DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 06/19/24 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 8, 9, 11, 13, 14, and 17-19 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
ISSUE - CLAIMS 1, 18, AND 19 - LACK OF ANTECEDENT BASIS FOR “THE ANALYSIS RESULT”
Claims 1, 18, and 19 each recite obtaining “the analysis result” before any “analysis result” has been positively introduced. Although the surrounding language suggests that the result is generated by analyzing the railway centralized signaling monitoring system data, the first occurrence of “the analysis result” lacks proper antecedent basis and renders the scope of the claim unclear.
Suggested correction: amend the first occurrence to “an analysis result,” and thereafter use “the analysis result” when referring back to the introduced result.
ISSUE - CLAIM 8 - UNCLEAR “DATA ANALYSIS MODULE OF THE AT LEAST ONE PROCESSOR” AND “BATCHED PARALLEL CIRCULATION”
Claim 8 recites that “an analysis procedure is provided in a data analysis module of the at least one processor.” The claim does not clearly define whether the data analysis module is software stored in memory, a hardware component, a logical module executed by the processor, or a subcomponent of the processor itself. The phrase “of the at least one processor” creates ambiguity as to the physical or logical relationship between the processor and the module.
Claim 8 also recites that the analysis procedure performs “batched parallel circulation.” The term “batched parallel circulation” does not have a reasonably clear technical meaning in the context of data processing. It is unclear whether the claim requires batch-parallel execution, loop iteration over batches, cyclic scheduling of batches, parallel circulation of data, or another processing technique.
Suggested correction: amend claim 8 to clarify that the memory stores an analysis procedure executed by the at least one processor in a data analysis module, and replace “batched parallel circulation” with definite language such as “batch-parallel processing of a plurality of data subsets” or “parallel batch processing using a plurality of processing threads.”
ISSUE - CLAIM 9 - UNCLEAR “CPU SHIELDING AFFINITY” AND “FIXED TASK”
Claim 9 recites that each of the N−1 threads “sets a CPU shielding affinity to ensure that a CPU core of the number N of CPU cores performs a fixed task.” The term “CPU shielding affinity” is unclear because it does not identify, with reasonable certainty, whether the claim requires processor affinity, CPU-core pinning, isolating a CPU core from unrelated operating-system tasks, shielding a process from migration, or another scheduling mechanism.
The phrase “performs a fixed task” is also unclear because the claim does not identify the “fixed task,” whether the fixed task is an analysis task, an operating-system task, a data-service task, or a permanently assigned thread, and whether the fixed task must be exclusive to the CPU core.
Suggested correction: amend claim 9 to recite the intended scheduling operation, for example: “setting processor affinity for each of the N−1 threads such that each thread is bound to a corresponding CPU core reserved for the thread’s assigned analysis task.”
ISSUE - CLAIM 11 - UNCLEAR “SAME TYPE”
Claim 11 recites storing analysis results of “a same day, a same type and a same analysis parameter.” The phrase “same type” lacks a clear antecedent and does not identify the category of “type” being compared. It is unclear whether “same type” refers to an analysis task type, acquisition-data type, device type, curve type, fault type, result type, or another category.
Suggested correction: amend claim 11 to specify the category, such as “same analysis task type,” “same acquisition-data type,” “same device type,” or “same curve type.”
ISSUE - CLAIMS 13 AND 14 - LACK OF ANTECEDENT BASIS IN THE DEPENDENCY CHAIN
Claims 13 and 14 are rejected under 35 U.S.C. § 112(b) as being indefinite.
Claim 13 depends from claim 12, which depends from claim 11, which depends from claim 10, which depends from claim 9, which depends from claim 8, which depends from claim 1. This dependency chain does not include claim 3. However, claim 13 recites “the daily curve analysis result and the turnout curve analysis result.” Because the daily curve analysis task and turnout curve analysis task are introduced in claim 3, and claim 13 does not depend from claim 3, the phrases “the daily curve analysis result” and “the turnout curve analysis result” lack clear antecedent basis within claim 13’s dependency chain.
Claim 14 depends from claim 13 and therefore inherits this indefiniteness.
Suggested correction: amend claim 13 to depend from claim 3, directly or indirectly, or amend claim 13 to introduce the daily curve analysis result and turnout curve analysis result without relying on claim 3’s antecedent basis.
ISSUE - CLAIM 14 - UNCLEAR “IS RELATED TO”
Claim 14 recites that “the specific analysis result description is related to corresponding abnormal turnout curve detailed information and abnormal analog quantity curve detailed information.” The phrase “is related to” does not define the required content of the specific analysis result description or the manner of the relationship. The claim does not make clear whether the description must include, link to, identify, summarize, index, retrieve, or otherwise associate with the abnormal curve detailed information.
Suggested correction: amend claim 14 to recite the actual relationship or required content, for example: “the specific analysis result description includes identifiers, abnormal time periods, abnormal values, threshold values, and links to corresponding abnormal turnout curve detailed information and abnormal analog quantity curve detailed information.”
ISSUE - CLAIM 17 - UNCLEAR DATA FLOW USING “FEED THE RECEIVED ... BACK”
Claim 17 recites that the data service module is configured to “feed the received analysis command back to the data analysis module” and “feed the received analysis result back to the central server.” The phrase “back” creates uncertainty because the claim does not clearly identify the source from which the analysis command and analysis result are received. The analysis command appears to originate from the interface module or central server, not the data analysis module, and the analysis result appears to originate from the data analysis module, not the central server. The claim therefore leaves unclear the required direction of data transmission among the data service module, data analysis module, and central server.
Suggested correction: amend claim 17 to recite the communication path expressly, for example: “the data service module receives the analysis command from the central server and forwards the analysis command to the data analysis module; and receives the analysis result from the data analysis module and forwards the analysis result to the central server.”
References Relied Upon
Reference 1: CN109271346A, “Browsing Method of Railway Signal Analog Quantity Curve.”
Reference 2: CN103345207B, “Mining Analyzing and Fault Diagnosis System of Rail Transit Monitoring Data.”
Reference 3: CN104091070B, “Rail Transit Fault Diagnosis Method and System Based on Time Series Analysis.”
Reference 4: CN109532949A, “Railway Switch Conversion Process Analysis and Evaluation System.”
Reference 5: CN110749785A, “Turnout Rotation Time Sequence Analysis Method and System.”
Reference 6: CN107959583A, “Management System for Centralized Monitoring Alarm Information.”
Reference 7: US5745778A, “Apparatus and Method for Improved CPU Affinity in a Multiprocessor System.”
Reference 8: US10528541B2, “Offline Access of Data in Mobile Devices.”
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.
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Claim 18 is rejected under 35 U.S.C. § 102(a)(1) as being anticipated by Reference 1.
Claim 18
A method of automatically analyzing railway centralized signaling monitoring system data, comprising:
generating an analysis command;
acquiring the railway centralized signaling monitoring system data to be analyzed according to the analysis command;
analyzing the railway centralized signaling monitoring system data to obtain the analysis result; and
displaying the analysis result.
Analysis
Reference 1 discloses a method for browsing railway signal analog quantity curves in which a railway signal centralized monitoring system collects device data, associates the data with acquisition time and device name, forms a data-and-curve file, and stores the file. Reference 1’s data analysis module receives user-set detection parameters and responds to the user’s analysis action on a computer screen interface. The user-set detection parameters and analysis-button operation correspond to generating an analysis command.
Reference 1 discloses acquiring railway centralized signaling monitoring system data according to that command because the data analysis module uses the stored data-and-curve files generated by the railway signal centralized monitoring system and applies the user-set detection parameters to the records in those files.
Reference 1 discloses analyzing the railway centralized signaling monitoring system data to obtain an analysis result because the data analysis module compares records one by one according to the user-set detection parameters, determines whether any index exceeds a detection parameter, judges such records as abnormal records, associates the abnormal records with corresponding curve sections, and saves the abnormal records in a form document.
Reference 1 discloses displaying the analysis result because the user opens the form document of abnormal records, selects an abnormal record, and the data analysis module recalls and displays the corresponding data and curve for the relevant time period.
Accordingly, Reference 1 discloses each limitation of claim 18.
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.
Claims 1-2 and 19 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2.
Claims 3-4 and 6 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2 and Reference 3.
Claims 5 and 7 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2, Reference 3, Reference 4, and Reference 5.
Claims 8-11 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2 and Reference 7.
Claim 12 is rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2 and Reference 7, and further in view of Reference 8.
Claims 13-14 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2, Reference 3, Reference 4, Reference 5, Reference 7, and Reference 8.
Claims 15-17 are rejected under 35 U.S.C. § 103 as being unpatentable over Reference 1 in view of Reference 2 and Reference 6.
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Claim 1
An intelligent browsing apparatus based on railway centralized signaling monitoring system data, comprising:
at least one processor; and
a memory coupled to the at least one processor to store instructions, which when executed by the processor, cause the at least one processor to:
generate an analysis command;
acquire the railway centralized signaling monitoring system data to be analyzed according to the analysis command and analyze the railway centralized signaling monitoring system data to obtain the analysis result; and
display the analysis result.
Analysis
Reference 1 teaches the core railway intelligent browsing functionality. Reference 1 discloses a railway signal analog quantity curve browsing method in which a railway signal centralized monitoring system collects device data, associates each data item with acquisition time and device name, forms and saves data-and-curve files, and allows a data analysis module to analyze the data according to user-set detection parameters. Reference 1 further discloses that the data analysis module compares stored records to the user-set detection parameters, identifies abnormal records, stores the abnormal records in a form document, and displays the corresponding data and curve when a user selects an abnormal record.
Reference 2 teaches the apparatus architecture for implementing such railway signal monitoring analysis. Reference 2 discloses signal monitoring system data processing unit 11, data analysis unit 12, knowledge base unit 13, fault diagnosis unit 14, human-machine interface unit 15, and data warehouse unit 16. Signal monitoring system data processing unit 11 acquires historical and real-time railway signal monitoring data from on-site CSM equipment. Data analysis unit 12 analyzes historical railway signal monitoring data. Human-machine interface unit 15 displays fault diagnosis results to the user. Data warehouse unit 16 stores historical and real-time monitoring data.
The “intelligent browsing apparatus based on railway centralized signaling monitoring system data” is taught by the combined railway signal monitoring/browsing system of References 1 and 2. The claimed processor and memory are taught or rendered obvious by Reference 2’s computer-implemented processing units 11, 12, 14, 15, and 16, which perform acquisition, analysis, storage, and display of railway signal monitoring data, and by Reference 1’s data analysis module operating through a computer screen interface and computer storage. Providing at least one processor and coupled memory storing executable instructions is the ordinary implementation for these software-controlled analysis and display units.
The limitation “generate an analysis command” is taught by Reference 1’s user-set detection parameters and analysis action. When the user sets detection parameters and initiates analysis, the system forms the command or parameterized request that causes the data analysis module to perform the analysis.
The limitation “acquire the railway centralized signaling monitoring system data to be analyzed according to the analysis command” is taught by Reference 1’s retrieval and use of data-and-curve files generated from railway signal centralized monitoring system data, and by Reference 2’s signal monitoring system data processing unit 11 acquiring railway signal monitoring data from on-site CSM equipment.
The limitation “analyze the railway centralized signaling monitoring system data to obtain the analysis result” is taught by Reference 1’s comparison of records against user-set detection parameters, abnormal-record determination, and association of abnormal records with curve sections. It is also taught by Reference 2’s data analysis unit 12 and fault diagnosis unit 14.
The limitation “display the analysis result” is taught by Reference 1’s form document browsing and display of corresponding data and curves, and by Reference 2’s human-machine interface unit 15 displaying fault diagnosis results.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to implement Reference 1’s railway signal analog quantity curve browsing method using the apparatus architecture of Reference 2 because both references address computerized acquisition, analysis, and presentation of railway signal monitoring data from centralized monitoring systems. The combination would have predictably provided Reference 1’s abnormal curve browsing and user-parameter analysis in a complete railway monitoring apparatus having data processing, data analysis, storage, and human-machine display units. The modification would have reduced manual browsing workload while preserving the same centralized signal monitoring data source and analysis purpose.
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Claim 2
The intelligent browsing apparatus according to claim 1, wherein the at least one processor is caused to:
customize an analysis task and form the analysis command; and
display or query the analysis result.
Analysis
Reference 1 teaches customization of an analysis task because the user inputs detection parameters, and the data analysis module saves those user-input detection parameters before performing the analysis. The detection parameters include values such as standard deviation tolerance, peak margin, average upper limit, and average lower limit. These user-entered parameters customize the analysis task performed by the system.
Reference 1 teaches forming the analysis command because the user’s parameter setting and analysis-button initiation cause the data analysis module to perform the selected analysis on the stored railway signal curve records. That parameterized initiation corresponds to forming an analysis command.
Reference 1 teaches displaying or querying the analysis result because abnormal records are saved in a form document, the user opens the form document, and when the user selects a specific abnormal record, the data analysis module recalls and displays the corresponding data-and-curve file for the relevant time period. Reference 2 further teaches human-machine interface unit 15 for displaying diagnosis results to the user.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to configure the combined system to customize analysis tasks and display or query the analysis results because Reference 1’s analysis depends on user-selected detection parameters and user review of abnormal records. Allowing user customization and subsequent query/display improves the usefulness of Reference 2’s interface-based railway monitoring analysis system by enabling operators to focus on relevant abnormal curve conditions.
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Claim 3
The intelligent browsing apparatus according to claim 2, wherein the analysis task comprises a daily curve analysis task and a turnout curve analysis task.
Analysis
Reference 1 teaches daily curve analysis because it describes a prior-art daily curve check window and replaces manual daily browsing of railway signal voltage/current curves with automated analysis. Reference 1’s method processes collected railway signal curve data, applies detection parameters, identifies abnormal records, and allows the user to browse abnormal records and corresponding curves.
Reference 3 teaches turnout curve analysis because it expressly applies rail-transit time-series analysis to turnout current curves and analog quantity change trend curves. Reference 3 explains that turnout current curves represent current values over time and include electrical and mechanical characteristics of the turnout conversion process.
Reference 2 provides the railway monitoring analysis architecture into which the daily curve analysis and turnout curve analysis tasks would be integrated, including CSM data acquisition by signal monitoring system data processing unit 11, analysis by data analysis unit 12, storage by data warehouse unit 16, and presentation by human-machine interface unit 15.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to include both daily curve analysis and turnout curve analysis in the combined system because Reference 1 already addresses daily review of railway signal analog curves, while Reference 3 identifies turnout current curves as a specific railway signal time-series curve suitable for automated fault analysis. Combining these task types would have predictably expanded the same railway signal curve browsing system to cover the ordinary daily-monitoring workflow and the known high-value turnout diagnosis workflow.
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Claim 4
The intelligent browsing apparatus according to claim 3, wherein the daily curve analysis task comprises one or more analysis items including at least one of:
an analysis item for a fluctuation, an analysis item for a rising trend, or an analysis item for a declining trend.
Analysis
Reference 1 teaches an analysis item for fluctuation because the data analysis module calculates statistical information for curve segments, including average value, mean square deviation, peak, and minimum, and compares these values against detection parameters. Standard deviation, peak, and minimum comparisons directly identify abnormal fluctuation in the railway signal analog quantity curve.
Reference 3 teaches trend-type analysis because it applies time-series analysis to rail-transit monitoring data, including analog quantity change trend curves. A rising trend and a declining trend are ordinary directional time-series changes in the analog quantity curve. Applying Reference 3’s time-series trend analysis to Reference 1’s daily railway signal analog quantity curves provides analysis items for rising trend and declining trend.
Reference 2 supplies the underlying railway signal monitoring architecture for acquiring and analyzing such historical railway signal monitoring data using data processing unit 11 and data analysis unit 12.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to include fluctuation, rising-trend, and declining-trend analysis items in the daily curve analysis because Reference 1 already detects abnormal curve behavior using statistical thresholds, and Reference 3 teaches time-series analysis of analog quantity trend curves. These items are predictable and useful curve features for identifying abnormal railway signal equipment behavior during daily monitoring.
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Claim 5
The intelligent browsing apparatus according to claim 3, wherein the turnout curve analysis task comprises one or more analysis items including at least one of:
an analysis item for a turnout rotation duration, an analysis item for a power curve beyond an upper limit, or an analysis item for a current curve beyond a lower limit.
Analysis
Reference 3 teaches that turnout current curves are suitable rail-transit time-series curves for fault diagnosis and that turnout current curves reflect electrical and mechanical characteristics in the turnout conversion process. Thus, Reference 3 provides the general turnout curve analysis context.
Reference 5 teaches an analysis item for turnout rotation duration. Reference 5 discloses MSS station machine 101 and switch indication circuit current collector 103. The system determines relay action timing from current waveforms and identifies time periods in the turnout rotation sequence, including the whole process time from unlocking to locking. That whole process time corresponds to turnout rotation duration.
Reference 4 teaches power-curve and current-curve analysis for turnout conversion. Reference 4 discloses a current curve acquisition module, voltage curve acquisition module, power curve acquisition module, central processing module, and state display module. The power curve acquisition module generates a turnout power curve from voltage and current. The central processing module receives and stores current and power curve signals, segments the curves into turnout processes such as unlocking, converting, and locking, compares them with preset normal-state parameters, and identifies normal, abnormal, or fault states. Comparing a power curve or current curve against preset normal-state parameters renders obvious detecting a power curve beyond an upper limit and a current curve beyond a lower limit, because threshold-based abnormality detection is the same type of parameterized curve screening taught by Reference 1.
Reference 1 supplies the user-parameterized abnormal curve browsing framework, including detection parameters and abnormal record display. Reference 2 supplies the CSM acquisition and analysis architecture.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to include turnout rotation duration, power upper-limit analysis, and current lower-limit analysis in the turnout curve analysis task because Reference 3 identifies turnout current curves as important rail-transit fault-diagnosis curves, Reference 5 teaches extracting turnout rotation timing from current waveforms, and Reference 4 teaches analyzing turnout current and power curves against preset normal-state parameters. Combining these known turnout diagnostics with Reference 1’s parameterized browsing system would have predictably provided selectable, operator-useful turnout abnormality checks.
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Claim 6
The intelligent browsing apparatus according to claim 4, wherein when the analysis command is the daily curve analysis task, the at least one processor is caused to form the analysis command by setting the one or more analysis items of the daily curve analysis task and a filter parameter thereof; and
wherein the filter parameter comprises one or more of a fluctuation amplitude corresponding to the analysis item for the fluctuation, a rising amplitude corresponding to the analysis item for the rising trend, or a declining amplitude corresponding to the analysis item corresponding to the declining trend.
Analysis
Reference 1 teaches forming the analysis command by setting analysis items and filter parameters because the user inputs detection parameters, the data analysis module stores those detection parameters, and the module analyzes records against those parameters. Reference 1’s detection parameters include threshold-type values such as standard deviation tolerance, peak margin, average upper limit, and average lower limit. These are filter parameters for screening abnormal curve records.
For the claimed daily curve analysis task, Reference 1’s daily curve browsing and abnormality screening teach setting filter parameters for daily railway signal analog quantity curves. Reference 1’s fluctuation-related parameters correspond to the claimed fluctuation amplitude filter parameter.
Reference 3 teaches analog quantity change trend curves and time-series analysis. Applying Reference 3’s trend analysis to Reference 1’s parameterized daily curve screening renders obvious using rising amplitude and declining amplitude as filter parameters for rising and declining trend analysis items. A rising trend is determined by upward amplitude change over time, and a declining trend is determined by downward amplitude change over time.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to form a daily curve analysis command by setting daily analysis items and associated fluctuation, rising-amplitude, and declining-amplitude filter parameters because Reference 1 already uses user-set detection thresholds to screen daily railway signal curves, and Reference 3 teaches time-series trend analysis of analog quantity curves. Providing these threshold parameters would have allowed operators to adjust sensitivity and reduce irrelevant abnormal records.
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Claim 7
The intelligent browsing apparatus according to claim 5, wherein
when the analysis command is the turnout curve analysis task, the at least one processor is caused to form the analysis command by setting the one or more analysis items of the turnout curve analysis task and a filter parameter thereof; and
wherein the filter parameter comprises one or more of a duration deviation corresponding to the analysis item for the turnout rotation duration, a power value upper limit corresponding to the analysis item for the power curve beyond the upper limit, or a current value lower limit corresponding to the analysis item for the current curve beyond the lower limit.
Analysis
Reference 1 teaches user-set detection parameters for curve analysis and the formation of a parameterized analysis request. Applying that same parameterized screening structure to turnout curve analysis provides the claimed formation of an analysis command by setting turnout analysis items and associated filter parameters.
Reference 5 teaches determining turnout rotation timing, including the whole process time from unlocking to locking of the turnout. Comparing that measured timing against a preset normal timing renders obvious a duration deviation filter parameter corresponding to turnout rotation duration.
Reference 4 teaches analyzing turnout current and power curves using preset normal-state parameters. The central processing module compares current and power curve sections against stored reference characteristics and identifies normal, abnormal, or fault states. In the combined system, a power value upper limit is an obvious filter parameter for detecting a power curve beyond an upper limit, and a current value lower limit is an obvious filter parameter for detecting a current curve beyond a lower limit.
Reference 3 supplies the broader rail-transit time-series turnout curve analysis context, and Reference 2 supplies the railway signal monitoring architecture.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide duration deviation, power upper-limit, and current lower-limit filter parameters for the turnout curve analysis task because Reference 1’s system is parameter-driven, Reference 5 extracts turnout timing values, and Reference 4 compares turnout current and power curves with normal-state parameters. The combination would have predictably allowed maintenance personnel to tune the turnout abnormality screening thresholds for the monitored equipment.
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Claim 8
The intelligent browsing apparatus according to claim 1, wherein an analysis procedure is provided in a data analysis module of the at least one processor, and the analysis procedure is used to perform batched parallel circulation and multi-thread concurrent operation on the railway centralized signaling monitoring system data to be analyzed to obtain the analysis result.
Analysis
Reference 1 teaches a data analysis module for processing railway signal centralized monitoring curve data and generating abnormal records. Reference 2 teaches data analysis unit 12, which analyzes historical railway signal monitoring data acquired by signal monitoring system data processing unit 11. These teachings provide the claimed analysis procedure in a data analysis module operating on railway centralized signaling monitoring system data.
Reference 1 further teaches batch-type processing because collected real-time sampling data is processed, segmented by set duration, saved as records, and compared record-by-record to detection parameters. Reference 2 teaches analysis of historical railway signal monitoring data, which is naturally batch-oriented.
Reference 7 teaches multi-thread concurrent operation and processor-affinity scheduling in a multiprocessor system. Reference 7 discloses process 200 having thread groups 210, 220, and 230, threads 212-214, 222-223, 232-233, and data 240. Reference 7 further teaches CPUs 100-107, cache 110, shared memory 120, and run queues for assigning thread groups to processors. Applying Reference 7’s multithreaded execution to Reference 1’s and Reference 2’s railway signal data analysis module provides concurrent processing of batches or subsets of railway monitoring data to obtain analysis results.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to implement the railway curve analysis procedure with batch-parallel and multithread concurrent operation because References 1 and 2 process large sets of historical railway monitoring data and curve records. Reference 7 teaches that multiple threads can be used to work on calculations and data subsets while improving processor affinity and scheduling. The combination would have predictably improved throughput and reduced the time required to analyze many railway signal curves.
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Claim 9
The intelligent browsing apparatus according to claim 8, wherein N−1 threads are provided in the analysis procedure according to a number N of central processing unit (CPU) cores provided in the data analysis module, and each of the threads is used to analyze and calculate the railway centralized signaling monitoring system data to be analyzed; and
each of the N−1 threads sets a CPU shielding affinity to ensure that a CPU core of the number N of CPU cores performs a fixed task.
Analysis
Reference 7 teaches assigning threads and thread groups to CPUs in a multiprocessor system using affinity and run queues. Reference 7 discloses CPUs 100-107, thread groups 210, 220, 230, and thread structures 212-214, 222-223, and 232-233. Reference 7 further teaches moving selected thread groups to a CPU-specific Level 0 run queue, a cache-shared Level 1 run queue, or a Level 2 run queue, thereby controlling processor affinity.
In the combined system, the data analysis module of References 1 and 2 analyzes and calculates railway centralized signaling monitoring system data. Providing multiple analysis threads according to available CPU cores is a predictable implementation of Reference 7’s multithreaded processor-affinity scheduling. Providing N−1 worker threads for N CPU cores is an obvious design choice in which one CPU core may be reserved for operating-system, user-interface, database, central-server, or communication activity while the remaining N−1 CPU cores perform analysis tasks.
The claimed “CPU shielding affinity” is taught or rendered obvious by Reference 7’s processor-affinity mechanism, including the ability to make a selected thread group affined solely with CPU 100 at Level 0 and to place tasks in CPU-specific run queues. Binding or affining analysis threads to fixed CPU cores ensures that corresponding CPU cores perform assigned analysis tasks.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide N−1 analysis threads and bind those threads to CPU cores using processor affinity because railway signal curve analysis involves many records and curves, and Reference 7 teaches processor-affinity scheduling to control where threads execute. Reserving one CPU core while using the remaining cores for fixed analysis tasks would have predictably maintained system responsiveness while increasing calculation efficiency for batch curve analysis.
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Claim 10
The intelligent browsing apparatus according to claim 9, wherein the data analysis module allocates analysis tasks to the corresponding threads according to the total number of the N−1 threads.
Analysis
Reference 7 teaches scheduling and assigning thread groups and threads to processors using run queues and affinity levels. Reference 7’s process 200 includes multiple thread groups and threads, and its scheduling system selects thread groups and individual threads for execution based on priority, queue location, and affinity.
In the combined system, Reference 1’s record-by-record curve analysis and Reference 2’s data analysis unit 12 provide the analysis workload. Once the number of worker threads is set to N−1, allocating analysis tasks among those corresponding threads according to the thread count is the predictable way to divide the workload. The data analysis module would partition curve records, device groups, time periods, or data files among the N−1 threads so that each thread analyzes and calculates a corresponding portion of the railway monitoring data.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to allocate analysis tasks according to the total number of N−1 worker threads because workload division is necessary to obtain the benefit of multithreaded processing. Reference 7 teaches assigning tasks/threads to processors, and applying that teaching to Reference 1’s railway curve analysis would predictably balance the analysis load among available worker threads.
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Claim 11
The intelligent browsing apparatus according to claim 10, wherein the data analysis module is further configured to store analysis results of a same day, a same type and a same analysis parameter.
Analysis
Reference 1 teaches storing analysis results because abnormal records are saved in a form document after the data analysis module determines that records exceed detection parameters. Reference 1 also associates records with acquisition time, device name, device attribute, abnormal index value, and corresponding curve sections.
Reference 2 teaches data warehouse unit 16 for storing historical railway signal monitoring data and analysis-related monitoring data. Reference 2 also teaches the use of historical and real-time railway signal monitoring data in the analysis and fault diagnosis system.
In the combined system, storing analysis results by same day is taught by Reference 1’s use of acquisition time and daily curve review context. Storing by same type is rendered obvious by Reference 1’s device attributes and curve/record categories and by Reference 2’s equipment-level railway monitoring data organization. Storing by same analysis parameter is taught by Reference 1’s user-set detection parameters saved by the data analysis module. These indexed storage fields allow the system to retrieve comparable results and avoid redundant analysis.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to store analysis results by day, type, and analysis parameter because Reference 1 already stores abnormal records with time, device, attribute, and parameter-based abnormality information, and Reference 2 provides a data warehouse for monitoring data. Such indexing would predictably improve later query, reporting, comparison, and reuse of analysis results.
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Claim 12
The intelligent browsing apparatus according to claim 11, wherein the analysis results are stored in a Sqlite format.
Analysis
References 1 and 2 teach storing railway signal analysis results and monitoring data, including abnormal records and historical monitoring data. Reference 7 is included because claim 12 depends through claims 8-11.
Reference 8 teaches storing structured data in SQLite format. Reference 8 discloses generating SQLite database 222, creating SQLite tables 304, 306, 308, and 310, and storing structured data in a compressed SQLite database 312. Reference 8 also teaches converting raw data and metadata into SQLite tables so the data can be stored and accessed in a structured database format.
Applying Reference 8’s SQLite storage format to the stored railway signal analysis results of References 1 and 2 yields the claimed analysis results stored in a Sqlite format.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to store the railway analysis results in SQLite format because Reference 8 teaches SQLite as a structured database format for storing data and metadata in tables, while References 1 and 2 require storage of abnormal records, monitoring data, parameters, and analysis results. SQLite would have provided a predictable lightweight, table-based, local storage format for station-level or apparatus-level analysis results.
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Claim 13
The intelligent browsing apparatus according to claim 12, wherein the analysis results are a summary report of the daily curve analysis result and the turnout curve analysis result.
Analysis
References 1 and 2 teach generating, storing, and displaying railway signal analysis results. Reference 1 saves abnormal records in a form document and allows browsing of abnormal records and corresponding curves. Reference 2 displays diagnosis results through human-machine interface unit 15 and stores monitoring data in data warehouse unit 16.
Reference 3 teaches time-series analysis of analog quantity change trend curves and turnout current curves. Reference 4 teaches turnout current/power curve analysis and state display of normal, abnormal, or fault statuses. Reference 5 teaches turnout rotation timing analysis using MSS station machine 101, waveform acquisition module 301, time determining module 302, and state determining module 303. These references provide the daily curve and turnout curve analysis results.
Reference 8 teaches storing structured result data in SQLite database format, and Reference 7 is included because claim 13 depends through the multithreaded processing limitations of claims 8-12.
It would have been obvious for the stored analysis results to be a summary report of the daily curve analysis result and turnout curve analysis result because Reference 1’s abnormal-record form document and Reference 2’s user-interface display already organize analysis results for user review. A summary report is the predictable presentation format for aggregated daily and turnout curve abnormalities.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to generate a summary report of the daily curve analysis result and the turnout curve analysis result because the combined system identifies multiple abnormal curve conditions from daily analog curves and turnout curves, and railway maintenance personnel need a consolidated output to review the results efficiently. Summarizing these results would predictably reduce manual review burden and support maintenance decisions.
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Claim 14
The intelligent browsing apparatus according to claim 13, wherein content of the summary report comprises: device information, acquisition content and specific analysis result description; and
the specific analysis result description is related to corresponding abnormal turnout curve detailed information and abnormal analog quantity curve detailed information.
Analysis
Reference 1 teaches report content including device information and acquisition content because its abnormal record content includes acquisition time, abnormal index value, corresponding device name, and device attribute, and because abnormal records are associated with corresponding data-and-curve sections. These items correspond to device information, acquisition content, and specific analysis result description.
Reference 1 further teaches abnormal analog quantity curve detailed information because the user can select an abnormal record and the data analysis module recalls and displays the corresponding data and curve for the relevant time period. This is detailed information for the abnormal analog quantity curve.
Reference 4 teaches abnormal turnout curve detailed information because its current curve acquisition module, voltage curve acquisition module, power curve acquisition module, central processing module, and state display module process turnout current and power curves, segment the curves into turnout processes, identify normal/abnormal/fault states, and display corresponding data values and state marks. Reference 5 further teaches turnout timing details based on current waveform points and relay action timing. These disclosures supply abnormal turnout curve details.
Reference 2’s human-machine interface unit 15 and data warehouse unit 16 support display and storage of such report content. Reference 8 provides the SQLite storage format inherited from claim 12, and Reference 7 provides the multithreaded processing inherited through claim 8.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to include device information, acquisition content, and specific abnormality descriptions in the summary report because Reference 1’s abnormal records already include device, time, abnormal value, and curve association information, and References 4 and 5 provide detailed turnout curve and timing information. Including these fields would have predictably allowed maintenance personnel to identify the affected equipment, understand the acquired data, and navigate to the abnormal analog or turnout curve details.
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Claim 15
The intelligent browsing apparatus according to claim 1, wherein the intelligent browsing apparatus comprises an interface module and a data analysis module, the intelligent browsing apparatus further comprising a central server, respectively connected to the interface module and the data analysis module, wherein the interface module transmits the analysis command to the data analysis module through the central server and the data analysis module feeds the analysis result back to the interface module through the central server.
Analysis
Reference 1 teaches an interface function and a data analysis module. Reference 1’s user sets detection parameters and initiates analysis through a computer screen interface, and the data analysis module performs the curve analysis, generates abnormal records, and displays corresponding curves.
Reference 2 teaches the railway signal monitoring system architecture, including data analysis unit 12 and human-machine interface unit 15. Human-machine interface unit 15 corresponds to the claimed interface module, and data analysis unit 12 corresponds to the claimed data analysis module.
Reference 6 teaches the central-server routing structure. Reference 6 discloses centralized management server 1 connected to information monitoring terminal 2 and information distribution terminal 3. Centralized management server 1 performs real-time data distribution, receives alarm/monitoring information from monitoring systems, processes information, stores alarm information in a database, and distributes information to terminals. This central management server 1 corresponds to the claimed central server connected between interface-side terminals and monitoring/analysis-side components.
In the combined system, the interface module transmits the analysis command to the data analysis module through the central server, and the data analysis module returns the analysis result to the interface module through the central server. This is the predictable command/result routing arrangement obtained by combining Reference 1’s user-initiated curve analysis, Reference 2’s interface and data-analysis units, and Reference 6’s centralized server connected to monitoring and distribution terminals.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to route analysis commands and results through a central server because Reference 6 teaches centralized management server 1 for centralized monitoring information distribution and terminal communication, while References 1 and 2 require interaction between user interface functions and railway signal data analysis functions. The central server would have predictably improved centralized coordination, data routing, storage, and distribution across railway monitoring terminals and analysis units.
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Claim 16
The intelligent browsing apparatus according to claim 15, wherein the data analysis module is arranged on a corresponding station acquisition layer.
Analysis
Reference 2 teaches acquisition of railway signal monitoring data from on-site signal centralized monitoring CSM equipment in each electric service workshop or work area by signal monitoring system data processing unit 11. Reference 2 also teaches that data analysis unit 12 analyzes the historical railway signal monitoring data acquired by data processing unit 11. Locating analysis functionality near the on-site acquisition source corresponds to arranging the data analysis module on a station acquisition layer.
Reference 6 teaches that centralized monitoring systems can preserve the existing monitoring network structure while enabling data interaction between centralized monitoring centers and station-layer data. Reference 6 further discloses information monitoring terminal 2 connected to corresponding monitoring network 5 and centralized management server 1 connected to terminals for data distribution. This supports an architecture in which acquisition-side station-layer components provide data to the centralized server/interface layer.
In the combined system, the data analysis module is arranged at the station acquisition layer so that railway signal curve data can be processed close to where station monitoring data is acquired, while the central server distributes commands and results.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to arrange the data analysis module on the corresponding station acquisition layer because Reference 2 obtains railway signal monitoring data from on-site CSM equipment, and Reference 6 teaches station-layer monitoring data interaction with centralized management. Placing analysis near the station acquisition source would predictably reduce data-transfer burden, reduce latency, and allow large curve datasets to be processed before only results are sent to the interface layer.
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Claim 17
The intelligent browsing apparatus according to claim 16, further comprises: a data service module, arranged on the corresponding station acquisition layer and respectively connected to the data analysis module and the central server, wherein
the data service module is configured to store the analysis result and the railway centralized signaling monitoring system data to be analyzed, and is further configured to feed the received analysis command back to the data analysis module and feed the received analysis result back to the central server.
Analysis
Reference 2 teaches storage and service functionality through data warehouse unit 16, which stores historical and real-time railway signal monitoring data. Reference 2 also teaches that signal monitoring system data processing unit 11 acquires railway signal monitoring data from on-site CSM equipment and reports the acquired data to data analysis unit 12. These functions correspond to a data service module storing the monitoring data to be analyzed and cooperating with the data analysis module.
Reference 1 teaches storage of analysis results because abnormal records are saved in a form document and associated with corresponding data-and-curve file sections. Reference 1 also teaches storage of user detection parameters and curve data files.
Reference 6 teaches centralized management server 1 connected to information monitoring terminal 2 and information distribution terminal 3, with server-side storage and data distribution. The architecture of Reference 6 renders obvious connecting the station-side data service/storage function to a central server for routing commands and results.
In the combined system, the data service module is arranged at the station acquisition layer to store both the railway centralized signaling monitoring system data to be analyzed and the analysis results. The data service module relays received analysis commands to the data analysis module and relays received analysis results back to the central server. This follows predictably from Reference 2’s acquisition/storage/analysis architecture and Reference 6’s centralized server distribution architecture.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to one of ordinary skill in the art, before the effective filling date of the claimed invention, to provide a station-layer data service module connected to the data analysis module and central server because the combined system requires local storage of raw monitoring data, local storage of analysis results, and communication between station-side analysis and centralized command/result distribution. The modification would have predictably improved data availability, reduced repeated acquisition, and allowed the central server to coordinate analysis commands and returned results.
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Claim 19
A non-transitory machine-readable medium having instructions stored therein, which when executed by at least one processor, cause the at least one processor to:
generate an analysis command;
acquire the railway centralized signaling monitoring system data to be analyzed according to the analysis command;
analyze the railway centralized signaling monitoring system data to obtain the analysis result; and
display the analysis result.
Analysis
Reference 1 teaches the claimed functional steps, including user parameter setting and analysis initiation, acquisition/use of railway signal centralized monitoring data and curve files, analysis of the data according to detection parameters, generation of abnormal records, and display of corresponding abnormal curve data.
Reference 2 teaches computer-implemented railway signal monitoring and analysis units, including signal monitoring system data processing unit 11, data analysis unit 12, fault diagnosis unit 14, human-machine interface unit 15, and data warehouse unit 16. These units perform data acquisition, analysis, storage, and display functions in a railway signal monitoring system.
Storing the software instructions for those functions on a non-transitory machine-readable medium is the ordinary implementation of the computer-implemented data analysis module and interface functions taught by References 1 and 2. The claimed medium does not require any additional functional step beyond the same command generation, acquisition, analysis, and display operations already taught or rendered obvious for claim 1.
Motivation
It would have been obvious to one of ordinary skill in the art, before the effective filling date of the claimed invention, to embody the software-controlled functions of References 1 and 2 in instructions stored on a non-transitory machine-readable medium because the references disclose computer-implemented railway signal data processing, analysis, storage, and display. Storing executable instructions on a machine-readable medium is the predictable way to implement such processor-based analysis and browsing functionality.
CONCLUSION
Claims 1-17 and 19 are rejected under 35 U.S.C. § 103 as set forth above. Claim 18 is rejected under 35 U.S.C. § 102(a)(1) as set forth above. Claims 1, 8, 9, 11, 13, 14, 17, 18, and 19 are rejected under 35 U.S.C. § 112(b) as set forth above. The following patent references were reviewed but are not relied upon in the present grounds of rejection because they are either cumulative of the applied references, less specific to the claimed intelligent browsing and automated curve-analysis architecture, or directed primarily to adjacent railway monitoring functions rather than the particular combination of user-customized analysis commands, railway centralized signaling monitoring data acquisition, daily curve/turnout curve abnormality analysis, report generation, multithreaded processing, station-layer deployment, and interface/server/data-service module communication.
JP2004034876A, “Monitoring Equipment for Point Machines,” is relevant to monitoring point-machine operating conditions. However, it was not used because the applied turnout-analysis references provide a closer teaching of turnout curve/time-sequence analysis and parameter-based abnormality evaluation.
CN101917237B, “Railway Signal Monitoring Method and System,” is relevant as general railway signal monitoring art. However, it was not used because it is broader and more cumulative of Reference 2’s railway monitoring acquisition, analysis, storage, and interface architecture.
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/Jason C Smith/ Primary Examiner, Art Unit 3615
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