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 .
Claim(s) is/are 1, 2, 4-12 are pending for examination. Claim 1, 8, and 9 have been amended. Claim 12 has been newly added. This action is Non-Final.
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/1/2026 has been entered.
Response to Arguments
Applicant's arguments filed 6/1/20206 with respect to the 35 U.S.C. 101 rejection have been fully considered and have been withdrawn and/or not persuasive.
The examiner respectfully notes the 35 U.S.C. 101 rejection of claim 8 for being directed to software per se has been withdrawn as the claim has been amended.
Applicant Argues: Claims 1-11 are rejected under 35 U.S.C. 101 as allegedly directed to abstract idea without significantly more.
Solely to advance prosecution, Applicant has amended Claim 1 to recite a computer- assisted method for performing maintenance operations of a fleet of vehicles according to a maintenance need, the method comprising: (A) for each vehicle in the fleet of vehicles, by a computer processor: ... ordering a list of the vehicles of the fleet of vehicles according to properties of the burst(s) detected for each vehicle; and (B) performing, by at least one maintenance operator, maintenance operations for one or more vehicles among a predefined number of first vehicles from the ordered list.
Amended Claim 1 as a whole integrates the alleged abstract idea into a practical application under Step 2A Prong Two, by applying and using an "ordered list" obtained from the alleged abstract idea in a concrete, tangible step of performing maintenance operations for one or more vehicles, effecting a transformation of the one or more vehicles to a "maintained" state. The method of amended Claim 1 improves the functioning of vehicle maintenance by, for example, automatically ordering vehicles according to the need for maintenance and helping maintenance operators to select the priority vehicles for maintenance in a quick and efficient way in order to carry out the required maintenance operations on the vehicles, optimizing maintenance tasks and limiting the time during which an item of equipment cannot be used. See specification at paragraphs [0008], [0024] and [0086].
Accordingly, amended Claim 1, as well as its dependent claims, are directed to patent eligible subject matter. Withdrawal of the 35 U.S.C. 101 rejection is respectfully requested.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes respectfully notes that the amended features that “(A) for each vehicle in the fleet of vehicles, by a computer processor: ... ordering a list of the vehicles of the fleet of vehicles according to properties of the burst(s) detected for each vehicle; and (B) performing, by at least one maintenance operator, maintenance operations for one or more vehicles among a predefined number of first vehicles from the ordered list” is noted to be a limitation that is still part of the abstract idea as it falls within the “Certain Methods of Organizing Human Activity” as it is still a form of a business relation. Thus, the purported improvement lies within the abstract idea itself, and therefore, does not provide integration into a practical application nor do they amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception.
Therefore, the examiner finds these arguments not persuasive.
Applicant's arguments filed 6/1/20206 with respect to the 35 U.S.C. 103 rejection have been fully considered but they are not persuasive.
Applicant Argues: Applicant respectfully disagrees with the Examiner's reading of Forrest. Applicant submits that Forrest discloses time series of events data (e.g., sensor data collected over time), rather than time series of the state of the vehicle, as recited in pending Claim 1. For example, Forrest teaches to analyze a series of event data to identify whether it is "significantly different" from the other rail vehicles of the fleet. In Forrest, identifying the "significantly different" data relies on "counting the number of occurrences of a predetermined event in each series, and means for comparing said numbers of occurrences, either graphically or numerically" (for example, using tools like histograms, bar charts, column charts). In Forrest, the whole time series of events for a given vehicle is thus analyzed to obtain a single piece of information on the status of this vehicle for that period: the vehicle either has a fault or it does not. See Forrest at paragraphs [0015], [0021] and[0022] and original claim 8.
Therefore, Forrest does not disclose or reasonably suggest "optimal sequence of states" (e.g., an algorithmic output that classifies the state of the vehicle at every single time step), or determining instantaneous probability for each individual time step (which results in a chronological sequence of states of "normal" or "abnormal" corresponding to each individual time step), as recited in amended Claim 1. Because Forrest fails to disclose the generation of a sequence of states for each time steps, it also cannot teach the claimed step of detecting a burst, which is characterized by a minimum number of consecutive time steps in the "abnormal" state, as recited in amended Claim 1. The method of Forrest cannot permit detecting consecutive time steps in an abnormal state if the system only outputs a single state data for a given period.
Rackley and Anderson are cited for disclosing other features and fail to cure the deficiencies of Forrest as discussed above. Indeed, neither Rackley nor Anderson teaches or suggests analyzing a time series of monitoring events to assign an instantaneous state at each time step. Since Rackley, Forrest and Anderson, alone or in combination, fail to teach each and every features of amended Claim 1, and of claims that depend from Claim 1, withdrawal of the 35 U.S.C.103 rejection is respectfully requested.
Examiner’s Response: The examiner respectfully disagrees.
As noted in the rejection below, Forest is shown to teach features of:
analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time step ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time), a state of the vehicle is either a "normal" state or an "abnormal" state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval..., so as to, determine ... for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time and [0044] - The dynamic attributes are parameters that are technically significant for the behaviour of the monitored component, e.g. parameters that may have a causal effect on the state of monitored component, or additional data useful for understanding the event, such as time of malfunction and operation being undertaken at the time of malfunction. For example, in trying to analyze wheels, the data will be visualised by car number, number of events. Accordingly, other aspects such as doors will be ignored. Filters can be used to select the analysed data, e.g. rail vehicle range, vehicle speed higher than a predetermined value, rail infrastructure range, etc.) [and]
detecting a presence, if any, of one or several burst(s) in the optimal sequence of states ([0015] and [0028] - a comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time), wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state ((0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time [0044] and [0045] - The database of historical events 44 can also be used to built a behaviour model for each monitored component of the rail system, i.e. a database containing data indicative of tolerances ranges, normal conditions and trends. The sensor data can then be compared to the behaviour model to more efficiently predict future faults). As construed a significant difference is a minimum number of time steps in an “abnormal sate”.
The examiner respectfully notes that Forest does in fact teach an “optimal sequence of states” as [0028] discuss - any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time. The examiner respectfully notes an event to be a state. If there is exhibition of significant difference for events (i.e., states) during a given time period step, it construed to be, a minimum number of consecutive steps during a given time period, thus representing burst in an “abnormal” state (i.e., significant difference is identified base on the exhibited events (i.e., state) during that time period). Therefore, the examiner finds this argument not persuasive.
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(s) 1, 2, and 4-12 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: claim(s) 1, 2, and 4-12 are directed to a process, manufacture, and/or machine. Therefore, the claims are directed to statutory subject matter under Step 1 (Step 1: YES). See MPEP 2106.03.
Prong 1, Step 2A: claim 1, and similar claim(s) 8, taken as representative, recites at least the following limitations that recite an abstract idea:
A
for each vehicle in the fleet of vehicles, by a computer processor:
determining a time series, the time series including, for each time step, an instantaneous value of at least one quantity of interest obtained from monitoring events,
analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time step, a state of the vehicle is either a "normal" state or an “abnormal” state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval, wherein said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and transition function, so as to, determine an instantaneous probability for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; and
detecting a presence, if any, of one or several burst(s) in the optimal sequence of states, wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state, and
ordering a list of the vehicles of the fleet of vehicles according to properties of the burst(s) detected for each vehicle; and
performing, by at least one maintenance operator, maintenance operations for one or more vehicles among a predefined number of first vehicles from the ordered list.
Claim 9 additionally recites at least, the following limitations that recite an abstract idea:
The above limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(II), in that they recite commercial or legal interactions, (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations). The broadest reasonable interpretation of these limitations includes for claim 1, and for similar claim(s) 8 and 9 includes determining a time series, for each time step, and instantaneous value of at least one quantity of interest obtained from monitoring events..., analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time step, a state of the vehicle is either a "normal" state or an “abnormal” state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval, wherein said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and transition function, so as to, determine an instantaneous probability for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; detecting a presence, if any, of one or several burst(s) in the optimal sequence of states, wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state, ordering a list of the vehicles of the fleet of vehicles according to properties of the burst(s) detected for each vehicle; and performing, by at least one maintenance operator, maintenance operations for one or more vehicles among a predefined number of first vehicles from the ordered list and further accessing contents... read, thus, the claim 1, and similar claim(s) 8 and 9 falls within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas as they recite business relations.
Accordingly, these claims recite an abstract idea. (Prong 1, Step 2A: YES). The types of identified abstract ideas are considered together as a single abstract idea for analysis purposes.
Prong 2, Step 2A: Limitations that are not indicative of integration into a practical application include: (1) Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)), (2) Adding insignificant extra-solution activity to the judicial exception (MPEP 2106.05(g)), (3) Generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h)). Claim 1, and for similar claim(s) 8 and 9, recite i.e., a computer-assisted method, program, system w/ processor and additionally acquiring from a monitoring system (i.e., data delivered by a sensor required vehicle) and a display interface;. These additional elements are described at a high level in Applicant’s specification without any meaningful detail about their structure or configuration (see Applicant’s Specification, p. 4, lines 19-32). These elements in the steps are recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component and merely invoke such additional elements as a tool to perform the abstract idea. See MPEP 2106.05(f). Accordingly, these additional elements, even in combination, do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
As such, under Prong 2 of Step 2A, when considered both individually and as a whole, the limitations of Claim 1, and for similar claim(s) 8 and 9 are not indicative of integration into a practical application (Prong 2, Step 2A: NO). See MPEP 2106.04(d).
Since claim 1, and similar claim(s) 8 and 9 recites an abstract idea and fails to integrate the abstract idea into a practical application, claim 1, and similar claim(s) 8 and 9 is “directed to” an abstract idea under Step 2A (Step 2A: YES). See MPEP 2106.04(d).
Step 2B: The recitation of the additional elements is acknowledged, as identified above with respect to Prong 2 of Step 2A. These additional elements do not add significantly more to the abstract idea for the same reasons as addressed above with respect to Prong 2 of Step 2A.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of for claim 1, and for similar claim(s) 8 and 9, i.e., a computer-assisted method, program, system w/ processor and additionally acquiring from a monitoring system (i.e., data delivered by a sensor required vehicle) and a display interface;; amounts to no more than mere instructions to apply the exception using a generic computer component and do not add anything that is not already present when they are considered individually or in combination. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, under Step 2B, there are no meaningful limitations in claim 1, and similar claim(s) 8 and 9 that transform the judicial exception into a patent eligible application such that the claims amount to significantly more than the judicial exception itself (Step 2B: NO). See MPEP 2106.05.
Accordingly, under the Subject Matter Eligibility test, claim 1, and similar claim(s) 8 and 9 is ineligible.
Regarding Claims 2, 4-7 and 10-12, claims 2, 4-7 and 10-12 further defines the abstract idea that is present in their respective independent claims and hence are abstract for at least the reasons presented above w/ respect to “Certain Methods of Organizing Human Activity” as the claims recite commercial or legal interactions, (including agreements in the form of contracts, legal obligations, advertising, marketing or sales activities or behaviors, and business relations) - i.e., further features related to ordering the vehicles of a fleet of vehicles according to a maintenance need and/or further recite “Mental Processes” as the claims recite further concepts that can be performed in the human mind, including observations, evaluations, judgments, and opinions. These dependent claim does not include any additional elements that integrate the abstract idea into a practical application; as such elements are recited at a high level of generality such that it amounts not more than mere instructions to apply the exception using a generic computer component. Even in combination, these additional elements do not integrate the abstract idea into a practical application and do no not amount to significantly more than the abstract idea itself. Thus, the aforementioned claims are not patent-eligible.
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.
Claim(s) 1, 2, 4-11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rackley, III et al. (US 11,068,958 B1) (hereinafter Rackley) in view of Forrest et al. (US 2010/0204857 A1) and Anderson (US 2012/0310872 A1).
Regarding Claim 1;
Rackley discloses a computer-assisted method for performing ordering vehicles of a fleet of vehicles according to a maintenance need (col. 4, lines 28-51- As all computing systems, the solution presented herein can be viewed as a state machine that receives certain inputs and produces deterministic outputs based on the received inputs. The system and method presented here in generally operates to monitor fleet financial and operational performance and orchestrate the trading actions on vehicles in a subscription pool by collecting and processing data from distributed sensors, devices, databases and third party resources. The various embodiments of the system and method are referred to herein as a Fleet Optimization Engine or FOE and col. 4, lines 39-560 - [...] and maintenance and/or repair [...]and col. 5, lines 49-col. 6, lines 8 and [...] and col. 7, lines 15-18 - The various embodiments of the FOE can analyze and interpret the collected data to determine the expected operation and financial behavior of each vehicle in the subscription fleet and col. 8, lines 55-59 - Thus, the FOE may automatically and/or autonomously initiate and cause the execution of vehicle sales, acquisitions and trades or may simply provide instructions to a user interface to enable an operator, such as a fleet manager to perform such actions), wherein the method comprising:
for each vehicle in the fleet of vehicles, by a computer processor (col. 7, lines 15-18 - The various embodiments of the FOE can analyze and interpret the collected data to determine the expected operation and financial behavior of each vehicle in the subscription fleet):
determining a time series, the time series including, for each time step, an instantaneous value of at least one quantity of interest obtained from monitoring events, the monitoring events being acquired, from monitoring data delivered by a sensor equipped in the vehicle, by a monitoring system for monitoring the vehicles of the fleet of vehicles (col. 3, lines 54-64 - The overall cost to own and operate a vehicle can be viewed in the short term but, typically, the cost factor is viewed over a period of time, such as a one to three year period. Looking at the vehicle cost over such a longer period of time helps to normalize the cost factor or to minimize the effect of any one factor on the vehicle valuation and col. 5, lines 49-col. 6, lines 8 - The Subscription Fleet Data 110 may include, as illustrated in the exemplary functional diagram, Vehicle Data 111 or Vehicle Usage Data. The Vehicle Data 111 may include the following items, as well as those listed elsewhere herein, but is not limited to, metrics such as the mileage on the vehicle, the mileage logged per time period, the distribution of the mileage, the type of mileage (city, highway, hybrid), the fuel efficiency, usage patterns, maintenance events, diagnostic trouble codes, etc. The Vehicle Data 111 can be collected via telematics hardware and sensors embedded in subscription fleet vehicles (whether gathered through sensors that were installed when the vehicle was originally manufactured or through telematics devices installed subsequent to manufacturing) as well as applications and/or hardware that may be operating on member's personal computing devices (PCD) and that are associated with particular vehicles. Further, information collected using a member's device may also be loaded into and stored in on-vehicle memory or transmitted to a system accessible database. As an example, a smart phone may include an accelerometer and an application that may provide geospatial information, velocity, acceleration, driving analysis, etc. Similarly, telematics hardware and/or software operating within the vehicle may also provide similar information. All of this information may be collected and used in real time and/or stored within memory on the vehicle or a database for later utilization and analysis.), then
ordering a list of the vehicles of the fleet of vehicles according to properties ...detected for each vehicle (col. 4, lines 28-51- As all computing systems, the solution presented herein can be viewed as a state machine that receives certain inputs and produces deterministic outputs based on the received inputs. The system and method presented here in generally operates to monitor fleet financial and operational performance and orchestrate the trading actions on vehicles in a subscription pool by collecting and processing data from distributed sensors, devices, databases and third party resources. The various embodiments of the system and method are referred to herein as a Fleet Optimization Engine or FOE and col. 4, lines 39-560 - [...] and maintenance and/or repair [...] and col. 7, lines 15-18 - The various embodiments of the FOE can analyze and interpret the collected data to determine the expected operation and financial behavior of each vehicle in the subscription fleet and col. 8, lines 55-59 - Thus, the FOE may automatically and/or autonomously initiate and cause the execution of vehicle sales, acquisitions and trades or may simply provide instructions to a user interface to enable an operator, such as a fleet manager to perform such actions); and
(B) performing by at least one maintenance operator, maintenance operations for one or more vehicles among a predefined number if first vehicles from the ordered list (col. 8, lines 55-59 - Thus, the FOE may automatically and/or autonomously initiate and cause the execution of vehicle sales, acquisitions and trades or may simply provide instructions to a user interface to enable an operator, such as a fleet manager to perform such actions and col. 10, lines 33-40 - The fleet status may also result in triggering various types of events. For instance, as an operator updates the SVS through the operator interface 280, the operator can enter changes to the fleet, such as the addition of new vehicles, removing of sold assets or tagging assets as unavailable due to delivery or maintenance, etc... and col. 14, lines 51-55 - For instance, if the FOE indicates that X number of a particular vehicle model should be purchased, the FOE can reach out to effectuate such purchases).
Rackley fails to explicitly disclose [concepts of]:
analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time step, a state of the vehicle is either a "normal" state or an "abnormal" state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval, wherein said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and transition function, so as to, determine an instantaneous probability for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; and
detecting a presence, if any, of one or several burst(s) in the optimal sequence of states, wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state, then
[...] according to properties of the burst(s) detected for each vehicle.
However, in an analogous art, Forrest teaches [concepts of]:
for each vehicle in the fleet of vehicles ([0015] - a comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time):
determining a time series, the time series including, for each time step, an instantaneous value of at least one quantity of interest obtained from monitoring events, the monitoring events being acquired, from monitoring data delivered by a sensor equipped in the vehicle, by a monitoring system for monitoring the vehicles of the fleet of vehicles ([0010] - on-board data acquisition means comprising sensors and pre-processing means responsive to the sensors for generating rail vehicle-related data representative of the operation of monitored rail vehicle components and/or of the rail vehicle environment of each rail vehicle of the fleet and [0015] and [0023] - generating rail vehicle-related data representative of the operation of monitored rail vehicle components and/or of the environment of each rail vehicle of the fleet and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time);
analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time step ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time), a state of the vehicle is either a "normal" state or an "abnormal" state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval..., so as to, determine ... for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time and [0044] - The dynamic attributes are parameters that are technically significant for the behaviour of the monitored component, e.g. parameters that may have a causal effect on the state of monitored component, or additional data useful for understanding the event, such as time of malfunction and operation being undertaken at the time of malfunction. For example, in trying to analyze wheels, the data will be visualised by car number, number of events. Accordingly, other aspects such as doors will be ignored. Filters can be used to select the analysed data, e.g. rail vehicle range, vehicle speed higher than a predetermined value, rail infrastructure range, etc.);
detecting a presence, if any, of one or several burst(s) in the optimal sequence of states ([0015] and [0028] - a comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time), wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state ((0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time [0044] and [0045] - The database of historical events 44 can also be used to built a behaviour model for each monitored component of the rail system, i.e. a database containing data indicative of tolerances ranges, normal conditions and trends. The sensor data can then be compared to the behaviour model to more efficiently predict future faults); As construed a significant difference is a minimum number of time steps in an “abnormal sate” [and]
[issuing recommendation] according to properties of the burst(s) detected for each vehicle ([0045]).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Forrest to the computer-implemented method for ordering the vehicles of a fleet of vehicles according to a maintenance need of Rackley to include analyzing, over a predetermined time interval, the time series, taking into account the time series determined for all the vehicles of the fleet of vehicles, by considering that, at each time, a state of the vehicle is either a "normal" state or an "abnormal" state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval..., so as to, determine ... for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state; detecting a presence, if any, of one or several burst(s) in the optimal sequence of states, wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state, [and] [issuing recommendation] according to properties of the burst(s) detected for each vehicle.
One would have been motivated to combine the teachings of Forrest to Rackley to do so as it provides / allows a system that more fully integrates the data from rail infrastructure and from the rail vehicles to allow more efficient monitoring of the complete rail system (infrastructure and vehicles), and in particular to enable identification of previously unknown failure signatures (Forrest, [0008]).
However, in an analogous art regarding computer-decision making, Anderson teaches
wherein said analyzing of the time series comprises ... a cost function, the cost function being associated a likelihood function and transition function, so as to, determine an instantaneous probability for each time step... ([0010] - The program facilitates input of relevant information from a database and/or from a user via the user input interface; validation, checking and correction of input errors; generation of elements from the input that are used for formulating a functional equation; solving the functional equation, and presenting the user with advice via the user output device. The elements generated from the input information include (1) a set of states that describe possible outcomes, (2) a set of possible actions that may be taken by a decision maker, (3) a transition probability function representative of the likelihood of a particular state occurring at a future time based on the current state and the particular action taken by the decision maker, (4) a reward function representative of the benefits and costs associated with each possible action and state, (5) a discount factor that is representative of the relative preference for receiving a benefit now and at a future time, and (6) a time index that establishes a special ordering of event).
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Anderson to the optimized cost function of Rackley to include being associated a likelihood function and transition function, so as to, determine an instantaneous probability for each time step...
One would have been motivated to combine the teachings of Anderson to Rackley and Forest to do so as it provides / allows analyze in the context of business, personal, and policy problems. (Anderson, [0004] and [0007]).
Regarding Claim 2;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Forest further teaches wherein the quantity of interest is the total number of monitoring events or the total number of monitoring events of a particular type affecting the vehicle during a reference time window ([0015] and [0021] - The comparison means may comprise counting means for counting the number of occurrences of a predetermined event in each series, and means for comparing said numbers of occurrences, either graphically or numerically. Such graphical displays may include, but are not limited to, histograms, bar charts, column charts, line charts, scatter plots and/or time series plots and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time.
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest and Anderson, as per claim 1, above.
Regarding Claim 4;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Forrest further teaches wherein the instantaneous [results] for each time step is compared with a reference [results] calculated from the quantities of interest of all vehicles of the fleet of vehicles, to determine an optimal state of the vehicle at each time step ([0015] and [0021] - The comparison means may comprise counting means for counting the number of occurrences of a predetermined event in each series, and means for comparing said numbers of occurrences, either graphically or numerically. Such graphical displays may include, but are not limited to, histograms, bar charts, column charts, line charts, scatter plots and/or time series plots and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time).
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest, as per claim 1, above.
Anderson further teaches [calculating] the instantaneous probability for each time step ([0010] - The program facilitates input of relevant information from a database and/or from a user via the user input interface; validation, checking and correction of input errors; generation of elements from the input that are used for formulating a functional equation; solving the functional equation, and presenting the user with advice via the user output device. The elements generated from the input information include (1) a set of states that describe possible outcomes, (2) a set of possible actions that may be taken by a decision maker, (3) a transition probability function representative of the likelihood of a particular state occurring at a future time based on the current state and the particular action taken by the decision maker, (4) a reward function representative of the benefits and costs associated with each possible action and state, (5) a discount factor that is representative of the relative preference for receiving a benefit now and at a future time, and (6) a time index that establishes a special ordering of event).
Similar rationale and motivation is noted for the combination of Anderson to Rackley in view of Forest and Anderson as per claim 1, above.
Regarding Claim 5;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Forest further teaches wherein detecting the presence of one or several burst(s) in the optimal sequence of states consists of determining a number of consecutive time steps the vehicle is in the "abnormal" state in the optimal sequence of states, and, when the number of consecutive time steps is greater than a predetermined threshold, considering the consecutive time steps as a burst ([0015] and [0021] - The comparison means may comprise counting means for counting the number of occurrences of a predetermined event in each series, and means for comparing said numbers of occurrences, either graphically or numerically. Such graphical displays may include, but are not limited to, histograms, bar charts, column charts, line charts, scatter plots and/or time series plots and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time). As construed events data that is significant different is noted to be a form of determining a number of consecutive time steps the vehicle is in the "abnormal" state in the optimal sequence of states and thus being greater than a threshold.
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest and Anderson, as per claim 1, above.
Regarding Claim 6;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Rackley further teaches wherein ordering the list of vehicles of the fleet of vehicles according to the properties [detected] (col. 4, lines 28-51- As all computing systems, the solution presented herein can be viewed as a state machine that receives certain inputs and produces deterministic outputs based on the received inputs. The system and method presented here in generally operates to monitor fleet financial and operational performance and orchestrate the trading actions on vehicles in a subscription pool by collecting and processing data from distributed sensors, devices, databases and third party resources. The various embodiments of the system and method are referred to herein as a Fleet Optimization Engine or FOE and col. 4, lines 39-560 - [...] and maintenance and/or repair [...] and col. 7, lines 15-18 - The various embodiments of the FOE can analyze and interpret the collected data to determine the expected operation and financial behavior of each vehicle in the subscription fleet and col. 8, lines 55-59 - Thus, the FOE may automatically and/or autonomously initiate and cause the execution of vehicle sales, acquisitions and trades or may simply provide instructions to a user interface to enable an operator, such as a fleet manager to perform such actions),.
Forest further teaches wherein each burst is characterized by a duration and/or an intensity, and wherein [recommending] according to the properties of the burst(s) detected for each vehicle of the fleet of vehicles is based on the duration and/or the intensity of each burst ([0015] and [0021] - The comparison means may comprise counting means for counting the number of occurrences of a predetermined event in each series, and means for comparing said numbers of occurrences, either graphically or numerically. Such graphical displays may include, but are not limited to, histograms, bar charts, column charts, line charts, scatter plots and/or time series plots and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time). As construed a series of events is a form of duration.
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest and Anderson, as per claim 1, above.
Regarding Claim 7;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Forest further teaches wherein the vehicle is a railway vehicle ([0009] – [...] one fleet of rail vehicles [...] and [0015] and [0028] – [...] any rail vehicle [...]).
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest and Anderson, as per claim 1, above.
Regarding Claim 8;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Rackley in view of Forest disclose a storage and memory device storing instructions which, when executed by a computer, implement the method according to claim 1, part (A). (see Claim 1, and further Rackley, FIG. 3 and col. 11, lines 42-col.12, line 16 and Forest, FIG. 2 and [0038]),
Regarding Claim 9;
Rackley in view of Forest and Anderson disclose the method to Claim 1.
Rackley in view of disclose computer system comprising hardware and software for implementing the method according to claim 1, part (A) (see Claim 1, and further Rackley, FIG. 3 and col. 11, lines 42-col.12, line 16 and Forest, FIG. 2 and [0038]), the computer system accessing the contents of a monitoring database for reading the monitoring events acquired by the monitoring system (Rackley, FIG. 2 – Datastore).
Regarding Claim 10;
Rackley in view of Forest and Anderson disclose the system to Claim 9.
Rackley further discloses further comprising:
a module for determining a time series of a quantity of interest for each vehicle of a fleet of vehicles (col. 3, lines 54-64 - The overall cost to own and operate a vehicle can be viewed in the short term but, typically, the cost factor is viewed over a period of time, such as a one to three year period. Looking at the vehicle cost over such a longer period of time helps to normalize the cost factor or to minimize the effect of any one factor on the vehicle valuation and col. 5, lines 49-col. 6, lines 8 - The Subscription Fleet Data 110 may include, as illustrated in the exemplary functional diagram, Vehicle Data 111 or Vehicle Usage Data. The Vehicle Data 111 may include the following items, as well as those listed elsewhere herein, but is not limited to, metrics such as the mileage on the vehicle, the mileage logged per time period, the distribution of the mileage, the type of mileage (city, highway, hybrid), the fuel efficiency, usage patterns, maintenance events, diagnostic trouble codes, etc. The Vehicle Data 111 can be collected via telematics hardware and sensors embedded in subscription fleet vehicles (whether gathered through sensors that were installed when the vehicle was originally manufactured or through telematics devices installed subsequent to manufacturing) as well as applications and/or hardware that may be operating on member's personal computing devices (PCD) and that are associated with particular vehicles. Further, information collected using a member's device may also be loaded into and stored in on-vehicle memory or transmitted to a system accessible database. As an example, a smart phone may include an accelerometer and an application that may provide geospatial information, velocity, acceleration, driving analysis, etc. Similarly, telematics hardware and/or software operating within the vehicle may also provide similar information. All of this information may be collected and used in real time and/or stored within memory on the vehicle or a database for later utilization and analysis.),;
a module for scheduling the vehicles of a fleet of vehicles according to properties of the ... detected (col. 4, lines 17-27).
Forest further teaches
a module for analyzing the time series of a quantity of interest, for determining an optimal sequence of states ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time), a state of the vehicle is either a "normal" state or an "abnormal" state, the "abnormal" state requiring maintenance, so as to obtain an optimal sequence of states over the predetermined time interval ([0015] and [0028] - comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time and [0044] - The dynamic attributes are parameters that are technically significant for the behaviour of the monitored component, e.g. parameters that may have a causal effect on the state of monitored component, or additional data useful for understanding the event, such as time of malfunction and operation being undertaken at the time of malfunction. For example, in trying to analyze wheels, the data will be visualised by car number, number of events. Accordingly, other aspects such as doors will be ignored. Filters can be used to select the analysed data, e.g. rail vehicle range, vehicle speed higher than a predetermined value, rail infrastructure range, etc.);
a module for detecting, if any, one or several burst(s) in the optimal sequence of states ([0015] and ] - a comparing the series of categorized event data representative of at least one category of events over any predetermined period of time and for identifying any location of the rail infrastructure and/or any rail vehicle which exhibits a series of events data that is significantly different from the other locations of the rail infrastructure and/or rail vehicles of the fleet over said predetermined period of time); and
a module for scheduling the vehicles of a fleet of vehicles according to properties of the bursts detected ([0045] - The data centre 16 is linked to rail vehicle maintenance facilities 40, rail infrastructure maintenance facilities 42 and can issue recommendations to the maintenances facilities 40, 42 and to the rail vehicles 12 when a fault is detected or preventive maintenance is advisable).
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest and Anderson, as per claim 1, above.
Regarding Claim 11;
Rackley in view of Forest and Anderson disclose the system to Claim 9.
Forest further teaches wherein the vehicle is a railway vehicle ([0009] – [...] one fleet of rail vehicles [...] and [0015] and [0028] – [...] any rail vehicle [...]).
Similar rationale and motivation is noted for the combination of Forest to Rackley in view of Forest, as per claim 1, above.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Rackley, III et al. (US 11,068,958 B1) (hereinafter Rackley) in view of Forrest et al. (US 2010/0204857 A1) and Anderson (US 2012/0310872 A1) and further in view of Schuchardt et al. (US 2006/0265235 A1).
Regarding Claim 12;
Rackley in view of Forest and Anderson disclose the system to Claim 1.
Rackey discloses receiving from the maintenance operator an input to select said one or more vehicles col. 8, lines 55-59 - Thus, the FOE may automatically and/or autonomously initiate and cause the execution of vehicle sales, acquisitions and trades or may simply provide instructions to a user interface to enable an operator, such as a fleet manager to perform such actions and col. 10, lines 33-40 - The fleet status may also result in triggering various types of events. For instance, as an operator updates the SVS through the operator interface 280, the operator can enter changes to the fleet, such as the addition of new vehicles, removing of sold assets or tagging assets as unavailable due to delivery or maintenance, etc... and col. 14, lines 51-55 - For instance, if the FOE indicates that X number of a particular vehicle model should be purchased, the FOE can reach out to effectuate such purchases).
Rackley fails to explicitly disclose wherein part (A) further comprises displaying on an interface identifiers of a predefined number first vehicles of the list.
However, in an analogous art, Schuchardt teaches [concepts of] wherein part (A) further comprises displaying on an interface identifiers of a predefined number first vehicles of the list, and receiving from the maintenance operator an input to select said one or more vehicles ([0110] - FIG. 21(d) lists individually lists the leased vehicles that made up the vehicle count selected by the user from the table of FIG. 21(c). The list of FIG. 21(d) also displays pertinent data about those vehicles' maintenance management attributes. Through the checkboxes adjacent each listed vehicle, the user can selectively choose one or more vehicles upon which a maintenance action will be simultaneously performed. Also, via the topmost checkbox adjacent the maintenance management header, the user can select all of the listed vehicles without the need to individually select each listed vehicle and In the claims - 15. The system of claim 14 wherein at least a plurality of the vehicle leases comprise customer vehicle leases, wherein the GUI that is configured to allow simultaneous maintenance actions is further configured to (1) receive input from the user indicative of a selection of a customer, (2) display a list of selectable activated customer vehicle leases for the selected customer, and (3) allow the user to select at least a plurality of the listed activated customer vehicle leases to thereby define the plurality of activated customer vehicle leases for which maintenance actions will be performed simultaneously.)
Therefore, it would have been obvious to one of ordinarily skill in the art before the effective filing date of the claimed invention to combine the teachings of Schuchardt to the list of Rackley in view of Forest and Anderson to include [concepts of] wherein part (A) further comprises displaying on an interface identifiers of a predefined number first vehicles of the list, and receiving from the maintenance operator an input to select said one or more vehicles.
One would have been motivated to combine the teachings of Schuchardt to Rackley in view of Forest and Anderson to do so as it provides / allows managing a plurality of vehicle leases, preferably on behalf of customers, a task often referred to as fleet management (Schuchardt, [0002]).
Conclusion
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/ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626
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