DETAILED ACTION
This is in response to the applicant’s communication filed on 5/5/26 wherein:
Claims 1-20 are currently pending.
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 .
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 63/085863, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application. 63/085863 fails to provide support for at least the following limitations: a machine learning algorithm combining road ageing curve data for said roadway by incrementally overlapping road aging curve data every quarter year to accumulate and average the aging curve data over a ten-year time span to create a conditional aging curve model for said roadway; said machine learning algorithm combining said conditional aging curve data, said distress measurement data, said photographic roadway data, and said accelerometer data for different climate and road use levels to create a set of optimized road aging curves for said roadway; said machine learning algorithm collecting dynamic road data to create a current dynamic road aging curve model; said machine learning algorithm updating the set of optimized road aging curve with the current dynamic road aging curve to predict road aging over a set period of time; the machine learning algorithm providing an analysis report of predicted distress extent, damage location, and repair estimates from analyzing said set of optimized road ageing curves for said roadway to a user tailored to user-defined end goals for analysis and road repair; (claim 1). Claim 11 contains similar limitations. All remaining claims are dependent on claim 1 or claim 11. Therefore, all of the claims are assigned the priority date of 9/21/21.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claim 1 recites a method and therefore, falls into a statutory category. Similar independent claim 11 recites a system, and therefore, also fall into a statutory category.
Step 2A – Prong 1 (Is a Judicial Exception Recited?): The underlined limitations of
collecting a data set of photographic images of roadway distress conditions in accordance with search criteria from a user;
collecting accelerometer data to identify specific road distress conditions from roughness patterns such distress conditions create in collected accelerometer data;
providing said photographic images and said accelerometer data to a machine learning algorithm, where said machine learning algorithm labels each photographic image with an initial indicator of an area or item of concern meeting said search criteria;
providing said photographic images and said machine learning algorithm supplied labels to a human evaluator;
providing said photographic roadway distress data and said accelerometer data to a machine learning algorithm;
said machine learning algorithm analyzing the received photographic roadway distress data and said accelerometer data for quantifiable roadway distress, to measure the quantifiable roadway distress and storing said quantifiable roadway distress as distress measurement data;
receiving collected roadway data to build a profile and history of said roadway;
a machine learning algorithm combining road ageing curve data for said roadway by incrementally overlapping road aging curve data every quarter year to accumulate and average the aging curve data over a ten-year time span to create a conditional aging curve model for said roadway;
said machine learning algorithm combining said conditional aging curve data, said distress measurement data, said photographic roadway data, and said accelerometer data for different climate and road use levels to create a set of optimized road aging curves for said roadway;
said machine learning algorithm collecting dynamic road data to create a current dynamic road aging curve model;
said machine learning algorithm updating the set of optimized road aging curve with the current dynamic road aging curve to predict road aging over a set period of time;
the machine learning algorithm providing an analysis report of predicted distress extent, damage location, and repair estimates from analyzing said set of optimized road ageing curves for said roadway to a user tailored to user-defined end goals for analysis and road repair;
the server transmitting machine learning algorithm determined GPS location, confirmation of damage and the type of road distress, a calculated estimate for repair of each type of damage and road distress to the user, and
providing maintenance recommendations based upon data analysis of road maintenance scenarios input by said user
are processes that, under their broadest reasonable interpretation, are considered certain methods of organizing human activity – commercial or legal interactions (including agreements in the form of contracts and marketing or sales activities or behaviors) and/or managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Examiner notes that road maintenance companies commonly plan and execute maintenance activities based upon analysis of roadways. The claims are directed to capturing and analyzing data, including photographic images, for use in planning road maintenance (Specification 5). Accordingly, the claim recites an abstract idea.
Alternatively, the following underlined limitations identify the abstract limitations which are considered mathematical concepts
a machine learning algorithm combining road ageing curve data for said roadway by incrementally overlapping road aging curve data every quarter year to accumulate and average the aging curve data over a ten-year time span to create a conditional aging curve model for said roadway;
said machine learning algorithm combining said conditional aging curve data, said distress measurement data, said photographic roadway data, and said accelerometer data for different climate and road use levels to create a set of optimized road aging curves for said roadway;
said machine learning algorithm updating the set of optimized road aging curve with the current dynamic road aging curve to predict road aging over a set period of time;
These limitations constitute creating various road aging curves and updating the curves, which are processes that, under their broadest reasonable interpretation, are considered mathematical concepts, in the form of a mathematical relationship, mathematical formulas or equations, and/or mathematical calculations. It is important to note that a mathematical concept need not be expressed in mathematical symbols. See MPEP 2106.04(a). Accordingly, the claim recites an abstract idea. The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Step 2A-Prong 2 (Is the Exception Integrated into a Practical Application?): This judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of:
Claim 1: a machine learning algorithm, a server, and a human evaluator
Claim 11: a machine learning algorithm, a server comprising at least one processor, at least one photographic image capture device, and at least one accelerometer.
The computer components and various devices are recited at a high-level of generality (i.e., as a generic processor performing generic computer functions), such that it amounts to no more than mere instructions to apply the exception using generic computer components and devices. Accordingly, the additional elements do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea when considered both individually and as a whole. The claims are directed to an abstract idea.
Step 2B (Does the claim recite additional elements that amount to Significantly More than the Judicial Exception?): The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer to perform the claimed steps amount to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible, as when viewed individually, and as a whole, nothing in the claim adds significantly more to the abstract idea.
Dependent claims 2-10 and 12-20 merely recite further embellishments of the abstract idea of independent claim 1 or claim 11 as discussed above with respect to integration of the abstract idea into a practical application, and these features only serve to further limit the abstract idea of independent claim 1 or claim 11; however, none of the dependent claims recite an improvement to a technology or technical field or provide any meaningful limits.
In light of the detailed explanation and evidence provided above, the Examiner asserts that the claimed invention, when the limitations are considered individually and as whole, is directed towards an abstract idea.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), first paragraph:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1 and 11 refer to:
said machine learning algorithm analyzing the received photographic roadway distress data and said accelerometer data for quantifiable roadway distress, to measure the quantifiable roadway distress and storing said quantifiable roadway distress as distress measurement data;
a machine learning algorithm combining road ageing curve data for said roadway by incrementally overlapping road aging curve data every quarter year to accumulate and average the aging curve data over a ten-year time span to create a conditional aging curve model for said roadway;
said machine learning algorithm combining said conditional aging curve data, said distress measurement data, said photographic roadway data, and said accelerometer data for different climate and road use levels to create a set of optimized road aging curves for said roadway;
said machine learning algorithm collecting dynamic road data to create a current dynamic road aging curve model;
said machine learning algorithm updating the set of optimized road aging curve with the current dynamic road aging curve to predict road aging over a set period of time;
the machine learning algorithm providing an analysis report of predicted distress extent, damage location, and repair estimates from analyzing said set of optimized road ageing curves for said roadway to a user tailored to user-defined end goals for analysis and road repair;
the server transmitting machine learning algorithm determined GPS location, confirmation of damage and the type of road distress, a calculated estimate for repair of each type of damage and road distress to the user,
providing maintenance recommendations based upon data analysis of road maintenance scenarios input by said user
but it is unclear how this is accomplished. This appears to be an end result without an explanation of how to accomplish this step. The specification does not describe how this is done, but provides only the bare steps included here. Specification 7-8. No specific algorithm is supplied for how the various inputs may be used by the machine learning algorithm to perform the analyzing of the data, combining the road ageing curve data, updating the road aging curve, providing the analysis report, determining the GPS location, confirmation of damage and the type of road distress and calculating estimates for repair, or making the maintenance recommendations. Simply hand-waving all of these calculations into a generic “machine learning algorithm” does not provide the required show of possession.
When examining computer implemented, functional claims, examiners should determine whether the specification discloses the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter. Specifically, if one skilled in the art would know how to program the disclosed computer to perform the necessary steps described in the specification to achieve the claimed function and the inventor was in possession of that knowledge, the written description requirement would be satisfied. If the specification does not provide a disclosure of the computer and algorithm in sufficient detail to demonstrate to one of ordinary skill in the art that the inventor possessed the invention including how to program the disclosed computer to perform the claimed function, a rejection under §112, ¶ 1 for lack of written description must be made. For more information regarding the written description requirement, see MPEP §2161.01–2163.07(b). In this case, applicant’s specification does not disclose an algorithm for performing the function in sufficient detail such that one of ordinary skill in the art can reasonably conclude that the inventor invented the claimed subject matter.
Claims 1-20 refer to using a computer to accomplish the claimed steps (claims 1-10) or as part of the system claimed (claims 11-20). At most, the specification discloses generic computers and processors. However, for a specific function, the specification must disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function sufficient detail such that one of ordinary skill can reasonably conclude that the inventor invented the claimed subject matter. It is not sufficient that one of ordinary skill in the art is capable of writing the software/program to achieve the claimed function. There must be an explanation of how the computer or component performs the claimed function. Here, the claimed functions appear to be specific functions that require a special purpose computer to perform, and the specification fails to disclose the corresponding structure and algorithm required to perform the claimed functions. As such, applicant has not met the requirements of 35 USC §112, first paragraph.
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
The claims refer to using a computer to accomplish the claimed steps (claims 1-10) or as part of the system claimed (claims 11-20). However, the specification does not disclose the computer and the algorithm (e.g., the necessary steps and/or flowcharts) that perform the claimed function. Without describing the computer and the algorithm, potential infringers cannot be sure whether they are infringing the claims or not.
Claim 1 refers to “the server transmitting machine learning algorithm determined GPS location, confirmation of damage and the type of road distress, a calculated estimate for repair of each type of damage and road distress to the user” but it is unclear how this information can be transmitted since it is not determined. Claim 11 includes a similar limitation.
Claim 1 refers to “providing maintenance recommendations based upon data analysis of road maintenance scenarios input by said user” which is confusing. It is unclear what road maintenance scenarios are input by the user. Is this a reference to the search criteria input by the user? Further, it is unclear how these maintenance recommendations are determined. Are they determined by the machine learning algorithm? Claim 11 includes a similar limitation.
Claim 1 refers to “the server transmitting…” which lacks antecedent basis. Examiner suggests amending the claim to state “a server transmitting…”
Claims 2-10 and 12-20 are rejected as dependent on claim 1 or claim 11.
Subject Matter Distinguished from Prior Art
The prior art of record neither anticipates nor supports a conclusion of obviousness without the use of impermissible hindsight with respect to claims x-z.
The most closely applicable prior art of record is Dickson (WO 2021076573). Dickson discloses a system for assessing infrastructure such as roads (abstract).
Druta (US 20200327631) is also closely related prior art of record. Druta discloses a similar system for optimization of programs of works in public roads (abstract).
Butler et al. (US 20140334689) is also closely related prior art of record. Butler discloses a system for infrastructure assessment via imaging sources (abstract).
The prior art of record neither anticipates not fairly and reasonable teach a method for delivering optimized road maintenance analysis comprising:
collecting a data set of photographic images of roadway distress conditions in accordance with search criteria from a user;
collecting accelerometer data to identify specific road distress conditions from roughness patterns such distress conditions create in collected accelerometer data;
providing said photographic images and said accelerometer data to a machine learning algorithm, where said machine learning algorithm labels each photographic image with an initial indicator of an area or item of concern meeting said search criteria;
providing said photographic images and said machine learning algorithm supplied labels to a human evaluator;
providing said photographic roadway distress data and said accelerometer data to a machine learning algorithm;
said machine learning algorithm analyzing the received photographic roadway distress data and said accelerometer data for quantifiable roadway distress, to measure the quantifiable roadway distress and storing said quantifiable roadway distress as distress measurement data;
receiving collected roadway data to build a profile and history of said roadway;
a machine learning algorithm combining road ageing curve data for said roadway by incrementally overlapping road aging curve data every quarter year to accumulate and average the aging curve data over a ten-year time span to create a conditional aging curve model for said roadway;
said machine learning algorithm combining said conditional aging curve data, said distress measurement data, said photographic roadway data, and said accelerometer data for different climate and road use levels to create a set of optimized road aging curves for said roadway;
said machine learning algorithm collecting dynamic road data to create a current dynamic road aging curve model;
said machine learning algorithm updating the set of optimized road aging curve with the current dynamic road aging curve to predict road aging over a set period of time;
the machine learning algorithm providing an analysis report of predicted distress extent, damage location, and repair estimates from analyzing said set of optimized road ageing curves for said roadway to a user tailored to user-defined end goals for analysis and road repair;
the server transmitting machine learning algorithm determined GPS location, confirmation of damage and the type of road distress, a calculated estimate for repair of each type of damage and road distress to the user, and
providing maintenance recommendations based upon data analysis of road maintenance scenarios input by said user.
Examiner notes that the underlined limitations above, in combination with the other limitations found within the independent claims are not found in the prior art. Claim 11 is distinguished from the prior art on a similar basis.
Response to Arguments
Examiner thanks Applicant for providing support for the amendments.
Claim Rejections under 35 USC 101
Applicant traverses the rejection, stating that the claims are patentable under Alice, particularly arguing that the “analysis in the Office Action is not accounting for the effect of claims 1 and 11, as amended, as a whole and the individual claim elements as required by the decision in McRO.” Remarks 8. McRO is not similar to the instant case, however. McRO improved the ability of the computer to perform specific animation tasks that previously could only be performed subjectively by humans. The instant claims are not directed to improving the ability of the computer to perform a task that previously could only be performed by humans. Rather, the claim limitations do not amount to more than mere instructions to implement an abstract idea on a computer. It is noted that the claims recite only the idea of a solution, but fail to recite technical details of how the solution to the problem is accomplished. Further, the claims invoke computers merely as a tool to perform the existing processes regarding road analysis.
Applicant argues that the claimed invention is not merely an abstract idea but “an improvement in the technology for detecting and optimization of ongoing repair for roadways in the form of a new, dynamically created family of aging curves that combines traditional aging curves with a set of aging curves created from dynamically collected data to permit a new analytical system and method that previously did not exist to improve the ability to identify and perform road maintenance as needed.” Remarks 9. This is not an improvement to technology, but merely an improvement to the abstract idea.
Applicant further argues that the invention is directed to determining factors “utilizing machine learning analysis as guided and directed by a pre-loaded set of criteria and conditions required by a user.” Remarks 10. Examiner respectfully disagrees. Machine learning, itself, makes use of a pre-loaded set of criteria and conditions as programed by a user. Applicant’s use of machine learning is recited at a high-level of generality (i.e., without specific technical details), such that it amounts to no more than mere instructions to apply the exception using generic computer components and devices. Again, Applicant does not provide any technical details which improves the machine learning algorithm itself. If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. An indication that the claimed invention provides an improvement can include a discussion in the specification that identifies a technical problem and explains the details of an unconventional technical solution expressed in the claim, or identifies technical improvements realized by the claim over the prior art. See MPEP 2106.05(a). In this case, the Specification does not provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. Neither has Applicant identified a specific improvement in technology, itself. At most, the Specification sets forth an alleged improvement in a conclusory manner. Therefore, the claim does not improve technology.
Applicant further argues that the claims recite “a new function for not only ongoing, but predictive road degradation over time that shows road determination in real time and future road deterioration based upon predictive analysis from the ongoing dynamic data collection and the inclusion of this information into a new type of aging curves” and that this “provides significantly more analysis and information for use in roadway maintenance activities than what is previously known in the industry” which provides significantly more than the judicial exception. Remarks 11. Examiner respectfully disagrees. Again, this is not an improvement in technology, but merely an improvement in the judicial exception itself.
Claim Rejections under 35 USC 112(a)
Applicant argues that the claims as amended are now in compliance with the written description. Remarks 12. Examiner has adjusted the rejection in response to the amendments.
Claim Rejections under 35 USC 112(b)
Applicant argues that the claims as amended are now in compliance with 112(b). Remarks 12. Examiner has adjusted the rejection in response to the amendments.
Conclusion
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CARRIE S GILKEY whose telephone number is (571)270-7119. The examiner can normally be reached Monday-Thursday 7:30-4:30 CT and Friday 7:30-12 CT.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached on 571-270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CARRIE S GILKEY/Primary Examiner, Art Unit 3626
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