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, 4-11 are pending for examination. Claim 1, 4, 9, and 11 have been amended. Claim 3 has been cancelled. This action is Final.
Response to Arguments
The objection to claim 11 is withdrawn the amendment to claim 11 has appropriately corrected the objection.
The 35 U.S.C. 112(b) rejection to Claim(s) 1-11 is withdrawn as the amendment to claim 1 has appropriately corrected the insufficient antecedent basis issue.
Applicant's arguments filed 11/14/2025 with respect to the 35 U.S.C. 103 rejection have been fully considered but they are not persuasive.
Applicant Argues: Without acquiescing to these rejections and merely to facilitate allowance of the present application, however, Applicant has amended independent Claim 1 to more precisely define the nature of the optimization and the burst detection so as to more clearly distinguish from the cited references. Specifically, Claim 1 has been amended to clarify the phrase "optimal sequence" by adding "said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and a transition function, so as to determine an instantaneous probability for each time step", and clarify the term "burst" by adding "the burst is characterized by a minimum number of consecutive time steps in the "abnormal" state" based on the paragraph at page 8, lines 3-6 in the specification as filed. Applicant respectfully submits that, even when combined, the cited references neither teach nor suggest at least these limitations recited in presently pending Claim 1.
As noted by the Examiner in the Office Action, Rackley does not disclose the "analyzing" and "detecting" features of the claimed invention. The Office Action, however, alleges that these features are disclosed in Forrest. Indeed, Forrest deals with identifying abnormalities by detecting deviations in categorized event data representative of the occurrence of categorized events on a given rail vehicle over time. Forrest, however, does not disclose or suggest the time series of the state of the train, especially after optimization, to obtain an "optimal sequence" that is more precisely defined in pending Claim 1.
Anderson, cited in connection with the rejection of Claims 3-4, only discloses a general method, and is not applied to the optimization of time series of "normal" or "abnormal" states. Thus, Anderson does not address the deficiencies in the teachings of Forrest
Accordingly, even when both cited references are combined, they do not teach or suggest the "optimal sequence" and "burst" defined in the claimed invention, therefore Applicant respectfully submits that Claim 1 is nonobvious and patentable over the cited references. The remaining rejected claims incorporate all the features of Claim 1 through their dependencies.
Therefore, the dependent claims are also patentable over the prior art for at least the same reasons that Claim 1 is patentable as well as for their own patentable features. For all the foregoing reasons, withdrawal of the rejections under 35 U.S.C. § 103, and allowance of the presently pending claims are respectfully requested.
Examiner’s Response: The examiner respectfully disagrees. In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
The examiner respectfully notes that the combination of Rackley in view of Forrest and Anderson disclose the limitations as found in amended claim 1. The examiner respectfully notes while Rackley does not disclose the "analyzing" and "detecting" features of the claimed invention; such limitations are indeed taught by Forest and Anderson.
As noted in the rejection below, Anderson 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 teaches concepts finding a significant difference for events during a given time period step, see ⁋[0028]. The examiner notes with reasonable construction such a teaching reads on “for each time step for each vehicle of the fleet of vehicle to be in the “normal” state or in the “abnormal” state” and further “wherein the burst is characterized by a minimum number of consecutive time steps in the “abnormal” state”. The examiner notes if a significant difference for events during a given time period step, it construed to be, a minimum number of consecutive steps during a given time period, thus represents a burst in an “abnormal” state as such significant different is identified base on the exhibited events during that time period.
As noted in the rejection below, Anderson is shown to teach concepts related to
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).
The examiner respectfully notes that Anderson teaches the general concept of “(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,” in [0010] which reads on the aforementioned limitation as Anderson discusses likelihood and transition of states/times with respect to probability.
The examiner notes based on the teachings Forest and Anderson can be combined to the disclosure of Rackley to in order to make obvious the features of as found in amended claim 1. Therefore, the examiner finds this argument not persuasive.
Applicant's arguments filed 11/14/2025 with respect to the 35 U.S.C. 101 rejection have been fully considered but they are not persuasive.
Applicant Argues: Claim 8 is rejected under 35 U.S.C. § 101 based on an assertion that the claimed invention is directed to software per se. Also, Claims 1-11 are rejected under 35 U.S.C. § 101 based on assertion that the claimed invention is directed to an abstract idea without significantly more.
Applicant respectfully traverses each rejection, and does not acquiesce in the validity of the rejections. To expedite allowance of this application, however, Applicant has amended independent Claim 1 to establish a link with physical reality, both through inputs (data comes from a sensor equipped in the vehicle) and outputs (displaying step in which only a predefined number of the first vehicles are displayed on the interface). Thus, the method of Claim 1 enables the operator to select the highest priority trains for maintenance by reducing and minimizing the time during which trains cannot be used.
Also, as stated above, Claim 1 has been amended to clarify the phrase "optimal sequence" by adding "said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and a transition function, so as to determine an instantaneous probability for each time step", and clarify the term "burst" by adding "the burst is characterized by a minimum number of consecutive time steps in the "abnormal" state" which are not disclosed or suggested by any of the cited references. Due to these features added, it became possible to quickly and simply identify and select vehicles from the fleet of vehicles which have a priority need for maintenance in efficient way by reducing and minimizing the time during which trains or equipment cannot be used or available as stated in paragraphs from page 1, line 24 to page 2, line 33; and paragraphs from page 8, line 26 to page 9, line 2 in the specification as filed. Thus, these steps provide significantly more than any abstract idea that may be recited in the claim. For this reason alone, the claims are directed to patent eligible subject matter.
Moreover, Applicant respectfully submits that any abstract ideas of Claim 1 are integrated into a practical application of improving the functioning of a computer. By determining "an instantaneous probability for each time step," Claim 1 minimizes computer resources because such resources not be consumed in time-consuming calculations. It is well-established that improving the functioning of a computer is a practical application that renders any abstract idea that might be recited in the claim into patent-eligible subject matter. Indeed, even Claim 8, which recites a computer program implementing the steps of Claim 1, is integrated into this practical application.
Accordingly, neither Claim 1 nor Claim 8 is directed to subject matter that is not merely abstract.
The remaining rejected claims incorporate all the features of Claim 1 through their dependencies. Therefore, the dependent claims are also patentable for at least the same reasons that Claim 1 is patentable. For all the foregoing reasons, withdrawal of the rejections under 35
U.S.C. § 101, and allowance of the presently pending claims are respectfully requested.
Examiner’s Response: The examiner respectfully disagrees. The examiner respectfully notes respectfully notes that the amended features that “link with physical reality, both through inputs (data comes from a sensor equipped in the vehicle) and outputs (displaying step in which only a predefined number of the first vehicles are displayed on the interface)” are noted to be additional elements that 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. Further even when considered separately and as an ordered combination, they do not add significantly more to the exception as they amount 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.
Applicant’s further amendments, i.e., “...clarify the phrase "optimal sequence" by adding "said analyzing of the time series comprises an optimization of a cost function, the cost function being associated a likelihood function and a transition function, so as to determine an instantaneous probability for each time step", and clarify the term "burst" by adding "the burst is characterized by a minimum number of consecutive time steps in the "abnormal" state" which are not disclosed or suggested by any of the cited references;” are noted to be features that are part of the abstract idea. 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.
Further, the examiner respectfully notes that claim 8 is still devoid of any actual structure that embodies the computer program. Therefore, it is still an idea without any physical embodiment, and therefore, does not fall within any statutory category.
Therefore, the examiner finds these arguments 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.
Claims 8 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter for being directed to software per se. Claim 8 recites “a computer program,” is noted to be software expressed as code or a set of instructions detached from any medium; thus is an idea without physical embodiment, and therefore does not fall within any statutory category, see MPEP 2106.03. For purposes of compact prosecution, claims 8 is reanalyzed under the full 2 step process, as if they passed step 1.
Claim(s) 1-11 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) 1-11 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:
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, then
ordering a list of the vehicles of the fleet of vehicles according to properties of the burst(s) detected for each vehicle; and
displaying
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 displaying... identifiers of a predefined number of first vehicles of the 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.
The above limitations, under their broadest reasonable interpretation, fall within the “Mental Processes” grouping of abstract ideas, enumerated in MPEP 2106.04(a)(2)(III), in that they recite as concepts performed in the human mind, including observations, evaluations, judgments, and opinions. That is, other than reciting for claim 1, and for similar claim(s) 8 and 9, i.e., a computer implemented method, program, system and additionally acquiring from a monitoring system (i.e., data delivered by a sensor required vehicle) and a display interface; nothing in these claim element(s) precludes the step(s) from practically being performed in the mind. For example, the broadest reasonable interpretation of these limitations for claim 1, and 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 displaying... identifiers of a predefined number of first vehicles of the list and further accessing contents... read, thus, encompasses steps that a user can manually perform in the human mind or by a human using a pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “mental processes” grouping of abstract ideas.
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 implemented method, program, system 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 implemented method, program, system 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 2-7 and 10-11, claims 2-7 and 10-11 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-implemented method for 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 (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
displaying on an interface, identifiers of a predetermined number of first vehicles on the 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] 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):
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 ] - 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 ] - 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 computer program including software instructions which, when executed by a computer, implement the method according to claim 1 (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 (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.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ASFAND M SHEIKH whose telephone number is (571)272-1466. The examiner can normally be reached Mon-Fri: 7a-3p (MDT).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, JESSICA LEMIEUX can be reached at (571)270-3445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/ASFAND M SHEIKH/ Primary Examiner, Art Unit 3626