DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after 16 March 2013, is being examined under the first inventor to file provisions of the AIA . This Office action is in response to the application filed on 27 December 2024. Claims 1-20 are pending.
Priority
The claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged.
The claim for foreign priority under 35 U.S.C. 119 (a)-(d) is acknowledged. A certified copy of the priority application has been received.
Information Disclosure Statement
The IDS received on 05 June 2024 has been considered.
Interpretation of Claims Under 35 USC 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f):
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are:
In claims 7, 9, and 11:
"positioning module" … "outputting a current position" (interpreted as a GNSS positioning receiver as described in para [0061] of the specification);
"video recognition module" … "to run [algorithms]" (interpreted as a video camera as described in para [0037]); and
"interface module gives a corresponding response" (interpreted as a computer interface as described in para [0081]).
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because the scope of the claim includes a transitory type of computer-readable storage medium, and therefore the invention would not have a physical or tangible form (MPEP 2106.03).
Claim Rejections - 35 USC § 102
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 1-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 9,796,400 B2 (Puttagunta et al.).
As to claim 1, Puttagunta discloses an active train obstacle detection method based on a positioning technique, comprising the following steps:
S1, acquiring an electronic map (col 16 ln 57-col 17 ln 2 - "auditing of map data by a local vehicle may be initiated by a centralized control server, communicating with the vehicle via communications module 2540. For example, if the time elapsed since last auditing of a map section exceeds a threshold, a centralized control server can request auditing from a local vehicle traveling through the target region. In another example, if one vehicle reports discrepancies between centralized map data and locally-observed conditions, the centralized control server may request confirmation auditing by one or more other vehicles moving within the area of the discrepancy. Auditing requests may pertain to various combinations of geographic regions and/or mapping layers", col 17 ln 39-45 - "semantic maps can be recorded and delivered in different coordinate and reference frames. There are also transformations allowing to project maps from one coordinate reference frame to the next. These maps can be packaged and delivered in different formats. Common formats include GeoJSON, KML, shapefiles, and the like");
S2, correcting initial parameters (col 17 ln 46-63 - "the geospatial data used for semantic map creation comes from LiDAR, visible spectrum cameras, infrared cameras, and other optical equipment. The act of obtaining machine vision data for map creation, where this data is georeferenced to a particular location on the planet, is called surveying. The output is a set of data points in three dimensions, along with images and video feeds in the visible spectrum and other frequencies. [...] The geospatial data is collected initially with the collection vehicle being the origin of the reference frame. By locating the vehicle throughout the survey (using, e.g., an Inertial Measurement Unit (IMU) and Global Positioning Systems (GPS)), the images, laser scans and video feeds are then registered to a fixed reference frame which which is georeferenced. The data generated in the survey can be streamed or saved locally for later consumption");
S3, performing parameter calibration of a video camera (col 9 ln 64-66 - "The Track Identification Algorithm (TIA), depicted in FIGS. 6-7 determines which track the rolling stock is currently utilizing", col 10 ln 64-col 11 ln 3 - "the TIA may utilize the global feature vectors to stitch together features from multiple points in space or from a single point in space using various image processing techniques (e.g., image stitching, geometric registration, image calibration, image blending). This results in a superset of feature data that has collated global feature vectors from multiple points or a single point in space");
S4, detecting a train obstacle (col 14 ln 36-40 - "The status of the infrastructure can also be verified, and the operational safety can be assessed, every time a vehicle with the vision apparatus travels down the track. For example, clearance measurements are performed making sure that no obstacles block the path of trains"); and
S5, outputting an obstacle recognition result (col 15 ln 14-16 - "The backend infrastructure also generates alerts and reports concerning the state of the assets for various railroad officers", col 18 ln 6-12 - "point-cloud data measured by a vehicle may be compared to previously-measured point cloud data to detect conditions or changes in a local environment, such as a fallen tree, overgrown vegetation, changed signage, lane closures, track or roadway obstructions, or the like. The detected changes in the environment can be used to further update the semantic maps").
As to claim 2, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, and further discloses wherein in S1, acquiring an electronic map specifically comprises:
acquiring an electronic map using different acquisition methods according to different positioning techniques (col 17 ln 64-col 18 ln 3 - "the vehicle localization and local environment sensing systems described herein benefit from use of point cloud survey data. Semantic maps derived from point cloud survey data may provide a vehicle with high levels of detail and information regarding the vehicle's current or anticipated local environment, which may be used, for example, to assist in relative vehicle localization", col 20 ln 63-col 21 ln 3 - "Map generator 1230 may also include an annotation integrity verifier operating to verify the integrity of annotated datasets over time. In some applications, locations may be surveyed repeatedly at different times. For example, in railway applications, trains equipped with LiDAR or other railway surveying vehicles may periodically survey the same length of railway, such as to monitor the health or status of assets along a track", col 21 ln 19-22 - "Feature maps (containing only the location, geometry and features of various assets), as well as semantic ones can also be stored in remotely accessible geodatabases. The map data can be retrieved either directly or through a server").
As to claim 3, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 2, and further discloses wherein acquiring an electronic map using different acquisition methods according to different positioning techniques specifically comprises:
(101) for a point positioning technique, acquiring an electronic map by means of trains running on railways or by converting an existing railway line data file (col 17 ln 39-45); or
(102) for a SLAM positioning technique, enabling a train to run along all railways at least once to record features of all rail operation regions, and generating an electronic railway map (col 20 ln 63-col 21 ln 3).
As to claim 4, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, and further discloses wherein in S2, correcting initial parameters specifically comprises:
correcting the initial parameters to realize registration of a coordinate system of a laser radar and a coordinate system of the electronic map to enable trajectories of tracks to overlap with actual railways in the coordinate system of the laser radar (col 4 ln 30-33 - "Railway embodiments can use a series of sensor fusion and data fusion techniques to obtain the track position with improved precision and reliability", col 17 ln 46-63).
As to claim 5, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, and further discloses wherein the initial parameters comprise translations XYZ and rotations YPR (col 9 ln 66-col 10 ln 4 - "The TIA creates a superimposed feature dataset by overlaying the features from the 3D LIDAR scanners and FLIR Cameras onto the onboard camera frame buffer. The superset of features (global feature vector) allows for three orthogonal measurements and perspectives of the tracks", col 10 ln 59-60 - "The TIA may further match global feature vector samples from a local or backend database with spatial transforms").
As to claim 6, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, and further discloses wherein in S3, by performing parameter calibration of the video camera, a transformational relation between two-dimensional coordinates of images acquired by the video camera, three-dimensional coordinates of a real world and a coordinate system of a laser radar is figured out (col 4 ln 30-33, col 10 ln 43-49 - "Raw or processed sensor data may include a point cloud (e.g., two-dimensional, three-dimensional), an image (e.g., jpg), a sequence of images, a video sequence (e.g., live, recorded playback), scanned map (e.g., two-dimensional, three-dimensional), an image detected by Light Detection and Ranging (e.g., LIDAR), infrared image, and/or low light image (e.g., night vision)", col 17 ln 46-63).
As to claim 7, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, and further discloses wherein in S4, detecting a train obstacle specifically comprises:
S401, acquiring point cloud data in front by a laser radar, acquiring image data in front by the camera, and inputting the point cloud data and the image data to an operational host (col 8 ln 53-56 - "Sensor data may be processed, whether by the VA module and/or the PTCC module, to detect and/or identify: Track used by the train, Distance to tracks, objects and/or infrastructure", col 17 ln 46-63);
S402, outputting a current position by a positioning module, finding a corresponding position in the electronic map by matching, and acquiring coordinates and attitude data of a positioning point according to the corresponding position (col 17 ln 46-63);
S403, inquiring information of a rail operation region in front of the positioning point according to the coordinates of the positioning point and a direction of travel, obtaining data of the rail operation region in front of the positioning point, and forming a three-dimensional rail operation region (col 10 ln 42-44 - "The list of potential absolute track IDs may be obtained through a query to a locally cached GIS dataset or a remotely hosted backend server", col 10 ln 48-51 - "The TIA compares the relative offset position of the train among multiple railway tracks and references to the list of potential absolute track IDs to identify the absolute track ID that the train is utilizing", col 10 ln 52-56 - "After the TIA obtains an absolute track ID, the global feature vector samples may be annotated with the geolocation (e.g., geographic coordinate) information and track ID. This allows the TIA to utilize the global feature vector datasets to directly determine a track position in the future");
S404, transforming the rail operation region into a coordinate system of the laser radar, plotting the rail operation region in a laser point cloud, and detecting whether there is an obstacle in the rail operation region by a point cloud processing algorithm (col 16 ln 31-35 - "By comparing the vehicle's observed position relative to local features or objects, with the position of those features and objects on maps, the vehicle's position can be refined with significantly more accuracy than typically possible using GPS");
S405, projecting the rail operation region into a coordinate system of a video image according to calibrated parameters of the video camera, plotting the rail operation region in the video image, and detecting whether there is an obstacle in the rail operation region by a video recognition algorithm (col 1 ln 45-46 - "all trains are monitored in real time to enable 'Positive Train Control' (PTC)", col 5 ln 4-7 - "a PTC vision system may include one or more of the following: [...] Vision Apparatus (VA) 230, Positive Train Control Computer (PTCC) 210", col 8 ln 16-21 - "The VA module may detect the environment using any type of conventional sensor that detects a physical property and/or a physical characteristic. Sensors of the VA module may include cameras (e.g., still, video), remote sensors (e.g., Light Detection and Ranging), radar, infrared, motion, and range sensors", col 8 ln 49-52 - "The VA module may perform some processing of sensor data. Processing may include data reduction, data augmentation, data extrapolation, and object identification", col 8 ln 53-56); and
S406, fusing obstacle information output by the laser radar and obstacle information output by video recognition, outputting finally confirmed obstacle information, and sending the obstacle information to an interface module (col 4 ln 30-33, col 9 ln 12-15 - "The PTCC module may also use information from any module of the PTC environment, including the PTC vision system, to qualify and/or interpret sensor information provided by the VA module").
As to claim 8, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 7, and further discloses wherein in S402, if the positioning module is able to output attitude data, and the attitude data output by the positioning module are used (col 8 ln 53-60 - "Sensor data may be processed, whether by the VA module and/or the PTCC module, to detect and/or identify: [...] Track curvature, [...] Track deviation from horizontal (e.g., declivity, acclivity)").
As to claim 9, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 7, and further discloses wherein in S405, a video recognition module is able to directly run a rail operation region recognition algorithm to automatically recognize the information of the rail operation region and then run the video recognition algorithm to detect whether there is an obstacle in the rail operation region (col 8 ln 53-62 - "Sensor data may be processed, whether by the VA module and/or the PTCC module, to detect and/or identify: Track used by the train, Distance to tracks, objects and/or infrastructure, Wayside signal indication (e.g., meaning, message, instruction, state, status), Track condition (e.g., passable, substandard), Track curvature, Direction (e.g., turn, straight) of upcoming segment, Track deviation from horizontal (e.g., declivity, acclivity), Junctions, Crossings, Interlocking exchanges, Position of train derived from environmental information, and Track identity (e.g., track ID)").
As to claim 10, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 7, and further discloses wherein the obstacle information output in S406 comprises a type, size, distance, direction and collision probability of an obstacle (col 8 ln 49-52, col 8 ln 53-62, col 14 ln 36-40, col 23 ln 40-46 - "The output of a track detection mechanism that includes the track centerline may be subsequently used as an input to a track clearance check mechanism. A bounding box is defined with respect to the track center line, and any objects that encroach within that bound are reported. The dimensions of the bounding box can be modified to fit various standards").
As to claim 11, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 7, and further discloses wherein in S5, the interface module gives a corresponding response according to obstacle recognition information, wherein the response comprises sound-light alarming, whistling, normal braking, emergency braking, log recording, and remote message sending (col 4 ln 30-36 - "Railway embodiments [...] can also be used for auto-braking of trains for committing red light violations on the track, for optimizing fuel based on terrain, synchronizing train speeds to avoid red lights, anti-collision systems", col 9 ln 61-63 - "The PTCC may also process the newly collected data (or forward it) to audit and augment the information in the backend database", col 13 ln 12-17 - "the PTC vision system issues notifications (local or remote), possibly raises alarms on-board the train, and can automatically control the train's inertial metrics by interfacing with various subsystems on-board (e.g., traction interface, braking interface, traction slippage system)").
As to claim 12, Puttagunta discloses the active train obstacle detection method based on a positioning technique according to claim 1, wherein in a case of multiple tracks, the active train obstacle detection method based on a positioning technique specifically comprises:
(a) acquiring, from a ground interlocking system, route information in front, figuring out a direction of a turnout from the route information, then determining a track where a train is about to run, and extending a rail operation region to the determined track (col 7 ln 54-col 8 ln 14 - "The VA module may detect the tracks upon which the train travels, tracks adjacent to the tracks traveled by the train [...] Additional examples include: PTC assets, ETCS assets, Tracks, Signals, Signal lights, Permanent speed restrictions, [...] Speed limit Signs, Roadside safety structures, [...] Clearance point locations for switches installed on the main and siding tracks, Clearance/structure gauge/kinematic envelope, Beginning and ending limits of track detection circuits in non-signaled territory, [...] Turnouts, [...] Switches, [...] Frog (crossing point of two rails), [...] Interchanges, Interlocking/control point locations", col 10 ln 48-51, col 10 ln 52-56); or
(b) determining the direction of the turnout by a video recognition method, and selecting a corresponding track, wherein the video camera is able to recognize, according to the position of a turnout gap, whether the turnout is located at a normal position or a reverse position and then inform a video recognition module and a laser radar detection module to select the corresponding track (col 7 ln 54-col 8 ln 14, col 8 ln 16-21, col 10 ln 48-51, col 10 ln 52-56); and
in a case of multiple turnouts, the turnouts are recognized in sequence to select the corresponding track (col 9 ln 6-9 - "The PTCC utilizes its access to all subsystems (e.g., modules) of the PTC system to derive (e.g., determine, calculate, extrapolate) track ID and signal state from the sensor data obtained from the VA module").
As to claim 13, Puttagunta further discloses an apparatus used for the active train obstacle detection method based on a positioning technique according to claim 1, comprising
a positioning module (Fig 2 - GPS (225)),
a laser radar detection module (Fig 2 - VA (230), col 5 ln 4-7),
a video recognition module (Fig 2, col 5 ln 4-7, col 8 ln 16-21, col 8 ln 49-52),
an operational host (Fig 2, col 8 ln 53-56) and
an interface module (Fig 2, col 9 ln 12-15),
wherein the operational host is connected to the positioning module, the laser radar detection module, the video recognition module and the interface module respectively (Fig 2).
As to claim 14, Puttagunta discloses the apparatus according to claim 13, and further discloses wherein the positioning module is configured to acquire a real-time position of a train and adopts one or more positioning techniques (col 17 ln 46-63), and
the positioning techniques comprise satellite navigation positioning, laser radar-based SLAM positioning, video-based VSLAM positioning, dead-reckoning positioning based on inertia navigation equipment, and integrated positioning based on wheel speed sensors, beacon transponders and Doppler speed sensors (col 17 ln 46-63).
As to claim 15, Puttagunta discloses the apparatus according to claim 13, and further discloses the laser radar detection module is mounted in front of a vehicle and configured to acquire a three-dimensional scanning point cloud in front of the vehicle to determine a size, direction and distance of an obstacle (col 8 ln 53-56, col 8 ln 64-65 - "The VA module may be coupled at any position on the train (e.g., top, inside, underneath)", col 17 ln 46-63).
As to claim 16, Puttagunta discloses the apparatus according to claim 13, and further discloses the video recognition module is mounted in front of the vehicle and configured to acquire colored image information in front of the vehicle and transmit the colored image information to the operational host (col 8 ln 64-65, col 12 ln 7-8 - "The color spectrum in an image captured through the PTC vision system may be segmented");
the video recognition module is able to automatically adjust exposure parameters according to a change in light intensity to ensure that a clear image is obtained (col 8 ln 16-21, col 8 ln 49-52).
As to claim 17, Puttagunta discloses the apparatus according to claim 13, and further discloses the operational host is configured to process received positioning data, laser radar data and video image data and output an obstacle detection result to the interface module (col 4 ln 30-33, col 9 ln 12-15, col 14 ln 36-40).
As to claim 18, Puttagunta discloses the apparatus according to claim 13, and further discloses the interface module is configured to receive the obstacle detection result output by the operational host and perform corresponding operations, including sound-light alarming, whistling and output braking, according to settings (col 4 ln 30-36, col 9 ln 61-63, col 13 ln 12-17).
As to claim 19, Puttagunta further discloses an electronic device, comprising
a memory (col 5 ln 31-35 - "The PTCC may be implemented using any conventional processing circuit including a microprocessor, a computer, a signal processor, memory, and/or buses. A PTCC may perform any computation suitable for performing the functions of the PTC vision system") and
a processor (col 5 ln 31-35),
a computer program being stored in the memory (col 5 ln 31-35),
wherein the processor implements the active train obstacle detection method according to claim 1 when executing the computer program (col 5 ln 31-35, col 9 ln 12-15).
As to claim 20, Puttagunta further discloses a computer-readable storage medium, having a computer program stored therein (col 5 ln 31-35),
wherein the active train obstacle detection method according to claim 1 is implemented when the computer program is executed by a processor (col 5 ln 31-35, col 9 ln 12-15).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Todd Melton whose telephone number is (571)270-3871. The examiner can normally be reached weekdays, 9:30am - 6:00pm (Eastern time). 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, Navid Mehdizadeh can be reached at 571-272-7691. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/TODD MELTON/Primary Examiner, Art Unit 3669
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