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 .
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-5, 11, 13-15 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Hellge et al (US 20230394744 A1) in view of Rus et al (US 20230202026 A1).
Regarding claim 1, Hellge discloses a computer-implemented method (Hellge [0014], “a method”) comprising:
obtaining a three-dimensional (3D) mesh of a 3D object (Hellge [0018], “a 3D model mesh … provided for a 3D object”);
segmenting the 3D mesh into two or more sub-meshes, wherein each sub-mesh corresponds to a respective part of the 3D object (Hellge [0111], “mesh may each comprise a plurality of subparts 215, wherein each subpart 215 comprises a set of portions, i.e. set of vertices and/or a set of faces.”);
determining a constraint for the 3D object using a transformer model (Hellge [0069], “Constrained Transformability”; [0070], “particular transformation of objects 212 are known beforehand, the described transformations (described transformations is interpreted as reading on a transformer model) are provided … transformation of an object due to interaction needs to be limited to affect parts of the body (determining a constraint for the 3D object using a transformer model)”)
wherein the two or more sub-meshes are provided as input to the transformer model (Hellge [0112], “a model is provided, e.g., by the first mesh information 210a, … by the second mesh information 210b, … provided to transfer the transformations”), and
wherein the constraint defines a set of joints such that each joint defines constraints on motion of respective pairs of the parts of the 3D object (Hellge [0101], “The joint constraint information, for example, restricts the space of freedom of the joints by way of restricting an angular movability range of the joints … the joint constraint information restricts the space of freedom of the joints by way of restricting an translational movability range of the joints, e.g., a translation compared to a previous position of the joint.”); and
wherein the sub-meshes, the constraint, and the plurality of parameters are usable to simulate motion of the 3D object in a virtual environment (Hellge [0022], “The moving information indicates, e.g., to the scene rendering apparatus, how to move (simulate motion of the 3D object in a virtual environment)”; [0061], “The viewer 400 views the scene … to observe virtual reality”; [0068], “two parameters per position 232a are given, the position 232a.sub.1 and range 232a2 … roll parameter 414 does not really influence on the viewing orientation but, it only indicates the tilting of the viewers head.”; [0094], “The constraints for the movability 244 of the movable 3D object 212 are to be obeyed in moving the movable 3D object”; [0119], “movement indicated by the moving information 240, of the subset 215.sub.1 of the surface of the mesh 214.sub.1,”).
Hellge does not disclose
determining a constraint graph for the 3D object
calculating a plurality of parameters for the constraint graph based on one or more objective functions
However, Rus discloses
determining a constraint graph for the 3D object (Rus [0107], “construct a constraint graph (for a robot object)”)
calculating a plurality of parameters for the constraint graph based on one or more objective functions (Rus [0082], “function of a constraint class is used to tune parameters of the constraint”; [0138], “3.3.5.1 Objective Function”; [0139], “find a trajectory that satisfies the constraints … an objective function”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hellge with Rus to determine a constraint graph and tune parameters for the constraint graph based on an objective function. This would have been done to clearly define motion constraints that satisfy an accurate representation of the object in an efficient manner. See for example, Rus [0139], “find a trajectory that satisfies the constraints but having a path that is as efficient as possible”.
Regarding claim 3, Hellge in view of Rus discloses the computer-implemented method of claim 1, wherein calculating the plurality of parameters for the constraint graph comprises determining values for one or more parameters associated with the set of joints based on user-specified criteria (Rus [0171], “inverse kinematics are computed to determine appropriate joint angles. To simplify this computation and to ensure arm configurations that appear natural to a human supervisor (user specifying a criteria), the arm is constrained to move in a vertical plane and then the main shoulder joint is allowed to rotate to achieve horizontal motion. A target arm length is also computed to keep the gripper close to the board as the shoulder joint rotates.”).
Regarding claim 4, Hellge in view of Rus discloses the computer-implemented method of claim 3, wherein determining the values for the one or more parameters associated with the set of joints comprises performing an optimization of the one or more objective functions that encode the user-specified criteria (Rus [0083], “The “evaluate” function of a constraint class is used … The function is formulated such that it is compatible with a nonlinear constrained optimization solver … constraint is satisfied for a specified set of waypoint positions.”).
Regarding claim 5, Hellge in view of Rus discloses the computer-implemented method of claim 3, further comprising:
receiving the user-specified criteria from a user (Rus [0035], “user wears a sensor on their arm that provides both trajectory data (user-specified criteria from a user)”); and
determining a respective type of the one or parameters based on the user-specified criteria (Rus [0082], “The “generalize” function of a constraint class is used to tune parameters of the constraint based on an existing trajectory (parameters based on the user-specified criteria)”).
Claim 11 recites a non-transitory computer-readable medium which corresponds to the function performed by the method of claim 1. As such, the mapping and rejection of claim 1 above is considered applicable to the non-transitory computer-readable medium of claim 11.
Additionally Hellge discloses
A non-transitory computer-readable medium with instructions stored thereon that, responsive to execution by a processing device, cause the processing device to perform operations (Hellge [0187], “A further embodiment of the inventive methods is, therefore, a data carrier (or a digital storage medium, or a computer-readable medium) comprising, recorded thereon, the computer program for performing one of the methods”).
Claim 13 recites a non-transitory computer-readable medium which corresponds to the function performed by the method of claim 3. As such, the mapping and rejection of claim 3 above is considered applicable to the non-transitory computer-readable medium of claim 13.
Claim 14 recites a non-transitory computer-readable medium which corresponds to the function performed by the method of claim 4. As such, the mapping and rejection of claim 4 above is considered applicable to the non-transitory computer-readable medium of claim 14.
Claim 15 recites a non-transitory computer-readable medium which corresponds to the function performed by the method of claim 5. As such, the mapping and rejection of claim 5 above is considered applicable to the non-transitory computer-readable medium of claim 15.
Claim 17 recites a system which corresponds to the function performed by the method of claim 1. As such, the mapping and rejection of claim 1 above is considered applicable to the system of claim 17.
Additionally Hellge discloses
A system comprising: a memory with instructions stored thereon; and a processing device, coupled to the memory, the processing device configured to access the memory and execute the instructions, wherein the instructions cause the processing device to perform operations (Hellge [0187], “A further embodiment of the inventive methods is, therefore, a data carrier (or a digital storage medium, or a computer-readable medium) comprising, recorded thereon, the computer program for performing one of the methods”).
Claims 6 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Hellge in view of Rus and further view of Guskov et al (US 20150187130 A1).
Regarding claim 6, Hellge in view of Rus discloses the computer-implemented method of claim 1, but does not disclose wherein segmenting the 3D mesh into the two or more sub-meshes comprises applying a trained classifier to the 3D mesh of the 3D object.
However, Guskov discloses
segmenting the 3D mesh into the two or more sub-meshes comprises applying a trained classifier to the 3D mesh of the 3D object (Guskov [0043], “polygon classifier 310 receives 3D model … Polygon classifier 310 can then classify each polygon in the mesh of polygons (applying a trained classifier to the 3D mesh of the 3D object) of 3D model 306 into separate groups … polygon classifier 310 may classify each polygon of 3D model 306 as corresponding to either a top surface or side surface of an object (segmenting the 3D mesh into the two or more sub-meshes) being represented in 3D model”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hellge further with Guskov to utilize a polygon classifier to segment polygon portions of 3D objects. This would have been done to separate logical portions of objects in an accurate manner so that a variety of additional representations of the object maybe generated.
Claim 18 recites a system which corresponds to the function performed by the method of claim 6. As such, the mapping and rejection of claim 6 above is considered applicable to the system of claim 18.
Claims 7-8 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Hellge in view of Rus and further view of Gandrud et al (US 20250364117 A1).
Regarding claim 7, Hellge in view of Rus and in further view of Guskov discloses the computer-implemented method of claim 6, but does not disclose further comprising training the classifier, wherein the training comprises training the classifier on a training dataset that includes 3D meshes of 3D objects and sub-meshes corresponding to the 3D meshes.
However, Gandrud discloses
training the classifier, wherein the training comprises training the classifier on a training dataset that includes 3D meshes of 3D objects and sub-meshes corresponding to the 3D meshes (Gandrud [0052], “FIG. 2 is an example technique 200 that can be used to train machine learning models … receive patient case data 204 (training dataset) … receiving module 202 can receive mesh data corresponding to 3D meshes (dataset that includes 3D meshes of 3D objects) … patient case data 204 may only include 3D mesh data concerning specific teeth (sub-meshes)”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hellge further with Gandrud to train a classifier using 3D meshes and related mesh segments. This would have been done to generate accurate data for improved representation of objects. See for example, Gandrud [0033], “iteratively improve a machine learning model”.
Regarding claim 8, Hellge in view of Rus discloses the computer-implemented method of claim 1, but does not disclose further comprising training the transformer model with an augmented training dataset, wherein the augmented training dataset includes sequences of segmented labeled parts of 3D meshes of parts of 3D objects included in the training dataset.
However, Gandrud discloses
comprising training the transformer model with an augmented training dataset, wherein the augmented training dataset includes sequences of segmented labeled parts of 3D meshes of parts of 3D objects included in the training dataset (Gandrud [0033], “techniques which involve labeling mesh elements”; [0034], “learning may, in some instances, augment samples in a training dataset to accentuate the differences in samples from difference classes and/or increase the similarity of samples of the same class.”).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify Hellge further with Gandrud to utilize a labeled augmented training dataset. This would have been done to improve the classification process. See, for example, Gandrud [0126], “image augmentations may improve the classifier”
Claim 19 recites a system which corresponds to the function performed by the method of claim 8. As such, the mapping and rejection of claim 8 above is considered applicable to the system of claim 19.
Allowable Subject Matter
Claims 2, 9-10, 12, 16 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
The following is a statement of reasons for the indication of allowable subject matter:
Regarding claim 2, none of the prior art of record, alone or in combination, disclose, “providing the two or more sub-meshes as a sequence of tokens to the transformer model, wherein the sequence of tokens is mapped by the transformer model to the constraint graph for the 3D object”.
Regarding claim 9, none of the prior art of record, alone or in combination, disclose, “segmenting the 3D mesh into the two or more sub-meshes comprises applying a trained regression model to the 3D mesh of the 3D object.”
Regarding claim 10, none of the prior art of record, alone or in combination, disclose, “providing to a physics solver a current state of the 3D object, the constraint graph, the plurality of parameters, and one or more forces acting on the 3D object in the virtual environment, wherein the physics solver determines an updated state of the 3D object; and displaying the 3D object in the virtual environment based on the updated state.”
Claim 12 is allowable similar to claim 2 for reciting similar subject matter as claim 2.
Claim 16 is allowable similar to claim 2 for reciting similar subject matter as claim 10.
Claim 20 is allowable similar to claim 2 for reciting similar subject matter as claim 10.
Conclusion
See the notice of references cited (PTO-892) for prior art made of record, including art that is not relied upon but considered pertinent to applicant's disclosure.
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/JITESH PATEL/Primary Examiner, Art Unit 2612
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