DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 3/21/2025 and 12/02/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description: “302” in Figure 3. Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
In addition to Replacement Sheets containing the corrected drawing figure(s), applicant is required to submit a marked-up copy of each Replacement Sheet including annotations indicating the changes made to the previous version. The marked-up copy must be clearly labeled as “Annotated Sheets” and must be presented in the amendment or remarks section that explains the change(s) to the drawings. See 37 CFR 1.121(d)(1). Failure to timely submit the proposed drawing and marked-up copy will result in the abandonment of the application.
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.
Claim 1 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The determination of whether a claim recites patent ineligible subject matter is a 2 step inquiry.
STEP 1: the claim does not fall within one of the four statutory categories of invention (process, machine, manufacture or composition of matter), see MPEP 2106.03, or
STEP 2: the claim recites a judicial exception, e.g. an abstract idea, without reciting additional elements that amount to significantly more than the judicial exception, as determined using the following analysis: see MPEP 2106.04
STEP 2A (PRONG 1): Does the claim recite an abstract idea, law of nature, or natural phenomenon? see MPEP 2106.04(II)(A)(1)
STEP 2A (PRONG 2): Does the claim recite additional elements that integrate the judicial exception into a practical application? see MPEP 2106.04(II)(A)(2)
STEP 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception? see MPEP 2106.05
101 Analysis – Step 1
Claim 1 is directed to a method of identifying characteristics of similar vehicles (i.e., a process). Therefore, claim 1 is within at least one of the four statutory categories.
101 Analysis – Step 2A, Prong I
Regarding Prong I of the Step 2A analysis, the claims are to be analyzed to determine whether they recite subject matter that falls within one of the follow groups of abstract ideas: a) mathematical concepts, b) certain methods of organizing human activity, and/or c) mental processes. see MPEP 2106(A)(II)(1) and MPEP 2106.04(a)-(c)
Independent claim 1 includes limitations that recite an abstract idea (emphasized below [with the category of abstract idea in brackets]) and will be used as a representative claim for the remainder of the 101 rejection. Claim 1 recites:
A method for fleet management, the method comprising:
receiving fleet management data for a target fleet of vehicles;
generating a target vehicle vector that represents a target vehicle of the target fleet based on the fleet management data [mental process/step];
identifying one or more similar vehicles, from a plurality of reference vehicles, that are similar to the target vehicle using a distance metric between the target vehicle vector and a plurality of reference vehicle vectors, the plurality of reference vehicle vectors representing the plurality of reference vehicles [mental process/step];
identifying vehicle characteristics of the target vehicle and the one or more similar vehicles, the vehicle characteristics affecting vehicle efficiency of the target vehicle [mental process/step]; and
displaying the vehicle characteristics affecting vehicle efficiency of the target vehicle and corresponding vehicle characteristics of the one or more similar vehicles.
The examiner submits that the foregoing bolded limitation(s) constitute a “mental process” because under its broadest reasonable interpretation, the claim covers performance of the limitation in the human mind. For example, “generating…” and “identifying…” in the context of this claim encompasses a person (driver) looking at data collected and forming a simple judgement. Accordingly, the claim recites at least one abstract idea.
101 Analysis – Step 2A, Prong II
Regarding Prong II of the Step 2A analysis, the claims are to be analyzed to determine whether the claim, as a whole, integrates the abstract into a practical application. see MPEP 2106.04(II)(A)(2) and MPEP 2106.04(d)(2). It must be determined whether any additional elements in the claim beyond the abstract idea integrate the exception into a practical application in a manner that imposes a meaningful limit on the judicial exception. The courts have indicated that additional elements merely using a computer to implement an abstract idea, adding insignificant extra solution activity, or generally linking use of a judicial exception to a particular technological environment or field of use do not integrate a judicial exception into a “practical application.”
In the present case, the additional limitations beyond the above-noted abstract idea are as follows (where the underlined portions are the “additional limitations” [with a description of the additional limitations in brackets], while the bolded portions continue to represent the “abstract idea”.):
A method for fleet management, the method comprising:
receiving fleet management data for a target fleet of vehicles [pre-solution activity (data gathering)];
generating a target vehicle vector that represents a target vehicle of the target fleet based on the fleet management data;
identifying one or more similar vehicles, from a plurality of reference vehicles, that are similar to the target vehicle using a distance metric between the target vehicle vector and a plurality of reference vehicle vectors, the plurality of reference vehicle vectors representing the plurality of reference vehicles;
identifying vehicle characteristics of the target vehicle and the one or more similar vehicles, the vehicle characteristics affecting vehicle efficiency of the target vehicle; and
displaying the vehicle characteristics affecting vehicle efficiency of the target vehicle and corresponding vehicle characteristics of the one or more similar vehicles [insignificant post-solution activity (displaying results of the mental process)].
For the following reason(s), the examiner submits that the above identified additional limitations do not integrate the above-noted abstract idea into a practical application.
Regarding the additional limitations of “receiving fleet management data…” and “displaying…” the examiner submits that these limitations are insignificant extra-solution activities. In particular, the receiving step is recited at a high level of generality (i.e. as a general means of gathering vehicle fleet data for use in the evaluating step), and amounts to mere data gathering, which is a form of insignificant extra-solution activity. The displaying results step is also recited at a high level of generality (i.e. as a general means of displaying the identified vehicle characteristics result from the identifying step), and amounts to mere post solution displaying, which is a form of insignificant extra-solution activity.
Thus, taken alone, the additional elements do not integrate the abstract idea into a practical application. Further, looking at the additional limitation(s) as an ordered combination or as a whole, the limitation(s) add nothing that is not already present when looking at the elements taken individually. For instance, there is no indication that the additional elements, when considered as a whole, reflect an improvement in the functioning of a computer or an improvement to another technology or technical field, apply or use the above-noted judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, implement/use the above-noted judicial exception with a particular machine or manufacture that is integral to the claim, effect a transformation or reduction of a particular article to a different state or thing, or apply or use 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 not more than a drafting effort designed to monopolize the exception. see MPEP § 2106.05. Accordingly, the additional limitation(s) do/does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
101 Analysis – Step 2B
Regarding Step 2B of the Revised Guidance, representative independent claim 1 does not include additional elements (considered both individually and as an ordered combination) that are sufficient to amount to significantly more than the judicial exception for the same reasons to those discussed above with respect to determining that the claim does not integrate the abstract idea into a practical application. As discussed above, regarding the additional limitations of “receiving fleet management data…” and “displaying…” the examiner submits that these limitations are insignificant extra-solution activities. In addition, these additional limitations (and the combination, thereof) amount to no more than what is well-understood, routine and conventional activity. Hence, the claim is not patent eligible.
Additional Claims
Independent claim 11 is not patent eligible under the same rationale as provided for in the rejection of claim 1.
Dependent claims 2-10 and 12-20 do not recite any further limitations that cause the claim(s) to be patent eligible. Rather, the limitations of dependent claims are directed toward additional aspects of the judicial exception and/or well-understood, routine and conventional additional elements that do not integrate the judicial exception into a practical application such as controlling the target vehicle to perform a remediation action. Therefore, dependent claims 2-10 and 12-20 are not patent eligible under the same rationale as provided for in the rejection of independent claim 1.
Therefore, claims 1-20 are ineligible under 35 USC §101.
Claim Rejections - 35 USC § 102
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)(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 and 11 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gupta et al. (U.S. Patent No. 11386161; hereinafter Gupta).
Regarding claim 1, Gupta teaches a method for fleet management, the method comprising: receiving fleet management data for a target fleet of vehicles (Gupta: Col. 1, lines 12-13; i.e., A dealer management system (DMS) helps a vehicle dealership manage an inventory of vehicles; Col. 6, lines 47-49; i.e., Vehicle recommendation system 140 may access a database (e.g., remote database 120) to retrieve vehicle attributes);
generating a target vehicle vector that represents a target vehicle of the target fleet based on the fleet management data (Gupta: Col. 4, lines 58-61; i.e., the vehicle recommendation systems use a machine learning model (e.g., a neural network) to determine numerical or alphanumerical representations, or “embeddings,” of vehicle attributes; Col. 1, lines 54-56; i.e., the vehicle recommendation system may use a target attribute against which similarity among vehicles should be analyzed);
identifying one or more similar vehicles, from a plurality of reference vehicles, that are similar to the target vehicle using a distance metric between the target vehicle vector and a plurality of reference vehicle vectors, the plurality of reference vehicle vectors representing the plurality of reference vehicles (Gupta: Col. 9, lines 18-25; i.e., the embeddings in database 210 may be accessed by embedding similarity module 235 to determine similarity metrics between embeddings and thus, a similarity between vehicle attributes. For example, a cosine similarity may be determined between a first concatenated embedding representing a first vehicle and its attributes and a second concatenated embedding representing a second vehicle and its attributes);
identifying vehicle characteristics of the target vehicle and the one or more similar vehicles, the vehicle characteristics affecting vehicle efficiency of the target vehicle (Gupta: Col. 7, lines 50-61; i.e., attribute database 200 stores vehicle attribute values. Vehicle attributes can include … vehicle performance information… Vehicle performance information may include fuel economy);
and displaying the vehicle characteristics affecting vehicle efficiency of the target vehicle and corresponding vehicle characteristics of the one or more similar vehicles (Gupta: Col. 14, lines 60-62; i.e., GUI module 240 uses the determined similarity metrics to provide identifiers of vehicles corresponding to embeddings from embeddings 450 for display at user device; Col. 17, lines 38-40; i.e., the results of the determination of similar vehicles to the user-specified vehicle or vehicle attributes are provided for display; fuel economy is an attribute that can be displayed along with other attributes of the one or more similar vehicles).
Regarding claim 11, Gupta teaches a system for fleet management, the system comprising: a processor; and a non-transitory computer-readable memory having computer-readable instructions that, when executed by a processor, cause the processor to: receive fleet management data for a target fleet of vehicles (Gupta: Col. 18, lines 8-11; i.e., an example machine able to read instructions from a machine-readable medium and execute them in a processor (or controller); Col. 1, lines 12-13; i.e., A dealer management system (DMS) helps a vehicle dealership manage an inventory of vehicles; Col. 6, lines 47-49; i.e., Vehicle recommendation system 140 may access a database (e.g., remote database 120) to retrieve vehicle attributes);
generate a target vehicle vector that represents a target vehicle of the target fleet based on the fleet management data (Gupta: Col. 4, lines 58-61; i.e., the vehicle recommendation systems use a machine learning model (e.g., a neural network) to determine numerical or alphanumerical representations, or “embeddings,” of vehicle attributes; Col. 1, lines 54-56; i.e., the vehicle recommendation system may use a target attribute against which similarity among vehicles should be analyzed);
identify one or more similar vehicles, from a plurality of reference vehicles, that are similar to the target vehicle using a distance metric between the target vehicle vector and a plurality of reference vehicle vectors, the plurality of reference vehicle vectors representing the plurality of reference vehicles (Gupta: Col. 9, lines 18-25; i.e., the embeddings in database 210 may be accessed by embedding similarity module 235 to determine similarity metrics between embeddings and thus, a similarity between vehicle attributes. For example, a cosine similarity may be determined between a first concatenated embedding representing a first vehicle and its attributes and a second concatenated embedding representing a second vehicle and its attributes);
identify vehicle characteristics of the target vehicle and the one or more similar vehicles, the vehicle characteristics affecting vehicle efficiency of the target vehicle (Gupta: Col. 7, lines 50-61; i.e., attribute database 200 stores vehicle attribute values. Vehicle attributes can include … vehicle performance information… Vehicle performance information may include fuel economy);
and display the vehicle characteristics affecting vehicle efficiency of the target vehicle and corresponding vehicle characteristics of the one or more similar vehicles (Gupta: Col. 14, lines 60-62; i.e., GUI module 240 uses the determined similarity metrics to provide identifiers of vehicles corresponding to embeddings from embeddings 450 for display at user device; Col. 17, lines 38-40; i.e., the results of the determination of similar vehicles to the user-specified vehicle or vehicle attributes are provided for display; fuel economy is an attribute that can be displayed along with other attributes of the one or more similar vehicles).
Claim Rejections - 35 USC § 103
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 2, 3, 12, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta and further in view of Maeng (U.S. Patent No. 8886392; hereinafter Maeng).
Regarding claim 2, Gupta teaches the method according to claim 1 but does not explicitly teach wherein receiving the fleet management data comprises receiving a maintenance history for the target fleet of vehicles.
However, in the same field of endeavor, Maeng teaches wherein receiving the fleet management data comprises receiving a maintenance history for the target fleet of vehicles (Maeng: Col. 2, lines 38-45; i.e., device 10 may be configured to generate, access, submit, track, process, compare, retrieve, display, and/or present information, such as: … a history of maintenance activities… The information may be associated with the particular vehicle and/or with one or more other vehicles).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Gupta to have further incorporated wherein receiving the fleet management data comprises receiving a maintenance history for the target fleet of vehicles, as taught by Maeng. Doing so would allow the system to update a maintenance standard for the target vehicle based on the maintenance history of the fleet (Maeng: Col. 10, lines 61-63; i.e., the predetermined maintenance standard may be revised and/or updated according to the normalized operational data associated with a fleet of substantially similar vehicles).
Regarding claim 3, Gupta teaches the method according to claim 1 but does not explicitly teach determining a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle.
However, in the same field of endeavor, Maeng teaches determining a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle (Maeng: Col. 4, lines 43-44; i.e., The operational data may comprise a fuel efficiency; Col. 5, lines 53-62; i.e., User interface 300 may be configured to generate and/or display one or more recommendations 340. Recommendations 340 may include a recommendation to perform a particular maintenance and/or repair activity, … a recommendation to alter vehicle operation, such as reducing vehicle speed).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Gupta to have further incorporated determining a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle, as taught by Maeng. Doing so would allow the system to improve reliability of the vehicles (Maeng: Col. 10, lines 4-7; i.e., System 600 may be configured to provide auto repair shop 610 with operation information for providing preventive services, and/or to provide auto manufacturer 670 with information for improving reliability of vehicles).
Regarding claim 12, Gupta teaches the system according to claim 11 but does not explicitly teach wherein the computer-readable instructions further cause the processor to: receive a maintenance history for the target fleet of vehicles.
However, in the same field of endeavor, Maeng teaches wherein the computer-readable instructions further cause the processor to: receive a maintenance history for the target fleet of vehicles (Maeng: Col. 2, lines 38-45; i.e., device 10 may be configured to generate, access, submit, track, process, compare, retrieve, display, and/or present information, such as: … a history of maintenance activities… The information may be associated with the particular vehicle and/or with one or more other vehicles).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gupta to have further incorporated wherein the computer-readable instructions further cause the processor to: receive a maintenance history for the target fleet of vehicles, as taught by Maeng. Doing so would allow the system to update a maintenance standard for the target vehicle based on the maintenance history of the fleet (Maeng: Col. 10, lines 61-63; i.e., the predetermined maintenance standard may be revised and/or updated according to the normalized operational data associated with a fleet of substantially similar vehicles).
Regarding claim 13, Gupta teaches the system according to claim 11 but does not explicitly teach wherein the computer-readable instructions further cause the processor to: determine a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle.
However, in the same field of endeavor, Maeng teaches wherein the computer-readable instructions further cause the processor to: determine a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle (Maeng: Col. 4, lines 43-44; i.e., The operational data may comprise a fuel efficiency; Col. 5, lines 53-62; i.e., User interface 300 may be configured to generate and/or display one or more recommendations 340. Recommendations 340 may include a recommendation to perform a particular maintenance and/or repair activity, … a recommendation to alter vehicle operation, such as reducing vehicle speed).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gupta to have further incorporated wherein the computer-readable instructions further cause the processor to: determine a remediation action for management of the target vehicle based on the vehicle characteristics affecting vehicle efficiency of the target vehicle, as taught by Maeng. Doing so would allow the system to improve reliability of the vehicles (Maeng: Col. 10, lines 4-7; i.e., System 600 may be configured to provide auto repair shop 610 with operation information for providing preventive services, and/or to provide auto manufacturer 670 with information for improving reliability of vehicles).
Claims 4-10 and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Gupta in view of Maeng and further in view of Song et al. (U.S. Publication No. 2019/0354939; hereinafter Song).
Regarding claim 4, Gupta in view of Maeng teaches the method according to claim 3, but does not teach generating a target fleet vector that represents the target fleet based on the fleet management data; generating a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles; and determining the remediation action for management of the target fleet based on the simulated fleet vector; wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle.
However, in the same field of endeavor, Song teaches generating a target fleet vector that represents the target fleet based on the fleet management data (Gupta: Col. 9, lines 42-43; i.e., attribute selector 215 may use a default attribute as a target attribute); generating a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module; Par. 46; i.e., the FPOT component replacement simulation tool ranks a given fleet of aircraft (or other vehicle types) by the highest predicted level of mission readiness; Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability); and determining the remediation action for management of the target fleet based on the simulated fleet vector (Song: Par. 7; i.e., the processor is also programmed to display, based on the ranking, the operational readiness of each vehicle on the user interface to facilitate actual replacement of at least one component on at least one of the vehicles); wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the method of Gupta and Maeng to have further incorporated generating a target fleet vector that represents the target fleet based on the fleet management data; generating a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles; and determining the remediation action for management of the target fleet based on the simulated fleet vector; wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle, as taught by Song. Doing so would allow for improved preventative maintenance scheduling (Song: Par. 46; i.e., The simulation feature described herein allows a user to explore preventative maintenance options and their corresponding effectiveness on operational readiness).
Regarding claim 5, Gupta in view of Maeng and Song teaches the method according to claim 4. Song further teaches wherein generating the simulated fleet vector comprises, for each vehicle in the target fleet, generating a simulated vehicle vector based on a corresponding target vehicle vector and the plurality of reference vehicle vectors using the distance metric (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module; Par. 46; i.e., the FPOT component replacement simulation tool ranks a given fleet of aircraft (or other vehicle types) by the highest predicted level of mission readiness; Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability; the highest simulated reliabilities are compared to the scheduled reliability).
Regarding claim 6, Gupta in view of Maeng and Song teaches the method according to claim 4. While Gupta does not explicitly teach wherein the distance metric is a Manhattan distance metric, Gupta does teach determining similarity through any suitable vector similarity calculation which includes Manhattan distance (Gupta: Col. 11, lines 26-30; i.e., The comparison may include determining similarity through cosine similarity, Euclidean distance, dot products, Manhattan length, Minkowski distance, Jaccard similarity, any suitable vector similarity calculation, or a combination thereof).
Regarding claim 7, Gupta in view of Maeng and Song teaches the method according to claim 4. Song further teaches wherein identifying the one or more similar vehicles comprises: sorting the plurality of reference vehicles and the target fleet of vehicles into a plurality of groups using a similarity algorithm (Song: Par. 56; i.e., the analysis steps automatically begin after FPOT queries maintenance data database 1102 and populates tables, for example within a fleet performance optimization tool (FPOT) database 1106, which in one embodiment is a maintenance database, for each analysis. In the analysis, item are grouped in groups such as fleet, series, and engine type).
Gupta further teaches calculating the distance metric between vehicles within a group of the plurality of groups (Gupta: Col. 9, lines 21-25; i.e., a cosine similarity may be determined between a first concatenated embedding representing a first vehicle and its attributes and a second concatenated embedding representing a second vehicle and its attributes; the distance metric is calculated between vehicles within the same group).
Regarding claim 8, Gupta in view of Maeng and Song teaches the method according to claim 4. Song further teaches wherein: the fleet management data represents a workload of the target fleet of vehicles (Song: Par. 47; i.e., FIG. 4 is a user interface display 400 of predicted aircraft reliability… Also shown is … the flight hours 412 for each of the aircraft; Figure 4 displays the service hour workload of each vehicle in the fleet);
and the simulated fleet of vehicles is generated to perform the workload of the target fleet of vehicles (Song: Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability 406. The reliability for aircraft “8448” has increased … due the simulated replacement of selected components; Figure 8 displays the service hour workload of each simulated vehicle in the fleet).
Regarding claim 9, Gupta in view of Maeng and Song teaches the method according to claim 8. Song further teaches wherein the simulated fleet of vehicles includes at least one target vehicle from the target fleet of vehicles having a modification indicated by the remediation action (Song: Par. 51; i.e., aircraft “8448” has moved from being the fifth most reliable (as shown in FIGS. 4 and 6) to being the third most reliable, based on the simulated replacement of the selected components).
Regarding claim 10, Gupta in view of Maeng and Song teaches the method according to claim 8. Song further teaches wherein: the simulated fleet of vehicles has a different number of vehicles than the target fleet of vehicles (Song: Par. 51; i.e., FIG. 8 is a user interface display 800 provided to the user of system 300 after the simulation defined by the user interface display 700 of FIG. 7 has been run; as displayed in Figure 8, one vehicle “8448” is simulated while the others in the fleet are not);
and a vehicle addition to or a vehicle removal from the target fleet of vehicles is indicated by the remediation action (Song: Par. 51; i.e., aircraft “8448” has moved from being the fifth most reliable (as shown in FIGS. 4 and 6) to being the third most reliable, based on the simulated replacement of the selected components; the simulated vehicle is no longer part of the target fleet due to the simulated remediation action).
Regarding claim 14, Gupta in view of Maeng teaches the system according to claim 13, but does not teach wherein the computer-readable instructions further cause the processor to: generate a target fleet vector that represents the target fleet based on the fleet management data; generate a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles; and determine the remediation action for management of the target fleet based on the simulated fleet vector; wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle.
However, in the same field of endeavor, Song teaches wherein the computer-readable instructions further cause the processor to: generate a target fleet vector that represents the target fleet based on the fleet management data (Gupta: Col. 9, lines 42-43; i.e., attribute selector 215 may use a default attribute as a target attribute); generate a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module; Par. 46; i.e., the FPOT component replacement simulation tool ranks a given fleet of aircraft (or other vehicle types) by the highest predicted level of mission readiness; Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability); and determine the remediation action for management of the target fleet based on the simulated fleet vector (Song: Par. 7; i.e., the processor is also programmed to display, based on the ranking, the operational readiness of each vehicle on the user interface to facilitate actual replacement of at least one component on at least one of the vehicles); wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module).
It would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Gupta and Maeng to have further incorporated wherein the computer-readable instructions further cause the processor to: generate a target fleet vector that represents the target fleet based on the fleet management data; generate a simulated fleet vector based on the target fleet vector and a plurality of reference fleet vectors using the distance metric, the plurality of reference fleet vectors representing a plurality of reference fleets of vehicles, wherein the simulated fleet vector represents a simulated fleet of vehicles; and determine the remediation action for management of the target fleet based on the simulated fleet vector; wherein generating the target fleet vector comprises generating, for each vehicle in the target fleet, a respective target vehicle vector that represents the vehicle, as taught by Song. Doing so would allow for improved preventative maintenance scheduling (Song: Par. 46; i.e., The simulation feature described herein allows a user to explore preventative maintenance options and their corresponding effectiveness on operational readiness).
Regarding claim 15, Gupta in view of Maeng and Song teaches the system according to claim 14. Song further teaches wherein the computer-readable instructions further cause the processor to: for each vehicle in the target fleet, generate a simulated vehicle vector based on a corresponding target vehicle vector and the plurality of reference vehicle vectors from the plurality of reference fleet vectors using the distance metric (Song: Par. 7; i.e., the processor is also programmed to generate a scheduled reliability for each vehicle in the fleet of vehicles using the statistical analysis module; Par. 46; i.e., the FPOT component replacement simulation tool ranks a given fleet of aircraft (or other vehicle types) by the highest predicted level of mission readiness; Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability; the highest simulated reliabilities are compared to the scheduled reliability).
Regarding claim 16, Gupta in view of Maeng and Song teaches the system according to claim 15. While Gupta does not explicitly teach wherein the distance metric is a Manhattan distance metric, Gupta does teach determining similarity through any suitable vector similarity calculation which includes Manhattan distance (Gupta: Col. 11, lines 26-30; i.e., The comparison may include determining similarity through cosine similarity, Euclidean distance, dot products, Manhattan length, Minkowski distance, Jaccard similarity, any suitable vector similarity calculation, or a combination thereof).
Regarding claim 17, Gupta in view of Maeng and Song teaches the system according to claim 15. Song further teaches wherein the computer-readable instructions further cause the processor to: sort the plurality of reference vehicles and the target fleet of vehicles into a plurality of groups using a similarity algorithm (Song: Par. 56; i.e., the analysis steps automatically begin after FPOT queries maintenance data database 1102 and populates tables, for example within a fleet performance optimization tool (FPOT) database 1106, which in one embodiment is a maintenance database, for each analysis. In the analysis, item are grouped in groups such as fleet, series, and engine type).
Gupta further teaches calculate the distance metric between vehicles within a group of the plurality of groups (Gupta: Col. 9, lines 21-25; i.e., a cosine similarity may be determined between a first concatenated embedding representing a first vehicle and its attributes and a second concatenated embedding representing a second vehicle and its attributes; the distance metric is calculated between vehicles within the same group).
Regarding claim 18, Gupta in view of Maeng and Song teaches the system according to claim 14. Song further teaches wherein: the fleet management data represents a workload of the target fleet of vehicles (Song: Par. 47; i.e., FIG. 4 is a user interface display 400 of predicted aircraft reliability… Also shown is … the flight hours 412 for each of the aircraft; Figure 4 displays the service hour workload of each vehicle in the fleet);
and the simulated fleet of vehicles is generated to perform the workload of the target fleet of vehicles (Song: Par. 51; i.e., user interface display 800 includes a simulated reliability 802 column for the five aircraft which can be compared to the scheduled reliability 406. The reliability for aircraft “8448” has increased … due the simulated replacement of selected components; Figure 8 displays the service hour workload of each simulated vehicle in the fleet).
Regarding claim 19, Gupta in view of Maeng and Song teaches the system according to claim 18. Song further teaches wherein the simulated fleet of vehicles includes at least one target vehicle from the target fleet of vehicles having a modification indicated by the remediation action (Song: Par. 51; i.e., aircraft “8448” has moved from being the fifth most reliable (as shown in FIGS. 4 and 6) to being the third most reliable, based on the simulated replacement of the selected components).
Regarding claim 20, Gupta in view of Maeng and Song teaches the system according to claim 18. Song further teaches wherein: the simulated fleet of vehicles has a different number of vehicles than the target fleet of vehicles (Song: Par. 51; i.e., FIG. 8 is a user interface display 800 provided to the user of system 300 after the simulation defined by the user interface display 700 of FIG. 7 has been run; as displayed in Figure 8, one vehicle “8448” is simulated while the others in the fleet are not);
and a vehicle addition to or a vehicle removal from the target fleet of vehicles is indicated by the remediation action (Song: Par. 51; i.e., aircraft “8448” has moved from being the fifth most reliable (as shown in FIGS. 4 and 6) to being the third most reliable, based on the simulated replacement of the selected components; the simulated vehicle is no longer part of the target fleet due to the simulated remediation action).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Additional prior art deemed pertinent in the art of fleet management and identifying characteristics of similar vehicles includes Davidson et al. (U.S. Publication No. 2010/0088163), Schmidt (U.S. Publication No. 2012/0253743), Ammoura et al. (U.S. Publication No. 2020/0200649), Barnwal et al. (U.S. Publication No. 2020/0364564), and Mitchell (U.S. Patent No. 9489845).
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRANDON Z WILLIS whose telephone number is (571)272-5427. The examiner can normally be reached Weekdays 8:00-5:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Erin D. Bishop can be reached at (571) 270-3713. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/BRANDON Z WILLIS/Examiner, Art Unit 3665