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 .
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.
Double Patenting
A rejection based on double patenting of the “same invention” type finds its support in the language of 35 U.S.C. 101 which states that “whoever invents or discovers any new and useful process... may obtain a patent therefor...” (Emphasis added). Thus, the term “same invention,” in this context, means an invention drawn to identical subject matter. See Miller v. Eagle Mfg. Co., 151 U.S. 186 (1894); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Ockert, 245 F.2d 467, 114 USPQ 330 (CCPA 1957).
A statutory type (35 U.S.C. 101) double patenting rejection can be overcome by canceling or amending the claims that are directed to the same invention so they are no longer coextensive in scope. The filing of a terminal disclaimer cannot overcome a double patenting rejection based upon 35 U.S.C. 101.
Claims 2, 10, 18 is/are rejected under 35 U.S.C. 101 as claiming the same invention as that of claims 1, 10, 18 of prior U.S. Patent No. 12,602,943. This is a statutory double patenting rejection.
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, 3-9, 11-17, 19-20 rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12,602,943. Although the claims at issue are not identical, they are not patentably distinct from each other because the claims are the same as those in patent 12,602,943 due to claims 2, 10, and 18 being incorporated into their respective independent claims of the ‘943 patent.
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, 4, 7–9, 12, 15–17 and 20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Takahashi et al. (U.S. 10,824,858 B2).
Regarding claim 1, Takahashi discloses a system for object identification based on a scalar score corresponding to a set of local microstructural features of an object, the system comprises:
a device camera configured to capture a plurality of digital images of one or more surfaces corresponding to the object of a plurality of objects; (Per Fig. 3, Takahashi’s image acquisition unit 101 and feature extraction unit 103 analyzes multiple images of surfaces of a plurality of objects. Takahashi col. 8 line 55 – col. 9 line 14. [t]he global common feature extraction unit 103, ... extracts a global common feature that appears in common on a plurality of objects produced with the use of the production die based on a plurality of images obtained by imaging the surfaces of the plurality of objects produced)
a feature extractor of a backend module, the feature extractor is configured to extract the set of local microstructural features from the plurality of digital images of the one or more surfaces of the object; (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 discloses local common structural feature for a plurality of captured images. Id. col. 10 lines 41–61. [t]he local common structural feature extraction unit 105 extracts a local structural feature for each of captured images of several tens of individuals and exhibits each image after extraction to the user.)
a database configured to store the plurality of objects and the set of local microstructural features extracted from the one or more surfaces of a corresponding object, wherein the set of local microstructural features represent the corresponding object in the database; and (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 stores the structural features in the storage unit 106. Id. col. 10 line 62 – col. 11 line 10. The local common structural feature extraction unit 105 stores the calculated local common structural feature as one template image into the local common structural feature storage unit 106.)
the backend module configured to:
receive a query object (a query object construed as management target product(s)) for identification; (Per Fig. 3, Takahashi’s global common structural feature extraction unit 103 conducts image process to analyze management target product. Id. col. 9 lines 16–42. [t]he global common structural feature extraction unit 105 retrieves at least one image stored in the image storage unit 102 and extracts a local structural feature which is common to a group of management target products.)
extract, using the feature extractor, a plurality of local microstructural features from images of a number of surfaces of the query object; (Per Fig. 3, Takahashi discloses that his feature extraction unit 105 analyzes microscopic irregularities of each surface of the products. Id. [i]n a case where management target products are produced by casting or heading with the use of a common production die, a structure such as local microscopic irregularities of the die is transferred onto the surface of each of the management target products,)
compare the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects stored in the database using a voting function, (Per Fig. 3, Takahashi discloses a feature value as he extracts a global feature—i.e., a non-microscopic pattern in the plurality of objects—in the captured images. Then his first feature extraction unit 108 analyzes a local structural feature—i.e., geometrical correction in the template image. Id. col. 5 line 30 – col. 6 line 18. The global common feature extraction unit 103 has a function to calculate a global feature that is common to a group of management targets from the captured images stored in the image storage unit 102 and output as a template image.) wherein the voting function (voting function construed as calculating a score to determine a degree of similarity among products) is a mathematical function that indicates agreements and disagreements of the plurality of local microstructural features of the query object and the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
generate a score for the corresponding object in response to the comparison of the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
select a potential match set of the plurality of objects based on the score; and (Per Fig. 3, Takahashi’s judgement unit 113 outputs a result of judgment of a management target. Id. The judgment unit 113 has a function to output the result of judgment of a management target based on the calculated score.)
identify a status of the query object based on the potential match set of the plurality of objects, wherein the status indicates a match of the query object with at least one object of the plurality of objects stored in the database. (Per Fig. 3, Takahashi’s global feature extraction unit 103 discloses accurate alignment based on the plurality of the images after comparing global common features between the images of objects and produced objects, which are in the particular process. Id. col. 8 line 55 – col. 9 line 14. As a result of using such a global common feature for each die in an image position normalization process of latter stage, highly accurate alignment becomes possible, and consequently, an effect of increasing the accuracy of individual identification and individual authentication can be expected.)
Regarding claim 9, Takahashi discloses a method for object identification based on a scalar score corresponding to a set of local microstructural features of an object, the method comprising:
capturing a plurality of digital images by a device camera of one or more surfaces corresponding to the object of a plurality of objects; (Per Fig. 3, Takahashi’s feature extraction unit 103 analyzes multiple images of surfaces of a plurality of objects. Takahashi col. 8 line 55 – col. 9 line 14. [t]he global common feature extraction unit 103, ... extracts a global common feature that appears in common on a plurality of objects produced with the use of the production die based on a plurality of images obtained by imaging the surfaces of the plurality of objects produced)
extracting the set of local microstructural features from the plurality of digital images of the one or more surfaces of the object by a feature extractor of a backend module; (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 discloses local common structural feature for a plurality of captured images. Id. col. 10 lines 41–61. [t]he local common structural feature extraction unit 105 extracts a local structural feature for each of captured images of several tens of individuals and exhibits each image after extraction to the user.)
storing the plurality of objects and the set of local microstructural features extracted from the one or more surfaces of a corresponding object, wherein the set of local microstructural features represent the corresponding object in the database; (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 stores the structural features in the storage unit 106. Id. col. 10 line 62 – col. 11 line 10. The local common structural feature extraction unit 105 stores the calculated local common structural feature as one template image into the local common structural feature storage unit 106.)
receiving a query object (a query object construed as management target product(s)) for identification; (Per Fig. 3, Takahashi’s global common structural feature extraction unit 103 conducts image process to analyze management target product. Id. col. 9 lines 16–42. [t]he global common structural feature extraction unit 105 retrieves at least one image stored in the image storage unit 102 and extracts a local structural feature which is common to a group of management target products.)
extracting a plurality of local microstructural features from images of a number of surfaces of the query object; (Per Fig. 3, Takahashi discloses that his feature extraction unit 105 analyzes microscopic irregularities of each surface of the products. Id. [i]n a case where management target products are produced by casting or heading with the use of a common production die, a structure such as local microscopic irregularities of the die is transferred onto the surface of each of the management target products,)
comparing the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects stored in the database using a voting function, (Per Fig. 3, Takahashi discloses a feature value as he extracts a global feature—i.e., a non-microscopic pattern in the plurality of objects—in the captured images. Then his first feature extraction unit 108 analyzes a local structural feature—i.e., geometrical correction in the template image. Id. col. 5 line 30 – col. 6 line 18. The global common feature extraction unit 103 has a function to calculate a global feature that is common to a group of management targets from the captured images stored in the image storage unit 102 and output as a template image.) wherein the voting function (voting function construed as calculating a score to determine a degree of similarity among products) is a mathematical function that indicates agreements and disagreements of the plurality of local microstructural features of the query object and the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
generating a score for the corresponding object in response to the comparison of the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
selecting a potential match set of the plurality of objects based on the score; and (Per Fig. 3, Takahashi’s judgement unit 113 outputs a result of judgment of a management target. Id. The judgment unit 113 has a function to output the result of judgment of a management target based on the calculated score.)
identify a status of the query object based on the potential match set of the plurality of objects, wherein the status indicates a match of the query object with at least one object of the plurality of objects stored in the database. (Per Fig. 3, Takahashi’s global feature extraction unit 103 discloses accurate alignment based on the plurality of the images after comparing global common features between the images of objects and produced objects, which are in the particular process. Id. col. 8 line 55 – col. 9 line 14. As a result of using such a global common feature for each die in an image position normalization process of latter stage, highly accurate alignment becomes possible, and consequently, an effect of increasing the accuracy of individual identification and individual authentication can be expected.)
Regarding claim 17, Takahashi discloses a computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause a data processing apparatus to perform operations, for object identification based on a scalar corresponding to a set of local microstructural features of an object, the operations comprising:
capturing a plurality of digital images by a device camera of one or more surfaces corresponding to the object of a plurality of objects; (Per Fig. 3, Takahashi’s feature extraction unit 103 analyzes multiple images of surfaces of a plurality of objects. Takahashi col. 8 line 55 – col. 9 line 14. [t]he global common feature extraction unit 103, ... extracts a global common feature that appears in common on a plurality of objects produced with the use of the production die based on a plurality of images obtained by imaging the surfaces of the plurality of objects produced)
extracting the set of local microstructural features from the plurality of digital images of the one or more surfaces of the object by a feature extractor of a backend module; (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 discloses local common structural feature for a plurality of captured images. Id. col. 10 lines 41–61. [t]he local common structural feature extraction unit 105 extracts a local structural feature for each of captured images of several tens of individuals and exhibits each image after extraction to the user.)
storing the plurality of objects and the set of local microstructural features extracted from the one or more surfaces of a corresponding object, wherein the set of local microstructural features represent the corresponding object in the database; (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 stores the structural features in the storage unit 106. Id. col. 10 line 62 – col. 11 line 10. The local common structural feature extraction unit 105 stores the calculated local common structural feature as one template image into the local common structural feature storage unit 106.)
receiving a query object (a query object construed as management target product(s)) for identification; (Per Fig. 3, Takahashi’s global common structural feature extraction unit 103 conducts image process to analyze management target product. Id. col. 9 lines 16–42. [t]he global common structural feature extraction unit 105 retrieves at least one image stored in the image storage unit 102 and extracts a local structural feature which is common to a group of management target products.)
extracting a plurality of local microstructural features from images of a number of surfaces of the query object; (Per Fig. 3, Takahashi discloses that his feature extraction unit 105 analyzes microscopic irregularities of each surface of the products. Id. [i]n a case where management target products are produced by casting or heading with the use of a common production die, a structure such as local microscopic irregularities of the die is transferred onto the surface of each of the management target products,)
comparing the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects stored in the database using a voting function, (Per Fig. 3, Takahashi discloses a feature value as he extracts a global feature—i.e., a non-microscopic pattern in the plurality of objects—in the captured images. Then his first feature extraction unit 108 analyzes a local structural feature—i.e., geometrical correction in the template image. Id. col. 5 line 30 – col. 6 line 18. The global common feature extraction unit 103 has a function to calculate a global feature that is common to a group of management targets from the captured images stored in the image storage unit 102 and output as a template image.) wherein the voting function (voting function construed as calculating a score to determine a degree of similarity among products) is a mathematical function that indicates agreements and disagreements of the plurality of local microstructural features of the query object and the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
generating a score for the corresponding object in response to the comparison of the plurality of local microstructural features of the query object with the set of local microstructural features of the plurality of objects; (Per Fig. 3, Takahashi’s score calculation unit 111 calculates a score to determine a degree of similarity between multiple features after processing captured images. Id. The score calculation unit 112 has a function to compare a feature value extracted from a captured image by the second feature extraction unit 110 with a feature value stored in the feature value storage unit 111 and calculate a score indicating the degree of similarity of both the feature values.)
selecting a potential match set of the plurality of objects based on the score; and (Per Fig. 3, Takahashi’s judgement unit 113 outputs a result of judgment of a management target. Id. The judgment unit 113 has a function to output the result of judgment of a management target based on the calculated score.)
identify a status of the query object based on the potential match set of the plurality of objects, wherein the status indicates a match of the query object with at least one object of the plurality of objects stored in the database. (Per Fig. 3, Takahashi’s global feature extraction unit 103 discloses accurate alignment based on the plurality of the images after comparing global common features between the images of objects and produced objects, which are in the particular process. Id. col. 8 line 55 – col. 9 line 14. As a result of using such a global common feature for each die in an image position normalization process of latter stage, highly accurate alignment becomes possible, and consequently, an effect of increasing the accuracy of individual identification and individual authentication can be expected.)
Regarding claim 4, Takahashi discloses the system, wherein the feature extractor comprises one of:
a convolutional neural network trained to extract the set of local microstructural features from the plurality of digital images of the one or more surfaces of the object of the plurality of objects; and (Per Fig. 3, Takahashi’s local common feature extraction unit 105 discloses a local common structural feature based on machine learning. Takahashi col. 10 lines 41–61. [t]he local common structural feature extraction unit 105 may obtain a local common structural feature by a statistical method or a method based on machine learning, instead of a simple pixel value average.)
a hand-crafted local feature extractor selected from the group using a range of modern methods designed for precise feature extraction. (Per Fig. 3, Takahashi discloses that his feature extraction unit 105 analyzes microscopic irregularities of each surface of the products. Id. col. 9 lines 16–42. [i]n a case where management target products are produced by casting or heading with the use of a common production die, a structure such as local microscopic irregularities of the die is transferred onto the surface of each of the management target products,)
Regarding claim 7, Takahashi discloses the system, wherein the device camera is a handheld device configured to capture the plurality of digital images in different environmental conditions. (Per Fig. 3, Takahashi’s information processing device 200 comprises a camera. Takahashi col. 6 lines 19–42. The information processing device 200 has an imaging part 201 such as a camera,)
Regarding claim 8, Takahashi discloses the system, wherein the backend module is further configured to identify at least one new local microstructural feature for the plurality of objects and update the set of local microstructural features in the database based on the at least one new local microstructural feature extracted from the one or more surfaces of the plurality of objects over time. (Per Fig. 3, Takahashi’s local common structural feature extraction unit 105 discloses local common structural feature for a plurality of captured images. Takahashi col. 10 lines 41–61. [t]he local common structural feature extraction unit 105 extracts a local structural feature for each of captured images of several tens of individuals and exhibits each image after extraction to the user.)
Regarding claim 12, it has been rejected in the same manner as claim 4.
Regarding claim 15, it has been rejected in the same manner as claim 7.
Regarding claim 16, it has been rejected in the same manner as claim 8.
Regarding claim 20, it has been rejected in the same manner as claim 4.
Allowable Subject Matter
Claims 3, 5–6, 11, 13–14, and 19 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.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Inoue et al. (U.S. 8,306,315 B2) discloses a method of generating a low-capacity model of identifying an object.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Ming Shui whose telephone number is (303)297-4247. The examiner can normally be reached 7-5 Pacific Time, M-Th.
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/Ming Shui/
Primary Examiner, Art Unit 2663
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