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 ACTION
This Non-Final action is in reply to the application filed 2/28/2024.
Claims 1-20 are pending.
Claim Rejections - 35 USC § 112
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 5, 6, 12, 13 and 16-20 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 5, 6, 12, 13, 19 and 20 recite the limitation “the greenhouse gas emissions models”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
Claims 16-20 recite the limitation, “the computer-readable medium”. There is insufficient antecedent basis for this limitation in the claim. Appropriate correction is required.
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. Claims 1-7 are directed to a system, claims 8-14 are directed to a process (an act, or series of acts or steps), and claims 15-20 are directed to a non-transient computer-readable medium. Hence, claims 1-20 fall within one of the four statutory categories.
Step 2A-Prong 1: Claims 1, 8 and 15 recite in part, “ an identifier decoder configured to receive indefinite article identifiers and to translate the received indefinite article identifiers into attribute sets, wherein each said indefinite article identifier non-uniquely identifies an article of manufacture, and wherein each said attribute set includes article attributes associated with the respective article of manufacture; an emissions database comprising a plurality of emissions records each including greenhouse gas emissions data indexed by the article attributes; and an emissions modeller configured to receive the attribute sets and to translate each said received attribute set into a greenhouse gas emissions model, wherein the emissions modeller is configured to translate each said received attribute set by locating a respective matching emissions record in the emissions database, and wherein amongst the plurality of emissions records the article attributes of each said received attribute set most closely match the article attributes of the respective matching emissions record”
The underlined limitations above demonstrate independent claims 1, 8 and 15 are directed toward the abstract idea for receiving and translating article identifiers into attribute sets, matching received attribute set with article attributes of a respective emissions record in a computing environment. Applicant’s specification ¶3 emphasizes a greenhouse gas emissions model generating (GHG-EMG) system that includes an identifier decoder, an emissions database, and an emissions modeler for translating each received attribute set with a respective matching emissions record whereby the article attributes of each received attribute set most closely match the article attributes of the respective matching emissions record (¶3-¶7). The disclosure further discusses that the identifier decoder and GHG emissions modeler may be implemented on a common computing platform via a single computer processing subsystem or on separate computing platforms via distinct computer processing subsystems (¶47) Claims 1, 8 and 15 are considered an abstract idea because the (underlined) limitations as claimed, pertains to (i) commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors, business relations) whereby a business that operates, sells, leases or otherwise transacts business using articles of manufacture that emit greenhouse gases can estimate the greenhouse gas (GHG) emissions of those articles.
With the exception of generic computing components, the limitations are merely using the “greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] as a tool to perform the abstract idea. The claimed steps for receiving, translating, matching (data) is a fundamental business practice (i.e. managing emissions records) that existed well before the advent of computers and the Internet. Also, the steps for an identifier decoder to receive input (article identifiers) and translate that input into an attribute set; and an emissions modeler translating received attribute set(s) into a greenhouse gas emissions model; and matching the article attributes of each received attribute set to a respective (matching) emissions record are steps of the same abstract idea that use rules logic for receiving and translating article identifiers into attribute sets, matching received attribute set with article attributes of a respective emissions record. Hence, the claims are direct to certain methods of organizing human activity grouping of abstract ideas-see MPEP 2106.04(II).
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] for (receiving, translating matching (data)), data gathering, analysis and merely to provide instructions for organizing human interactions, and to implement the abstract idea recited above utilizing ““greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] as a tool to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h).
Independent claim 1 fails to operate “greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] (which is merely a nominal recitation of a standard computer technology, database and hardware/software components) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for receiving and translating article identifiers into attribute sets, matching received attribute set with article attributes of a respective emissions record in a computing environment (see applicant’s disclosure ¶10: “The emissions database includes a plurality of emissions records. Each emissions record of the emissions database includes greenhouse gas emissions data indexed by the article attributes”; ¶21: “the GHG-EMG system 100 includes an operator terminal 150, a greenhouse gas (GHG) emissions modelling platform 200, an asset database 300 and a greenhouse gas (GHG) emissions database 400.”; ¶22: “The operator terminal 150 is implemented as a computer terminal, and includes an input device, a display device, a network interface and a computer processing subsystem that is coupled to the input device, the display device and the network interface. The computer processing subsystem accepts operator input from the input device, outputs information on the display device, and communicates with the network interface. The network interface allows the operator terminal 150 to communicate with the GHG emissions modelling platform 200 via a computer network 120.”; ¶29: “the functionality of the operator terminal 150, asset database 300 and/or GHG emissions database 400 may be incorporated into the GHG emissions modelling platform 200. Similarly, although the asset database 300 and GHG emissions database 400 are depicted in Fig. 1 as being deployed on separate and distinct database servers, the asset database 300 and GHG emissions database 400 may be deployed instead on a common database server”; ¶30: “the GHG emissions modelling platform 200 may be implemented as a computer server, and includes a network interface 202 and a computer processing subsystem 204 that is coupled to the network interface 202”; ¶32: “the computer processing subsystem 204 includes one or more microprocessors 206, a volatile computer-readable memory 208, and a non-transient non-volatile computer-readable memory 210”; ¶35: “The attribute database 214 includes a plurality of attribute records”; ¶39: “The identifier decoder 218 is configured to receive indefinite article identifiers and to translate the received indefinite article identifiers into attribute sets. Each attribute set includes the article attributes that are associated with the respective article of manufacture”; ¶47: “he identifier decoder 218 and the GHG emissions modeller 220 may be implemented on a common computing platform (GHG emissions modelling platform 200) via a single computer processing subsystem 204”). Applicant’s limitations as recited above do nothing more than supplement the abstract idea using generic computer components performing generic computer functions (receiving, translating, matching) such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f) and linking the use of the judicial exception to a particular technological environment or field of use as discussed in MPEP 2106.05(h). Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a).
Dependent claims 2-7, 9-14 and 16-20 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2, 9 and 16 recite in part, “wherein each said indefinite article identifier identifies”; claims 3, 10 and 17 recite in part, “wherein each said article of manufacture is uniquely identified by a”; claims 4, 11 and 18 recite in part, “further including an asset database and an attribute database, wherein the asset database includes”; claims 5, 12 and 19 recite in part, “wherein: the articles of manufacture include”; claims 6, 13 and 20 recite in part, “wherein each said asset record of the asset database further includes”; claims 7 and 14 recite in part, wherein the emissions modeller is configured to”, which is still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, the additional elements “greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] ”, and “asset database”, “attribute database”, [claims 4, 11 and 17]; “fuzzy name matching algorithm [claims 7 and 14] amount to no more than mere instructions to apply the exception using a generic computer component which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea). Applicant’s disclosure teaches at ¶17: “asset database includes a plurality of asset records. Each asset record of the asset database may be associated with one of the articles of manufacture, and may include the indefinite article identifier of the article of manufacture. The attribute database may include a plurality of attribute records. Each attribute record of the attribute database may be associated with one of the articles of manufacture, and may include the attribute set of the article of manufacture”; ¶44: “the GHG emissions modeller 220 may be configured to locate each matching emissions record by applying a fuzzy name matching algorithm to the GHG emissions database 400 (i.e. without first performing a database query of the GHG emissions database 400).
Applicant’s “greenhouse gas emissions model”, “system”, “identifier decoder”, emissions database” emissions modeller” [claims 1, 8]; and “non-transient computer-readable medium” [claim 15] ”, “asset database”, “attribute database”, [dependent claims 4, 11 and 17]; “fuzzy name matching algorithm [dependent claims 7 and 14] are generically used for data gathering, analysis and to further process received information. As evidenced by applicant's disclosure, the additional elements are not described in any way other than merely being conventionally “known” and used as a tool to perform the functions for receiving, translating and matching data/information. There is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing generic computing components as a tool for data gathering and analysis, and to provide instructions to automate and perform the abstract idea. The claims do not solve any problem which is inherently rooted to computing technology and generally links the abstract idea to a particular technological environment-see MPEP 2106.05 (f-h). The additional elements also fail to provide a practical application or significantly more than the abstract idea because they are merely standard computer technology and hardware/software components recited at a high-level of generality and under their broadest reasonable interpretation includes generic computer and networking components performing generic computer functions such that it amounts to no more than mere instruction to apply the exception using a generic computer component-see MPEP 2106.05(f).
Hence, the 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.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for receiving and translating article identifiers into attribute sets, matching received attribute set with article attributes of a respective emissions record in a computing environment. Hence, claims 1-20 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101. See 2019 PEG and MPEP 2106.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-6, 8-13 and 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Frazier et al., (WO 2023/141102 A1).
With respect to claims 1, 8 and 15,
Frazier discloses,
an identifier decoder configured to receive indefinite article identifiers and to translate the received indefinite article identifiers into attribute sets, (¶20: “The systems, apparatuses, and methods disclosed herein relate to and/or are further capable of performing operations including detecting, sensing, predicting, or otherwise determining operational data associated with a vehicle”; ¶22: “The operation data may, in some embodiments, include metadata such as a vehicle or engine identifier, a timestamp, a geo-location stamp, and/or other suitable metadata”; ¶54: “The vehicle 202 is shown to include an engine 210, an air system 220, engine sensors 230, air sensors 240, a battery 250, fuel systems 260, external sensors 270, and a controller 300… The sensors are coupled to the controller 300, such that the controller 300 can monitor, receive, and/or acquire data indicative of operation of the vehicle 202 (which may be referred to as operational data associated with the vehicle herein)”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”))
wherein each said indefinite article identifier non-uniquely identifies an article of manufacture, and wherein each said attribute set includes article attributes associated with the respective article of manufacture (¶22: “The operation data may, in some embodiments, include metadata such as a vehicle or engine identifier, a timestamp, a geo-location stamp, and/or other suitable metadata”; ¶40: “the data includes information associated with each of the components of the system 100. For example, the data includes information about one or more vehicles 202, 204, 206 of the fleet 200. The information about the fleet 200 includes information received from one or more vehicles 202, 204, 206, predicted or estimated regarding one or more of the vehicles, and/or information about the one or more vehicles 202, 204, 206 (e.g., vehicle identification number, etc.). For example, the information includes a vehicle or equipment powertrain type (e.g., an internal combustion engine powered vehicle, a hybrid engine, a mild-hybrid powertrain, a parallel hybrid powertrain, a series hybrid powertrain, a series-parallel powertrain, a battery electric vehicle a range extender electric vehicle, a fuel-cell vehicle, etc.), a chassis type, a drag coefficient, tire sizes, tire pressures, a vehicle connectivity indicator (e.g., an ability of the vehicle to communicatively couple and/or coordinate operations to/with other vehicles), etc.”; ¶41: “The fleet data and/or the third part computing systems data is retrievable, viewable, and/or editable by the remote computing system 110 (e.g., by a user input). The information also includes an identifier for a vehicle (e.g., the vehicle 202). The vehicle identifier is a unique code or string of alpha, numeric, and/or alpha-numeric values that is associated with a specific vehicle, such as a vehicle identification number (VIN), a serial number, an engine serial number, a fleet identifier, a controller IP address, and so on”; Fig 1, ¶52: “the fleet 200 includes a first vehicle 202, a second vehicle 204, and a third vehicle 206. In some embodiments, the fleet 200 includes more or fewer (e.g., at least one) vehicles. While shown as vehicles, in other embodiments, the fleet includes other equipment (e.g., gensets, etc.) in addition to or in place of the vehicle fleet. In yet other embodiments, the fleet includes off-road equipment (e.g., power generators, mining equipment, construction equipment, marine equipment, excavation equipment, etc.). The fleet 200 is associated with at least one of the service provider, a direct customer of the service provider, a third party customer, a location (e.g., a city, a state, a region, a country, etc.), a vehicle type (e.g., engine type, chassis type, workload type, etc.), and/or any other categorizing parameter associated with the vehicle”; ¶54: “The vehicle 202 includes a sensor array that includes a plurality of sensors. The sensors are coupled to the controller 300, such that the controller 300 can monitor, receive, and/or acquire data indicative of operation of the vehicle 202 (which may be referred to as operational data associated with the vehicle herein)”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”))
an emissions database comprising a plurality of emissions records each including greenhouse gas emissions data indexed by the article attributes; (¶115: “The values may also include a NOx value (or other emissions value, such as PM, GHG, etc.) regarding one or more vehicles (e.g., a cumulative NOx output over a predefined amount of time (e.g., operating time, hours, etc.) and/or distance (e.g., a predefined amount of miles), an instantaneous NOx output, a NOx reading at various locations such as an engine out NOx amount versus an aftertreatment system NOx output amount, a NOx output rate over time and/or distance, etc.), a NOx output of a system (e.g., the vehicle 202, the fleet 200), etc. Similarly, the emissions value may include an instantaneous particulate matter output, a cumulative particulate matter output, a cumulative GHG output, and/or any other operational data associated with a cylinder (e.g., cylinder 212), engine (e.g., engine 210), vehicle (e.g., vehicle 202), and/or fleet 200. The cumulative values are defined for a period (e.g., an operating time, a time between a predetermined start and endpoint, a distance, etc.), which may be associated with a route and/or location of the system (e.g., state, region, etc.). In any of the above-described embodiments, the predicted emissions values (e.g., NOx values, GHG values, cumulative values, etc.) may be determined based on at least one of a model (e.g., a statistical model, a lookup table, a machine learning model, etc.), as described herein with respect to the modeling circuit 140”; ¶139: “remote computing report data packet may provide an indication of vehicle operational data including one or more vehicle operational parameters, an indication of whether the operational data value(s) is/are non-complaint and/or exceeds/is below one or more predefined thresholds, an indication of a corrective action taken to return to compliance and/or not exceed/be below one or more predefined threshold values, and so on”)
and an emissions modeller configured to receive the attribute sets and to translate each said received attribute set into a greenhouse gas emissions model, wherein the emissions modeller is configured to translate each said received attribute set by locating a respective matching emissions record in the emissions database, (¶119: “a well-to-wheel emissions value for a plug-in hybrid vehicle may be determined by adding a well-to-battery emissions value to a “battery-to-wheel” emissions value. As used herein the “battery-to-wheel” emissions value refers to an amount of emissions produced by providing battery charge to an electric motor that drives a wheel. The battery-to-wheel emissions value may be predicted and/or estimated based on a model (e.g., a statistical model, a regression model, a machine learning model, etc.) and/or one or more lookup tables. In either case, the model and/or the one or more lookup tables may correlate an amount of work performed by the wheels of the plug-in hybrid vehicle and/or an amount of energy provided by the battery to an emissions value. More specifically, the model and/or the one or more lookup tables may correlate a difference between the amount of work performed by the wheels of the plug-in hybrid vehicle and the amount of energy provided by the battery to an emissions value”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”))
wherein amongst the plurality of emissions records the article attributes of each said received attribute set most closely match the article attributes of the respective matching emissions record (¶45-¶49; ¶45: “the modeling circuit 140 includes one or more specialized circuits having any combination of hardware and software. For example, and as shown in FIG. 1, the modeling circuit 140 includes a NOx modeling circuit 142, a particulate modeling circuit 144, and a GHG modeling circuit 146. Accordingly, the modeling circuit 140, including one or more of the components of the modeling circuit 140 (e.g., the NOx modeling circuit 142, the particulate modeling circuit 144, and the GHG modeling circuit 146), is structured to generate a computer-generated prediction of NOx, particulate matter, and/or GHG (or another exhaust gas constituent) exhaust emissions of a fleet (e.g., fleet 200), a vehicle (e.g., vehicle 202, 204, 206), and/or engine (e.g., an engine of the vehicle 202)… The GHG modeling circuit 146 is structured to generate a computer-generated prediction of GHG concentration (e.g., a percentage, parts per unit, etc.) in exhaust emissions. In some embodiments, the GHG modeling circuit 146 is structured to generate a computer-generated prediction of GHG concentration for one or more types of GHG (e.g., carbon dioxide, methane, etc.) as a total amount, individually, or both”; ¶47: “the modeling circuit 140 may utilize a regression analysis, machine learning techniques, and/or other statistical techniques to correlate historical data with new data. In some embodiments, the modeling circuit 140 may utilize a comparison between new data and historical data for similar vehicles, similar duty cycles, similar operating conditions, and so on to predict future output for a vehicle under similar conditions (e.g., same route, time of day, altitude, etc.)”; ¶48: “the remote computing system 110 includes a vehicle tracking circuit 148 and associated software for tracking the vehicles 202, 204, 206 of the fleet 200. For example, the vehicle tracking circuit 148 is structured to receive (e.g., via the communications interface 150) information about the vehicles 202, 204, 206 of the fleet 200 such as a vehicle owner, a vehicle type, a vehicle history including past and present owners, locations, maintenance reports, fuel efficiency, exhaust emissions (e.g., NOx, particulate matter, and/or GHG concentrations), and/or any other information associated with the vehicle”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”)
Applicant’s disclosure teaches ¶12: “Each attribute set includes article attributes that are associated with the respective article of manufacture”; ¶24: “Each indefinite article identifier non-uniquely identifies an article of manufacture. Therefore, each indefinite article identifier may identify a group of articles that includes the respective article of manufacture. Each attribute set includes one or more article attributes that are associated with the respective article of manufacture. Therefore, each attribute set includes the article attribute(s) common to the respective article(s)”; ¶25: “Each asset record of the asset database 300 is associated with an article of manufacture and includes the indefinite article identifier of the respective article of manufacture”; and ¶27: “each indefinite article identifier non-uniquely identifies a respective motor vehicle”; ¶47: “the identifier decoder 218 and the GHG emissions modeller 220 may be implemented on a common computing platform (GHG emissions modelling platform 200) via a single computer processing subsystem 204”)
Giving the broadest reasonable interpretation of applicant’s claim limitations in light of the specification, Examiner interprets at least the metadata such as a vehicle or engine identifier, as taught by Frazier as teaching applicant’s indefinite article identifier. The operation and fleet data as taught by Frazier further teaches applicant’s attribute set(s). Examiner also interprets at least the GHG modeling circuit and tracking circuit utilizing statistical techniques (regression analysis, machine learning) to track and correlate historical (emissions) data with new data as taught by Frazier as teaching applicant’s emissions modeller.
Frazier discloses a method/system for managing fleet vehicles and/or individual vehicles or components, cumulative information/data recording and/or monitoring performing operations and determining engine performance based on one or more factors such as fuel economy and reporting results. Frazier further discloses determining, by a remote computing system, whether the first predicted set of emissions values regarding operation of a vehicle is within a predetermined threshold of the first actual set of emissions values utilizing a statistical model and/or machine learning model (artificial intelligence).
A person of ordinary skill in the art before the effective filing date of applicant’s invention would have been motivated to modify the prior art reference to achieve the claimed invention (emissions modeller, attribute sets, indefinite article identifier) since all of the claimed elements were known in Frazier, and there would have been a reasonable expectation of success in doing so. “DyStar Textifarben GMbH & Co., Deutschland KG v. C.H. Patrick Co., 464 F.3d 1356, 1360, 80 USPQ2d 1641, 1645 (Fed. Cir. 2006).
Independent claims 8 and 15 recite substantially similar limitations as independent claim 1, hence they are rejected based on the same rationale noted above.
With respect to claims 2, 9 and 16,
Frazier discloses all of the above limitations, Frazier further discloses,
wherein each said indefinite article identifier identifies a group of articles, wherein the group of articles includes the respective article of manufacture and each said attribute set includes the article attributes common to the respective group of articles (¶27: “The remote computing system may also receive a plurality of operational data packets. Each of the operational data packets may be associated with a respective vehicle of a plurality of vehicles. The remote computing system may also transmit the plurality of emissions data packets to the third-party computing system. The plurality of vehicles may be grouped by at least one of a fleet, a territory, or a vehicle type. The first emissions data packet further comprises a fuel type and a fuel source”; ¶40: “the data includes information associated with each of the components of the system 100. For example, the data includes information about one or more vehicles 202, 204, 206 of the fleet 200. The information about the fleet 200 includes information received from one or more vehicles 202, 204, 206, predicted or estimated regarding one or more of the vehicles, and/or information about the one or more vehicles 202, 204, 206 (e.g., vehicle identification number, etc.). For example, the information includes a vehicle or equipment powertrain type (e.g., an internal combustion engine powered vehicle, a hybrid engine, a mild-hybrid powertrain, a parallel hybrid powertrain, a series hybrid powertrain, a series-parallel powertrain, a battery electric vehicle a range extender electric vehicle, a fuel-cell vehicle, etc.), a chassis type, a drag coefficient, tire sizes, tire pressures, a vehicle connectivity indicator (e.g., an ability of the vehicle to communicatively couple and/or coordinate operations to/with other vehicles), etc.”; Fig 6, ¶123: “FIG. 6 is a flow diagram of a method 600 of reporting emissions for the vehicle 202 or a group of vehicles (e.g., a fleet), according to an example embodiment. In some embodiments, one or more of the computing systems of the system 100 is configured to perform method 600”; ¶124: “As an overview of method 600, at process 602, a predicted emissions output for one or more vehicles 202, 204, 206 is generated. At process 604, an operational data packet is received from one or more vehicles 202, 204, 206. At process 606, the predicted emissions and/or the actual emissions are compared to a threshold. . At process 608, instructions to change one or more operational parameters are provided to the vehicle controller 300”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”))
With respect to claims 3, 10 and 17,
Frazier discloses all of the above limitations, Frazier further discloses,
wherein each said article of manufacture is uniquely identified by a respective unique sequence of characters, and each said indefinite article identifier includes a sub-set of the respective unique sequence of characters, the sub-set including fewer of the characters than the respective unique sequence of characters (¶41: The fleet data and/or the third part computing systems data is retrievable, viewable, and/or editable by the remote computing system 110 (e.g., by a user input). The information also includes an identifier for a vehicle (e.g., the vehicle 202). The vehicle identifier is a unique code or string of alpha, numeric, and/or alpha-numeric values that is associated with a specific vehicle, such as a vehicle identification number (VIN), a serial number, an engine serial number, a fleet identifier, a controller IP address, and so on. Accordingly, any of the information described above may include metadata that includes the identifier such that the information can be associated with a particular vehicle”; Fig 7, ¶133: “the vehicle data request refers to information about one or more vehicles 202, 204, 206 and/or a fleet 200 (e.g., vehicle operational data such as sensed and detected values (e.g., sensor data from an actual sensor), predicted or determined values (e.g., predicted or determined values from a virtual sensor, computing system, and/or statistical model), metadata including timestamps, geo-location stamps, and/or identifiers (e.g., vehicle identifiers, engine identifiers, etc.), and/or any other information described herein). In some embodiments, the request includes a vehicle identifier (e.g., a serial number, a VIN, a controller IP address), such that the request can be sent to and/or received by a specific vehicle or group of vehicles”; ¶134: “the vehicle data request requests data for one or more vehicles (e.g., one or more of the vehicles on the fleet 200), a component of the vehicles (e.g., the engine 210, the cylinder 212), or any combination thereof”; ¶139: “remote computing report data packet may provide an indication of vehicle operational data including one or more vehicle operational parameters, an indication of whether the operational data value(s) is/are non-complaint and/or exceeds/is below one or more predefined thresholds, an indication of a corrective action taken to return to compliance and/or not exceed/be below one or more predefined threshold values, and so on”; ¶140: “The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on”)
With respect to claims 4, 11 and 18,
Frazier discloses all of the above limitations, Frazier further discloses,
further including an asset database and an attribute database, wherein the asset database includes a plurality of asset records each associated with one of the articles of manufacture and including the indefinite article identifier of the one article of manufacture (¶40: “The vault 130 retrievably stores data associated with the remote computing system 110 and/or any other component of the system 100. That is, the data includes information associated with each of the components of the system 100. For example, the data includes information about one or more vehicles 202, 204, 206 of the fleet 200. The information about the fleet 200 includes information received from one or more vehicles 202, 204, 206, predicted or estimated regarding one or more of the vehicles, and/or information about the one or more vehicles 202, 204, 206 (e.g., vehicle identification number, etc…. the information includes a vehicle or equipment powertrain type (e.g., an internal combustion engine powered vehicle, a hybrid engine, a mild-hybrid powertrain, a parallel hybrid powertrain, a series hybrid powertrain, a series-parallel powertrain, a battery electric vehicle a range extender electric vehicle, a fuel-cell vehicle, etc.), a chassis type, a drag coefficient, tire sizes, tire pressures, a vehicle connectivity indicator (e.g., an ability of the vehicle to communicatively couple and/or coordinate operations to/with other vehicles), etc.).
wherein the attribute database includes a plurality of attribute records each associated with one of the articles of manufacture and including the attribute set of the one article of manufacture (¶40: “ a renewable source or a non-renewable source and/or an actual or an estimated GHG emissions associated with fuel formulation (commonly known as “well to tank” or “WTT”)… the fuel characteristics include an emissions value of using the fuel in the vehicle for powering the vehicle in combination with the well to tank emissions values (the collective value may be known as “well to wheel” or “WTW”). The data also includes information associated with the third party computing systems 190 such as a history of emissions data requests, emissions tests for fleets, vehicles, engines, and/or engine cylinders, vehicle purchase history, vehicle sales history, and/or any other information associated with the third party computing systems 190”; ¶41: “The fleet data and/or the third part computing systems data is retrievable, viewable, and/or editable by the remote computing system 110 (e.g., by a user input). The information also includes an identifier for a vehicle (e.g., the vehicle 202). The vehicle identifier is a unique code or string of alpha, numeric, and/or alpha-numeric values that is associated with a specific vehicle, such as a vehicle identification number (VIN), a serial number, an engine serial number, a fleet identifier, a controller IP address, and so on”; ¶42: “The vault 130 may be configured to store one or more applications and/or executables to facilitate tracking data (e.g., vehicle data, operation parameters, operation data, fleet data, and/or emissions data), managing incoming emissions data requests or emissions tests, managing on-vehicle control systems, or any other operation described herein”; ¶133, ¶134).
wherein the identifier decoder is configured to retrieve said indefinite article identifiers from the asset database each via one of a plurality of threads, and to translate the received indefinite article identifiers by at least saving the attribute sets in the attribute database each via the respective one thread (¶40: “The data also includes information associated with the third party computing systems 190 such as a history of emissions data requests, emissions tests for fleets, vehicles, engines, and/or engine cylinders, vehicle purchase history, vehicle sales history, and/or any other information associated with the third party computing systems 190”; ¶48: “remote computing system 110 includes a vehicle tracking circuit 148 and associated software for tracking the vehicles 202, 204, 206 of the fleet 200. For example, the vehicle tracking circuit 148 is structured to receive (e.g., via the communications interface 150) information about the vehicles 202, 204, 206 of the fleet 200 such as a vehicle owner, a vehicle type, a vehicle history including past and present owners, locations, maintenance reports, fuel efficiency, exhaust emissions (e.g., NOx, particulate matter, and/or GHG concentrations), and/or any other information associated with the vehicle”; Fig 1, Fig 3- Fig 7, ¶136: “eelative to the operational data packet, the remote computing reporting data packet may include additional information, such as information regarding compliance with one or more standards or thresholds (e.g., emissions compliance, engine noise compliance, etc.), historical or trend information regarding the vehicle, and so on. The remote computing reporting data packet may be specific to an individual vehicle or include information regarding a fleet of vehicles or equipment (e.g., powertrain units, gensets, etc.) … The remote computing reporting data packet may also include historical and/or trend data. For example, the remote computing reporting data packet may include an indication that a particular component and/or system is degrading over time. The remote computing reporting data packet also includes an indication of lube-oil status (e.g., a time since last changed), operation time or an age of a catalyst, filter, or other exhaust treatment element, a time since last service of the exhaust treatment element, and/or other operational data”; ¶139: “the remote computing report data packet may include a cumulative emissions output compared to a limit, such as a regulatory value (e.g., minimum amount, limit, etc.). For example, the cumulative emissions may include NOx, PM, etc. versus one or more regulation limits. In these embodiments, multiple operational data packets may be utilized from multiple vehicles or equipment (e.g., a fleet subgroup, a territory subgroup, an engine type subgroup, etc.). The multiple operational data packets may be grouped together based on a variety of conditions, such as engine identifiers, powertrain identifiers, a fuel type and/or a fuel source (fuel properties, fuel source location, renewable versus non-renewable, WTT analysis, etc.), and so on. ¶141-¶155; ¶141: “the remote computing report data packet may also include a fuel economy data packet for monitoring, reporting, and/or optimizing a fuel economy of a vehicle, a set of vehicles of the fleet, the fleet itself, or a combination thereof. The fuel economy data packet may include a historical fuel economy value for a defined period of time or distance (e.g., an operating time, hours, or miles”), a given route, and/or within a predetermined geographic area (e.g., a state, a territory, etc.)”; ¶142: “s, the vehicle reporting data packet may be the same or similar to the remote computing reporting data packet, described herein above, except that the vehicle reporting data packet is generated and/or transmitted by the controller 300 and includes data (e.g., operational parameters, operational data, and/or other data described herein) associated with a particular vehicle associated with the controller 300, such as the vehicle 202”).
With respect to claims 5, 12 and 19,
Frazier discloses all of the above limitations, Frazier further discloses,
wherein: the articles of manufacture include motor vehicles; and the emissions modeller configured to translate the attribute sets by at least generating emissions intensity values and incorporating the emissions intensity values into the greenhouse gas emissions models, wherein each said emissions intensity value identifies a volume of greenhouse gas emitted per unit of distance travelled for the respective group of the motor vehicles (¶115: “The values may also include a NOx value (or other emissions value, such as PM, GHG, etc.) regarding one or more vehicles (e.g., a cumulative NOx output over a predefined amount of time (e.g., operating time, hours, etc.) and/or distance (e.g., a predefined amount of miles), an instantaneous NOx output, a NOx reading at various locations such as an engine out NOx amount versus an aftertreatment system NOx output amount, a NOx output rate over time and/or distance, etc.), a NOx output of a system (e.g., the vehicle 202, the fleet 200), etc. Similarly, the emissions value may include an instantaneous particulate matter output, a cumulative particulate matter output, a cumulative GHG output, and/or any other operational data associated with a cylinder (e.g., cylinder 212), engine (e.g., engine 210), vehicle (e.g., vehicle 202), and/or fleet 200. The cumulative values are defined for a period (e.g., an operating time, a time between a predetermined start and endpoint, a distance, etc.), which may be associated with a route and/or location of the system (e.g., state, region, etc.). In any of the above described embodiments, the predicted emissions values (e.g., NOx values, GHG values, cumulative values, etc.) may be determined based on at least one of a model (e.g., a statistical model, a lookup table, a machine learning model, etc.), as described herein with respect to the modeling circuit 140”; ¶116: “received well-to-tank emissions values may include an emissions amount of an exhaust gas species (e.g., mass, volume, concentration, etc. of NOx, GHG, particulate matter, etc.) per unit of fuel (e.g., gallon, liter, etc.). More specifically, the received well-to- tank emissions values may correspond to the fuel received by a fuel system 260 of the vehicle 202”; ¶120: “predicted values also include a predicted operational cost based on a time period, a route, a location, a destination, a vehicle operator, vehicle history, and/or any other metric. The predicted values may also include a predicted and/or simulated fleet outcome… The simulated fleet outcome may include a predicted output for a defined period (e.g., an operating time, hours, or miles), a given route, a defined territory (e.g., geographical area, state, etc.), etc:”; ¶121: “the predicted emissions are compared to the actual emissions of the one or more vehicles 202, 204, 206 based on information from the operational data packet(s). The remote computing system 110 is structured to compare the predicted values generated at process 502 with the actual, detected values received in the operational data packet at process 504”; ¶122: “Re-calibrating the predicted emissions output value(s) may include re-calculating one or more statistical models using real sensor data of a particular vehicle and/or similar vehicles. For example, the remote computing system 110 may recalculate a regression model to generate one or more new regression questions, retrain a machine learning model to output more accurate values, and/or provide new data to existing statistical models such that the statistical models output an updated, and, advantageously, more accurate values”; ¶123: “FIG. 6 is a flow diagram of a method 600 of reporting emissions for the vehicle 202 or a group of vehicles (e.g., a fleet)”; ¶126: “predicted emissions and/or the actual emissions are compared to a threshold. The threshold may include at least one of a GHG threshold, a PM threshold, or a NOx value threshold. In some embodiments, the remote computing system 110 is also structured to compare a predicted or actual value for a fuel efficiency, an operating cost, and/or any other operational data of the vehicle against a threshold associated for that value”) Examiner interprets at least the emissions output values of Frazier as teaching applicant’s emissions intensity values.
With respect to claims 6, 13 and 20,
Frazier discloses all of the above limitations, Frazier further discloses,
wherein each said asset record of the asset database further includes a monetary amount associated with the one article of manufacture; and wherein the emissions modeller is configured to retrieve the monetary amounts from the asset database (¶50: “The reports may be used to monitor, manage, report, and optimize operations (cost, emissions, routes, etc.”; ¶70: . The simulated fleet outcome(s) may include a predicted output for a defined period (e.g., an operating time, hours, or miles), a given route, a defined territory (e.g., geographical area, state, etc.), etc. … The simulated fleet outcome may also include predicted operational costs based on a period, a route, and/or a territory “).
and the emissions modeller is configured to translate the attribute sets by at least generating asset intensity values and incorporating the asset intensity values into the greenhouse gas emissions models (¶115:“The values may also include a NOx value (or other emissions value, such as PM, GHG, etc.) regarding one or more vehicles (e.g., a cumulative NOx output over a predefined amount of time (e.g., operating time, hours, etc.) and/or distance (e.g., a predefined amount of miles), an instantaneous NOx output, a NOx reading at various locations such as an engine out NOx amount versus an aftertreatment system NOx output amount, a NOx output rate over time and/or distance, etc.), a NOx output of a system (e.g., the vehicle 202, the fleet 200), etc. Similarly, the emissions value may include an instantaneous particulate matter output, a cumulative particulate matter output, a cumulative GHG output, and/or any other operational data associated with a cylinder (e.g., cylinder 212), engine (e.g., engine 210), vehicle (e.g., vehicle 202), and/or fleet 200. The cumulative values are defined for a period (e.g., an operating time, a time between a predetermined start and endpoint, a distance, etc.), which may be associated with a route and/or location of the system (e.g., state, region, etc.). In any of the above described embodiments, the predicted emissions values (e.g., NOx values, GHG values, cumulative values, etc.) may be determined based on at least one of a model (e.g., a statistical model, a lookup table, a machine learning model, etc.), as described herein with respect to the modeling circuit 140”; ¶116: “the predicted values may be based on and/or consider an electric power source (e.g., grid electricity versus electricity generated by an ICE), electricity from renewable and nonrenewable source, fuel or electricity created from or using fossil fuels such as diesel, natural gas, hydrogen, etc. For example, the predicted emissions values (e.g., NOx, GHG, etc.) may include at least one well-to-tank emissions value of a fuel input… received well-to-tank emissions values may include an emissions amount of an exhaust gas species (e.g., mass, volume, concentration, etc. of NOx, GHG, particulate matter, etc.) per unit of fuel (e.g., gallon, liter, etc.). More specifically, the received well-to- tank emissions values may correspond to the fuel received by a fuel system 260 of the vehicle 202”; ¶119; ¶122)
wherein each said asset intensity value identifies a volume of greenhouse gas emitted per unit of the monetary amount.(¶70: “The simulated fleet outcome(s) may include a predicted output for a defined period (e.g., an operating time, hours, or miles), a given route, a defined territory (e.g., geographical area, state, etc.), etc. In some embodiments, the simulated fleet outcome may also include a predicted output in consideration of differing WTT inputs including grid electricity versus electricity generated by an ICE, electricity from renewable versus electricity from nonrenewable sources, and/or electricity from fossil fuel (e.g., diesel, natural gas, etc.). The simulated fleet outcome may also include predicted operational costs based on a period, a route, and/or a territory”; ¶120: “the predicted values also include a predicted operational cost based on a time period, a route, a location, a destination, a vehicle operator, vehicle history, and/or any other metric”; ¶121: “the simulated fleet outcome may also include a predicted output in consideration of differing WTT inputs including grid electricity versus electricity generated by an ICE, electricity from renewable versus electricity from non-renewable sources, and/or electricity from fossil fuel (e.g., diesel, natural gas, hydrogen, etc.). The simulated fleet outcome may also include predicted operational costs based on a time period, distance, a route, and/or a territory”; ¶126: “the predicted emissions and/or the actual emissions are compared to a threshold. The threshold may include at least one of a GHG threshold, a PM threshold, or a NOx value threshold. In some embodiments, the remote computing system 110 is also structured to compare a predicted or actual value for a fuel efficiency, an operating cost, and/or any other operational data of the vehicle against a threshold associated for that value”)
Claims 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Frazier et al., (WO 2023/141102 A1), in view of Denninghoff, US Patent Application Publication No US 2023/0401274 A1.
With respect to claims 7 and 14,
Frazier discloses all of the above limitations, Frazier further discloses,
wherein the emissions modeller is configured to use a fuzzy name matching algorithm to locate each respective matching emissions record in the emissions database (Abstract: “A fuzzier match scheme implies more results and fewer false negatives and a less fuzzy match scheme implies fewer results and fewer false positives. This fuzziness relationship allows, without changing a search string, for users to quickly change from a match scheme to a fuzzier or less fuzzy match scheme depending on user evaluation of results as containing too many false negatives or too many false positives”; ¶126: “FIG. 1 illustrates high quality search, very fuzzy search, cross-document search within a tab, application of a targeted busy indicator, and retrieval of additional content to support search. This process begins with one of four user-initiated event types: selection 105 of a snippet (which becomes a search string) in a web search-results window; activation 110 of a precision hyperlink (that identifies a specific target textual content in a document); initialization 115 of either a forward (which is default) or backward search for a target search string in context of a HTML document display, and 117 selection of a match scheme and/or a new search string, which initializes search using the currently selected match scheme and search string as illustrated in FIG. 22”; ¶137: “there are multiple fuzzy match schemes and in some such embodiments a particular one of the multiple fuzzy match schemes is used by default for the processing outlined in FIG. 19; however, when a fuzzier (e.g. a match scheme expansion) of the default fuzzy match scheme is supported by the embodiment, and there are no <low_qual_matches> having scores that are at or above 45, then the next fuzzier match scheme (e.g. the closest match scheme expansion) relative to the default match scheme is chosen for the match scheme that is used in the processing described in FIG. 19”)
Applicant’s disclosure generally describes at ¶43: “the GHG emissions modeller 220 may be configured to then use a fuzzy name matching algorithm to locate the greenhouse gas emissions data most closely associated with the attribute set (generated by the identifier decoder 218) in the GHG emissions database 400”; ¶44: “the GHG emissions modeller 220 may be configured to locate each matching emissions record by applying a fuzzy name matching algorithm to the GHG emissions database 400 (i.e. without first performing a database query of the GHG emissions database 400)”.
Frazier teaches a modeling circuit utilizing a regression analysis, machine learning techniques, and/or other statistical techniques for correlating emissions data (¶47, ¶114, ¶119). It does not distinctly describe a fuzzy name matching algorithm. Denninghoff discloses a fuzzy match scheme for finding and disiplaying search information. Frazier and Denninghoff are directed to the same inventive concept since they are related to correlating and producing search results in a computing environment. Therefore, it would have been obvious to one of ordinary skill of the art before the effective filing date of applicant’s invention to combine the system/method for managing fleet vehicles and/or individual vehicles or components of Frazier and the fuzzy name matching scheme as taught by Denninghoff since it allows for producing both binary (match or no match) and non-binary (multiple matching values) search results having relative fuzziness relationships, implying more results and fewer false negatives (Abstract, Fig 1, Fig 19, Fig 22, ¶126, ¶137)
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cardoso et al., US Patent Application Publication No US 2013/0185001 A1, “Vehicle Emissions Testing and Toll Collection System”, relating to a system and method for testing a motorized vehicle's exhaust emissions in a non-controlled emissions testing environment.
Saurav et al., US Patent Application Publication No US 2023/0153733 A1, “Generating Greenhouse Gas Emissions Estimations associated with Logistics Contexts using Machine Learning Techniques”, directed to techniques for generating GHG emissions estimations associated with logistics contexts using machine learning techniques.
Nishiwada et al., (JP 2024146539-A), “Greenhouse Gas Emissions Management Method”, relating to method/system of efficiently performing management of greenhouse gas emissions, including computation of the amount of emissions, by a business operator.
Conclusion
Any inquiry of a general nature or relating to the status of this application or concerning this communication or earlier communications from the Examiner should be directed to Kimberly L. Evans whose telephone number is 571.270.3929. The Examiner can normally be reached on Monday-Friday, 9:30am-5:00pm. If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached at 571.272.6782.
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/KIMBERLY L EVANS/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629