US20230228042A1 - Method for automatic autonomous control of a packing machine - Google Patents

Method for automatic autonomous control of a packing machine Download PDF

Info

Publication number
US20230228042A1
US20230228042A1 US18/008,433 US202118008433A US2023228042A1 US 20230228042 A1 US20230228042 A1 US 20230228042A1 US 202118008433 A US202118008433 A US 202118008433A US 2023228042 A1 US2023228042 A1 US 2023228042A1
Authority
US
United States
Prior art keywords
work
track
packing
ballast bed
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
US18/008,433
Other languages
English (en)
Inventor
Bernhard Lichtberger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
HP3 Real GmbH
Original Assignee
HP3 Real GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by HP3 Real GmbH filed Critical HP3 Real GmbH
Assigned to HP3 REAL GMBH reassignment HP3 REAL GMBH ASSIGNMENT OF ASSIGNORS INTEREST (SEE DOCUMENT FOR DETAILS). Assignors: LICHTBERGER, BERNHARD
Publication of US20230228042A1 publication Critical patent/US20230228042A1/en
Pending legal-status Critical Current

Links

Images

Classifications

    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01B—PERMANENT WAY; PERMANENT-WAY TOOLS; MACHINES FOR MAKING RAILWAYS OF ALL KINDS
    • E01B27/00—Placing, renewing, working, cleaning, or taking-up the ballast, with or without concurrent work on the track; Devices therefor; Packing sleepers
    • E01B27/12—Packing sleepers, with or without concurrent work on the track; Compacting track-carrying ballast
    • E01B27/13—Packing sleepers, with or without concurrent work on the track
    • E01B27/16—Sleeper-tamping machines
    • E01B27/17—Sleeper-tamping machines combined with means for lifting, levelling or slewing the track
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01B—PERMANENT WAY; PERMANENT-WAY TOOLS; MACHINES FOR MAKING RAILWAYS OF ALL KINDS
    • E01B27/00—Placing, renewing, working, cleaning, or taking-up the ballast, with or without concurrent work on the track; Devices therefor; Packing sleepers
    • E01B27/12—Packing sleepers, with or without concurrent work on the track; Compacting track-carrying ballast
    • E01B27/13—Packing sleepers, with or without concurrent work on the track
    • E01B27/16—Sleeper-tamping machines
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01B—PERMANENT WAY; PERMANENT-WAY TOOLS; MACHINES FOR MAKING RAILWAYS OF ALL KINDS
    • E01B35/00—Applications of measuring apparatus or devices for track-building purposes
    • E—FIXED CONSTRUCTIONS
    • E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
    • E01B—PERMANENT WAY; PERMANENT-WAY TOOLS; MACHINES FOR MAKING RAILWAYS OF ALL KINDS
    • E01B2203/00—Devices for working the railway-superstructure
    • E01B2203/12—Tamping devices

Definitions

  • the invention relates to a method for the automatic autonomous control of a track-building machine having a position-measuring device and precise synchronization to the track, position detection of the working assemblies of the packing machine, with the aid of which the control computer of a packing machine is given positionally accurate work instructions for each sleeper area to be packed and the packing machine carries this out fully automatically and autonomously depending on the current position in the track and the associated work instruction data.
  • track maintenance is planned on the basis of the track geometry, which is recorded via the position of the rails.
  • Track measurement vehicles drive over the tracks at regular intervals and record their geometric position.
  • the track position is usually divided into sections of about 200 m in length and the standard deviation of the elevation, direction, superelevation and twist is recorded.
  • singular individual errors are also measured. If the statistical values exceed certain comfort tolerances then maintenance work is planned and carried out. If the individual faults exceed certain critical values, immediate action is taken to rectify them, otherwise slow running points or track closures will have to be imposed because of the danger to train traffic.
  • the maintenance machine is given the target track geometry, previously recorded and measured track defects and the area to be maintained. No further specifications are made.
  • a second operator i.e. the tamper
  • the packing operation Regardless of the type of defect in the ballast, he usually performs a standard packing. The methodology whether multiple packing, lifting over the positions etc. is left to him.
  • the actual track position is measured with various known measuring systems and compared with the target track position.
  • the differences in height and direction are transferred to the packing machines as track correction values with the track set geometry.
  • packing machines There are packing machines specialized in packing of switches (divisible packing units—so called splithead units, additional lifting devices for the branching line, pivotable compacting picks etc.) and packing machines which are preferably built for line packing. Packing machines are known in cyclic but also in continuous working advance. In addition, there are single-sleeper and multi-sleeper packing machines. Multiple sleeper packing machines pack several sleepers at once in one working cycle. However, they can also be used in such a way that only one sleeper is packed.
  • ballast may be destroyed, rounded and crushed in the area of a rail joint, similar defects occur on very hard substrates and are referred to as “white spots”.
  • the driving dynamics cause ballast to be pulverized and these spots are indicated by escaping mineral dust.
  • the ballast can be very damaged if it lies for a long time.
  • a large amount of fines and organic material or soil pressed up from the subsoil may have filled the interstices of the ballast grains. It is known from practice that the track position of such ballast structures cannot be durably corrected with track packing machines. It is also known from practice that individual faults occur randomly distributed in the track.
  • Track geometry defects are usually recorded by independent measurement methods prior to a packing operation, stored, and transferred to the packing machine computers in electronic form. Track geometry faults typically have 10-25 m wavelengths with amplitudes of 10-40 mm. Long wavelength faults in the 25-70 m range also occur and have higher fault amplitudes.
  • Packing units fix the position of a track during a maintenance measure. This is done by packing tools, so-called packing picks, which plunge into the ballast next to the sleepers and compact the ballast under the sleeper by means of a linear closing movement superimposed by a compression vibration.
  • packing tools so-called packing picks
  • the linear closing movement is superimposed by a hydraulic cylinder and the vibration amplitude mechanically generated by an eccentric shaft.
  • Newer fully hydraulic packing drives generate the linear closing motion and the vibration simultaneously.
  • a track-building machine which collects network data and transmits them to a system control center.
  • the track-building machine has a sensor system and collects raw data. From this, it is to be planned when and where operations of the track-building machine are to be performed.
  • raw data is collected to update the network data, i.e. data such as rebuilds or faults and the like, and not specific ballast parameters recorded during packing.
  • the course of the compression forces cannot be obtained from the collection of network data.
  • a fully hydraulic drive of a packing unit is disclosed in AT 513 973 A, for example. To regulate and control this drive, the adjusting movement is recorded by means of integrated displacement sensors.
  • the packing pressure is measured by pressure sensors.
  • parameters such as compression work, ballast hardness, ballast bed contamination, compression force, compression times and ballast stiffness, etc. can be measured and derived. It is known from AT 515 801 A how optimum packing times can be specified depending on measurements. The opening width of the packing tools can also be freely and continuously adjusted via these fully hydraulic packing drives.
  • the packing operator is currently responsible for selecting the correct setting of the packing unit, such as packing pressure, packing time, lowering speed of the packing unit, opening width, packing depth, elevation of the track or multiple packing, etc. Further planning of the work, such as the packing work itself, but also preparatory work such as ballast replacement in the area of local disturbances, local drainage improvement, etc., does not take place. This increases the track maintenance costs and reduces the durability of the achieved track position.
  • Points in the track with high bedding hardness form high points, which change little in elevation due to train traffic.
  • Singular short faults in the track have the tendency to expand longitudinally under the high dynamic forces acting in the track, to increase in the height of the track fault and to produce consequential faults caused by the excited track vehicles.
  • Sensors are known that can determine the position of the sleepers in the track when a packing machine passes over them. With the aid of such devices, the machine can be positioned correctly for packing fully automatically. Machines that operate fully automatically are thus known from practice.
  • Machine learning systems are state of the art. Machine learning is a generic term for computer-aided generation of knowledge from experience. For this purpose, algorithms build a statistical model based on training data. Patterns and regularities are recognized in the learning data. This enables the system to assess even unknown data. With the help of GPS systems installed on packing machines, an exact assignment of the sleepers and the recorded measurement parameters to the track kilometer can be made via the GPS coordinates.
  • RTK-GPS has the advantage that it can determine the absolute location very precisely (about 5 mm in position and 10-15 mm in height) using RTK correction data.
  • the more satellites and satellite systems are received simultaneously by a GPS receiver the more accurate the results.
  • Modern satellite receivers simultaneously receive and utilize the GPS, GLONASS, GALILEO, BeiDou, QZSS, IRNSS and SBAS satellite systems. They can send data to the correction service and receive correction data on a second channel.
  • Sleepers in the track and other places can be located just as precisely and provided with GPS coordinates. These places or sleepers can be precisely and unambiguously located with permanent way machines equipped with an RTK-GPS system.
  • the invention is based on the object of providing a method for the automatic autonomous control of a track-building machine which avoids the above-mentioned disadvantages.
  • the method is to supply the track-building machine not only generally with nominal geometry data and track position correction data, but also with exact locally unambiguously assigned work instructions, so that these are autonomously packed with high quality adapted also to the properties and requirements of the ballast bed and thus avoid the error susceptibility by humans.
  • the packing machine should record the ballast bed parameters during the work, analyze them with the computer and, at the end of the work, transfer them preferably to an infrastructure operator in preparation for the next pass.
  • the invention solves the given object with the features of claim 1 .
  • Advantageous further developments of the invention are shown in the subclaims.
  • the invention solves the object in that during packing the ballast bed data is recorded via sensors and from this the current ballast bed parameters are recorded and stored for a subsequent work pass and analyzed with a machine learning device, wherein an analysis of the ballast bed state data is made on the basis of machine learning methods and the ballast bed parameters are analyzed with regard to a drop in the compression forces occurring in the longitudinal direction of the track and work instructions for an optimum working method are ascertained therefrom and stored, wherein, in a subsequent work pass, depending on the current position in the track and the associated work instruction data, the packing machine carries out said work instructions fully automatically and autonomously.
  • a packing machine control computer is given positionally accurate (via GPS coordinates, for example) work instructions for each sleeper area to be packed (this may include: Multiple packing, larger opening width of the packing tools, packing pressure, overlifting, specification of the maximum compression force, packing time, automatic packing time depending on the compression, etc. or specification of the work sequence in switches—at which points, for example, the splithead units are to be split in the case of a switch packing machine and the outer part is to be swung outwards, etc.). These working parameters were recorded in a previous work pass, a complete or partial packing of a track, and stored for a following work pass.
  • the packing machine is positioned precisely at the sleeper areas to be packed via automatic sleeper detection or GPS coordinates. At the position reached, the packing machine can then carry out this work fully automatically and autonomously, depending on the specified work instructions, generating new work instructions for a next pass if necessary and then moving to the next sleeper area via an automatic travel system, where the sequence is repeated accordingly until the entire intended work area has been worked through.
  • the predetermined work instructions do not have to be performed fully automatically, but can be displayed to an operator for each sleeper area, with the operator setting and performing the predetermined work modes.
  • the ballast bed data and work data are recorded during packing with the aid of the fully hydraulic packing drive and its sensors, and the current ballast bed parameters (such as ballast bed hardness, compression force, packing time, penetration time of the packing units, deceleration acceleration of the packing units during the penetration process, current GPS position or track km, current lifting and leveling value, current lifting force and leveling force, etc.) are calculated, stored and analyzed using a machine learning device with machine learning techniques.
  • the current ballast bed parameters such as ballast bed hardness, compression force, packing time, penetration time of the packing units, deceleration acceleration of the packing units during the penetration process, current GPS position or track km, current lifting and leveling value, current lifting force and leveling force, etc.
  • a ballast bed state record is generated during the work and displayed to the packing operator or pre-car operator for information, and a ballast state report is generated from the measurement data after the work, both of which are sent to the infrastructure manager as a basis for work preparation for the upcoming packing pass.
  • the packing is provided with appropriate instructions for the optimum working method from the analysis of the ballast bed data that runs along with the packing work.
  • the measurement data of the packing work are analyzed by a rule-based expert system (AI system or other machine learning program) with regard to a sudden drop in the compression forces in the longitudinal direction (individual error) or statistical parameters such as standard deviation, mean value, correlation with the track position level error, etc., and instructions for the optimum mode of operation are determined and specified from this.
  • AI system AI system or other machine learning program
  • AI artificial intelligence
  • ML machine learning
  • a rule-based expert system can support the operator by providing concrete suggestions.
  • XPS have a great advantage in areas where profound expertise is available for the interpretation of the algorithmic models and data situation.
  • the various work instructions can be coordinated and standardized in consultation between the infrastructure manager and the machine operator.
  • the work instructions could mean the following:
  • FIG. 1 shows a schematic side view of a packing machine
  • FIG. 2 shows a schematic representation of a fully hydraulic packing unit
  • FIG. 3 shows a circuit diagram of a track geometry computer with the control devices of the packing machine
  • FIG. 4 shows a ballast bed acceptance record
  • FIG. 1 shows a packing machine 38 , C with trailer 39 which travels on track-mounted undercarriages 34 , 36 on railroad tracks S.
  • the packing machine 38 , C has a packing unit 30 with fully hydraulic drive and measuring sensors 37 , a lifting and straightening unit 42 , 43 for introducing lifting forces FH and straightening forces FR into the track, a working measuring system aw, bw, 35 and an acceptance recorder measuring system ar, br, 35 .
  • Working measuring system aw, bw, 35 and acceptance recorder measuring system ar, br, 35 are, for example, chord measuring systems.
  • the trailer is coupled to the packing machine by a drawbar 40 .
  • the packing unit 30 has a standard opening width B of the packing tools 29 .
  • the packing machine 38 , C also has a control system 19 , a track geometry guidance computer 17 with screen 20 . Data is exchanged wirelessly with the infrastructure operator via an antenna 33 .
  • the working area is precisely coordinated via a GPS system 32 .
  • FIG. 2 shows a packing unit B with fully hydraulic drive Z.
  • the adjusting distance 31 and the compression force are recorded and transferred to the control computer 18 , which forwards them to the track geometry computer 17 for processing.
  • An acceleration sensor by measures the braking deceleration of the packing unit when it dips into the ballast bed. The harder this is, the higher the braking deceleration.
  • the fully hydraulic drive can adjust the opening width of the packing arms 30 with the packing tools 29 from the normal opening B to a larger width BE.
  • ballast granules from the intermediate compartment under the sleeper in a compacting manner through the larger opening BE in order to supplement the partially damaged crushed ballast granules there with intact ballast granules to increase the durability of the track layer.
  • the rails S are fastened to sleepers 41 .
  • FIG. 3 shows a circuit diagram of the track geometry computer 17 with the control devices 19 of the machine.
  • the sensors of the fully hydraulic packing units 18 , 26 are read in and analyzed with a machine learning program ML. Via the screen 20 , the machine operator is informed about the ballast bed state and can receive work instructions.
  • a ballast bed report 22 and a ballast bed record 21 are generated by the track geometry computer 17 and the machine learning program ML.
  • This data is sent wirelessly 25 to an infrastructure operator or machine owner database or to a cloud.
  • the ballast bed parameters under each sleeper are accurately recorded via GPS and assigned to them.
  • a distance measuring wheel WMS is used to assign the local position over the track km.
  • FIG. 4 schematically shows a ballast bed diagram A.
  • Recording channel 1 shows the braking delay by of the packing units
  • channel 2 the track height error before work determined from preliminary measurements of the current track position and comparison with the target track position
  • channel 3 shows the ballast hardness
  • channel 4 the compression force achieved.
  • Channel 5 is the event channel which indicates various special track conditions or track characteristics via markers 6 , 7 , 8 , Br.
  • Symbol 6 stands for a rail joint
  • symbol 7 marks a place in the track where the ballast is destroyed and therefore no satisfactory compression forces can be achieved.
  • Symbol 8 stands for deposited pictures and Br indicates a bridge. At singular individual faults, photos are embedded in the record. If the operator activates them, the corresponding photo 8 is shown.
  • FIG. 10 shows singular fault locations with destroyed ballast, evident on the one hand from the rapid drop in the compression forces and also from the fact that the packing unit braking delay 11 drops because the ballast does not have a high penetration resistance at these locations.
  • Another fault location is formed by 9 which occurs at a weld joint as shown by symbol 6 .
  • Such singular fault locations can be detected and recognized relatively easily by a machine learning program (or a rule-based system). If the course of the height defects (channel 2 ) is compared with with the course of the ballast bed hardness (channel 3 ), it is recognized that they behave in approximately inverse proportion 12 . At hard places, high points form in the height. Where there are soft places, settlements (troughs) are formed. Correlation functions can be used to determine how well these two channels are correlated.
  • ballast bed hardness 16 , 17 indicates the degree of contamination-wear of the ballast. The more contaminated the ballast bed, the higher the ballast hardness 16 , 17 .
  • the compression force (channel 4 ) is proportional to the ballast hardness. Very low values of the compression force indicate either a new layer 14 (new ballast) or a singular place 9 , 10 with defective ballast.
  • the lower the standard deviation ⁇ V is, the lower the stiffness variations and the better the durability of the track position.
  • the cross lines indicate the track kilometer (76, 400, . . . ).
  • ballast bed analysis report An example of a ballast bed analysis report is shown below.
  • the analysis with machine learning system ML provides statements about the durability of the track position and the ballast bed hardness. If there are any faults 9 , 10 , they are indicated with their type, exact location, length and characteristic values.
  • the transmission of these data to the infrastructure manager or a responsible work scheduler forms the basis for the specification of the work instructions for the next pass.
  • the analysis also gives an estimate of the track deterioration rate which is essential for the timing of the next pass. This data is also easily converted into a machine-readable form and transmitted.
  • Packing Mean value Mean value operations Compressive Bedding Number of per sleeper force (kN) hardness (Nm) sleepers 1 18.53 264.66 472 2 15.62 194.80 101 3 0.00 0.00 0 >3 0.00 0.00 0 Mean value 18.31 254.82 573 Standard deviation 5.29 64.33
  • the ballast bed has defects. There is a low durability of the track layer.
  • the estimate results in a track deterioration rate of 1.6 mm/year.
  • the mean value of the ballast bed hardness was 254 Nm.
  • ballast bed is in borderline highly contaminated condition. Track bed cleaning is recommended. A critical fault (with crushed/rounded ballast was found in the tamped area).

Landscapes

  • Engineering & Computer Science (AREA)
  • Architecture (AREA)
  • Civil Engineering (AREA)
  • Structural Engineering (AREA)
  • Machines For Laying And Maintaining Railways (AREA)
US18/008,433 2020-06-08 2021-06-04 Method for automatic autonomous control of a packing machine Pending US20230228042A1 (en)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
ATA50499/2020A AT523900A1 (de) 2020-06-08 2020-06-08 Verfahren zur automatischen autonomen Steuerung einer Stopfmaschine
ATA50499/2020 2020-06-08
PCT/AT2021/060198 WO2021248170A1 (de) 2020-06-08 2021-06-04 Verfahren zur automatischen autonomen steuerung einer stopfmaschine

Publications (1)

Publication Number Publication Date
US20230228042A1 true US20230228042A1 (en) 2023-07-20

Family

ID=76444183

Family Applications (1)

Application Number Title Priority Date Filing Date
US18/008,433 Pending US20230228042A1 (en) 2020-06-08 2021-06-04 Method for automatic autonomous control of a packing machine

Country Status (6)

Country Link
US (1) US20230228042A1 (zh)
EP (1) EP4162111A1 (zh)
JP (1) JP2023529091A (zh)
CN (1) CN115667632A (zh)
AT (1) AT523900A1 (zh)
WO (1) WO2021248170A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230278599A1 (en) * 2022-03-04 2023-09-07 Bnsf Railway Company Automated Tie Marking

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AT519218B1 (de) * 2017-02-06 2018-05-15 Hp3 Real Gmbh Verfahren zur Optimierung einer Gleislage
AT519738B1 (de) * 2017-07-04 2018-10-15 Plasser & Theurer Export Von Bahnbaumaschinen Gmbh Verfahren und Vorrichtung zum Verdichten eines Gleisschotterbetts
AT520117B1 (de) * 2017-07-11 2019-11-15 Hp3 Real Gmbh Verfahren zum Verdichten eines Schotterbettes eines Gleises
EP3707468B1 (de) * 2017-11-09 2021-12-08 Track Machines Connected Gesellschaft m.b.H. System und verfahren zum navigieren innerhalb eines gleisnetzes
AT521263B1 (de) * 2018-08-20 2019-12-15 Hp3 Real Gmbh Verfahren zur Einzelfehlerbehebung
AT521850A1 (de) * 2018-10-24 2020-05-15 Plasser & Theurer Export Von Bahnbaumaschinen Gmbh Gleisbaumaschine und Verfahren zum Unterstopfen von Schwellen eines Gleises

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20230278599A1 (en) * 2022-03-04 2023-09-07 Bnsf Railway Company Automated Tie Marking

Also Published As

Publication number Publication date
EP4162111A1 (de) 2023-04-12
JP2023529091A (ja) 2023-07-07
AT523900A1 (de) 2021-12-15
CN115667632A (zh) 2023-01-31
WO2021248170A1 (de) 2021-12-16

Similar Documents

Publication Publication Date Title
US11255055B2 (en) Method and system for maintenance of a permanent way for rail vehicles
US9631325B2 (en) Apparatus for improving the track position by residual error compensation
EP3160817B1 (en) Lead rail vehicle with drone vehicle and method
EP0688902B1 (de) Verfahren zur kontinuierlichen Messung des Querverschiebewiderstandes eines Gleises
CN101374713B (zh) 检测及考虑行驶中的轨道车辆所受侧风负荷的方法和所相应实施的其端部车厢
JP2008513633A (ja) ポイント、交差あるいは渡り線付交差ならびにレール継ぎ目および鉄道車輌による線路の不均質性の診断および状態の監視
RU2674726C1 (ru) Способ и устройство для уплотнения щебеночного балластного слоя рельсового пути
US11982056B2 (en) Method for automatic correction of the position of a track
US20230228042A1 (en) Method for automatic autonomous control of a packing machine
DE19801311A1 (de) Schienengebundene Instandhaltungsmaschine
US20130220162A1 (en) Drone vehicle
US8245646B1 (en) Articulated rail vehicle
Swift et al. New practices for railway condition monitoring and predictive analysis
Vikesland Track geometry degradation cause identification and trend analysis
Presle et al. The EM 250 high-speed track recording coach and the EM-SAT 120 track survey car, as networked track geometry diagnosis and therapy systems
RU2738026C1 (ru) Информационно-управляющая система перемещения рельсов, шпал и балласта для обеспечения работоспособности железнодорожного пути по заданным критериям
AT526338B1 (de) Verfahren zur automatischen Auswertung von Gleismessdaten
Ivona et al. TRACK GEOMETRY DEGRADATION AND MAINTENANCE OF THE RAILWAYS
Schild et al. Inertial Sensing and Mapping of Railway Track Properties
RU2038441C1 (ru) Способ определения состояния рельсошпальной решетки эксплуатируемого бесстыкового железнодорожного пути
DE102004015244B4 (de) Verfahren und Vorrichtung zur Überwachung der Gleislagequalität
DE10022684A1 (de) Sicherheitssystem für Schienenfahrzeuge und Schienenfahrwege
KR20240081798A (ko) 궤도 정밀진단 및 성능평가용 야장기입부
AT525332A1 (de) Verfahren zur Berichtigung des Seitenabstandes und des Höhenabstandes einer Bahnsteigkante zur Gleisachse
Matiwos Railway Track Inspection and Maintenance Scheduling For Addis Ababa Light Rail Transit

Legal Events

Date Code Title Description
AS Assignment

Owner name: HP3 REAL GMBH, AUSTRIA

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:LICHTBERGER, BERNHARD;REEL/FRAME:062066/0510

Effective date: 20221107

STPP Information on status: patent application and granting procedure in general

Free format text: DOCKETED NEW CASE - READY FOR EXAMINATION