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 12/05/2022 is being considered by the examiner.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-10 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 1–10 are rejected under 35 U.S.C. §112(a).
As amended, claim 1 now affirmatively requires:
“a machine learning device trained to predict occurrence of track deterioration or track faults”
While the specification discusses machine learning at a general conceptual level, it does not provide sufficient disclosure to enable a person of ordinary skill in the art to practice the full scope of the claims without undue experimentation, including but not limited to:
what constitutes “track deterioration” versus a “track fault”;
how training data is generated, labeled, or validated;
how the model is trained to distinguish among the recited operational modes;
how the “predetermined critical value” is derived or incorporated into the training or inference process; and
how the prediction output is translated into stored work instruction data.
The amendment narrows the claim from generic data analysis to a predictive, trained machine-learning system, but the specification does not demonstrate possession of or enablement for that predictive scope across varying ballast conditions, track classes, and machine configurations.
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 1-10 are 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.
Claim 1 is rejected under 35 U.S.C. 112(b) as failing to particularly point out and distinctly claim the subject matter regarded as the invention for the following reasons:
Claims 1–10 remain indefinite because claim 1 now recites:
“a drop in compression forces … that exceeds a predetermined critical value”
However, the claims do not define:
the reference baseline against which the “drop” is determined (e.g., adjacent sleeper, moving average, historical value, target force);
whether the “drop” is absolute, relative, rate-based, or spatially normalized; or
how the “predetermined critical value” is established, expressed, or applied.
Merely stating that a drop “exceeds a predetermined critical value” does not provide objective boundaries for determining when the limitation is met. The metes and bounds of the claims therefore remain unclear.
In addition, claim 1 now classifies conditions as:
“a single fault in the track,”
“a highly polluted ballast area,” or
“a general default.”
The claims do not define how these classifications are determined, what parameters separate them, or how a given ballast condition is objectively assigned to one category versus another. These terms operate as functional labels without definitional boundaries, rendering the scope of the claims uncertain.
Accordingly, claims 1–10 are rejected under 35 U.S.C. §112(b).
References Relied Upon
Reference 1 – WO 2020/037343 A1 (WO ’343): “Procedure for automatic position correction of a track” – Relevant numerals: single-fault tamping machine 2; tamping unit 7 with sensors 8, 9; control computer 18; display 18; inertial carriage 10, 11; ballast hardness/compaction‐force plots Fig. 6; data-based planning of subsequent corrections (“moving average… target compaction force”).
Reference 2 – WO 2019/091681 (WO ’681): “System and method for navigating within a track network” – Relevant numerals: system centre 20; big-data framework 27; machine-learning algorithms; sensors 14 on tamping machine 1; navigation device 19; communication means 26; object chains 30; updated work parameters to machine.
Reference 3 – AT 520 117 B1 (AT ’117): “Method for compacting a ballast bed” – Relevant numerals: tamping tools 4; hydraulic cylinders 7; pressure sensors 9; calculation of drop in compaction force (Fig. 4, Fv); generation of target values FvNom from moving average k·WN.
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.
Rejections
35 U.S.C. § 103 – Claims 1-10 Rejected over WO ’343 in view of WO ’681 and further in view of AT ’117
• Independent claim 1 and its dependents 2, 4, 5, 8 are treated together because they rely on autonomous execution based on stored work instructions. • Dependent claims 3, 6, 7, 9, 10 add operator display / report features; they are treated as a second sub-group relying on the same combination plus explicit operator-interface teachings already present in WO ’343.
Independent Claim 1
A method for automatic autonomous control of a packing machine having a position-measuring device detecting a position of a track-building machine in a track, with signal detection by actuators of working assemblies of the packing machine, said method comprising: • acquiring ballast bed data via sensors during packing; and • acquiring current ballast bed parameters from the ballast bed data; • storing the current ballast bed parameters for a subsequent work pass; and • analyzing the current ballast bed parameters with a machine learning device so as to create an analysis of ballast bed state data based on machine learning methods, wherein the current ballast bed parameters are analyzed with regard to a drop in compression forces occurring in a longitudinal direction of the track; and • determining and storing work instruction data defining work instructions of an optimum mode of operation from the current ballast bed parameters; and • wherein, in a subsequent work pass, based on a current position in the track and associated work instruction data, the packing machine carries out the work instructions of said work instruction data fully automatically and autonomously.
Analysis of Each Limitation
“Packing machine having a position-measuring device…” – WO ’343 discloses a tamping (packing) machine 2 equipped with front and rear inertial measuring carriages 10, 11 for precise position determination relative to the track. These carriages constitute the claimed position-measuring device.
“Signal detection by actuators of working assemblies…” – In WO ’343 the tamping unit 7 includes hydraulic cylinders whose pressures (sensors 8, 9) and strokes are captured, exactly matching “signal detection by actuators.”
“Acquiring ballast bed data via sensors during packing” – WO ’343 Fig. 6 records compaction-force curves and bedding hardness while the tamping tools are in the ballast, satisfying the requirement.
“Acquiring current ballast bed parameters from the ballast bed data” – WO ’343 expressly converts force/displacement signals into parameters such as hardness and predicted durability (ballast-condition metrics).
“Storing the current ballast bed parameters for a subsequent work pass” – WO ’343 teaches saving those parameters in the on-board computer 18 and later transferring them to an infrastructure manager for planning the next tamping cycle (see dashed arrows “analysis… basis for upcoming correction”). The storage for later use is therefore met.
“Analyzing … with a machine learning device …” – WO ’343 alone employs deterministic statistical rules, not ML. WO ’681’s big-data framework 27 and machine-learning algorithms analyze continuously gathered sensor data to refine network models and to output work parameters to the tamping machine via communication means 26. When combined, the two references teach ML analysis of the same ballast parameters.
“…analyzed with regard to a drop in compression forces occurring in a longitudinal direction of the track” – AT ’117 Fig. 4 explicitly examines the longitudinal drop ΔFv in compaction force and computes WN and FvNom accordingly. This supplies the precise analysis criterion.
“Determining and storing work instruction data defining work instructions of an optimum mode of operation…” – WO ’343 develops target compaction forces (FvNom) and corresponding tamping parameters, storing them for later execution. WO ’681 transmits optimized, location-bound “working parameters” back to the machine. Together they teach the claimed step.
“In a subsequent work pass … the packing machine carries out the work instructions … fully automatically and autonomously” – WO ’343 already operates tamping automatically once the machine reaches the defined start point S (Fig. 3), and it iterates to the end point E without operator intervention. WO ’681 further automates navigation to the next work site through navigation device 19. The combined system therefore meets the autonomous execution requirement.
Accordingly, every element of claim 1 is present in, or would have been obvious by combination of, WO ’343, WO ’681, and AT ’117.
Motivation to Combine
A person of ordinary skill in the art (POSITA) of rail-maintenance automation would reasonably combine (i) WO ’343’s detailed tamping-quality framework with (ii) WO ’681’s well-known machine-learning big-data architecture to automate analysis and instruction generation, because both disclosures focus on improving planning accuracy and reducing human error. The addition of AT ’117’s compaction-force-drop analytics is a predictable use of a known technique (monitor ΔFv) to achieve a known benefit (identify weak ballast), exactly the concern highlighted in WO ’343 (“defects recur quickly if ballast hardness is low”). The combination merely unites old elements with no unpredictable result; motivation is therefore explicit and rational under KSR.
Claim 2
The method of claim 1 wherein a control computer of the packing machine is supplied with positionally accurate work instructions for each sleeper region … the packing machine moves to each longitudinal position … and autonomously performs work … the cycle being repeated until the intended work area has been processed.
Additional limitation addressed
WO ’343 Fig. 2 shows precise sleeper-indexed lifting points (MAX, MIN) and instructs automatic movement of the packing unit from start S to end E over each sleeper. WO ’681 teaches transmitting position-indexed work lists to the navigation device. The combined teachings fully satisfy claim 2.
Motivation
Both references aim to minimize re-work and human error by tying instructions to absolute sleeper locations; combining them is an obvious optimization that merely yields predictable positioning accuracy.
Claim 3
The method of claim 1 further comprising displaying predetermined work instructions for each sleeper area to an operator, and the operator sets and executes the predetermined work instructions.
Analysis
WO ’343 control computer 18 includes a graphical display that shows the target compaction parameters sleeper-by-sleeper, enabling the operator to confirm or override them (see explanatory text around Fig. 2). Thus claim 3 is met by WO ’343 alone.
Motivation
No additional combination required; WO ’343 already provides this user-interface feature.
Claim 4
The method of claim 2 further comprising generating a ballast bed state record during the work and a ballast bed state report with the result of the analysis … and transmitting both … to an infrastructure manager.
Analysis
WO ’343 explicitly produces (i) a record (“moving average table”) and (ii) a report (durability prediction) which are transmitted via wireless link to the infrastructure manager. Thus, limitation met.
Motivation
Contained within WO ’343; no further motivation needed.
Claim 5
The method of claim 1 wherein the analysis … provides an operator with indications for an optimum working procedure.
Analysis
WO ’343’s analysis module highlights “hard” vs “soft” zones and suggests multiple tamping or ballast replacement, thereby giving the operator optimum procedure indications. Limitation met.
Motivation
Already present in WO ’343.
Claim 6
The method of claim 3 further comprising generating a ballast bed state record … report … transmitting both to an infrastructure manager.
Analysis
Same teachings identified for claim 4 apply where the operator interface of claim 3 is used; limitations met.
Motivation
Combination as above.
Claim 7
The method of claim 6 wherein the analysis … provides an operator with indications for an optimum working procedure.
Analysis
Indications shown on display 18 in WO ’343 fulfil this requirement.
Motivation
Contained in WO ’343.
Claim 8
The method of claim 2 wherein the analysis … provides an operator with indications for an optimum working procedure.
Analysis
Same rationale as claim 5; satisfied.
Motivation
Contained in WO ’343.
Claim 9
The method of claim 3 wherein the analysis … provides an operator with indications for an optimum working procedure.
Analysis
Satisfied by display in WO ’343 as above.
Motivation
Contained in WO ’343.
Claim 10
The method of claim 4 wherein the analysis … provides an operator with indications for an optimum working procedure.
Analysis
Same teachings satisfy.
Motivation
Contained in WO ’343.
Response to Arguments
STATUS OF AMENDMENT
The Amendment filed December 15, 2025 amending claims 1, 2, 5, 7, 8, 9, and 10 is entered. Claims 3, 4, and 6 remain as previously presented.
RESPONSE TO AMENDMENTS AND REMARKS
Applicant’s arguments filed December 15, 2025 have been fully considered but are not persuasive for the reasons set forth below.
I. RESPONSE TO §112 ISSUES
A. Prior §112 Issues – Withdrawn
The following prior issues under 35 U.S.C. §112 are considered overcome by the amendments:
“track-building machine” Claim 1 has been amended to consistently recite the packing machine as the machine whose position is detected. This resolves the internal inconsistency previously noted.
“signal detection by actuators” This language has been removed and replaced with acquisition of ballast bed data using a hydraulic packing drive and associated sensors, curing the antecedent and clarity issues.
“optimum mode” The subjective term “optimum” has been deleted and replaced with language directed to a mode of operation selected to respond to indicated conditions. The prior indefiniteness concern is therefore moot.
“fully automatically and autonomously” Applicant has removed “autonomously” and clarified “automatically,” resolving the ambiguity identified in the first Office Action.
Accordingly, the original §112(b) rejections based on these issues are withdrawn.
B. New §112 Issues Raised by the Amendments
Notwithstanding the above, the amendments introduce new issues under 35 U.S.C. §112, as explained below.
1. Indefiniteness – §112(b)
Claims 1–10 remain indefinite because claim 1 now recites:
“a drop in compression forces … that exceeds a predetermined critical value”
However, the claims do not define:
the reference baseline against which the “drop” is determined (e.g., adjacent sleeper, moving average, historical value, target force);
whether the “drop” is absolute, relative, rate-based, or spatially normalized; or
how the “predetermined critical value” is established, expressed, or applied.
Merely stating that a drop “exceeds a predetermined critical value” does not provide objective boundaries for determining when the limitation is met. The metes and bounds of the claims therefore remain unclear.
In addition, claim 1 now classifies conditions as:
“a single fault in the track,”
“a highly polluted ballast area,” or
“a general default.”
The claims do not define how these classifications are determined, what parameters separate them, or how a given ballast condition is objectively assigned to one category versus another. These terms operate as functional labels without definitional boundaries, rendering the scope of the claims uncertain.
Accordingly, claims 1–10 are rejected under 35 U.S.C. §112(b).
2. Enablement / Written Description – §112(a)
Claims 1–10 are also rejected under 35 U.S.C. §112(a).
As amended, claim 1 now affirmatively requires:
“a machine learning device trained to predict occurrence of track deterioration or track faults”
While the specification discusses machine learning at a general conceptual level, it does not provide sufficient disclosure to enable a person of ordinary skill in the art to practice the full scope of the claims without undue experimentation, including but not limited to:
what constitutes “track deterioration” versus a “track fault”;
how training data is generated, labeled, or validated;
how the model is trained to distinguish among the recited operational modes;
how the “predetermined critical value” is derived or incorporated into the training or inference process; and
how the prediction output is translated into stored work instruction data.
The amendment narrows the claim from generic data analysis to a predictive, trained machine-learning system, but the specification does not demonstrate possession of or enablement for that predictive scope across varying ballast conditions, track classes, and machine configurations.
Accordingly, claims 1–10 are rejected under 35 U.S.C. §112(a).
II. RESPONSE TO §103 ARGUMENTS
Applicant argues that the amended claims distinguish over WO2020/037343 A1 (“WO ’343”), WO2019/091681 A1 (“WO ’681”), and AT520117 B1 (“AT ’117”). These arguments are not persuasive.
III. §103 REJECTION MAINTAINED
Claims 1–10 are rejected under 35 U.S.C. §103 as being unpatentable over:
WO2020/037343 A1 (WO ’343) in view of
WO2019/091681 A1 (WO ’681) and further in view of
AT520117 B1 (AT ’117)
A. Amended Claim 1
Applicant asserts that none of the references teaches or suggests:
analyzing ballast bed parameters based on a drop in compression forces exceeding a critical value;
selecting among modes of operation based on that analysis; or
automatically implementing the selected work instructions.
These assertions are not supported.
1. Hydraulic packing drive and sensor-based acquisition
AT ’117 expressly teaches tamping units with hydraulic auxiliary cylinders (7), pressure sensors (9), and path sensors used to derive compaction force and work. WO ’343 similarly discloses sensor-based measurement of ballast condition during tamping. The amended limitation is therefore taught or at least rendered obvious.
2. Drop in compression forces exceeding a predetermined critical value
AT ’117 teaches evaluating compaction force relative to predetermined criteria, including target values and percentage thresholds, and identifying defective sleepers when those criteria are not met. This is a threshold-based evaluation of force behavior along the longitudinal direction of the track.
WO ’343 teaches identifying defective ballast zones based on abnormally low achievable compaction force and correlating such drops with recurrence of faults. The combination teaches the claimed analysis of compression-force drops relative to a critical value.
3. Machine learning trained to predict deterioration or faults
WO ’681 discloses a big-data framework employing machine-learning algorithms to evaluate sensor-derived track data and infer unstable or deteriorating track conditions, and to output location-dependent working parameters.
WO ’343 independently teaches correlating measured ballast parameters with durability and fault recurrence. Applying machine learning to that same parameter set to predict deterioration is a predictable implementation of known analytics using known tools.
The amended claim does not require any specific model architecture or training methodology beyond this general predictive function.
4. Selection among operational modes
WO ’343 is fundamentally directed to correction of single faults and distinguishes such faults from normal track sections. AT ’117 addresses handling of contaminated / hard ballast requiring modified compaction strategies. Both references implicitly operate with baseline or default tamping conditions for normal ballast.
Encoding these known conditions into selectable operational modes is an obvious organization of known tamping practices.
5. Automatic execution in a subsequent work pass
WO ’343 discloses computer-controlled tamping sequences executed once the machine is positioned at defined start and end points. WO ’681 teaches navigation and direct transmission of working parameters to working units, enabling automatic execution of location-dependent instructions.
The amended removal of “autonomously” further broadens the scope and does not distinguish over the combined teachings.
B. Dependent Claims 2–10
Claims 2–10 add limitations directed to:
positionally accurate work instructions per sleeper,
cyclic automatic travel and execution,
operator display of instructions, and
generation and transmission of ballast bed records and reports.
Each of these features is taught or suggested by WO ’343 and WO ’681, as previously mapped, and Applicant’s amendments do not introduce a limitation that overcomes the combination.
Because claim 1 remains unpatentable, claims 2–10 likewise remain unpatentable.
IV. CONCLUSION
The prior §112 issues identified in the first Office Action are withdrawn.
New §112(a) and §112(b) rejections are maintained based on the amended claim language.
The §103 rejection of claims 1–10 is maintained over WO ’343 in view of WO ’681 and AT ’117.
Applicant has not demonstrated that the amendments place the claims in condition for allowance.
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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 JASON C SMITH whose telephone number is (703)756-4641. The examiner can normally be reached Monday - Friday 8:30 AM - 5:00 PM.
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, Allen Shriver can be reached at (303) 297-4337. 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.
/Jason C Smith/ Primary Examiner, Art Unit 3613