DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendment filed on 11/07/2025.
Claims 1, 3-5, and 7 have been amended and are hereby entered.
Claim 9 has been added.
Claims 1-9 are currently pending and have been examined.
This action is made FINAL.
Response to Applicant’s Arguments
Claim Rejections – 35 USC § 112
The present amendments of Claim 1 obviate the previous 112(b) rejection thereto; therefore, this rejection is withdrawn.
Claim Rejections – 35 USC § 101
Applicant’s arguments regarding the 101 analysis have been considered and are unpersuasive.
Applicant first argues that the newly drafted limitation “(b) collecting real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the buses for each group classified according to the operation (a)” does not recite a mental process, and therefore “claim 1 does not recite a mental process, because the claimed steps of claim 1 as amended cannot practically be performed within the human mind.” Examiner agrees in part and disagrees in part. Despite the details of some exemplary CAN networks (e.g., “dozens or even hundreds of sensors,” “data throughput capacity of 125 kbit/s at low speed, or 1 Mbit/s at high speed”) which are neither embodied in the claim language itself nor supported in the original disclosure (and thus are irrelevant to the analysis of the claims as presently drafted), Examiner nonetheless agrees that this step cannot practically be performed in the human mind, and thus does not recite a mental process. While Applicant makes no mention of the separate abstract idea categories of certain methods of organizing human activity and mathematical concepts, Examiner finds that this limitation does not recite either of these as well. Examiner, however, disagrees with Applicant’s conclusion as to what this means.
This argument and the conclusion Applicant draws from it improperly conflate the standards of Step 2A, Prong One (ie: the recitation of judicial exceptions) and Prong Two (ie: whether any recited judicial exceptions are integrated into a practical application, or what a claim is “directed to”). Here, Applicant presents a Prong One argument and immediately concludes that this necessitates a particular Prong Two outcome. This is not the case, nor does it properly apply the steps of the 101 subject matter eligibility analysis (nor, indeed, does this argument appear to attempt to perform a Prong Two analysis at all). In particular regarding Prong One, this step is performed on a limitation-by-limitation basis (see any of the myriad individually analyzed limitations in MPEP 2106.04(a)(2) or the Examples of the most recent PEG updates), after which the claim is analyzed “as a whole” in Steps 2A, Prong Two and 2B. Under the Prong One analysis, that one claim limitation does not recite a judicial exception (and as such instead recites an additional element) in no way prevents other claim limitations from reciting judicial exceptions (such as those pointed out in the 101 rejections of the previous Office Action, against which Applicant provides no substantive arguments). In other words, as applied to the Prong One analysis of the present argument, while the singularly argued and newly drafted limitation of Claim 1 does not recite an abstract idea, the remainder of the presently drafted steps of Claim 1 continue to recite abstract ideas in the same manner as set forth in the previous Office Action. Indeed, if the mere presence of a claim limitation reciting an additional element were to immediately have the effect argued by Applicant, Steps 2A, Prong Two and 2B would be entirely superfluous, which is clearly not in keeping with the law. Thus, Applicant’s argument here fails to support Applicant’s conclusions that (a) the presence of a singular step which does not recite an abstract idea somehow indicates that all steps of Claim 1 “cannot practically be performed within the human mind,” nor that (b) this would somehow result in a conclusion that the claims were not directed to the recited abstract ideas under the Step 2A, Prong Two analysis.
Applicant next argues that the claims embody an improvement to a technology under Step 2A, Prong Two, particularly that the claims “address[] deficiencies in the technical field of vehicle monitoring and maintenance,” citing to pg. 2 of the specification as filed. Examiner disagrees, finding Applicant’s argument and supporting reasoning flawed for multiple reasons, most critically that Applicant’s argument (and its broad classification of “vehicle monitoring and maintenance” as a “technical field”) fails to consider the distinction between abstract ideas and technological elements and steps. MPEP 2106.05(a) explains that such an improvement to a technology is a “technological solution to a technological problem,” and further states that “it is important to keep in mind that an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology.” While discrete aspects of “vehicle monitoring and maintenance” might be considered technological, the aspect which is actually improved in the present claims (abstract data analysis of vehicle information to determine bus maintenance priorities) is not, instead representing an abstract business-related concept. Merely performing a single step of this process at a high level by way of an artificial intelligence model does not make this otherwise (see, e.g., Examples 47-49 of the July 2024 PEG Update). Similarly, the argued CAN-based data gathering step does not render the entire improvement itself as technological, nor does this step in isolation represent its own separate technological improvement (rather, this CAN-based data gathering merely claims a standard communication protocol of vehicle ECUs which long pre-dates Applicant’s effective filing date and as such could not be reasonably considered an improvement to a technology by one of ordinary skill in the art).
The supporting assertions made within this argument also fail to support a conclusion that the claims embody an improvement to a technology under Step 2A, Prong Two. The repeated assertion of real-time monitoring in step (b) as presently drafted does not somehow render the other steps of the claims as anything other than abstract. Even if those separate steps found to recite abstract ideas also contained language requiring their performance in real time, this in no way prevents recitation of abstract ideas. Similarly, other details presently argued (e.g., “a vast array of sensors,” “diagnosing multi-variable problems in very high-dimensional data,” the use of location information at all, etc.) are likewise not embodied in the claims, and as such are likewise irrelevant. Lastly, Examiner also notes that Applicant’s appeal to the primary Chen reference here is inapplicable and inappropriate, as (a) the standards of 101 differ from those of 102 and 103, (b) even were this not the case, Applicant’s appeal to Chen in isolation does not address Chen as modified by the recited secondary references, and (c) Applicant’s assertion that the system set forth in Chen “is insufficient for modern vehicles which include a vast array of sensors, and may miss problems that would manifest not as an out-of-bounds value of a single sensor, but as a combination of values across multiple sensors” both does not comport with the invention as currently drafted in the claims and represents an unsupported hypothetical, which Applicant’s use of “may” essentially admits may or may not be true.
Claim Rejections – 35 USC § 103
Applicant’s arguments regarding the 103 analysis have been considered and are unpersuasive.
Regarding the citations provided in the previous 103 rejections, Applicant argues that “Examiner has mistakenly misinterpreted Chen,” asserting generally that the previous citations fail to properly disclose at least steps (d) though (g) (prior steps (c) through (f)). Applicant’s only substantive support for this assertion is to argue that the AI/ML disclosed in Chen “describes various machine learning and AI algorithms being used for operating the autonomous vehicle, but nowhere does Chen teach or suggest training a machine learning model on vehicle monitoring information or using a trained model to determine maintenance priority” (Applicant’s emphases) followed by a vague assertion that the secondary Lim and Han references do not cure this purported deficiency.
Applicant is mistaken. Indeed, despite Applicant’s explicit citation of Paragraphs 0051-0052 of Chen, the very first line of Paragraph 0051 states that “The AI/ML platform 454 can provide the infrastructure for training and evaluating machine learning algorithms for operating the AV 402, the simulation platform 456, the remote assistance platform 458, the ridesharing platform 460, the map management system platform 462, and other platforms and systems,” making it abundantly clear that the AI/ML of Chen may be used for far more than merely the operating of the autonomous vehicles. Rather, these paragraphs indicate that the AI/ML platform trains machine learning algorithms for operating the various platforms of the management system. As said management system is explicitly disclosed as implementing steps for determining vehicle maintenance priorities (see, e.g., Paragraphs 0020-0025 and 0029-0031, a relevant selection of which were included in the previous citations provided for the claimed machine learning-based claim functions). By way of these paragraphs, in combination with the aforementioned Paragraphs 0051-0052, disclose the use of AI/ML for performing the claimed maintenance priority determination steps. As such, it is Applicant rather than Examiner who has misunderstood the content of Chen.
Regarding the newly added and amended claim limitations, for which Applicant presents no corresponding arguments, see the updated 103 rejections below for more information.
Claim Objections
Claim 1 is objected to because of the following informality: there should be a semicolon separating as-amended steps (b) and (c) to maintain consistency with the remainder of Claim 1’s formatting. Appropriate correction is required.
Claim Interpretation
While the claims contain several terms which constitute generic placeholders (e.g., “a data processing unit,” “a monitoring unit,” and “a data analysis unit” of Claim 1; “an analysis unit” of Claim 8) which are modified by functional language, these terms are not given interpretation under 112(f) as they are all modified by sufficient structure (e.g., “an operational server”) to perform the respectively recited functions, either directly or indirectly (e.g., “an analysis unit” of Claim 8 is not directly tied to the operational server, but it is tied to the data processing unit, which is in turn modified by the operational server in language of Claim 1). See MPEP 2181 for more information on this standard.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding Claim 1, the limitations of (a) grouping and classifying a plurality of buses to be monitored according to a preset classification criterion; (c) receiving bus monitoring information including the real-time CAN data and a plurality of additional types of data collected; (d) performing refinement on the bus monitoring information; (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; (f) re-receiving the bus monitoring information collected for each group classified according to the operation (a); and (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f), as drafted, are processes that, under their broadest reasonable interpretations, cover certain methods of organizing human activity. For example, these limitations fall at least within the enumerated categories of commercial or legal interactions and/or managing personal behavior or relationships or interactions between people (see MPEP 2106.04(a)(2)(II)).
Additionally, the limitations of (a) grouping and classifying a plurality of buses to be monitored according to a preset classification criterion; (c) receiving bus monitoring information including the real-time CAN data and a plurality of additional types of data collected; (d) performing refinement on the bus monitoring information; (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; (f) re-receiving the bus monitoring information collected for each group classified according to the operation (a); and (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f), as drafted, are processes that, under their broadest reasonable interpretations, cover mental processes. For example, these limitations recite activity comprising observations, evaluations, judgments, and opinions (see MPEP 2106.04(a)(2)(III)).
Additionally, the limitations of (e) inputting the bus monitoring information refined in the operation (d) as training data to build a model for determining bus maintenance priorities; and (g) deriving a maintenance priority of the plurality of buses through the model based on the bus monitoring information re-received in the operation (f), as drafted, are processes that, under their broadest reasonable interpretations, cover mathematical concepts. For example, these limitations recite mathematical relationships and/or calculations (see MPEP 2106.04(a)(2)(I)).
If a claim limitation, under its broadest reasonable interpretation, covers fundamental economic principles or practices, commercial or legal interactions, managing personal behavior or relationships, or managing interactions between people, it falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind or with the aid of pen and paper but for recitation of generic computer components, it falls within the “Mental Processes” grouping of abstract ideas. If a claim limitation, under its broadest reasonable interpretation, covers mathematical relationships, mathematical formulae or equations, or mathematical calculations, it falls within the “Mathematical Concepts” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of a data processing unit, an operational server, a plurality of buses, (b) collecting real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the buses for each group classified according to the operation (a), a monitoring unit, vehicle information collection devices installed on the buses, an artificial intelligence machine, an artificial intelligence model, and a data analysis unit. A data processing unit, an operational server, a monitoring unit, vehicle information collection devices installed on the buses, an artificial intelligence machine, an artificial intelligence model, and a data analysis unit amount to no more than mere instructions to apply a judicial exception (see MPEP 2106.05(f)). (b) collecting real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the buses for each group classified according to the operation (a) amounts to no more than insignificant extra-solution activity (see MPEP 2106.05(g)). A plurality of buses amounts to no more than generally linking the use of a judicial exception to a particular technological environment or field of use (see MPEP 2106.05(h)). Accordingly, these additional elements do not integrate the abstract ideas into a practical application because they do not, individually or in combination, impose any meaningful limits on practicing the abstract ideas. The claim is therefore directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the judicial exception into a practical application, the additional elements amount to no more than mere instructions to apply a judicial exception, insignificant extra-solution activity, and generally linking the use of a judicial exception to a particular technological environment or field of use for the same reasons as discussed above in relation to integration into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional activity as "receiving or transmitting data over a network" in MPEP 2106.05(d). These cannot provide an inventive concept. Therefore, when considering the additional elements alone and in combination, there is no inventive concept in the claim, and thus the claim is not patent eligible.
Claims 2-9, describing various additional limitations to the method of Claim 1, amount to substantially the same unintegrated abstract idea as Claim 1 (upon which these claims depend, directly or indirectly) and are rejected for substantially the same reasons.
Claim 2 discloses wherein the preset classification criterion in the operation (a) has at least one criterion among a manufacturer, fuel, a model, year, and a system type (further defines the abstract idea already set forth in Claim 1), which does not integrate the claim into a practical application.
Claim 3 discloses (d-1) filtering, by the data processing unit, a data error in the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); (d-2) performing, by the data processing unit, preprocessing on data of the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); and (d-3) backing up and loading, by the data processing unit, the data of the bus monitoring information into a database of the operational server (insignificant extra-solution activity), which do not integrate the claim into a practical application. The limitation found to recite insignificant extra-solution activity is further found to be well-understood, routine, and conventional as storing and retrieving information in memory (see MPEP 2106.05(d)).
Claim 4 discloses (g-1) generating, by the data analysis unit, a state prediction model of the bus to be monitored through the artificial intelligence model (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); (g-2) generating, by the data analysis unit, a steady state model of the bus to be monitored through the artificial intelligence model (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); (g-3) calculating, by the data analysis unit, a Euclidean distance between the state prediction model and the steady state model (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and (g-4) deriving, by the data analysis unit, maintenance priorities in the decreasing order of the Euclidean distance calculated in the operation (g-3) (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 5 discloses between the operations (d) and (e), operation (ex1) of setting, by the data processing unit of the operational server, a threshold standard for determining normal/abnormal data based on the bus monitoring information refined in the operation (d) (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 6 discloses wherein the operation (ex1) includes one or more operations selected from the group consisting of: (ex1-1) receiving, by the data processing unit, an expert threshold value calculated by an automobile-related expert and setting the received expert threshold value as a first threshold standard (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex1-2) deriving, by the data processing unit, a numerical threshold value calculated through a statistical technique and setting the derived numerical threshold value as a second threshold standard (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept); and (ex1-3) deriving, by the data processing unit, a displacement difference threshold value calculated through an offset method and setting the derived displacement difference threshold value as a third threshold standard (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 7 discloses after the operation (ex1), operation (ex2) of processing, by the data processing unit, the bus monitoring information to build the training data to be input to the artificial intelligence machine in the operation (e) (an abstract idea in the form of a certain method of organizing human activity and a mental process), which does not integrate the claim into a practical application.
Claim 8 discloses (ex2-1) standardizing, by the data processing unit, the bus monitoring information (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex2-2) performing, by the data processing unit, preprocessing on the bus monitoring information standardized in the operation (ex2-1) (an abstract idea in the form of a certain method of organizing human activity and a mental process); (ex2-3) setting, by the data processing unit, an analysis unit (mere instructions to apply a judicial exception) for the bus monitoring information preprocessed in the operation (ex2-2) (an abstract idea in the form of a certain method of organizing human activity and a mental process); and (ex2-4) classifying, by the data processing unit, each piece of data of the bus monitoring information as normal data or abnormal data according to the threshold standard set in the operation (ex1) (an abstract idea in the form of a certain method of organizing human activity, a mental process, and a mathematical concept), which do not integrate the claim into a practical application.
Claim 9 discloses (h) displaying the maintenance priority of the plurality of buses (an abstract idea in the form of a certain method of organizing human activity and a mental process) on a graphical user interface of a video output device connected to the operational server (mere instructions to apply a judicial exception); and (i) updating the maintenance priority displayed on the graphical user interface in real-time as new bus monitoring information is received by the monitoring unit (an abstract idea in the form of a certain method of organizing human activity and a mental process), which do not integrate the claim into a practical application.
Claim Rejections – 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
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 1-3, 5-6, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Chen et al (PGPub 20230071833) (hereafter, “Chen”) in view of Lim et al (KR 102204199) (hereafter, “Lim”), Han (WO 2014168371) (hereafter, “Han”), and Leone et al (PGPub 20190154453) (hereafter, “Leone”).
Regarding Claim 1, Chen discloses (c) receiving, by a monitoring unit of the operational server, vehicle monitoring information including a plurality of types of data collected by the vehicle information collection devices installed on the vehicles (Abstract; ¶ 0015-0016, 0020; Figs. 1-2; a system that can be used to triage work orders using dynamic priority number, including a management system that can be configured to receive field data from one or more machines, e.g., AVs of a fleet of AVs; field data can include measurements, statistics, and other operational status updates for any of a variety of AV systems; field data is received from one or more devices/machines (e.g., the AVs) can include any of a variety of reported states/metrics/conditions for one or more corresponding systems or parts). Chen does not explicitly disclose but Leone does disclose wherein the plurality of types of data comprising the vehicle monitoring information includes the real-time CAN data (¶ 0026, 0029, 0036, 0061; Fig. 2; the computer is generally arranged for communications on a vehicle communication network, e.g., including a communication bus such as a controller area network (CAN) or the like; the computer may include or be communicatively coupled to, e.g., via a vehicle communications bus, more than one processor, e.g., controllers or the like included in the vehicle for monitoring and/or controlling various subsystems such as a powertrain, brake, steering, etc.; sensors may include a variety of devices to provide data via the vehicle communications bus; the computer receives vehicle data such as a first destination, a diagnostic status, and/or a current location; for example, the computer may be programmed to receive the current location of the vehicle from a vehicle GPS sensor, the diagnostic status from a computer memory, and the first destination from the vehicle HMI). Chen does not explicitly disclose Lim Han does disclose wherein the vehicles are buses (Title; Abstract; pgs. 3-4; Fig. 1; method for performing vehicle fault diagnosis and prediction in realtime with respect to autonomous public buses; vehicle sensor information collected from autonomous public buses).
Chen additionally discloses (e) inputting, by the data processing unit, the vehicle monitoring information as training data for an artificial intelligence machine to build an artificial intelligence model for determining vehicle maintenance priorities (Abstract; ¶ 0013-0014, 0020, 0023, 0025, 0036, 0051-0052; Figs, 2A, 3A, 4; triage of work orders for detected failure events (or predicted failure events) for a fleet of vehicles; FIG. 2A illustrates an example process that can be implemented by a management system to perform work order scheduling; a dynamic priority number (DPN) is calculated for each potential failure event; once DPNs have been calculated for each (potential) failure event, the failure events can be sorted based on immediacy/severity; the AI/ML platform can provide the infrastructure for training and evaluating machine learning algorithms for operating the various platforms of the management system’s data center). Chen does not explicitly disclose but Lim does disclose wherein the vehicles are buses; wherein the vehicle monitoring information is refined in the operation (d) (Title; Abstract; pgs. 4-5; Claim 1; vehicle fault diagnosis and prediction in real time with respect to an autonomous public bus; the data management unit performs data purification operations such as correction of missing values, data smoothing, and data normalization for the collected vehicle sensor information).
Chen additionally discloses (g) deriving, by a data analysis unit of the operational server, a maintenance priority of the plurality of vehicles through the artificial intelligence model based on the vehicle monitoring information (Abstract; ¶ 0013-0014, 0020, 0023, 0025, 0036, 0051-0052; Figs, 2A, 3A, 4; triage of work orders for detected failure events (or predicted failure events) for a fleet of vehicles; FIG. 2A illustrates an example process that can be implemented by a management system to perform work order scheduling; a dynamic priority number (DPN) is calculated for each potential failure event; once DPNs have been calculated for each (potential) failure event, the failure events can be sorted based on immediacy/severity; the AI/ML platform can provide the infrastructure for training and evaluating machine learning algorithms for operating the various platforms of the management system’s data center). Chen does not explicitly disclose but Lim does disclose wherein the vehicles are buses; wherein the model results are based on the bus monitoring information re-received in the operation (f) (Title; Abstract; pgs. 2-4, 8-9; Fig. 1; vehicle fault diagnosis and prediction in real time with respect to an autonomous public bus; collecting and analyzing vehicle sensor data of autonomous public buses; raw sensor data collected in the data collection and monitoring process; vehicles can continuously obtain data through real-time monitoring; a continuous data collection process that can observe changes in vehicle parts performance over time; in order to predict the future state through deterioration model inference, it is necessary to obtain historical data of state change before and after due to continuous long-term data collection; the vehicle condition-based diagnosis and prediction system is required to secure the status change history data before and after due to continuous long-term data collection for vehicle parts and part condition diagnosis and maintenance activities).
Chen does not explicitly disclose but Lim does disclose:
(d) performing, by the data processing unit, refinement on the bus monitoring information (pgs. 4-5; Claim 1; the data management unit performs data purification operations such as correction of missing values, data smoothing, and data normalization for the collected vehicle sensor information); and
(f) re-receiving, by the monitoring unit, the bus monitoring information collected by the vehicle information collection devices installed on the buses for each group classified according to the operation (a) (pgs. 2-4, 8-9; Fig. 1; collecting and analyzing vehicle sensor data of autonomous public buses; raw sensor data collected in the data collection and monitoring process; vehicles can continuously obtain data through real-time monitoring; a continuous data collection process that can observe changes in vehicle parts performance over time; in order to predict the future state through deterioration model inference, it is necessary to obtain historical data of state change before and after due to continuous long-term data collection; the vehicle condition-based diagnosis and prediction system is required to secure the status change history data before and after due to continuous long-term data collection for vehicle parts and part condition diagnosis and maintenance activities).
Chen does not explicitly disclose but Han does disclose (a) grouping and classifying, by a data processing unit of an operational server, a plurality of vehicles to be monitored according to a preset classification criterion (Abstract; pgs. 2, 7, 9; Fig. 1; a plurality of maintenance details performed by each maintenance company are received in real time through the Internet, and according to the characteristics of the vehicle (manufacturer, model, name of vehicle); the statistics module of the server is liked with the maintenance history database to read a plurality of pre-stored maintenance history and then statistically processing by car features to predict when the failure occurs preventative maintenance; the list is updated in real time, and is classified by manufacturer, model, vehicle name, type and year, chassis number, prime mover type, displacement, transmission type, driver type, etc.). Chen does not explicitly disclose but Lim does disclose wherein the vehicles are buses (Title; Abstract; pgs. 3-4; Fig. 1; method for performing vehicle fault diagnosis and prediction in realtime with respect to autonomous public buses; vehicle sensor information collected from autonomous public buses).
Chen does not explicitly disclose but Han does disclose (b) collecting real-time vehicle data on the vehicles for each group classified according to the operation (a) (Abstract; pgs. 2, 7, 9; Fig. 1; a plurality of maintenance details performed by each maintenance company are received in real time through the Internet, and according to the characteristics of the vehicle (manufacturer, model, name of vehicle); the statistics module of the server is linked with the maintenance history database to read a plurality of pre-stored maintenance history and then statistically processing by car features to predict when the failure occurs preventative maintenance; the list is updated in real time, and is classified by manufacturer, model, vehicle name, type and year, chassis number, prime mover type, displacement, transmission type, driver type, etc.). Chen does not explicitly disclose but Leone does disclose wherein the collecting of real-time vehicle data is the collecting of real-time Controller-Area-Network (CAN) data with vehicle information collection devices installed on the vehicles (¶ 0026, 0029, 0036, 0061; Fig. 2; the computer is generally arranged for communications on a vehicle communication network, e.g., including a communication bus such as a controller area network (CAN) or the like; the computer may include or be communicatively coupled to, e.g., via a vehicle communications bus, more than one processor, e.g., controllers or the like included in the vehicle for monitoring and/or controlling various subsystems such as a powertrain, brake, steering, etc.; sensors may include a variety of devices to provide data via the vehicle communications bus; the computer receives vehicle data such as a first destination, a diagnostic status, and/or a current location; for example, the computer may be programmed to receive the current location of the vehicle from a vehicle GPS sensor, the diagnostic status from a computer memory, and the first destination from the vehicle HMI). Chen does not explicitly disclose but Lim does disclose wherein the vehicles are buses (Title; Abstract; pgs. 3-4; Fig. 1; method for performing vehicle fault diagnosis and prediction in realtime with respect to autonomous public buses; vehicle sensor information collected from autonomous public buses).
It would have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the bus maintenance prediction techniques of Lim with the vehicle maintenance prediction and prioritization system of Chen because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Lim are applicable to the base device (Chen), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the vehicle maintenance grouping techniques of Han with the vehicle maintenance prediction and prioritization system of Chen and Lim because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Han are applicable to the base device (Chen and Lim), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined. It would further have been obvious to one of ordinary skill in the art before the filing date of the claimed invention to include the maintenance analysis-based vehicle data gathering techniques of Leone with the vehicle maintenance prediction and prioritization system of Chen, Lim, and Han because the combination merely applies a known technique to a known device/method/product ready for improvement to yield predictable results (see KSR Int’l Co. v. Teleflex, Inc., 550 U.S. 398, 415-421 (2007) and MPEP 2143). The known techniques of Leone are applicable to the base device (Chen, Lim, and Han), the technical ability existed to improve the base device in the same way, and the results of the combination are predictable because the function of each piece (as well as the problems in the art which they address) are unchanged when combined.
Regarding Claim 2, Chen in view of Lim, Han, and Leone discloses the limitations of Claim 1. Chen does not explicitly disclose but Han does disclose wherein the preset classification criterion in the operation (a) has at least one criterion among a manufacturer, fuel, a model, year, and a system type (Abstract; pgs. 2, 7, 9; Fig. 1; a plurality of maintenance details performed by each maintenance company are received in real time through the Internet, and according to the characteristics of the vehicle (manufacturer, model, name of vehicle); the statistics module of the server is liked with the maintenance history database to read a plurality of pre-stored maintenance history and then statistically processing by car features to predict when the failure occurs preventative maintenance; the list is updated in real time, and is classified by manufacturer, model, vehicle name, type and year, chassis number, prime mover type, displacement, transmission type, driver type, etc.).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 3, Chen in view of Lim, Han, and Leone discloses the limitations of Claim 1. Chen does not explicitly disclose but Lim does disclose:
(d-1) filtering, by the data processing unit, a data error in the bus monitoring information (pgs. 4-5; Claim 1; the data management unit performs data purification operations such as correction of missing values, data smoothing, and data normalization for the collected vehicle sensor information; data smoothing includes removal of errors/noise in the data; missing values are a form of error);
(d-2) performing, by the data processing unit, preprocessing on data of the bus monitoring information (pgs. 4-5; Claim 1; the data management unit performs data purification operations such as correction of missing values, data smoothing, and data normalization for the collected vehicle sensor information); and
(d-3) backing up and loading, by the data processing unit, the data of the bus monitoring information into a database of the operational server (pgs. 3-5; Claim 1; the database records and stores data collected in real time from vehicle sensors, and data processed at each stage; the distributed messaging system facilitates real-time processing of various large amounts of data in the monitoring system of the present invention, and is used for linkage between internal processors and for storing and retrieving databases; the data management unit performs data purification operations such as correction of missing values, data smoothing, and data normalization for the collected vehicle sensor information).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 5, Chen in view of Lim, Han, and Leone discloses the limitations of Claim 1. Chen does not explicitly disclose but Lim does disclose between the operations (d) and (e), operation (ex1) of setting, by the data processing unit of the operational server, a threshold standard for determining normal/abnormal data based on the bus monitoring information refined in the operation (d) (pg. 5; for data-based anomaly detection composed of complex elements, the normal performance range value of each vehicle part is predetermined and registered in the system, and then, as shown in FIG.5, after grasping the normal performance cluster at the same coordinates, the distance d1 between the new data and the cluster Or, if the d2 value is outside the normal performance distribution range of C1 and C2, it is judged as an anomalous value; the normal performance range value is defined in the system according to the performance acceptance criteria set by the manufacturer for each vehicle part).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 6, Chen in view of Lim, Han, and Leone discloses the limitations of Claim 5. Chen does not explicitly disclose but Lim does disclose wherein the operation (ex1) includes one or more operations selected from the group consisting of: (ex1-1) receiving, by the data processing unit, an expert threshold value calculated by an automobile-related expert and setting the received expert threshold value as a first threshold standard; (ex1-2) deriving, by the data processing unit, a numerical threshold value calculated through a statistical technique and setting the derived numerical threshold value as a second threshold standard; and (ex1-3) deriving, by the data processing unit, a displacement difference threshold value calculated through an offset method and setting the derived displacement difference threshold value as a third threshold standard (pg. 5; for data-based anomaly detection composed of complex elements, the normal performance range value of each vehicle part is predetermined and registered in the system, and then, as shown in FIG.5, after grasping the normal performance cluster at the same coordinates, the distance d1 between the new data and the cluster Or, if the d2 value is outside the normal performance distribution range of C1 and C2, it is judged as an anomalous value; the normal performance range value is defined in the system according to the performance acceptance criteria set by the manufacturer for each vehicle part).
The rationale to combine remains the same as for Claim 1.
Regarding Claim 9, Chen in view of Lim, Han, and Leone discloses the limitations of Claim 1. Chen does not explicitly disclose but Han does disclose (h) displaying a maintenance priority on a graphical user interface of a video output device connected to the operational server (Abstract; pgs. 3, 6, 9; the user terminal receives the accumulated distance information of the instrument cluster odometer in real time and 5000km according to the life cycle of each vehicle feature configured in each 5000km unit from the server; the personalized preventive maintenance list is guided and displayed or saved on the screen; according to the 5000km personalized preventive maintenance list according to the guidance and display the information in real time according to the user terminal; the user terminal is a user’s vehicle terminal, PC, mobile and maintenance company PC, smart phone of the user, tablet PC, desktop computer, etc. (a standard embodiment of any one of which would be recognized by one of ordinary skill in the art at the time of filing as capable of video output)). Chen additionally discloses wherein the maintenance priority is the maintenance priority of the plurality of vehicles (Abstract; ¶ 0013-0014, 0020, 0023, 0025, 0036, 0051-0052; Figs, 2A, 3A, 4; triage of work orders for detected failure events (or predicted failure events) for a fleet of vehicles; FIG. 2A illustrates an example process that can be implemented by a management system to perform work order scheduling; a dynamic priority number (DPN) is calculated for each potential failure event; once DPNs have been calculated for each (potential) failure event, the failure events can be sorted based on immediacy/severity; the AI/ML platform can provide the infrastructure for training and evaluating machine learning algorithms for operating the various platforms of the management system’s data center). Chen does not explicitly disclose but Lim does disclose wherein the vehicles are buses (Title; Abstract; pgs. 2-4, 8-9; Fig. 1; vehicle fault diagnosis and prediction in real time with respect to an autonomous public bus; collecting and analyzing vehicle sensor data of autonomous public buses; raw sensor data collected in the data collection and monitoring process; vehicles can continuously obtain data through real-time monitoring; a continuous data collection process that can observe changes in vehicle parts performance over time; in order to predict the future state through deterioration model inference, it is necessary to obtain historical data of state change before and after due to continuous long-term data collection; the vehicle condition-based diagnosis and prediction system is required to secure the status change history data before and after due to continuous long-term data collection for vehicle parts and part condition diagnosis and maintenance activities).
Chen additionally discloses (i) updating the maintenance priority in real-time as new vehicle monitoring information is received by the monitoring unit (Abstract; ¶ 0013-0014, 0020, 0023-0025, 0025, 0031, 0036, 0042, 0050-0052; Figs, 2A, 3A, 4; triage of work orders for detected failure events (or predicted failure events) for a fleet of vehicles; AV sensor data is captured in real-time by the sensor systems; FIG. 2A illustrates an example process that can be implemented by a management system to perform work order scheduling; a dynamic priority number (DPN) is calculated for each potential failure event; once DPNs have been calculated for each (potential) failure event, the failure events can be sorted based on immediacy/severity; the AI/ML platform can provide the infrastructure for training and evaluating machine learning algorithms for operating the various platforms of the management system’s data center; the DPN may also be a function of an occurrence multiplier, e.g., that is calculated based on a ratio of the current occurrence percentage to the last known occurrence percentage; the occurrence multiplier can provide a quantitative comparison between two time periods, e.g., a current time period, and a previously measured time period; as such, the occurrence multiplier can function to provide a time derivative indicator for the P.N., e.g., to highlight cases where the issue is trending towards a failure over time; once DPNs have been calculated for each (potential) failure event, the failure events can be sorted based on immediacy/severity; as the dynamic priority number (DPN) is updated over time, and in some instances based on differences in real-time sensor data analysis versus sensor data analysis of a previous time period, the sorted maintenance list is consequently updated in real time). Chen does not explicitly disclose but Han does disclose wherein the maintenance priority is displayed on the graphical user interface (Abstract; pgs. 3, 6, 9; the user terminal receives the accumulated distance information of the instrument cluster odometer in real time and 5000km according to the life cycle of each vehicle feature co