DETAILED CORRESPONDENCE
This Office action is in response to the application filed 5/13/2025.
Claim Status
Claims 1-20 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 5/13/2025 complies with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Howard et al., US 2021/0107538 hereinafter “Howard”.
Claim 1. Howard teaches a file retrieval management system configured to implement one or more algorithms to facilitate retrieval of files and logs of railroad events for various systems, comprising:
a memory having a train system log ([0047] The on-board controller 200 and the off-board remote controller interface 204 may include any means for monitoring, recording, storing, indexing, processing, and/or communicating various operational aspects of the locomotive 208, 248. These means may include components such as, for example, a memory, one or more data storage devices, a central processing unit, or any other components that may be used to run an application.); and
a processor operably coupled to the memory and capable of executing machine-readable instructions to perform program steps, the program steps comprising: receiving incoming messages related to railroad event notifications ([0066]—“The analytics engine 318 and virtual system modeling engine 324 may be configured to implement pattern/sequence recognition into a real-time decision loop that, e.g., is enabled by machine learning… Associative memory is built through “experiential” learning in which each newly observed state is accumulated in the associative memory as a basis for interpreting future events. Thus, by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” [0067]—“The machine learning algorithms assist in uncovering the patterns and sequencing of alarms to help pinpoint the location and cause of any actual or impending failures of physical systems or computer systems.” Here computer systems have processor.), the program steps comprising:
querying an internal service queue ([0068] describes this element as such—“…to access the database as needed”);
classify the incoming messages and the notifications ([0076] reads on this element as such—“ the learning system may include a classification engine, such as a support vector machine (SVM). The SVM can be configured to generate a mapping of first input conditions to first response maneuvers. For example, the machine learning engine may be configured to train the SVM to generate one or more rules configured to classify training pairs (e.g., each first input condition and its corresponding first response maneuver). The classification of training pairs can enable the mapping of first input conditions to first response maneuvers by classifying particular first response maneuvers as corresponding to particular first input conditions. Once trained, the learning system can generate the second response maneuver based on the second input condition by applying the mapping or classification to the second input condition.”);
parsing the incoming messages and the notification for relevant information ([0066] reads on this element as such—“Thus, by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses. The analytics engine 318 is also better able to understand component mean time to failure rates through observation and system availability characteristics. This technology in combination with the virtual system model can present a novel way to digest and comprehend alarms in a manageable and coherent way.”); and
wherein the relevant information includes information about a train enforcement event ([0076] read on this element as such—“For example, the machine learning engine may be configured to train the SVM to generate one or more rules configured to classify training pairs (e.g., each first input condition and its corresponding first response maneuver)”).
Claim 2. Howard teaches the system of Claim 1 and further teaches, wherein the railroad event notification can include enforcement events, such as a PTC brake event ([0002]—“For example, the supplied tractive and/or braking efforts may be based on Positive Train Control (PTC) instructions or control information for an upcoming trip. The control information may be used by a software application to determine the speed of the rail vehicle for various segments of an upcoming trip of the rail vehicle.”).
Claim 3. Howard teaches the system of Claim 1 and further teaches, the program steps further comprising:
receiving the incoming messages from and transmitting outgoing messages to a file retrieval manager (FRM) ([0059] read on this element as such—“Analytics engine 318 can be configured to generate predicted data for the monitored systems and analyze differences between the predicted data and the real-time data received from data acquisition hub 312. Analytics server 316 may be interfaced with a monitored train control system 302 via sensors, e.g., sensors 304, 306, and 308. The various sensors are configured to supply real-time data from the various physical components and computer systems and subsystems of train 102. The real-time data is communicated to analytics server 316 via data acquisition hub 312 and network 314. Hub 312 can be configured to provide real-time data to analytics server 316 as well as alarming, sensing and control featured for the monitored system 302, such as the train control system 100.”).
Claim 4. Howard teaches the system of Claim 3 and further teaches, wherein the outgoing messages can request train system logs corresponding to one or more of the enforcement events ([0003]—“The control system must be capable of transmitting data messages having the information used to control the tractive and/or braking efforts of the rail vehicle and other operational characteristics of the various consist subsystems while the rail vehicle is moving”).
Claim 5. Howard the system of Claim 4 and further teaches, the program steps further comprising:
generating a time window based on the data surrounding a time of the enforcement event ([0065] describes this element as such—“….A divergence between the real-time sensor output values and the predicted output values may generate either an alarm condition for the values in question and/or a calibration request that is sent to a calibration engine 334.”).
Claim 6. Howard teaches the system of Claim 1 and further teaches, the program steps further comprising: generating an authentication token for a particular user, session, or request (Howard in [0040] reads on this element as such “Access to the GUI may require user authentication, such as, for example, a username, a password, a pin number, an electromagnetic passkey, etc., to display certain information and/or functionalities of the GUI.”).
Claim 7. Howard teaches a watchdog system configured to implement one or more algorithms to facilitate status monitoring of train system logs, comprising:
a memory having a train system log ([0047] The on-board controller 200 and the off-board remote controller interface 204 may include any means for monitoring, recording, storing, indexing, processing, and/or communicating various operational aspects of the locomotive 208, 248. These means may include components such as, for example, a memory, one or more data storage devices, a central processing unit, or any other components that may be used to run an application.); and
a processor operably coupled to the memory and capable of executing machine- readable instructions to perform program steps (Taken together the following cited section reads on this element. [0066]—“The analytics engine 318 and virtual system modeling engine 324 may be configured to implement pattern/sequence recognition into a real-time decision loop that, e.g., is enabled by machine learning… Associative memory is built through “experiential” learning in which each newly observed state is accumulated in the associative memory as a basis for interpreting future events. Thus, by observing normal system operation over time, and the normal predicted system operation over time, the associative memory is able to learn normal patterns as a basis for identifying non-normal behavior and appropriate responses, and to associate patterns with particular outcomes, contexts or responses.” [0067]—“The machine learning algorithms assist in uncovering the patterns and sequencing of alarms to help pinpoint the location and cause of any actual or impending failures of physical systems or computer systems.” Here computer systems have processor.), the program steps comprising:
querying an internal service queue ([0068] describes this element as such—“…to access the database as needed”);
determining whether an internal service queue includes a new enforcement message ([0033] teaches a scenario that read on this element as such—“Any data packet of information received from the off-board remote controller interface 104 may include header information or other means of identifying which locomotive in which locomotive consist the information is intended for. Although the lead communication unit 120 on the lead consist may be the unit that initiates the transmission of data packets forming a message to the off-board, remote controller interface 104, all of the lead and trailing communication units may be configured to receive and transmit data packets forming messages. Accordingly, in various alternative implementations according to this disclosure, a command control signal providing operational commands for the lead and trailing locomotives may originate at the remote controller interface 104 rather than at the lead powered unit 108 of the lead consist 114.”);
identifying whether an automation service is executing and execute an automation process ([0048] reads on this element as such—“The off-board remote controller interface 204 may be configured to execute instructions stored on non-transitory computer readable medium to perform methods of remote control of the locomotive 230.”);
receiving processed events including results from the automation process ([0071]—[0062]“The off-board remote controller interface 204 may be configured to execute instructions stored on non-transitory computer readable medium to perform methods of remote control of the locomotive 230.”);
generating a notification when the automation process is complete ([0062] reads on this element as such—“If the differential exceeds the alarm threshold value, an alarm or notification message may be generated by the analytics engine 318. The alarm or notification message may be sent directly to the client (i.e., user) 328 for display in real-time on a web browser, pop-up message box, e-mail, or equivalent on the client 328 display panel. In another embodiment, the alarm or notification message may be sent to a wireless mobile device to be displayed for the user by way of a wireless router or equivalent device interfaced with the analytics server 316.”).
Claim 8. Howard teaches the system of Claim 7 and further teaches, the program steps further comprising:
generating a record when the internal service queue includes a new enforcement message (Accordingly, in various alternative implementations according to this disclosure, a command control signal providing operational commands for the lead and trailing locomotives may originate at the remote controller interface 104 rather than at the lead powered unit 108 of the lead consist 114.).
Claim 9. Howard teaches the system of Claim 8, wherein the record includes a collection of enforcement events based on enforcement messages from the internal service queue ([0062]— “If the differential exceeds the alarm threshold value, an alarm or notification message may be generated by the analytics engine 318. The alarm or notification message may be sent directly to the client (i.e., user) 328 for display in real-time on a web browser, pop-up message box, e-mail, or equivalent on the client 328 display panel. In another embodiment, the alarm or notification message may be sent to a wireless mobile device to be displayed for the user by way of a wireless router or equivalent device interfaced with the analytics server 316.”).
Claim 10. Howard teaches the system of Claim 8 and further teaches, the program steps further comprising:
updating the record to indicate whether train system logs are available or not ([0061] teaches a scenario that reads on this element as such—“These models may be continuously and automatically synchronized with the actual status of the control systems based on the real-time data provided by the data acquisition hub 312 to analytics server 316. In other words, the models are updated based on current switch status, breaker status, e.g., open-closed, equipment on/off status, etc. Thus, the models are automatically updated based on such status, which allows a simulation engine to produce predicted data based on the current train operational status. This in turn, allows accurate and meaningful comparisons of the real-time data to the predicted data. Example models that can be maintained and used by analytics server 316 may include models used to calculate train trip optimization, determine component operational requirements for improved asset life expectancy, determine efficient allocation and utilization of computer control systems and computer resources, etc. In certain embodiments, data acquisition hub 312 may also be configured to supply equipment identification associated with the real-time data. This identification can be cross referenced with identifications provided in the models.”).
Claim 11. Howard teaches the system of Claim 7 and further teaches, wherein the processed events can include a root cause of the enforcement event ([0067] describes this element as such “The machine learning algorithms assist in uncovering the patterns and sequencing of alarms to help pinpoint the location and cause of any actual or impending failures of physical systems or computer systems”).
Claim 12. Howard teaches the system of Claim 7 and further teaches, wherein the notification corresponds to a result of processed events ([0062] teaches a scenario that reads on this element as such—“ If the differential exceeds the alarm threshold value, an alarm or notification message may be generated by the analytics engine 318. The alarm or notification message may be sent directly to the client (i.e., user) 328 for display in real-time on a web browser, pop-up message box, e-mail, or equivalent on the client 328 display panel. In another embodiment, the alarm or notification message may be sent to a wireless mobile device to be displayed for the user by way of a wireless router or equivalent device interfaced with the analytics server 316. The alarm can be indicative of a need for a repair event or maintenance, such as synchronization of any computer control systems that are no longer communicating within allowable latency parameters.”).
Claims 13-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wang et al., US 2018/0312180 hereinafter “Howard”.
Claim 13. Wang teaches an automated production system configured to implement one or more algorithms to facilitate automated workflow and identify a root cause of a train event, comprising:
a memory having a train system log ([0048] reads on this element as such—“ The output 530 may output data to an embedded display of the device 500, an externally connected display, a cloud, another device, and the like. The storage device 540 is not limited to any particular storage device and may include any known memory device such as RAM, ROM, hard disk, and the like.” [0055] –“memory”); and
a processor operably coupled to the memory and capable of executing machine- readable instructions to perform program steps ([0048]—The processor 520 may include one or more processing devices each including one or more processing cores. In some examples, the processor 520 is a multicore processor or a plurality of multicore processors. Also, the processor 520 may be fixed or it may be reconfigurable. The output 530 may output data to an embedded display of the device 500, an externally connected display, a cloud, another device, and the like.), the program steps
comprising: receiving data corresponding to the train system log ([0033] along with [0046]-[0047] teaches —“In response to receiving information about a road failure event associated with the locomotive 110, the application hosted by the cloud computing platform 130 may determine a root cause for the current locomotive failure by determining a similarity of the keywords of each root cause of the historical road failures with respect to the received textual data of the current locomotive failure…. receiving textual data associated with a current locomotive failure. For example, the textual data may be provided from multiple different sources, for example, textual data related to machine parameter readings, repair comments, material usage descriptions, symptom descriptions, defect descriptions, action comments, incident descriptions, part descriptions, root cause descriptions, symptom codes, and the like.”);
collecting second data surrounding a time of an enforcement event to generate a time window ([0053]—“For example, the search may be performed by the virtual machine (VM) 640. Here, the search may be performed based on a fleet number, a predetermined time period, a company, a geographic location, a type of failure, and the like.”);
labeling the data with unique identifiers ([0007] describes this element as such “storing information about a plurality of root causes of historical equipment failures and respective keywords associated with each root cause, receiving textual data associated with a current equipment failure, determining a similarity for each root cause, from among the plurality of root causes, with respect to the current equipment failure, by determining a similarity of the keywords of the root cause and the received textual data of the current equipment failure, selecting at least one root cause for the current equipment failure based on the determined similarities of the plurality of root causes, and outputting information about the at least one selected root cause for the current equipment failure for display on a display device.”);
extracting data using data manipulation techniques ([0025] along with [0052] reads on this element as such—“The cloud computing platform 130 can further include or can be coupled with one or more other processor circuits or modules configured to perform a specific task, such as to perform tasks related to asset maintenance, analytics, data storage, security, or some other function…. In this example, historical data 610 is provided to the database 620 on a periodic basis such as daily, weekly, and the like. An ETL (Extract, Transform, Load) node 630 may perform learning based on the historical data 610 and generate a listing of predetermined root causes of locomotive failure and associate the root causes with various keywords and weights as described in the examples herein.”);
analyze the extracted data to generate an analysis result ([0052] read on this element as such—“For example, the ETL node 630 may process the historical data 610 and calculate weights, keywords, relevance, etc., associated with each cause of historic locomotive failures included in the historical data 610. Furthermore, the ETL node 630 may receive new locomotive failure data and process the new locomotive failure data (e.g., in real-time) to determine a cause of failure for the new locomotive failure and output one or more determined causes of the locomotive failure to a user interface such as interface 650 and/or 660.”).
Claim 14. Wang teaches the system of Claim 13 and further teaches, wherein the time window can include time measurements before the enforcement event, after the enforcement event, or both before and after the enforcement event ([0053]—“For example, the search may be performed by the virtual machine (VM) 640. Here, the search may be performed based on a fleet number, a predetermined time period, a company, a geographic location, a type of failure, and the like”).
Claim 15. Wang teaches the system of Claim 13 and further teaches, further comprising:
labeling the train system logs based on one or more characteristics (fig. 2 illustrates an example of determining weights for keywords of root causes and calculating similarities based thereon, in accordance with an example embodiment.).
Claim 16. Wang teaches the system of Claim 13 and further teaches, further comprising:
determining whether the data is structured or unstructured ([0037] describes this element as such—“The textual data 220 comprises normalized data that may be provided from multiple different sources, for example, textual data related to machine parameter readings, repair comments, material usage descriptions, symptom descriptions, defect descriptions, action comments, incident descriptions, part descriptions, root cause descriptions, symptom codes, and the like. Prior to being analyzed, the raw textual data may be cleaned to generate textual data 220. For example, the raw text data may have uppercase strings converted to lowercase, punctuation removed, stop words removed, extra spaces and trailing spaces removed, abbreviations converted into standard definitions, word stemming performed, and the like, to generate textual data 220.”).
Claim 17. Wang teaches the system of Claim 16 and further teaches, further comprising:
identifying a pattern to unstructured data and transforming the unstructured data into structured data ([0037]—“Prior to being analyzed, the raw textual data may be cleaned to generate textual data 220. For example, the raw text data may have uppercase strings converted to lowercase, punctuation removed, stop words removed, extra spaces and trailing spaces removed, abbreviations converted into standard definitions, word stemming performed, and the like, to generate textual data 220.” Figs. 2 and 5 illustrates the unstructured data to structured data. ).
Claim 18. Wang teaches the system of Claim 17 and further teaches, wherein the unstructured data is transformed by assigning elements of the unstructured data to one or more categories, fields, or metadata (this element is best illustrated in fig. 1).
Claim 19. Wang teaches the system of Claim 13 and further teaches, wherein the extracted data is analyzed using a defect detection analysis model, a historical analysis model, a decision tree model, a classification model, or a clustering model ([0022] describes this element as such—“By analyzing the locomotive defect information, work orders, repair notes, material usages, and the like, the example embodiments recommend one or a small list of potential root causes (e.g. water leak, high pressure fuel pipe, etc.) from a historical root cause pool that includes hundreds and in some cases even thousands of choices.”).
Claim 20. Wang teaches the system of Claim 19 and further teaches, wherein the analysis identifies when a defect during the analysis occurs ([0052] describes this element as such—“Furthermore, the ETL node 630 may receive new locomotive failure data and process the new locomotive failure data (e.g., in real-time) to determine a cause of failure for the new locomotive failure and output one or more determined causes of the locomotive failure to a user interface such as interface 650 and/or 660.”).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANA D THOMAS whose telephone number is (571)272-8549. The examiner can normally be reached Monday - Friday 8 - 5.
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, Ramya Burgess can be reached at 571-272-6011. 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.
/A.D.T/Examiner, Art Unit 3661
/RUSSELL FREJD/Primary Examiner, Art Unit 3661
Prosecution Insights