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 12/20/2024.The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Priority
Acknowledgement is made of applicants claim for foreign priority under 35 U.S.C. 119(a)-(d) and (f). The certified copy has been filed in parent application DE10 2022 115 662.1 filed on 06/23/2022.
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 1-9, 11-14 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Wagner (US20200130935).
Regarding claim 1, Wagner teaches a method for handling an object (20) by means of a robotic arm (10), the method comprising the following steps([0072]-[0084] disclosing robotic arm for manipulating objects based on perception data from sensor such as camera ):
a. detecting at least one object (20) and measuring at least one measurement
value of at least one object property of the object (20) by means of at least
one sensor (31, 32) ([0085]-[0089] disclosing the sensors producing additional information about the object such as weight and dimensions. [0099]-0101] disclosing the system identifies the object class using weight or mass or dimensions determined by sensor the sensors, processing parameters can also indicate various motion planning methodologies that balance the speed of the effector moving the object with the ability to maintain the object in the effector's grasp. The processing parameters may also include information related to releasing the object from the effector, including release height, release orientation, release location.);
b. determining at least one handling property by querying the measurement
value in a database [[and/]] or by inference with a statistical model
involving entering the measurement value ([0099]-[[0101] disclosing the parameters for handling the object are based on the classified object class based on the weight and dimensions, i.e., the classification is based on the measurements included in database indicating the object class includes that weight and control parameters for such class of objects. See also [0105] disclosing the classification of objects based on their characteristics similarity);
C. creating a set of control data for the robotic arm (10) based on the
handling property ([0099]-[0102] disclosing the control of the robot to process the object based on the parameters); and
d. handling the object (20) by means of the robotic arm (10) according to the
set of control data ([0099]-[0102] disclosing the handling of the object by the robotic arm according to control data specified by the parameters).
Regarding claim 2, Wagner teaches the method according to claim 1, wherein the object property in step a. is a property inherent to the object (20), a property
influenced by the environment [[and/]]or a property assigned to the
object (20), or both a property inherent to the object (20), a property
influenced by the environment and a property assigned to the object (20) ([0099]-[0102] disclosing the property is either assigned as a barcode or inherent as a shape, weight or both).
Regarding claim 3, Wagner teaches method according to claim 1, wherein the handling property in step b. is a grip strength, a point of attack, a pickup place, a pickup orientation, an obstruction, a placement orientation, a transport orientation, a placement location, a maximum acceleration, a maximum speed and/or the a minimum lifting height or any combination thereof ([0099]-[0102] disclosing the property includes a pickup location).
Regarding claim 4, Wager teaches method according to claim1, wherein, in step b., the inference by means of the statistical model takes place if querying the measurement value in the database fails ([0085]-[0088], [0100]-[0104] discloses the inference of properties via machine learning and probabilities, i.e., statistical model of handling the object, if the object is not able to be classified as a known object. Since [0090]-[0104] discloses the classification of the object based on measurements such as weight and querying the measurement to obtain class and properties of the handling, thus it is interpreted from the citations that the statistical model inference takes place when the database fails to identify the object based on properties such as weight).
Regarding claim 5, Wagner teaches the method according to claim 1,
wherein, in step b., at least one other measurement value is detected by means
of at least one other sensor (32) and the inference by means of the statistical
model involving entering the other measurement value takes place if querying
the measurement value in the database fails ([0085]-[0088], [0100]-[0104 discloses the inference of properties via machine learning and probabilities, i.e., statistical model of handling the object, if the object is not able to be classified as a known object. Since [0090]-[0104] discloses the classification of the object based on measurements such as weight and querying the measurement to obtain class and properties of the handling, thus it is interpreted from the citations that the statistical model inference takes place when the database fails to identify the object based on properties such as weight. When no Sku measurement helps to identify a measurement of the object, the camera sensors are used to measure as taught in [0090]-[0104]).
6. (Currently Amended) The method according to claim 1, , wherein, in step b., the database or the statistical model is extended or updated with the measurement value measured in step a. or the other measurement value measured in step b., or the database or the statistical model is extended or updated with the measurement value
measured in step a. and the other measurement value measured in step b ([0085]-[0090], [0099]-[0104] disclosing the updating of the database and or the learning model to include the newly learned products and their new property for handling including the classification based on weight, see [0088], [0100] disclosing the handling of heavy objects).
Regarding claim 7, Wagner teaches the method according to claim 1,
wherein the handling comprises gripping, picking up, lifting, transporting, sorting, feeding and/or shaking, or any combination thereof ([0085]-[0104] disclosing transporting objects).
Regarding claim 8, Wagner teaches the method according to claim 1, wherein step d. further comprises: evaluating the handling and receiving an evaluation ([0085]-[0090], [0099]-[0101] disclosing the evaluation the success of the grasp and receiving feedback of evaluation).
Regarding claim 9, Wagner teaches the method according to claim 8, wherein step d. further comprises: updating or extending the set of control data based on the
evaluation, or both updating and extending the set of control data based on the
evaluation ([0085]-[0090], [0099]-[0104] disclosing updating the handling process and the handling of the object based on success and score).
Regarding claim 11, Wagner teaches the device for handling objects (20), the device comprising a robotic arm (10), at least one sensor (31, 32), and a computer
system (40), wherein the device uses the method
according to claim 1, the computer system (40) comprises a computer-readable storage medium, which comprises the database or the statistical model for executing step b., and a data processing device for executing steps b. and C ([0085]-[0104] disclosing the device including robotic arm and sensors and computer to determine the processes based on statistical model and or database learned, the database stored is indicative of a storage medium).
Regarding claim 12, Wagner teaches device according to claim 11, the sensor (31, 32) is a light sensor, a torque sensor, a pressure sensor, a barcode scanner, an RFID scanner, an image capturing device, a lidar, a radar, a tactile sensor system, a measuring device, in particular a spectrometer, an ultrasonic measuring device, an X-ray device, a refractometer, a thermometer, a moisture meter and/or, a scale or any combination thereof ([0064] disclosing a camera image capturing and a scanner).
Regarding claim 13, Wagner teaches the device according to claim 11 wherein the robotic arm (10) has one axis or multiple axes ([0043] and figure 1 disclosing an articulated robot with at least one axis).
Regarding claim 14, Wagner teaches the device according to claim 11
wherein the computer system (40) comprises an interface (41) or a network connection (42), or wherein the computer system (40) comprises an interface (41) and a network connection (42) ([0064]-[0065] disclosing the network, [0074] disclosing an interface of the system where the user sends the feedback).
Claim Rejections - 35 USC § 103
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 10, 15, 16 are rejected under 35 U.S.C. 103 as being unpatentable by Wagner (US20200130935) in view of Chang (US20240385204).
Regarding claim 10, Wagner teaches the method according to claim 1,
the handling property in step b. is a placement location (at least [0100] disclosing the release location as a handling property),
Wagner does not teach wherein the object (20) is a medical sample or a container holding a medical sample, the placement location being the position of a feeding device (24) feeding the object to at least one medical analytical instrument, and the handling in step d. comprises feeding the object to the feeding device (24).
Chang teaches wherein the object (20) is a medical sample or a container holding a medical sample, the placement location being the position of a feeding device (24) feeding the object to at least one medical analytical instrument, and the handling in step d. comprises feeding the object to the feeding device (24) ([0040] disclosing the picking and placing of a medical container specimen from one location to a target feeding location such as a carrier for analysis of the sample).
Wagner already teaches the classification of objects to identify a target location and orientation and forces, thus it is obvious to one of ordinary skill in the art to combine the placement method of Wagner to the sample transfer of Chang yielding predictable results in order to enhance the sample transfer by the ability to classify the sample and infer its target location automatically.
Regarding claim 15, Wagner teaches the device according to claim 11,
wherein the objects (20) to be handled are medical samples or containers each holding a medical sample, and the device comprises at least one medical analytical instrument.
Chang teaches wherein the objects (20) to be handled are medical samples or containers each holding a medical sample, and the device comprises at least one medical analytical instrument ([0040] disclosing the picking and placing of a medical container specimen from one location to a target feeding location such as a carrier for analysis of the sample).
Wagner already teaches the classification of objects to identify a target location and orientation and forces, thus it is obvious to one of ordinary skill in the art to combine the placement method of Wagner to the sample transfer of Chang yielding predictable results in order to enhance the sample transfer by the ability to classify the sample and infer its target location automatically.
Regarding claim 16, Wagner as modified by Chang teaches the device according to claim 15, wherein the medical analytical instrument is a polymer-chain-reaction (PCR) test device, a real-time-PCR test device, a sequence analyzing device, a blood analyzing device, in particular a blood glucose meter a lactate meter, a
coagulometer, a blood alcohol meter, a refractometer, a pH meter, a urinalysis
device and/or, a photomicroscope, or any combination of the foregoing.
Specicially, Chang teaches wherein the medical analytical instrument is a polymer-chain-reaction (PCR) test device, a real-time-PCR test device, a sequence analyzing device, a blood analyzing device, in particular a blood glucose meter a lactate meter, a coagulometer, a blood alcohol meter, a refractometer, a pH meter, a urinalysis device and/or, a photomicroscope, or any combination of the foregoing ([0023] disclosing the analysis of specimen including blood and urine).
Wagner already teaches the classification of objects to identify a target location and orientation and forces, thus it is obvious to one of ordinary skill in the art to combine the placement method of Wagner to the sample transfer of Chang yielding predictable results in order to enhance the sample transfer by the ability to classify the sample and infer its target location automatically.
Conclusion
The prior art made of record and not relied upon is considered pertinent to
applicant's disclosure. The prior art cited in PTO-892 and not mentioned above disclose related devices and methods by storing such data indicative of locations as parameters as taught by Wagner.
US20190261566 disclosing statistical model for estimating picking success probability might take the form of a multivariate histogram or gaussian defined on the space of all picking success indicator, i.e., combine different values to ensure verification.
US20140277742 disclosing statistical model learning to update sensory map and the grasp poses and storing for future use.
US20200016756 disclosing statistical model for grasping poses.
US20210214163 disclosing Statistical model for object gripping.
US20210046642 disclosing statistical model for selecting grasp and determining location of material to pickup.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMAD O EL SAYAH whose telephone number is (571)270-7734. The examiner can normally be reached on M-Th 6:30-4:30.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached on (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MOHAMAD O EL SAYAH/Primary Examiner, Art Unit 3658B
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