its type. Sensor data 220 indicate a status of paving machine (e.g. configuration), a status of components and operational parameters of the paving machine, that all read on actuator control values (See at least [0047]; also, criteria defect 224 database reads on sensor data recited in the claim. __) and outputting – by use of a human interface and/or a wireless communication link to a mobile device- an operator message to an operator, the operator message being indicative of the specific quality situation ([0019], “The control interface 134 may also display indications (e.g., map) of the predicted defects, or determined defects, in the paving mat 108.”, [0057], “This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108”, [0070]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with the step of determining a specific quality situation for the paved surface by the combination of the sensor values and the control values obtained from plurality of sensors as taught by Steffen, with a reasonable expectation of success, with the motivation of determining and classifying the defects/faults based on the data from multiple sensors (or one sensor data over time) and the current machine control value. This approach enhances the accuracy and reliability of defect detection and improve the performance of the diagnosis control unit in detecting the faults.
Regarding claims 2 and 14, although Mansell discloses the specific sensor value combination (See rejection of claim 1 or 11), and wherein the sensor values are used by the leveling system or by the layer thickness measurement system for controlling the paving procedure or the screed of the road finishing machine via the one or more actuator control values (Abstract, [0007]-[0008], [0038], Fig. 5 and [0052]- [0054], feed sensor reads on thickness measurement system as it monitors the level of the auger/paving material by measuring the height (thickness) of the material.__, [0023], “a tow arm 116 which couples a height adjustable screed portion” , “The tow arm 116 is actuated by a hydraulic actuator, an electric actuator (not shown), and/or any other type of actuator “, “[…] the screed portion 118 can receive commands from the mat defect ID component 214 to adjust settings”, [0031]), however, Mansell doesn’t explicitly disclose wherein the specific sensor value combination is out of a plurality of specific sensor value combinations; and/or wherein a specific sensor value combination is out of a plurality of specific sensor value combinations stored by a database; wherein a specific sensor value combination is a pattern comprising sensor values and one or more actuator control values.
Steffen teaches wherein the specific sensor value combination is out of a plurality of specific sensor value combinations ([0068], “include a plurality of sensor(s) for generating sensor data to be used by the computing device(s) 102,”, [0086], [0100]); and/or wherein a specific sensor value combination is out of a plurality of specific sensor value combinations stored by a database ([0050], “defect database”, [0057], [0061], [0063], “the defect database 226, for example, stored in memory for determining the defects in the paving mat 108.”, [0075]); wherein a specific sensor value combination is a pattern comprising sensor values and one or more actuator control values (__Figs. 4 and 5 and associated paragraphs, and Table 1 is an example of a combination pattern of sensor values and actuator control values. According to the cited figures and paragraphs of Steffen, patterns between different sensor and control parameters can be related to a specific problem that can be recognized/predicted in step 512 and 502 as shown in Fig. 5__).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with the step of analyzing patterns in data obtained from one or more sensors as taught by Steffen, with a reasonable expectation of success, with the motivation of classifying the defects/faults based on the data from multiple sensors (or one sensor data over time). This approach enhances the accuracy and reliability of defect detection and improve the performance of the diagnosis control unit in detecting the faults.
Regarding claims 3 and 15, although Mansell discloses specific sensor value combinations (See rejection of claim 1), however, Mansell doesn’t explicitly disclose wherein the plurality of specific sensor value combinations comprises another specific sensor value combination being assigned to another specific quality situation, such that another operator message can be output dependent on the another specific quality situation.
Steffen teaches wherein the plurality of specific sensor value combinations comprises another specific sensor value combination being assigned to another specific quality situation, such that another operator message can be output dependent on the another specific quality situation ([0006], “determining first type of paving map defect and second type paving mat defect”, [0008], [0050], “different operating conditions at the worksite or different paving machine 100 parameters lead to different types of defects in the paving mat 108.”, [0057], “real-time for assessing a quality of the paving mat”, [0060]-[0061], “the defect database 226 grows to accommodate sensor data received in different conditions, at different times of day, using different paving materials,”, [0070], [0084], “Such process may be performed in real-time and as the paving machine 100 is preparing a paving surface. This allows the paving machine 100 to operate continuously and increase productivity.”, [0101]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with the step of analyzing patterns in data obtained from one or more sensors and determining different type of mat defect as taught by Steffen, with a reasonable expectation of success, with the motivation of classifying the defects/faults based on the data from multiple sensors continuously and in real-time. This approach enhances the accuracy and reliability of defect detection and improve the performance of the diagnosis control unit in detecting the faults and providing instantaneous and continuous feedback to the operator about potential defects in the paving mats.
Regarding claims 4 and 16, Mansell discloses method of claim 1 and paving machine of claim 11 (See rejections for claim 1 and 11), wherein the operator message comprises an information regarding the specific quality situation and/or a suggestion for improving the specific quality situation and/or an information on a parameter to be adapted or an actuator control value to be adapted ([0076], “provide a command [] which instructs the operator on how to change a setting of the paving machine 100 to remedy the detected defect.”).
Regarding claims 5 and 17, Mansell discloses method of claim 1 and paving machine of claim 11 (See rejections for claim 1 and 11), wherein the sensor values are received over time and wherein the sensor value combination is determined taking into account a value deviation of sensor values received over the time of at least one sensor value of the sensor values ( [0035], “The defect database 216, in some cases, includes sensor data [] at different times of day”, [0038], “the mat defect ID component 214 includes a comparison component 220 that is configured to compare a potential mat defect in sensor data (such as an image) to known defects in the defect database 216.”, “determines a value associated with the detected feature in the sensor data, and compares the value to known values (and/or value ranges) associated the known defects in the defect database 216”, [0047], “at different times of day”)
Regarding claims 6 and 18, Mansell discloses the method of claim 1 and the paving machine of claim 11 (See rejections for claim 1 and 11), further comprising receiving one or more process control parameters, wherein determining is performed taking into account the one or more process control parameters; or further comprising receiving one or more process control parameters ([0031], [0033]), wherein determining is performed taking into account the one or more process control parameters and wherein the one or more process control parameters are received as an input of the operator ([0024], [0046], “the mat defect ID component 214 solicits user input in determining a defect type of a suspected mat defect, such as via the interactive mat defect component 212.”,__according to the cited reference, the interactive mat defect component enables the user to select the image similar to the defect by a touch screen/interface (which reads on controlling the process as input from the user) and upon receiving the selection from the user, the command component generate the command based on the defect type (which reads on determining by taking into account the process control parameters as an input from the operator)__, [0053], “the command 404 instructs the paving machine 100 (and/or an operator of the paving machine 100 via the interactive mat defect component 212 of the mobile device 208)”).
Regarding claim 7, Mansell discloses wherein one sensor value of the sensor values is determined using a distance sensor, a cross slope sensor and/or a 3D process control system ([0030], “the LIDAR sensor 150 measures distance to a target (e.g., the mat 120)”, __Lidar sensor is a distance sensor__).
Regarding claims 8 and 19, Mansell discloses the method of claim 1 and the paving machine of claim 11 (See rejections for claim 1 and 11), wherein an actuator control value of the one or more actuator control values describes a height position of a tool or a screed, and/or a width information of the tool or the screed, and/or a speed information of the construction machine ([0023], “a tow arm 116 which couples a height adjustable screed portion […] The tow arm 116 is actuated […] the screed portion 118 can receive commands from the mat defect ID component 214 to adjust settings such as speed, height, and the like to remedy defects detected in the mat 120.”); and/or wherein a process control parameter comprises a temperature information and/or a material temperature information of a paving or installed road surface (e.g., [0026]-[0028]).
Regarding claim 10, Mansell discloses a non-transitory digital storage medium having stored thereon a computer program for performing a method for evaluating of a quality of a generated pavement generated by use of a road finishing machine ([0069], “instructions stored in memory”, “to perform the recited operations”, “the method […] with reference to the paving machine”), comprising: receiving -by use of an interface- sensor values from one or more sensors of a leveling system of the road finishing machine and/or a layer thickness measurement system of the road finishing machine (Abstract, “receiving sensor data indicative of a paved surface”, [0007], “receive, from the sensing device, sensor data indicative of a paved surface,”, [0030], “paving machine 100 may include various other sensors to measure various other parameters related to [] the mat 120,”, [0031]-[0032], [0034], “receives sensor data […] associated with the mat”, [0038], “values characterizing such example characteristics include size values (e.g., length, width, height, depth, and the like of the suspected defect)”, __ According to the cited reference, the sensor data indicative of a paved surface/pavement, is indicative of the characteristic values of the paved surface (defected paved surface) including size of the paved surface such as height and depth. Furthermore, according to Fig. 5, element 508, and paragraphs [0052]- [0054] of the cited reference, feed sensor reads on thickness measurement system as it monitors the level of the auger/paving material by measuring the height (thickness) of the material.__) and one or more actuator control values for controlling a screed of the road finishing machine performing the paving procedure ([0023], “a tow arm 116 which couples a height adjustable screed portion 118 to the tractor portion 102 so as to spread and compact the paving material 110 into a mat 120 on the paving surface 122. The tow arm 116 is actuated by a hydraulic actuator, an electric actuator (not shown), and/or any other type of actuator “, “[…] the screed portion 118 can receive commands from the mat defect ID component 214 to adjust settings”);
Mansell doesn’t clearly teach determining - by use of processor- a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation; and outputting – by use of a human interface and/or a wireless communication link to a mobile device- an operator message to an operator, the operator message being indicative of the specific quality situation.
Steffen teaches determining - by use of a processor - a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being assigned to a specific quality situation (Abstract, [0006]-[0008], [0050], [0051], “he monitoring component 106 is configured to receive the sensor data 220 and compare the sensor data 220 to the criteria 224 for use in determining whether a defect is predicted and/or the cause of defect.”, [0076], “the monitoring component 106 may compare the information received from the controller 144 of the paving machine 100 (at 402) to determine whether the information satisfies (e.g., matches) the criteria 224. In some instances, this involves the monitoring component 106 processing, in parallel or sequentially, the sensor data 220 and comparing the sensor data 220 to the criteria 224 associated with each of the paving mat defects. As discussed herein, such processing may be performed in real-time for providing substantially instantaneous feedback for informing the operation of potential defects in the paving mat 108.”, __ Accordingly to the disclosure of Steffen and the cited paragraphs, the data from monitoring value in parallel with sensor data regarding the criteria 224 (which is associated with respective effects in the paving amt), is used to predict the potential defects in the paving mat__, [0032], [0038], [0039], “components of the paving machine 100 (e.g., hydraulic actuators, lines, pumps, etc.),”, [0047], “The sensor data 220, as discussed herein, is usable to determine defects in the paving mat 108. That is, the sensor data 220 generated by the various sensor(s) of the paving machine 100, or components thereof, is usable to determine defects in the paving mat 108. For example, the sensor data 220 may indicate a status of the paving machine 100 (e.g., configurations), or a status of components of the paving machine 100, operational parameters of the paving machine 100 (e.g., material feed), and/or worksite conditions (e.g., temperature).”, [0057], “The monitoring component 106 is therefore configured to receive the sensor data 220 from the controller 144 and map the sensor data 220 (or information associated therewith) to the criteria 224 in the defect database 226 to determine whether a defect is predicted, as well as the type or cause of the defect. […] This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108 and/or a productivity of the paving machine 100.”, [0060]-[0061], See also Table 1,__Note: according to paragraph [0057], sensor data from the controller is used in combination with the defect database to predict the defect and its type. Sensor data 220 indicate a status of paving machine (e.g. configuration), a status of components and operational parameters of the paving machine, that all read on actuator control values (See at least [0047]; also, criteria defect 224 database reads on sensor data recited in the claim. __) and outputting – by use of a human interface and/or a wireless communication link to a mobile device- an operator message to an operator, the operator message being indicative of the specific quality situation ([0019], “The control interface 134 may also display indications (e.g., map) of the predicted defects, or determined defects, in the paving mat 108.”, [0057], “This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108”, [0070]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with the step of determining a specific quality situation for the paved surface by the combination of the sensor values and the control values obtained from plurality of sensors as taught by Steffen, with a reasonable expectation of success, with the motivation of determining and classifying the defects/faults based on the data from multiple sensors (or one sensor data over time) and the current machine control value. This approach enhances the accuracy and reliability of defect detection and improve the performance of the diagnosis control unit in detecting the faults.
Regarding claim 11, Mansell discloses a paving machine comprising a leveling system and/or a layer thickness measurement system and a diagnosis control unit ([0022], “a mat defect ID component”) and a process control unit ([0031], [0033]), the diagnosis control unit for evaluating of a quality of a generated pavement ([0022], “a mat defect ID component 214 configured to identify defects in a paving mat”), generated by use of a paving machine ([0022], “paving machine”), comprising: an interface for receiving sensor values from one or more sensors of the leveling system of the paving machine and/or the layer thickness measurement system of the paving machine ([0024], “The operator station 124 includes a console […] console 126 includes a control interface […] The control interface 128 may comprise… touchscreen display, and such a control interface 128 is configured to display, for example, commands that when executed by the paving machine 100, remedy defects of mat 120 according to the present disclosure.”, or ,for example, [0034], [0046], “interactive mat defect component 212 of the mobile device 208, which in turn displays the notification including the images”) and one or more actuator control values for controlling a screed of the road finishing machine performing the paving procedure ([0007], [0023], [0023], “a tow arm 116 which couples a height adjustable screed portion” , “The tow arm 116 is actuated by a hydraulic actuator, an electric actuator (not shown), and/or any other type of actuator “, “[…] the screed portion 118 can receive commands from the mat defect ID component 214 to adjust settings”, [0031]);
Mansell doesn’t clearly disclose a processor configured to determine a specific sensor value combination within the sensor values in combination with the one or more actuator control values; the specific sensor value combination being associated with a specific quality situation; and an interface for outputting an operator message to an operator, the operator message being indicative of the specific quality situation; wherein the interface for outputting an operation message comprises a human machine interface and/or a wireless communication link to a mobile device.
Steffen teaches a processor configured to determine a specific sensor value combination within the sensor values in combination with the one or more actuator control values, the specific sensor value combination being associated with a specific quality situation; (Abstract, [0006]-[0008], [0050], [0051], “he monitoring component 106 is configured to receive the sensor data 220 and compare the sensor data 220 to the criteria 224 for use in determining whether a defect is predicted and/or the cause of defect.”, [0076], “the monitoring component 106 may compare the information received from the controller 144 of the paving machine 100 (at 402) to determine whether the information satisfies (e.g., matches) the criteria 224. In some instances, this involves the monitoring component 106 processing, in parallel or sequentially, the sensor data 220 and comparing the sensor data 220 to the criteria 224 associated with each of the paving mat defects. As discussed herein, such processing may be performed in real-time for providing substantially instantaneous feedback for informing the operation of potential defects in the paving mat 108.”, __ Accordingly to the disclosure of Steffen and the cited paragraphs, the data from monitoring value in parallel with sensor data regarding the criteria 224 (which is associated with respective effects in the paving amt), is used to predict the potential defects in the paving mat__, [0032], [0038], [0039], “components of the paving machine 100 (e.g., hydraulic actuators, lines, pumps, etc.),”, [0047], “The sensor data 220, as discussed herein, is usable to determine defects in the paving mat 108. That is, the sensor data 220 generated by the various sensor(s) of the paving machine 100, or components thereof, is usable to determine defects in the paving mat 108. For example, the sensor data 220 may indicate a status of the paving machine 100 (e.g., configurations), or a status of components of the paving machine 100, operational parameters of the paving machine 100 (e.g., material feed), and/or worksite conditions (e.g., temperature).”, [0057], “The monitoring component 106 is therefore configured to receive the sensor data 220 from the controller 144 and map the sensor data 220 (or information associated therewith) to the criteria 224 in the defect database 226 to determine whether a defect is predicted, as well as the type or cause of the defect. […] This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108 and/or a productivity of the paving machine 100.”, [0060]-[0061], See also Table 1,__Note: according to paragraph [0057], sensor data from the controller is used in combination with the defect database to predict the defect and its type. Sensor data 220 indicate a status of paving machine (e.g. configuration), a status of components and operational parameters of the paving machine, that all read on actuator control values (See at least [0047]; also, criteria defect 224 database reads on sensor data recited in the claim. __); and an interface for outputting an operator message to an operator, the operator message being indicative of the specific quality situation; wherein the interface for outputting an operation message comprises a human machine interface ([0019], “The control interface 134 may also display indications (e.g., map) of the predicted defects, or determined defects, in the paving mat 108.”, [0057], “This allows operators of the paving machine 100 to observe the defects (e.g., via the control interface 134) in real-time for assessing a quality of the paving mat 108”, [0070]).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with the step of determining a specific quality situation for the paved surface by the combination of the sensor values and the control values obtained from plurality of sensors as taught by Steffen, with a reasonable expectation of success, with the motivation of determining and classifying the defects/faults based on the data from multiple sensors (or one sensor data over time) and the current machine control value. This approach enhances the accuracy and reliability of defect detection and improve the performance of the diagnosis control unit in detecting the faults.
Regarding claim 13, Mansell discloses wherein the diagnosis control unit further comprises a database or an interface for generating access to a database, the database storing a plurality of specific sensor value combinations ([0035], “the mat defect ID component 214 includes a defect database 216 that includes images or other sensor data types that are known to illustrate mat defects and the defect types of the mat defects.”, __map defect ID component reads on diagnostic control unit and defect database reads on database which stores sensor data type illustrating mat defects and the type of the mat defects which all read on the claim limitation).
Claims 9 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Mansell, in view of Steffen, further in view of Marko et al., DE 10319493 A1, hereinafter “Marko”.
Regarding claim 9, although Mansell discloses a network for receiving the sensor data and communicating with computing devices via a network ([0008], Fig. 2, “network 206”, and [0025], “via the network 206 (e.g., by way of the controller 154”), however Mansell doesn’t explicitly disclose wherein a sensor value of the sensor values or an actuator control value and/or a process control parameter are extracted from a CAN bus.
Nevertheless, using a CAN bus is a well-known method in the art. For instance, Marko, teaches wherein a sensor value of the sensor values or an actuator control value and/or a process control parameter are extracted from a CAN bus ([0038], “a standard controller area network (CAN bus)”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the method of evaluating quality of a pavement generated by a paving machine as taught by Mansell with extracting sensor data and control parameters from a CAN bus as taught by Marko, with a reasonable expectation of success, with the motivation of using a real-time, reliable and efficient system to access the data in order to perform diagnostic analysis.
Regarding claim 12, although Mansell discloses the paving machine according to claim 11 (See rejection for claim 11), however, Mansell doesn’t explicitly disclose wherein the interface for receiving sensor values comprises a CAN bus interface;
Nevertheless, Marko wherein the interface for receiving sensor values comprises a CAN bus interface ([0038], “a standard controller area network (CAN bus)”).
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to include the paving machine as taught by Mansell with a CAN bus interface or a wireless communication as taught by Marko, with a reasonable expectation of success, with the motivation of using a real-time, reliable and efficient system to access the data in order to perform diagnostic analysis.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/H.H./Examiner, Art Unit 3669
/Erin M Piateski/Supervisory Patent Examiner, Art Unit 3669