DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
2. Receipt is acknowledged of papers submitted under 35 U.S.C. 119(a)-(d), which papers have been placed of record in the file.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory obviousness-type double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); and In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on a nonstatutory double patenting ground provided the conflicting application or patent either is shown to be commonly owned with this application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement.
Effective January 1, 1994, a registered attorney or agent of record may sign a terminal disclaimer. A terminal disclaimer signed by the assignee must fully comply with 37 CFR 3.73(b).
4. Claims 1-11, 13 and 16-20 are rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claims 1-14, 16 and 17 of Patent No. 12,229,993. Although the conflicting claims are not identical, they are not patentably distinct from each other because the instant application claims are broader in every aspect than the patent claims and are therefore an obvious variant thereof.
5. Regarding claim 1, the application claim discloses A computer-implemented method for automatically determining a canonical pose of a 3D object represented by data points of a 3D data set, the method comprising: a processor of a computer providing one or more blocks of data points of the 3D data set associated with a first coordinate system to the input of a first 3D deep neural network, the first 3D neural network being trained to generate canonical pose information associated with a canonical coordinate system defined relative to a position of part of the 3D object; the processor receiving canonical pose information from the output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of a data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates; the processor using the canonical coordinates to determine an orientation and scaling of the axes of the canonical coordinate system and a position of the origin of the canonical coordinate system relative to the axis and the origin of the first 3D coordinate system and using the orientation and the position to determine transformation parameters, including rotation, translation and/or scaling parameters, for transforming coordinates of the first coordinate system into canonical coordinates; and, the processor determining a canonical representation of the 3D object, the determining including applying the transformation parameters to coordinates of the data points of the 3D data set. Claim 1 of Patent No. 12,229,993 discloses A computer-implemented method for automatically determining a canonical pose of a 3D dental structure represented by data points of a 3D data set, the method comprising: providing one or more blocks of data points of the 3D data set associated with a first 3D coordinate system to an input of a first 3D deep neural network, the 3D data set representing a 3D dental structure comprising one or more dental features having a first orientation in a 3D space defined by axes of a first 3D coordinate system, the first 3D deep neural network being trained to generate canonical pose information associated with a 3D space defined by axes of a 3D canonical coordinate system relative to a position of part of the 3D dental structure, the orientation of the one or more dental features in the 3D space of the canonical coordinate system being in alignment with the axes of the 3D canonical coordinate system; receiving canonical pose information from an output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of the data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates; using the canonical coordinates to determine an orientation and scaling of the axes of the 3D canonical coordinate system and a position of an origin of the 3D canonical coordinate system relative to the axes and an origin of the first 3D coordinate system and using the orientation, the scaling, and the position to determine transformation parameters, including rotation, translation, and/or scaling parameters, for transforming coordinates of the first 3D coordinate system into canonical coordinates of the 3D canonical coordinate system; and determining a canonical representation of the 3D dental structure in which the one or more dental features of the 3D dental structure are in alignment with the axes of the 3D canonical coordinate system, the determining of the canonical representation including applying the transformation parameters to coordinates of the data points of the 3D data set. Regarding claim 1, the only difference is that claim 1 of the instant application recites “object, a processor of a computer” and does not recite “dental structure, the 3D data set representing a 3D dental structure comprising one or more dental features having a first orientation in a 3D space defined by axes of a first 3D coordinate system, the orientation of the one or more dental features in the 3D space of the canonical coordinate system being in alignment with the axes of the 3D canonical coordinate system, the 3D canonical coordinate system, dental structure in which the one or more dental features of the 3D dental structure are in alignment with the axes of the 3D canonical coordinate system,” while claim 1 of Patent No. 12,229,993 recites. A dental structure is an object and a computer-implemented method includes a processor of a computer. Regarding claims 4 and 13, the analyses are similar to that of claim 1, the rationale of claim 1 rejection is applied in rejecting claims 4 and 13. Therefore, the claims in the present application recite a broader scope than the claims in the Patent No. 12,229,993.
6. The following table shows the claims of the current application being examined and the conflicting claims of Patent No. 12,229,993.
Current Application No.
19/045,142
Patent No.
12,229,993
1-3
1-3
4
5
5+18
6
6-9
7-10
10
12
11
13
13
16
16
4
17
14
19
11
20
17
The following table shows an example of the corresponding conflicting claims of the current application and Patent No. 12,229,993.
Current Application No.
19/045,142
Claim 1
Patent No.
12,229,993
Claim 1
A computer-implemented method for automatically determining a canonical pose of a 3D object represented by data points of a 3D data set,
A computer-implemented method for automatically determining a canonical pose of a 3D dental structure represented by data points of a 3D data set,
the method comprising: a processor of a computer providing one or more blocks of data points of the 3D data set associated with a first coordinate system to the input of a first 3D deep neural network,
the method comprising: providing one or more blocks of data points of the 3D data set associated with a first 3D coordinate system to an input of a first 3D deep neural network,
the first 3D neural network being trained to generate canonical pose information associated with a canonical coordinate system defined relative to a position of part of the 3D object;
the 3D data set representing a 3D dental structure comprising one or more dental features having a first orientation in a 3D space defined by axes of a first 3D coordinate system, the first 3D deep neural network being trained to generate canonical pose information associated with a 3D space defined by axes of a 3D canonical coordinate system relative to a position of part of the 3D dental structure, the orientation of the one or more dental features in the 3D space of the canonical coordinate system being in alignment with the axes of the 3D canonical coordinate system;
the processor receiving canonical pose information from the output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of a data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates;
receiving canonical pose information from an output of the first 3D deep neural network, the canonical pose information comprising for each data point of the one or more blocks a prediction of a position of the data point in the canonical coordinate system, the position of the data point being defined by canonical coordinates;
the processor using the canonical coordinates to determine an orientation and scaling of the axes of the canonical coordinate system and a position of the origin of the canonical coordinate system relative to the axis and the origin of the first 3D coordinate system and using the orientation and the position to determine transformation parameters, including rotation, translation and/or scaling parameters, for transforming coordinates of the first coordinate system into canonical coordinates;
using the canonical coordinates to determine an orientation and scaling of the axes of the 3D canonical coordinate system and a position of an origin of the 3D canonical coordinate system relative to the axes and an origin of the first 3D coordinate system and using the orientation, the scaling, and the position to determine transformation parameters, including rotation, translation, and/or scaling parameters, for transforming coordinates of the first 3D coordinate system into canonical coordinates of the 3D canonical coordinate system;
and, the processor determining a canonical representation of the 3D object, the determining including applying the transformation parameters to coordinates of the data points of the 3D data set.
and determining a canonical representation of the 3D dental structure in which the one or more dental features of the 3D dental structure are in alignment with the axes of the 3D canonical coordinate system, the determining of the canonical representation including applying the transformation parameters to coordinates of the data points of the 3D data set.
Allowable Subject Matter
7. Claim 15 is objected to being dependent upon rejected base claims. The claim would be allowable if the base claim got allowed that including all the limitations.
Conclusion
8. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michelle Chin whose telephone number is (571)270-3697. The examiner can normally be reached on Monday-Friday 8:00 AM-4:30 PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner's supervisor, Kent Chang can be reached on (571)272-7667. The fax phone number for the organization where this application or proceeding is assigned is (571)273-8300.
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/MICHELLE CHIN/
Primary Examiner, Art Unit 2614
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