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 .
Response to Preliminary Amendment
The Preliminary Amendment filed on 11/25/2024 has been entered.
Response to Preliminary Remarks
Applicant's Preliminary Remarks filed 11/25/2024 concerning the Preliminary Amendment have been considered and those amendments have been entered.
Specification
The disclosure is objected to because of the following informalities:
Amended paragraph [0001] needs to have the patent number added.
Appropriate correction is required.
CLAIM INTERPRETATION
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 following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
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.
Claims 1-30 have been interpreted under 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) to not invoke 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) claim interpretation.
Double Patenting
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 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); 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 nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-30 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 12,154,265. Although the claims at issue are not identical, they are not patentably distinct from each other because the pending claims are broader versions of the patented claims, thus, the patented claims anticipate the pending claims, refer to the following table which compares this application’s claims filed on 11/25/2024 with patented claims in U.S. Patent No. 12,154,265.
This application’s claims filed on 11/25/2024
1. A method for identifying similar three-dimensional (3D) representations of an object, the method comprising:
receiving at an interface:
a target 3D model, and at least one candidate 3D model;
executing at least one feature identification procedure to identify a feature of the target 3D model;
generating a target feature tensor based on the identified feature of the target 3D model;
executing the at least one feature identification procedure on the candidate 3D model;
generating a candidate feature tensor based on the identified feature of the candidate 3D model;
executing at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generating a feature comparison tensor based on the execution of the at least one comparison function; and
identifying a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.
2. The method of claim 1 wherein identifying the degree of similarity between the target feature tensor and the candidate feature tensor includes providing an explanation of a result of the comparison function.
3. The method of claim 1 wherein the comparison function comprises one or more of an L1 norm, L2 norm, Hamming distance, Cartesian distance, cosine similarity, graph isomorphism, or tensor-component-wise difference.
4. The method of claim 1 wherein the at least one feature identification procedure applied to the target 3D model is identified according to at least one feature identified in a candidate model or another property of the candidate model.
5. The method of claim 1 wherein at least one of the target 3D model and the candidate 3D model is associated with metadata including a textual description, technical data files, user inputs, a stock keeping unit (SKU), or material specification, wherein the metadata is provided as an input to at least one of the feature identification procedure or the at least one comparison function.
6. The method of claim 1 wherein at least one comparison function is configured to treat as identical at least one feature of the target 3D model if it differs from at least one feature of the candidate 3D model by at most a specified tolerance.
7. The method of claim 1 further comprising executing at least one classifier on the target 3D model to identify at least one classification of the target 3D model.
8. The method of claim 7 further comprising selecting the at least one candidate 3D model from a corpus of stored 3D models based on the selected candidate 3D model having at least one classification matching at least one classification of the target 3D model.
9. The method of claim 7 wherein the at least one classification is associated with at least one feature, and the at least one classification is determined according to the target 3D model having the at least one feature.
10. The method of claim 9 further comprising selecting the at least one feature identification procedure based on the classification.
11. A system for identifying similar three-dimensional (3D) representations of an object, the system comprising:
an interface for receiving:
a target 3D model, and at least one candidate 3D model; and
a processor executing instructions stored on memory and configured to:
execute at least one feature identification procedure to identify a feature of the target 3D model,
generate a target feature tensor based on the identified feature of the target 3D model,
execute the at least one feature identification procedure on the candidate 3D model,
generate a candidate feature tensor based on the identified feature of the candidate 3D model,
execute at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generate a feature comparison tensor based on the execution of the at least one comparison function, and
identify a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.
12. The system of claim 11 wherein the processor is further configured to
provide an explanation of a result of the comparison function.
13. The system of claim 11 wherein the comparison function comprises one or more of L1 norm, L2 norm, Hamming distance, Cartesian distance, cosine similarity, graph isomorphism, or tensor-component-wise difference.
14. The system of claim 11 wherein the at least one feature identification procedure applied to the target 3D model is identified according to at least one feature identified in a candidate model or another property of the candidate model.
15. The system of claim 11 wherein at least one of the target 3D model and the candidate 3D model is associated with metadata including a textual description, technical data files, user inputs, a stock keeping unit (SKU), or material specification, wherein the metadata is provided as an input to at least one of the feature identification procedure or the at least one comparison function.
16. The system of claim 11 wherein the at least one comparison function is configured to treat as identical at least one feature of the target 3D model if it differs from at least one feature of the candidate 3D model by at most a specified tolerance.
17. The system of claim 11 wherein the processor is further configured to execute a classifier on the target 3D model to identify at least one classification of the target 3D model.
18. The system of claim 17 wherein the processor is further configured to select the at least one candidate 3D model from a corpus of stored 3D models based on the selected candidate 3D model having at least one classification matching at least one classification of the target 3D model.
19. The system of claim 18 wherein the at least one classification is associated with at least one feature, and the at least one classification is determined according to the target 3D model having the at least one feature.
20. The system of claim 19 wherein the processor is further configured to select the at least one feature identification procedure based on the classification.
21. A method for determining a difference between two 3D models, the method comprising:
receiving at an interface:
a first 3D model, and a second 3D model;
executing a first feature identification procedure to identify at least a first feature of the first 3D model;
generating a first feature tensor based on at least the first feature of the first 3D model;
executing the first feature identification procedure to identify the at least the first feature of the second 3D model;
generating a second feature tensor based on at least the first feature of the second 3D model;
executing at least one comparison function to compare the first feature tensor and the second feature tensor;
generating a feature comparison tensor based on the execution of the at least one comparison function; and
identifying a difference between the first 3D model and the second 3D model based on the feature comparison tensor.
22. The method of claim 21 wherein at least one identified difference
includes an explanation of the difference.
23. The method of claim 21 wherein the identified difference includes a reference to a location in at least one of the first 3D model and second 3D model where the identified difference is present.
24. The method of claim 21 wherein the at least one comparison function identifies that the identified difference is due to disparate discretization of curves or discretization of parametric curves.
25. The method of claim 21 wherein the second 3D model is generated by converting the first 3D model into a different format than the first 3D model, and the identified difference is used to assess at least fidelity or accuracy of the second 3D model compared to the first 3D model.
26. The method of claim 21 wherein the second 3D model is a putative copy or updated version of the first 3D model, and the identified difference is used to determine whether the first 3D model is isomorphic with the second 3D model or whether the identified difference is due to tampering, sabotage, file corruption, or an unauthorized or unanticipated modification of the first model.
27. The method of claim 21 wherein the comparison function treats as equal a portion of the first 3D model and the second 3D model that represent identical geometries or real-world objects but may differ in respective encodings, 3D model or mesh formats or implementations, triangulation or polygonization density or degree, curve parameterization, coordinate system, orientation, or certain metadata.
28. The method of claim 21 wherein the at least one feature identification procedure is selected based on at least one of user input;
metadata of the first 3D model or the second 3D model;
cached or persisted calculations from previous operations on the first 3D model or the second 3D model; or
heuristics, documents, files, or technical data associated with the first 3D model or the second 3D model.
29. The method of claim 21 wherein the at least one comparison function comprises calculating graph isomorphism between the first 3D model and the second 3D model using at least one identified feature that is common to the first 3D model and the second 3D model.
30. The method of claim 21 wherein the method further includes receiving a user selection of at least one element of the feature comparison tensor indicating a difference between the first 3D model and the second 3D model, and modifying at least one of the first 3D model and the second 3D model to remedy the identified difference.
US 12,154,265 B2
1. A computer-implemented method for identifying similar three-dimensional (3D) representations of an object, the method comprising:
receiving at an interface:
a machine-readable target 3D model representing a target object, and at least one machine-readable candidate 3D model;
executing at least one feature identification procedure to identify a feature of the target 3D model;
generating a target feature tensor that includes data regarding the identified feature of the target 3D model;
executing the at least one feature identification procedure on the candidate 3D model;
generating a candidate feature tensor that includes data regarding the identified feature of the candidate 3D model;
executing at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generating a feature comparison tensor encoding data based on the execution of the at least one comparison function; and
identifying a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.
2. The method of claim 1 wherein identifying the degree of similarity between the target feature tensor and the candidate feature tensor includes providing an explanation of a result of the comparison function.
3. The method of claim 1 wherein the comparison function comprises one or more of an L1 norm, L2 norm, Hamming distance, Cartesian distance, cosine similarity, graph isomorphism, or tensor-component-wise difference.
4. The method of claim 1 wherein the at least one feature identification procedure applied to the target 3D model is identified according to at least one feature identified in a candidate model or another property of the candidate model.
5. The method of claim 1 wherein at least one of the target 3D model and the candidate 3D model is associated with metadata including a textual description, technical data files, user inputs, a stock keeping unit (SKU), or material specification, wherein the metadata is provided as an input to at least one of the feature identification procedure or the at least one comparison function.
6. The method of claim 1 wherein at least one comparison function is configured to treat as identical at least one feature of the target 3D model if it differs from at least one feature of the candidate 3D model by at most a specified tolerance.
7. The method of claim 1 further comprising executing at least one classifier on the target 3D model to identify at least one classification of the target 3D model.
8. The method of claim 7 further comprising selecting the at least one candidate 3D model from a corpus of stored 3D models based on the selected candidate 3D model having at least one classification matching at least one classification of the target 3D model.
9. The method of claim 7 wherein the at least one classification is associated with at least one feature, and the at least one classification is determined according to the target 3D model having the at least one feature.
10. The method of claim 9 further comprising selecting the at least one feature identification procedure based on the classification.
11. A computer-implemented system for identifying similar three-dimensional (3D) representations of an object, the system comprising:
an interface for receiving:
a machine-readable target 3D model representing a target object, and at least one machine-readable candidate 3D model; and
a processor executing instructions stored on memory and configured to:
execute at least one feature identification procedure to identify a feature of the target 3D model,
generate a target feature tensor that includes data regarding the identified feature of the target 3D model,
execute the at least one feature identification procedure on the candidate 3D model,
generate a candidate feature tensor that includes data regarding the identified feature of the candidate 3D model,
execute at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generate a feature comparison tensor encoding data based on the execution of the at least one comparison function, and
identify a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.
12. The system of claim 11 wherein the processor is further configured to provide an explanation of a result of the comparison function.
13. The system of claim 11 wherein the comparison function comprises one or more of L1 norm, L2 norm, Hamming distance, Cartesian distance, cosine similarity, graph isomorphism, or tensor-component-wise difference.
14. The system of claim 11 wherein the at least one feature identification procedure applied to the target 3D model is identified according to at least one feature identified in a candidate model or another property of the candidate model.
15. The system of claim 11 wherein at least one of the target 3D model and the candidate 3D model is associated with metadata including a textual description, technical data files, user inputs, a stock keeping unit (SKU), or material specification, wherein the metadata is provided as an input to at least one of the feature identification procedure or the at least one comparison function.
16. The system of claim 11 wherein the at least one comparison function is configured to treat as identical at least one feature of the target 3D model if it differs from at least one feature of the candidate 3D model by at most a specified tolerance.
17. The system of claim 11 wherein the processor is further configured to execute a classifier on the target 3D model to identify at least one classification of the target 3D model.
18. The system of claim 17 wherein the processor is further configured to select the at least one candidate 3D model from a corpus of stored 3D models based on the selected candidate 3D model having at least one classification matching at least one classification of the target 3D model.
19. The system of claim 18 wherein the at least one classification is associated with at least one feature, and the at least one classification is determined according to the target 3D model having the at least one feature.
20. The system of claim 19 wherein the processor is further configured to select the at least one feature identification procedure based on the classification.
21. A computer-implemented method for determining a difference between two 3D models, the method comprising:
receiving at an interface:
a machine-readable first 3D model representing a first object, and a machine-readable second 3D model;
executing a first feature identification procedure to identify at least a first feature of the first 3D model;
generating a first feature tensor that includes data regarding at least the first feature of the first 3D model;
executing the first feature identification procedure to identify the at least the first feature of the second 3D model;
generating a second feature tensor that includes data regarding at least the first feature of the second 3D model;
executing at least one comparison function to compare the first feature tensor and the second feature tensor;
generating a feature comparison tensor encoding data based on the execution of the at least one comparison function; and
identifying a difference between the first 3D model and the second 3D model based on the feature comparison tensor.
22. The method of claim 21 wherein at least one identified difference includes an explanation of the difference.
23. The method of claim 21 wherein the identified difference includes a reference to a location in at least one of the first 3D model and second 3D model where the identified difference is present.
24. The method of claim 21 wherein the at least one comparison function identifies that the identified difference is due to disparate discretization of curves or discretization of parametric curves.
25. The method of claim 21 wherein the second 3D model is generated by converting the first 3D model into a different format than the first 3D model, and the identified difference is used to assess at least fidelity or accuracy of the second 3D model compared to the first 3D model.
26. The method of claim 21 wherein the second 3D model is a putative copy or updated version of the first 3D model, and the identified difference is used to determine whether the first 3D model is isomorphic with the second 3D model or whether the identified difference is due to tampering, sabotage, file corruption, or an unauthorized or unanticipated modification of the first model.
27. The method of claim 21 wherein the comparison function treats as equal a portion of the first 3D model and the second 3D model that represent identical geometries or real-world objects but may differ in respective encodings, 3D model or mesh formats or implementations, triangulation or polygonization density or degree, curve parameterization, coordinate system, orientation, or certain metadata.
28. The method of claim 21 wherein the at least one feature identification procedure is selected based on at least one of user input;
metadata of the first 3D model or the second 3D model;
cached or persisted calculations from previous operations on the first 3D model or the second 3D model; or
heuristics, documents, files, or technical data associated with the first 3D model or the second 3D model.
29. The method of claim 21 wherein the at least one comparison function comprises calculating graph isomorphism between the first 3D model and the second 3D model using at least one identified feature that is common to the first 3D model and the second 3D model.
30. The method of claim 21 wherein the method further includes receiving a user selection of at least one element of the feature comparison tensor indicating a difference between the first 3D model and the second 3D model, and modifying at least one of the first 3D model and the second 3D model to remedy the identified difference.
Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Hughes et al., US Patent Application Publication No. 2021/0248446, describes matching a first product with a second product by converting first product metadata with image metadata and textual data to a first product feature vector and by comparing the first product feature vector and a second product feature vector of a second product with regard to a threshold, refer to the abstract, but is silent at least with regard to applicant’s claimed comparing tensors.
Maranzana et al., US Patent Application Publication No. 2017/0004621, describes Comparing 3D Models by comparing intrinsic 3D model specifications, refer to paragraphs [0061]-[0066], but is silent at least with regard to applicant’s claimed comparing tensors.
Allowable Subject Matter
Claims 1-30 would be allowable if a proper terminal disclaimer is filed addressing the nonstatutory double patenting in the manner discussed above.
The following is a statement of reasons for the indication of allowable subject matter: Refer to the parent file history, nonprovisional application no. 18/420,191, which compared prior art to and distinguished prior art from the claims in the parent nonprovisional application both of which applies to the pending claims. The prior art of record in this pending nonprovisional application and in the parent nonprovisional application fails to teach or suggest in the context of each of independent claims 1 and 21:
“generating a candidate feature tensor based on the identified feature of the candidate 3D model;
executing at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generating a feature comparison tensor based on the execution of the at least one comparison function; and
identifying a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.”, and fails to teach or suggest in the context of independent claim 11:
“generate a candidate feature tensor based on the identified feature of the candidate 3D model,
execute at least one comparison function to compare the target feature tensor and the candidate feature tensor;
generate a feature comparison tensor based on the execution of the at least one comparison function, and
identify a degree of similarity between target 3D model and the candidate 3D model based on the feature comparison tensor.”.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JEFFERY A BRIER whose telephone number is (571)272-7656. The examiner can normally be reached on Mon-Fri from 8:30am-3:00pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Xiao M Wu, can be reached at telephone number 571-272-7761. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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JEFFERY A. BRIER
Primary Examiner
Art Unit 2613
/JEFFERY A BRIER/Primary Examiner, Art Unit 2613
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