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 .
DETAILED CORRESPONDENCE
Status of Claims
Claim 1 has been amended.
No claims have been cancelled.
Claims 8 – 20 have been added.
Claim Rejections - 35 USC § 112(b)
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.
In regards to claim 1, the manner in which the claim has been formatted renders certain limitations indefinite. Specifically, it is unclear whether the digital twin is used to perform the third limitation or limitations three to six. If it is only being used for the third limitation, then the third limitation should be intended. If it is being used for other limitations in addition to the third limitation, those limitations should be intended.
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. The claims recite:
acquire and hold data including map data, sensing data acquired by sensing a state of an infrastructure at a high frequency, and repair actual result data of the infrastructure;
construct a digital twin to visualize a current state of the infrastructure on the basis of the sensing data acquired at high frequency, and use the digital twin to:
selecting sensing data to be a processing target for learning by Artificial Intelligence (AI) as teacher data based on a data attribute of the sensing data, the teacher data being selected from at least one of sensing data acquired by the system or external sensing data;
display a map image of an area specified by a user based on the map data with information regarding the state of the infrastructure determined on the basis of the sensing data in a superimposed manner on the map image, wherein the information regarding the state of the infrastructure is mapped on the map image by color coding or a mark;
predict a future degree of deterioration of each infrastructure at a future time specified by the user on the basis of data including the sensing data, extract the infrastructure having the predicted future degree of deterioration exceeding a predetermined repair reference value, and display the extracted infrastructure on the map image as a candidate for an infrastructure to be repaired by combining related information including at least one of weather information or traffic volume specified by the user; and
display a result of aggregation of the sensing data and indexes related to the infrastructure maintenance management on a dashboard in addition to the map image
The invention is directed towards the abstract idea of maintenance management based on the collection and comparison of information and, based on a rule(s), identify options, which corresponds to “Mental Processes” and “Certain Methods of Organizing Human Activities” as it is directed towards steps that can be performed by a human(s) and/or with the aid of pen and paper and fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions), e.g., having a user collect information regarding the state of an infrastructure by sensing the condition using the sense of sight or sense of touch, looking at a map, writing on the map and predicting based on the collected data (e.g., writing/drawing a prediction on the map), and presenting the map to convey the location of where there is an issue, as well as comparing the information collected against expected information, i.e. expected condition or reference value of the infrastructure, determine the degradation/deterioration of the infrastructure based on the comparison and any applicable rules, and presenting the results of the analysis.
The limitations of:
acquire and hold data including map data, sensing data acquired by sensing a state of an infrastructure at a high frequency, and repair actual result data of the infrastructure;
construct a digital twin to visualize a current state of the infrastructure on the basis of the sensing data acquired at high frequency, and use the digital twin to:
selecting sensing data to be a processing target for learning by Artificial Intelligence (AI) as teacher data based on a data attribute of the sensing data, the teacher data being selected from at least one of sensing data acquired by the system or external sensing data;
display a map image of an area specified by a user based on the map data with information regarding the state of the infrastructure determined on the basis of the sensing data in a superimposed manner on the map image, wherein the information regarding the state of the infrastructure is mapped on the map image by color coding or a mark;
predict a future degree of deterioration of each infrastructure at a future time specified by the user on the basis of data including the sensing data, extract the infrastructure having the predicted future degree of deterioration exceeding a predetermined repair reference value, and display the extracted infrastructure on the map image as a candidate for an infrastructure to be repaired by combining related information including at least one of weather information or traffic volume specified by the user; and
display a result of aggregation of the sensing data and indexes related to the infrastructure maintenance management on a dashboard in addition to the map image
are processes that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor(s), generic Artificial Intelligence (AI), and generic digital twin. That is, other than reciting generic processor(s), generic Artificial Intelligence (AI), and generic digital twin nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the generic processor(s), generic Artificial Intelligence (AI), and generic digital twin in the context of this claim encompasses a user can observe, using their senses, the condition of an infrastructure and analyze the information by comparing it against known/expected information, referring to a map, marking the location of where they have determined, based on the comparison and any applicable rules, the location where there will be future deterioration, and presenting the results of the analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of a generic processor(s), generic Artificial Intelligence (AI), and generic digital twin, then it falls within the “Mental Processes” and “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites additional elements – generic processor(s) to communicate, store (hold), and display information, as well as performing operations that a human can perform in their mind and/or pen and paper, i.e. analyzing the information, as was discussed above, to determine the future deterioration of an infrastructure and marking it on a map. The generic processor(s) in the steps are recited at a high-level of generality (i.e., as a generic processor(s) can perform the insignificant extra solution steps of communicating, storing (holding), and displaying information (See MPEP 2106.05(g) while also reciting that the a generic processor(s)are merely being applied to perform the steps that can be performed in the human mind and/or pen and paper; "[use] of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) does not integrate a judicial exception into a practical application or provide significantly more.” Therefore, according to the MPEP, this is not solely limited to computers but includes other technology that, recited in an equivalent to “apply it,” is a mere instruction to perform the abstract idea on that technology (See MPEP 2106.05(f)) such that it amounts no more than mere instructions to apply the exception using generic processor(s).
With regards to “digital twin”, this is nothing more than a representation of an actual asset and, more specifically, encompasses a human drawing and writing down information about an asset based on historical and current information and extrapolating when sufficient deterioration has occurred to determine when maintenance should be performed. The claimed invention is not directed towards creating and maintaining a “digital twin” of a real-world object in order to simulate how it is operating, how it is being affected by its environment, and the like to determine the deterioration rate of the real-world asset so that a determination can be made on when it will require maintenance. The simulation discussed in the specification is directed toward a cost analysis not an analysis that is simulating how the asset is/will perform or operate.
Although the claim recites “using Artificial Intelligence (AI),” the claimed invention and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying AI is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being AI is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards managing the maintenance/repair of infrastructure based on collected information about the condition of the infrastructure. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the selection and presentation by AI are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with AI. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, 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. The claims are directed to an abstract idea.
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 generic processor(s), generic AI, and generic digital twin to perform the steps of:
acquire and hold data including map data, sensing data acquired by sensing a state of an infrastructure at a high frequency, and repair actual result data of the infrastructure;
construct a digital twin to visualize a current state of the infrastructure on the basis of the sensing data acquired at high frequency, and use the digital twin to:
selecting sensing data to be a processing target for learning by Artificial Intelligence (AI) as teacher data based on a data attribute of the sensing data, the teacher data being selected from at least one of sensing data acquired by the system or external sensing data;
display a map image of an area specified by a user based on the map data with information regarding the state of the infrastructure determined on the basis of the sensing data in a superimposed manner on the map image, wherein the information regarding the state of the infrastructure is mapped on the map image by color coding or a mark;
predict a future degree of deterioration of each infrastructure at a future time specified by the user on the basis of data including the sensing data, extract the infrastructure having the predicted future degree of deterioration exceeding a predetermined repair reference value, and display the extracted infrastructure on the map image as a candidate for an infrastructure to be repaired by combining related information including at least one of weather information or traffic volume specified by the user; and
display a result of aggregation of the sensing data and indexes related to the infrastructure maintenance management on a dashboard in addition to the map image
amounts 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.
Additionally:
Claim 2 is directed towards human activities directed towards describing a rule and providing a suggestion of when to perform inspections.
Claim 3 is directed towards collecting and comparing/analyzing information to determine the condition of the infrastructure.
Claim 4 is directed to human activities (select), extra-solution activities (present/display), and reciting generic technology at a high level of generality and applying it to the abstract idea.
Although the claim recites “AI,” (which the Examiner presumes is the acronym for “artificial intelligence”) the claims and specification fail to provide sufficient disclosure regarding an improvement to how a machine learning algorithm can be trained, but simply recites a high-level generic recitation that a machine learning algorithm is being trained. There is insufficient evidence from the specification to indicate that the use of the machine learning algorithm involves anything other than the generic application of a known technique in its normal, routine, and ordinary capacity or that the claimed invention purports to improve the functioning of the computer itself or the machine learning algorithm. None of the limitations reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field, applies or uses a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implements a judicial exception with, or uses a judicial exception in conjunction with, a particular machine or manufacture that is integral to the claim, effects a transformation or reduction of a particular article to a different state or thing, or applies or uses the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception.
Even training and applying AI is simply application of a computer model, itself an abstract idea manifestation. Further, such training and applying of a model is no more than putting data into a black box machine learning operation. The nomination as being AI is a functional label, devoid of technological implementation and application details. The specification does not contend it invented any of these activities, or the creation and use of such machine learning models. In short, each step does no more than require a generic computer to perform generic computer functions. As to the data operated upon, "even if a process of collecting and analyzing information is 'limited to particular content' or a particular 'source,' that limitation does not make the collection and analysis other than abstract." SAP America, Inc. v. InvestPic LLC, 898 F.3d 1161, 1168 (Fed. Cir. 2018).
The Examiner asserts that the scope of the disclosed invention, as presented in the originally filed specification, is not directed towards the improvement of machine learning, but directed towards managing the maintenance/repair of infrastructure based on collected information about the condition of the infrastructure. The specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP § 2106.05(f), the selection and presentation by AI are merely being used to facilitate the tasks of the abstract idea, which provides nothing more than a results-oriented solution that lacks detail of the mechanism for accomplishing the result and is equivalent to the words “apply it,” per MPEP § 2106.05(f). The Examiner asserts that in light of the 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, the claimed invention is analogous to Example 47, Claim 2.
Further, the combination of these elements is nothing more than a generic computing system with AI. Because the additional elements are merely instructions to apply the abstract idea to a computer, as described in MPEP § 2106.05(f), they do not integrate the abstract idea into a practical application.
Accordingly, 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. The claim is directed to an abstract idea.
Claim 5 is directed towards simulating or predicting the life cycle cost based on the collected information and comparison and/or reference to a rule in order to predict/simulate the cost and the extra-solution activity of presenting/displaying information.
Claim 6 is directed towards human activities for creating a repair plan and performing the repair work based on the repair plan.
Claim 7 is directed towards the extra-solution activity of displaying and receiving information and descriptive subject matter describing the displayed information. The filtering process is directed towards displaying and organizing desired information in response to what a user wants to have displayed.
In summary, the dependent claims are simply directed towards providing additional descriptive factors that are considered for managing the repair of an asset. Accordingly, the claims are not patent eligible.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1 – 14, 16 – 20 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by Sunde et al. (US PGPub 2022/0101272 A1).
In regards to claim 1, Sunde discloses an infrastructure maintenance management support system that supports infrastructure maintenance management, the system comprising a processor or processors configured to:
acquire and hold data including map data, sensing data acquired by sensing a state of an infrastructure at high frequency, and repair actual result data of the infrastructure (Fig. 3A, 3B, 4A, 4B, 6; ¶ 70, 73 wherein the system collects and stores map information; ¶ 48, 53, 54, 55 wherein the system further collects and stores sensing data; ¶ 51, 58, 59 wherein historical repair information is stored
With regards to “high frequency” the specification fails to define what is considered “high frequency” or how it differentiates itself from a “low frequency”, “medium frequency”, or etc. At best, ¶ 39 recites, “As the cycle and frequency of the inspection, for example, several times a year, several times a month, several times a week and the like can be set, and an appropriate cycle can be appropriately set according to a characteristic of the individual infrastructure (for example, in a case of the road, difference in traffic volume, deterioration speed of the road surface and the like among roads).” As a result, the Examiner asserts that the time period that a practitioner of the invention believes, in their opinion and/or available data, is a sufficient amount of time to properly assess the infrastructure.
With that said, see ¶ 45 “Another machine learning algorithm analyzes the road distress data and combines it with data that most affect road quality such as, by way of non-limiting example, maintenance type, traffic volume and flow, environmental factors, census data, and both current and prospective budget constraints. The system would then deliver planning criteria and information to a user for anticipated road maintenance for a multiple-year period from the image capture date.”
¶ 50 “The system and machine-learning method can also build family curves-for example providing a family of aging curves for roads in X climate and Y number of cars per day. Although this is not unknown in the industry the system herein described has created novel model for generating these family curves with a very small amount of data. In a non-limiting example, a family of curves may be generated from as few as 20 records that don' t cover the entire 20-year life of a road, yet provides an optimized set of road aging curves for various conditions and road use levels. The algorithm involves incrementally taking averages of roads whose aging curves overlap at 1 to 1.25 years of age, 1.25 to 1.5 years, and so on. Other models need a lot more data to build these curves, and, thus, often have to use a stock curve where the system and machine-learning method can often build unique curve models on smaller subsets of roads.”;
¶ 51 “We can fuel our predictive analytics algorithms that artificially age and improve roads to create our 10-year model. So instead of a static aging curve, the aging curve is modified with dynamic data combined with one or more stock aging curves to create a dynamic, blended family of aging curves utilizing collected data.”;
¶ 58 “In an embodiment, the instant innovation's aggregation of data around road quality, maintenance, safety, and past quality history is utilized to find trends and best practices in road maintenance.”;
¶ 59 “The system and platform herein described is designed to be a rapid feedback loop for reporting on road aging and optimize response and repair based upon the more rapid, as compared to systems currently in place, feedback and analysis from collected and calculated road condition data. Instead of no feedback or 5-10 year feedback, the data collection effort for the platform is offered as annual feedback on how a city is progressing. To accomplish this feedback optimization, the data collection by the system and platform occurs on an annual basis.”;
¶ 60 “In a non-limiting example, the system may utilize information from traffic accidents as data elements to consider then prioritizing which roads require maintenance.”);
construct a digital twin to visualize a current state of the infrastructure on the basis of the sensing data acquired at high frequency, and use the digital twin to (The Examiner refers to Fig. 5 and ¶ 57 of the applicant’s specification to determine the metes and bounds of “digital twin”. In light of the specification, a “digital twin” is a virtual simulation performed the system to provide a visualization of its predictions, analysis, and the like. ¶ 20 recites “…and “digital twin” to visualize the state of the infrastructure on the basis of the accumulated sensing data and easily analyze the same…”
With that said, see ¶ 45 “Captured images may then be analyzed by a machine-learning algorithm utilizing criteria corresponding to standard road distresses, including, but not limited to fatigue cracking, block cracking, utility patches, longitudinal and transverse cracks, and potholes. Another machine-learning algorithm analyzes the road distress data and combines it with data that most affect road quality such as, by way of non-limiting example, maintenance type, traffic volume and flow, environmental factors, census data, and both current and prospective budget constraints. The system would then deliver planning criteria and information to a user for anticipated road maintenance for a multiple-year period from the image capture date.”;
¶ 49 “Instead of an ASTM rating, the system may predict how much "fatigue cracking" will progress on a street using this same approach or how rough the ride may become as measured by the IRI, or other rating system as supplied by a user. A standard capability currently is to replace a stock aging curve with actual data measured using various technique. The system herein described permits the melding of a stock aging curve and actual measured data together to form a blended road aging curve to provide an optimized measurement for the aging of a road surface or road system.”
¶ 50 “The system and machine-learning method can also build family curves-for example providing a family of aging curves for roads in X climate and Y number of cars per day. Although this is not unknown in the industry the system herein described has created novel model for generating these family curves with a very small amount of data. In a non-limiting example, a family of curves may be generated from as few as 20 records that don' t cover the entire 20-year life of a road, yet provides an optimized set of road aging curves for various conditions and road use levels. The algorithm involves incrementally taking averages of roads whose aging curves overlap at 1 to 1.25 years of age, 1.25 to 1.5 years, and so on. Other models need a lot more data to build these curves, and, thus, often have to use a stock curve where the system and machine-learning method can often build unique curve models on smaller subsets of roads.”;
¶ 51 “We can fuel our predictive analytics algorithms that artificially age and improve roads to create our 10-year model. So instead of a static aging curve, the aging curve is modified with dynamic data combined with one or more stock aging curves to create a dynamic, blended family of aging curves utilizing collected data.”;
¶ 52 “The system provides a tool for cities to (1) model the effect of various road maintenance strategies, such as worst first, and (2) given a maintenance strategy, the system may compute an optimal maintenance schedule multiple years into the future by treating it as a discrete optimization problem, such as knapsack.”
¶ 54 “Upon collection and receipt of data from aerial or satellite resources having sufficient visual resolution, the data may be stored and used by the machine learning algorithm to provide a more accurate, comprehensive, and optimized maintenance and replacement schedule for all types of roadway assets, such as pavement and pavement marking repair and replacement, and assets associated with a roadway such as street signs, pavement markings, sidewalks, curbs, manholes, water valves, catch basins, park benches, household and city-owned trash cans, power lines and fleet vehicle movements, as previously recited.”
¶ 58 “In an embodiment, the instant innovation's aggregation of data around road quality, maintenance, safety, and past quality history is utilized to find trends and best practices in road maintenance. The instant innovation returns one or more results of road maintenance choices to a user based upon data analysis of road maintenance scenarios input by the user. The results supplied by the system permit the user to optimize maintenance plans for roadways, municipal assets, transportation assets, and other pre-configured assets.”;
Fig. 6; ¶ 73 “Turning now to FIG. 6, a view of a third web application user experience consistent with certain embodiments of the present invention is shown. At 600 the system has returned to the user a dashboard showing a map of the geographical location of surveyed assets, a description of determined asset ratings, and an estimate of repair costs associated with distressed assets.”):
select sensing data to be a processing target for learning by Artificial Intelligence (AI) as teacher data based on a data attribute by the system or external sensing data (¶ 44 “In a non-limiting embodiment, the system may train a machine-learning algorithm to identify each of these items, read them for those items that may contain text, and locate them.”
¶ 48, 49, 50, 51, 52, 53, 58, 59, 60 wherein the system is not only provided with data of expected degradation to monitor/determine the state of the infrastructure, but will dynamically modify expected degradation based on the data that is being collected and will monitor the location to determine if a trend exists, which further assists with the generation of the dynamically based maintenance plan. The system will also attach sensors to vehicles in order to more closely monitor the condition of the infrastructure, wherein the vehicles have assigned routes, e.g., garbage trucks, recycling trucks, and sweepers are vehicles that follow an assigned schedule and route and the system can utilize this information to attach a sensor and monitor the condition of the roads these vehicles are traveling on to predict degradation of the road and dynamically generate a maintenance plan. The system can also utilize traffic information to determine the current usage of the road, which, in turn, further allows the system to dynamically generate the maintenance plan. The system further blends stock aging curves and actual measured data to form a blended aging curve for the infrastructure to provide an optimized measurement for the agent of the infrastructure to determine when maintenance should be performed, i.e. when the condition of the road has exceeded an acceptable threshold value that triggers its maintenance/repair. Finally, the system can also deploy vehicles to drive onto certain streets to collect information regarding the state of the street and continue monitoring the infrastructure over time to determine the priority or decision to repair.);
display a map image of an area specified by a user based on the map data with information regarding the state of the infrastructure determined on the basis of the sensing data in a superimposed manner on the map image, wherein the information regarding the state of the infrastructure is mapped on the map image by color coding or a mark (Fig. 3A, 3B, 4A, 4B, 6; ¶ 48, 53, 54, 55, 67, 69, 70, 73; Claim 1 wherein, based on the collected and stored information, the system displays a map of the location that requires servicing, e.g., repair, maintenance plan, etc., and wherein the information is marked by superimposing the additional information regarding the condition of the infrastructure onto the map;
Fig. 4B, 6; Abstract; ¶ 25 and corresponding examples disclosed in ¶ 26 – 43, 53, 65 wherein the system is configured to capture, process, and display information for assisting a user with evaluating the condition of a plurality of different infrastructure types, e.g., roads, signs, power lines, and etc., and presents the user with a dashboard to allow them to filter through information and present information that the user is interested in. As a non-limiting example, the user selects the chevron of Fig. 6 to select roads and is presented with a plurality of additional filtering options to mark roads requiring a particular maintenance type, e.g., “Mill and overlay” and marking the roads (e.g., marking the roads or using a different color to mark the roads to differentiate the roads for the treatment against the roads that do not) in the center map image of roads satisfying this filter option. As stated above, Sunde discloses a plurality of infrastructure types and Fig. 6 provides an example for when the user selects “Roads” (see also: ¶ 58, 76). Sunde provides another example where the system displays of map identifying the presence and type of signage.);
predict a future degree of deterioration of each infrastructure at a future time specified by the user using the Artificial Intelligence trained with the teacher data selected via the digital twin on the basis of data including the sensing data, extract the infrastructure having the predicted future degree of deterioration exceeding a predetermined repair reference value, and display the extracted infrastructure on the map image as a candidate for an infrastructure to be repaired by combining related information including at least one of weather information or traffic volume specified by the user (¶ 49, 51, 52, 58, 59, 61, 63 wherein the system provides predictions regarding deterioration of the infrastructure based on the collected and stored information and will provide a maintenance plan for the infrastructure based on the prediction and request by a user.;
¶ 44, 45, 50, 54, 65, 66 wherein machine learning is used to analyze the information and present its results to assist with the generation and implementation of the maintenance plan at the request of a user;
¶ 45, 49, 50, 51, 52, 54, 58, 73 wherein the system constructs and utilizes a digital twin and machine learning to analyze and present predicted results
Fig. 4B, 6; Abstract; ¶ 25 and corresponding examples disclosed in ¶ 26 – 43, 53, 65 wherein the system is configured to capture, process, and display information for assisting a user with evaluating the condition of a plurality of different infrastructure types, e.g., roads, signs, power lines, and etc., and presents the user with a dashboard to allow them to filter through information and present information that the user is interested in. As a non-limiting example, the user selects the chevron of Fig. 6 to select roads and is presented with a plurality of additional filtering options to mark roads requiring a particular maintenance type, e.g., “Mill and overlay” and marking the roads (e.g., marking the roads or using a different color to mark the roads to differentiate the roads for the treatment against the roads that do not) in the center map image of roads satisfying this filter option. As stated above, Sunde discloses a plurality of infrastructure types and Fig. 6 provides an example for when the user selects “Roads”. Sunde provides another example where the system displays of map identifying the presence and type of signage.
¶ 48, 49, 50, 51, 52, 53, 58, 59, 60 wherein the system is not only provided with data of expected degradation to monitor/determine the state of the infrastructure, but will dynamically modify expected degradation based on the data that is being collected and will monitor the location to determine if a trend exists, which further assists with the generation of the dynamically based maintenance plan. The system will also attach sensors to vehicles in order to more closely monitor the condition of the infrastructure, wherein the vehicles have assigned routes, e.g., garbage trucks, recycling trucks, and sweepers are vehicles that follow an assigned schedule and route and the system can utilize this information to attach a sensor and monitor the condition of the roads these vehicles are traveling on to predict degradation of the road and dynamically generate a maintenance plan. The system can also utilize traffic information to determine the current usage of the road, which, in turn, further allows the system to dynamically generate the maintenance plan. The system further blends stock aging curves and actual measured data to form a blended aging curve for the infrastructure to provide an optimized measurement for the agent of the infrastructure to determine when maintenance should be performed, i.e. when the condition of the road has exceeded an acceptable threshold value that triggers its maintenance/repair. Finally, the system can also deploy vehicles to drive onto certain streets to collect information regarding the state of the street and continue monitoring the infrastructure over time to determine the priority or decision to repair); and
display a result of aggregation of the sensing data and indexes related to the infrastructure maintenance management on a dashboard in addition to the map image (¶ 73 wherein a dashboard is displayed that includes information of the location for the maintenance plan, repair cost estimates, determined rating of the asset, and so forth;
Fig. 3A, 3B, 4A, 5A, 5B, 6 wherein the system also provides a dashboard/user interface that displays sensed data and indexes related to the infrastructure maintenance management in addition to the map image
Fig. 4B, 6; Abstract; ¶ 25 and corresponding examples disclosed in ¶ 26 – 43, 53, 65 wherein the system is configured to capture, process, and display information for assisting a user with evaluating the condition of a plurality of different infrastructure types, e.g., roads, signs, power lines, and etc., and presents the user with a dashboard to allow them to filter through information and present information that the user is interested in. As a non-limiting example, the user selects the chevron of Fig. 6 to select roads and is presented with a plurality of additional filtering options to mark roads requiring a particular maintenance type, e.g., “Mill and overlay” and marking the roads (e.g., marking the roads or using a different color to mark the roads to differentiate the roads for the treatment against the roads that do not) in the center map image of roads satisfying this filter option. As stated above, Sunde discloses a plurality of infrastructure types and Fig. 6 provides an example for when the user selects “Roads”. Sunde provides another example where the system displays of map identifying the presence and type of signage.).
In regards to claim 2, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to:
determine a difference between the future degree of deterioration of each infrastructure at a certain point of time predicted on the basis of data including the sensing data and an inspection value acquired by sensing a state of the infrastructure at the certain point of time;
set a first inspection cycle for each infrastructure if the difference is equal to or smaller than a predetermined threshold;
set a second inspection cycle for each infrastructure if the difference is larger than the predetermined threshold, the second inspection cycle being shorter than the first inspection cycle; and
display the first inspection cycle or the second inspection cycle on the map image as the information regarding the state of the corresponding infrastructure (¶ 48, 49, 50, 51, 52, 53, 58, 59, 60 wherein the system is not only provided with data of expected degradation to monitor/determine the state of the infrastructure, but will dynamically modify expected degradation based on the data that is being collected and will monitor the location to determine if a trend exists, which further assists with the generation of the dynamically based maintenance plan. The system will also attach sensors to vehicles in order to more closely monitor the condition of the infrastructure, wherein the vehicles have assigned routes, e.g., garbage trucks, recycling trucks, and sweepers are vehicles that follow an assigned schedule and route and the system can utilize this information to attach a sensor and monitor the condition of the roads these vehicles are traveling on to predict degradation of the road and dynamically generate a maintenance plan. The system can also utilize traffic information to determine the current usage of the road, which, in turn, further allows the system to dynamically generate the maintenance plan. The system further blends stock aging curves and actual measured data to form a blended aging curve for the infrastructure to provide an optimized measurement for the agent of the infrastructure to determine when maintenance should be performed, i.e. when the condition of the road has exceeded an acceptable threshold value that triggers its maintenance/repair. Finally, the system can also deploy vehicles to drive onto certain streets to collect information regarding the state of the street and continue monitoring the infrastructure over time to determine the priority or decision to repair.).
In regards to claim 3, Sunde discloses the infrastructure maintenance management support system according to claim 2, wherein the processor or processors are further configured to determine soundness of the infrastructure on the basis of the sensing data acquired by the inspection (¶ 48, 49, 51, 52, 53, 58, 59, 60 wherein the data is collected to determine the condition of the infrastructure).
In regards to claim 4, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to select a method to be adopted for repairing the candidate infrastructure by Artificial Intelligence (AI), and present the selected method (¶ 44, 45, 50, 54, 65, 66 wherein machine learning is used to analyze the information and present its results to assist with the generation and implementation of the maintenance plan).
In regards to claim 5, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to perform simulation of a life cycle cost when repairing the candidate infrastructure, and present a result of the simulation (¶ 50, 51, 52, 58, 59, 60, 61, 73 wherein the system performs a simulation of the life cycle cost for the maintenance plan and presents the results to optimize the coordination effort to maintain and upgrade the infrastructure).
In regards to claim 6, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to:
create a repair plan including a method, a work schedule, and a budget for an infrastructure selected from the candidate and record the repair plan as repair plan data; and
perform project management related to repair work performed on the basis of the repair plan
(¶ 50, 51, 52, 58, 59, 60, 61, 73 wherein the system generates and provides a maintenance plan that includes the repair plan, schedule, and budget for the maintenance project and stores the plan for viewing by a user, monitoring the progress of the entity implementing the plan, and the management of the plan).
In regards to claim 7, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are configured to:
digitally present a screen display including the image and a filter;
electrically receive an input through the filter digitally presented on the screen display to specify the information regarding the state of the infrastructure; and
digitally present the specified information regarding the state of the infrastructure on the map image in the superimposed manner by the color coding or the mark
(Fig. 4B, 6; Abstract; ¶ 25 and corresponding examples disclosed in ¶ 26 – 43, 53, 65 wherein the system is configured to capture, process, and display information for assisting a user with evaluating the condition of a plurality of different infrastructure types, e.g., roads, signs, power lines, and etc., and presents the user with a dashboard to allow them to filter through information and present information that the user is interested in. As a non-limiting example, the user selects the chevron of Fig. 6 to select roads and is presented with a plurality of additional filtering options to mark roads requiring a particular maintenance type, e.g., “Mill and overlay” and marking the roads (e.g., marking the roads or using a different color to mark the roads to differentiate the roads for the treatment against the roads that do not) in the center map image of roads satisfying this filter option. As stated above, Sunde discloses a plurality of infrastructure types and Fig. 6 provides an example for when the user selects “Roads”. Sunde provides another example where the system displays of map identifying the presence and type of signage.).
In regards to claim 8, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to set a priority for inspection and repair for a plurality of infrastructures based on inspection and repair priority master data including information specifying whether a route is an emergency transportation road (¶ 52 “The system provides a tool for cities to (1) model the effect of various road maintenance strategies, such as worst first, and (2) given a maintenance strategy, the system may compute an optimal maintenance schedule multiple years into the future by treating it as a discrete optimization problem, such as knapsack.”
¶ 59 “In this manner, when a street maintenance plan is created for the next 10 years, the system is active to match up other data sources like traffic accidents, real estate developments, known utility failures, and census data that may affect the priority or even decision to repair a particular roadway prior to the issuance of a request for proposal for street resurfacing activity.”
¶ 60 “In a non-limiting example, the system may utilize information from traffic accidents as data elements to consider then prioritizing which roads require maintenance.”
With regards to, “…inspection and repair priority master data including information specifying whether a route is an emergency transportation road” the Examiner refers to MPEP § 2111.04 and 2111.05. The Examiner asserts that the limitation is directed towards describing an intended result. Identifying the information as “inspection and repair priority master data including information specifying whether a route is an emergency transportation road” is a label for the information and adds little, if anything, to the claimed invention and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of information), which does not explicitly alter or impact the structure or function of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability. Sunde discloses that the system sets priority for inspection and repair for a plurality of infrastructures and whether the route is an emergency transportation road is directed towards descriptive subject matter and describes an intended result.).
In regards to claim 9, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the processor or processors are further configured to predict the future degree of deterioration by incorporating regional characteristic master data reflecting environmental factors including at least one of salt damage, snowfall, or whether the infrastructure is in a mountainous area (¶ 45 “Captured images may then be analyzed by a machine-learning algorithm utilizing criteria corresponding to standard road distresses, including, but not limited to fatigue cracking, block cracking, utility patches, longitudinal and transverse cracks, and potholes. Another machine-learning algorithm analyzes the road distress data and combines it with data that most affect road quality such as, by way of non-limiting example, maintenance type, traffic volume and flow, environmental factors, census data, and both current and prospective budget constraints. The system would then deliver planning criteria and information to a user for anticipated road maintenance for a multiple-year period from the image capture date.”
¶ 49 “Instead of an ASTM rating, the system may predict how much "fatigue cracking" will progress on a street using this same approach or how rough the ride may become as measured by the IRI, or other rating system as supplied by a user. A standard capability currently is to replace a stock aging curve with actual data measured using various technique. The system herein described permits the melding of a stock aging curve and actual measured data together to form a blended road aging curve to provide an optimized measurement for the aging of a road surface or road system.”
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, the data identifying the environmental factors as “at least one of salt damage, snowfall, or whether the infrastructure is in a mountainous area” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of environmental factors), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability.).
In regards to claim 10, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the data held in the system includes structure master data comprising at least one of an external photograph or three-dimensional model data for each infrastructure (¶ 45 “Captured images may then be analyzed by a machine-learning algorithm utilizing criteria corresponding to standard road distresses, including, but not limited to fatigue cracking, block cracking, utility patches, longitudinal and transverse cracks, and potholes. Another machine-learning algorithm analyzes the road distress data and combines it with data that most affect road quality such as, by way of non-limiting example, maintenance type, traffic volume and flow, environmental factors, census data, and both current and prospective budget constraints. The system would then deliver planning criteria and information to a user for anticipated road maintenance for a multiple-year period from the image capture date.”
See also ¶ 53, 65, 67, 68, 69, 70 wherein sensors, drones, and photographs are collected and utilized by the system
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, the data identifying the structure master data as “at least one of an external photograph or three-dimensional model data for each infrastructure” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of structure master data), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability).
In regards to claim 11, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the selection of the teacher data includes extracting monitoring data that exceeds a predetermined threshold from a monitoring data database (¶ 44 “In a non-limiting embodiment, the system may train a machine-learning algorithm to identify each of these items, read them for those items that may contain text, and locate them.”
¶ 48, 49, 50, 51, 52, 53, 58, 59, 60 wherein the system is not only provided with data of expected degradation to monitor/determine the state of the infrastructure, but will dynamically modify expected degradation based on the data that is being collected and will monitor the location to determine if a trend exists, which further assists with the generation of the dynamically based maintenance plan. The system will also attach sensors to vehicles in order to more closely monitor the condition of the infrastructure, wherein the vehicles have assigned routes, e.g., garbage trucks, recycling trucks, and sweepers are vehicles that follow an assigned schedule and route and the system can utilize this information to attach a sensor and monitor the condition of the roads these vehicles are traveling on to predict degradation of the road and dynamically generate a maintenance plan. The system can also utilize traffic information to determine the current usage of the road, which, in turn, further allows the system to dynamically generate the maintenance plan. The system further blends stock aging curves and actual measured data to form a blended aging curve for the infrastructure to provide an optimized measurement for the agent of the infrastructure to determine when maintenance should be performed, i.e. when the condition of the road has exceeded an acceptable threshold value that triggers its maintenance/repair. Finally, the system can also deploy vehicles to drive onto certain streets to collect information regarding the state of the street and continue monitoring the infrastructure over time to determine the priority or decision to repair.).
In regards to claim 12, Sunde discloses the infrastructure maintenance management support system according to claim 6, wherein the processor or processors are further configured to set a budget upper limit for the repair plan and create the plan such that an estimated cost does not exceed the budget upper limit (¶ 24 “Because municipal road-maintenance budgets are finite, if such allotments exist at all, there is a need for a system and method for mechanically scanning roads and locating portions of roadways that appear rough, patchy or in need of repair. Such a system and method would capture and analyze at least photographic images to provide information that assists in the planning for road maintenance.”
¶ 45 “Captured images may then be analyzed by a machine-learning algorithm utilizing criteria corresponding to standard road distresses, including, but not limited to fatigue cracking, block cracking, utility patches, longitudinal and transverse cracks, and potholes. Another machine-learning algorithm analyzes the road distress data and combines it with data that most affect road quality such as, by way of non-limiting example, maintenance type, traffic volume and flow, environmental factors, census data, and both current and prospective budget constraints. The system would then deliver planning criteria and information to a user for anticipated road maintenance for a multiple-year period from the image capture date.”
¶ 59 “However, an additional aspect of the system and platform that may improve both recognition of road issues and spur efforts at repair and improvement is that the system and platform may insert critical information at specific decision points. In a nonlimiting example, vehicles may be deployed to drive the streets that a city has advertised in a Street Resurfacing contractor on which general paving contractors may place bids. The measurement and data collection capabilities of the system may measure and inventory the cracks and other road surface conditions to ensure that contractor bids submitted to the city are accurate. Additionally, the system can predict locations that may require change-orders that are unforeseen by humans.”).
In regards to claim 13, Sunde discloses the infrastructure maintenance management support system according to claim 4, wherein the method to be adopted for repairing includes at least one of a surface coating method, a crack injection filling method, a sectional repair method, or a steel plate bonding method (¶ 59 “However, an additional aspect of the system and platform that may improve both recognition of road issues and spur efforts at repair and improvement is that the system and platform may insert critical information at specific decision points. In a nonlimiting example, vehicles may be deployed to drive the streets that a city has advertised in a Street Resurfacing contractor on which general paving contractors may place bids. … In this non-limiting example, many times, a street is milled by a contractor and after milling but before resurfacing, the city or DOT staff will identify areas of full-depth asphalt replacement rather than just resurfacing.”
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, the data identifying the repair as “at least one of a surface coating method, a crack injection filling method, a sectional repair method, or a steel plate bonding method” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of repair), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability).
In regards to claim 14, Sunde discloses the infrastructure maintenance management support system according to claim 6, wherein the processor or processors are further configured to display the repair plan as a list including a predicted deterioration level, a scheduled year for repair, and a cost estimate for each infrastructure (¶ 52 regarding predicted deterioration level, scheduled year for repair; ¶ 24, 45, 59 regarding cost
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, describing the displayed information as “the repair plan as a list including a predicted deterioration level, a scheduled year for repair, and a cost estimate for each infrastructure” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of displayed information), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability).
In regards to claim 16, Sunde discloses the infrastructure maintenance management support system according to claim 7, wherein the map image includes a plurality of layers assigned to different types of infrastructures, including a first layer for roads and a second layer for signage (Fig. 3A, 3B, 4A, 4B, 6 wherein the system is capable of displaying a plurality of different layers with each layer displaying corresponding information that may include information that is different from the other layers, as well as further corresponding to different types of infrastructures, e.g., roads and signs).
n regards to claim 17, Sunde discloses the infrastructure maintenance management support system according to claim 8, wherein the priority is set to a higher value for infrastructures located on routes with higher traffic volume compared to routes with lower traffic volume (¶ 48, 49, 50, 51, 52, 53, 58, 59, 60 wherein the system is not only provided with data of expected degradation to monitor/determine the state of the infrastructure, but will dynamically modify expected degradation based on the data that is being collected and will monitor the location to determine if a trend exists, which further assists with the generation of the dynamically based maintenance plan. The system will also attach sensors to vehicles in order to more closely monitor the condition of the infrastructure, wherein the vehicles have assigned routes, e.g., garbage trucks, recycling trucks, and sweepers are vehicles that follow an assigned schedule and route and the system can utilize this information to attach a sensor and monitor the condition of the roads these vehicles are traveling on to predict degradation of the road and dynamically generate a maintenance plan. The system can also utilize traffic information to determine the current usage of the road, which, in turn, further allows the system to dynamically generate the maintenance plan. The system further blends stock aging curves and actual measured data to form a blended aging curve for the infrastructure to provide an optimized measurement for the agent of the infrastructure to determine when maintenance should be performed, i.e. when the condition of the road has exceeded an acceptable threshold value that triggers its maintenance/repair. Finally, the system can also deploy vehicles to drive onto certain streets to collect information regarding the state of the street and continue monitoring the infrastructure over time to determine the priority or decision to repair
With regards to, “…on routes with higher traffic volume… routes with lower traffic volume…” the Examiner refers to MPEP § 2111.04 and 2111.05. The Examiner asserts that the limitation is directed towards describing an intended result. Describing what the priority is intended to represent, i.e. “…on routes with higher traffic volume… routes with lower traffic volume…” is a label for the priority/priority rule and adds little, if anything, to the claimed invention and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of priority/priority rule), which does not explicitly alter or impact the structure or function of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability. Sunde discloses that the system sets priority for inspection and repair for a plurality of infrastructures and whether the route is higher or lower traffic volume is directed towards descriptive subject matter and describes an intended result.).
In regards to claim 18, Sunde discloses the infrastructure maintenance management support system according to claim 1, wherein the predicted future degree of deterioration is determined as one of a plurality of deterioration levels including level 1 (small), level 2 (medium), and level 3 (large) (Fig. 5B; ¶ 47, 66, 67 wherein the system displays corresponding deterioration levels.
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, describing the predicted future degree of deterioration as “level 1 (small), level 2 (medium), and level 3 (large)” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of predicted future degree of deterioration), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability).
In regards to claim 19, Sunde discloses the infrastructure maintenance management support system according to claim 7, wherein the processor or processors are further configured to display degradation states at two different points of time side-by-side on the map image for comparison (Fig. 4B, 6 wherein the system is capable of displaying information side-by-side
Finally, the Examiner asserts that, in light of MPEP § 2111.04 and § 2111.05, describing the displayed information as “degradation states at two different points of time side-by-side on the map image for comparison” is a label for the items and adds little, if anything, to the claimed structure and/or function and, thus, does not serve to distinguish over the prior art. Any differences related merely to the meaning and information conveyed through labels (i.e., the type of displayed information), which does not explicitly alter or impact the structure or functions of the claimed invention, does not patentably distinguish the claimed invention from the prior art, in terms of patentability).
In regards to claim 20, Sunde discloses the infrastructure maintenance management support system according to claim 7, wherein the filter comprises a condition selection column allowing the user to select the information regarding the state of the infrastructure to be displayed by infrastructure type or individual infrastructure ID (Fig. 6 wherein the system provides an interface to allow a user to view a column having a filter and allow the user to select the filter to display desired information, e.g., roads and further filtering this type of information).
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Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Sunde et al. (US PGPub 2022/0101272 A1) in view of Page Laubheimer (Drag–and–Drop_ How to Design for Ease of Use).
In regards to claim 15, the infrastructure maintenance management support system according to claim 14, wherein the processor or processors are further configured to update the repair plan in response to a user operation of moving a repair item to a different scheduled year on the list via [interaction with the system] (¶ 51 “We can fuel our predictive analytics algorithms that artificially age and improve roads to create our 10-year model. So instead of a static aging curve, the aging curve is modified with dynamic data combined with one or more stock aging curves to create a dynamic, blended family of aging curves utilizing collected data.”
¶ 59 “The system and platform herein described is designed to be a rapid feedback loop for reporting on road aging and optimize response and repair based upon the more rapid, as compared to systems currently in place, feedback and analysis from collected and calculated road condition data. Instead of no feedback or 5-10 year feedback, the data collection effort for the platform is offered as annual feedback on how a city is progressing. To accomplish this feedback optimization, the data collection by the system and platform occurs on an annual basis. … In this manner, when a street maintenance plan is created for the next 10 years, the system is active to match up other data sources like traffic accidents, real estate developments, known utility failures, and census data that may affect the priority or even decision to repair a particular roadway prior to the issuance of a request for proposal for street resurfacing activity.”
¶ 60 “In a non-limiting example, the system may utilize information from traffic accidents as data elements to consider then prioritizing which roads require maintenance.”).
Sunde discloses the utilization of software and operating systems to allow a user to interface with the system and allow for the performance of the claimed invention (¶ 19 “Reference throughout this document to "device" refers to any electronic communication device with network access such as, but not limited to, a cell phone, smart phone, tablet, iPad, networked computer, internet computer, laptop, watch or any other device, including Internet of Things devices, a user may use to interact with one or more networks.”; see also ¶ 21). Despite this, Sunde fails to explicitly recite the use of dragging and dropping.
To be more specific, Sunde fails to explicitly disclose:
the infrastructure maintenance management support system according to claim 14, wherein the processor or processors are further configured to update the repair plan in response to a user operation of moving a repair item to a different scheduled year on the list via a drag-and-drop or swipe operation.
However, Laubheimer teaches that drag and drop has been around since the dawn of GUIs and is familiar to most users. It is a type of direction manipulation, particularly useful for grouping, reordering, moving, or resizing objects. Laubheimer teaches that drag and drop makes actions visible and immediate and can thus improve usability.
Although Laubheimer discloses that there are some disadvantages, one of ordinary skill in the art would have found that based on the teachings of Laubheimer to determine whether the advantages outweigh the disadvantages. However, Laubheimer teaches that the downsides can be addressed by accompanying more precise interactions. Laubheimer teaches that, when appropriate, drag and drop is well understood, and quickly adopted by users.
One of ordinary skill in the art looking upon the Sunde would have found that there is a plurality of different input devices that a user can use on a computer (e.g., laptop, iPad, networked computer, internet computer, tablet, and etc.) that can be used, such as, but not limited to, mouse, keyboard, and touchscreen, and that these various options would still achieve the same predictable result of receiving user input and converting user actions into information that can be used by the system in order to perform the user’s intended goal.
One of ordinary skill in the art looking upon the teachings of the teachings of Laubheimer would have found that many of the input devices that can be utilized by the computer/device of Sunde are devices that can be used to enable a user to interact with a computing system using the drag and drop technique and, therefore, if appropriate, one of ordinary skill in the would not have found it uniquely challenging or difficult to incorporate drag and drop into the system and method of Sunde in order to achieve the predictable result of receiving user input and converting user actions into information that can be used by the system in order to perform the user’s intended goal while providing the benefits of making actions visible and immediate and can thus improve usability.
(See Pages 1 – 15)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to incorporate the drag and drop technique, as disclosed by Laubheimer, in the system and method of Sunde, which requires a user interacting with a computer system via an input device, since dragging and dropping is familiar and effective means of allowing a user to interact with a computer system while providing the benefit of providing clear feedback through the interaction, and ensure that it is accessible.
Response to Arguments
Applicant's arguments filed 7/21/2026 have been fully considered but they are not persuasive.
Rejection under 35 USC 101
The rejection under 35 USC 101 has been maintained.
The Examiner asserts that the claimed invention is not improving upon infrastructure maintenance or the infrastructure itself, but directed towards the collection and comparison of information and, based on a rule(s), identify options for the purpose of managing the maintenance of an infrastructure, e.g., notifying when maintenance is required. The claimed invention can be performed by a human(s) in their mind and/or through the aid of pen and paper. The claimed invention encompasses having a user collect information regarding the state of an infrastructure by sensing the condition using the sense of sight or sense of touch, looking at a map, writing on the map, and presenting the map to convey the location of where there is an issue, as well as comparing the information collected against expected information, i.e. expected condition or reference value of the infrastructure, determine the degradation/deterioration of the infrastructure based on the comparison and any applicable rules, and presenting the results of the analysis.
With regards to “digital twin”, this is nothing more than a representation of an actual asset and, more specifically, encompasses a human drawing and writing down information about an asset based on historical and current information and extrapolating when sufficient deterioration has occurred to determine when maintenance should be performed. The claimed invention is not directed towards creating and maintaining a “digital twin” of a real-world object in order to simulate how it is operating, how it is being affected by its environment, and the like to determine the deterioration rate of the real-world asset so that a determination can be made on when it will require maintenance. The simulation discussed in the specification is directed toward a cost analysis not an analysis that is simulating how the asset is/will perform or operate.
With regards to “reduce a load of information collection”, the Examiner asserts that the claimed invention does not recite such a feature nor does it present elements directed towards such a technological improvement. The claimed invention does not rise to the level of Enfish, DDR Holdings, or CoreWireless wherein demonstrable technological improvements were discussed, identified, and provided to show a demonstrable difference in technological performance against the prior state of the art. The claimed invention recites generic technology at a high level of generality to perform extra-solution activities of communicating, storing/holding, and displaying/presenting information and performing operations that a human can perform in their mind and/or with the aid of pen and paper, i.e. analyzing the information, as was discussed above, to determine the future deterioration of an infrastructure and marking it on a map.
With regards to selecting specifically teacher data, the Examiner asserts that this is an activity that can be performed by a human. Moreover, the claimed invention is not directed towards nor does it recite, “automatically determining the suitability of data for machine learning” or “ensuring integrity and reliability of the training dataset,” but is, in fact, describing the type of data that the applicant believes, in their opinion, is the “best” data to utilize. The claimed invention is not concerned with nor is it directed towards improving upon training techniques or resolving an issue that arose in machine learning training, but directed towards describing, at a high level of generality, the type of data to use as training data.
As a result, the claimed invention is not directed towards a technological improvement, but reciting generic technology at a high level of generality and applying it to the abstract idea to perform steps that can be performed by a human(s) and/or with the aid of pen and paper, e.g., having a user collect information regarding the state of an infrastructure by sensing the condition using the sense of sight or sense of touch, looking at a map, writing on the map, and presenting the map to convey the location of where there is an issue, as well as comparing the information collected against expected information, i.e. expected condition or reference value of the infrastructure, determine the degradation/deterioration of the infrastructure based on the comparison and any applicable rules, and presenting the results of the analysis. The claimed invention’s application of Artificial Intelligence (AI) has been recited at a high level and applied to the abstract idea for the benefits that AI provides, i.e. faster, more efficient, and etc. and not improving upon AI. Reciting, at a high level, the type of data to be used as training data is not an improvement to AI training, but simply a description of information that the applicant believes, in their mind and opinion, is the “best” type of data to utilize while relying on generic training techniques. Similarly, describing an inspection should be performed is not an improvement to technology, but directed towards the management of human activities based on the collection and comparison of information and, based on a rule(s), identify options, as was discussed above.
Finally, with regards to the Berkheimer analysis, the Examiner asserts that the rejection never stated “well-understood, routine, and conventional” and, therefore, there is no requirement to provide any evidence because the Examiner’s analysis is not based on this analysis.
Rejection under 35 USC 102
Claim 1
The Examiner asserts that the applicant’s arguments are directed towards newly amended limitations and are, therefore, considered moot. However, the Examiner has responded to the newly submitted amendments, which the arguments are directed to, in the rejection above, thereby addressing the applicant’s arguments.
Claim 2
In response to applicant's argument that the references fail to show certain features of applicant’s invention, it is noted that the features upon which applicant relies (i.e., “error-correction feedback mechanism”) 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). Additionally, as was discussed in the rejection, Sunde discloses a dynamic analysis system to determine if changes to a maintenance plan/schedule should be made based on historical and current data.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure can be found in the attached PTO-892 Notice of References Cited.
Park et al. (KR 20140078000 A); Wei et al. (CN 110516820 B); Liao et al. (CN 114548512 A); Zhao et al. (CN 113888043 A); Williams (US PGPub 2021/0117897 A1); Davenport et al. (US Patent 9,311,615 B2) – which disclose infrastructure maintenance/condition monitoring and management systems
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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 GERARDO ARAQUE JR whose telephone number is (571)272-3747. The examiner can normally be reached Monday - Friday 8-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Sarah Monfeldt can be reached at 571-270-1833. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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GERARDO ARAQUE JR
Primary Examiner
Art Unit 3629
/GERARDO ARAQUE JR/Primary Examiner, Art Unit 3629 8/11/2026
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