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 .
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on October 10, 2024, December 17, 2024 and April 28, 2025 comply with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. Citations which have not been considered, have not been considered because they do not comply with 37 CFR 1.98(b) which states “The date of publication supplied must include at least the month and year of publication, except that the year of publication (without the month) will be accepted if the applicant points out in the information disclosure statement that the year of publication is sufficiently earlier than the effective U.S. filing date and any foreign priority date so that the particular month of publication is not in issue”.
Related applications
Applicant should amend the cross-reference to related applications portion of the specification to update the current status of the cited applications.
35 USC § 101 Statutory Analysis
The claims do not recite any of the judicial exceptions enumerated in the 2019 Revised Patent Subject Matter Eligibility Guidance. Further, the claims do not recite any method of organizing human activity, such as a fundamental economic concept or managing interactions between people. Finally, the claims do not recite a mathematical relationship, formula, or calculation. Thus, the claims are eligible because they do not recite a judicial exception.
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 claims at issue 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 reference 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. 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 §§ 706.02(l)(1) - 706.02(l)(3) 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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/forms/. The 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:
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Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,033,372. Although the conflicting claims are not identical, they are not patentably distinct from each other because both sets of claims are directed towards the common subject matter.
The claims in the present application define the invention differently from the claims in the issued U.S. Patent No. 12,033,372, however they are not patentably distinguishable from the claims in the other copending applications. In re White et al., 160 USPQ 417, In re Thorington et al., 163 USPQ 644.
For example, comparing representative claim 1 of the present application with representative claim 1 of issued U.S. Patent No. 12,033,372. Claim 1 of the present application recites: A non-transitory computer readable medium containing instructions that when executed by at least one processor cause the at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising (Claim 1 of issued U.S. Patent No. 12,033,372 recites: A non-transitory computer readable medium containing instructions for causing at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising); receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (Claim 1 of issued U.S. Patent No. 12,033,372 recites: receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (Claim 1 of issued U.S. Patent No. 12,033,372 recites: calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (Claim 1 of issued U.S. Patent No. 12,033,372 recites: determining one or more properties of the first visual content); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (Claim 1 of issued U.S. Patent No. 12,033,372 recites: for each training example of the plurality of training examples, analyzing the visual content associated with the training example to determine one or more properties of the visual content associated with the training example); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (Claim 1 of issued U.S. Patent No. 12,033,372 recites: using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples).
As the comparison shows the claims recite common subject matter, and the differences relate to variations of the claimed limitations, and the processing is carried out on the data and/or elements in no way affects how the data would be received from an input, processed and output within the context of the claims. Therefore, the substitution of the different variations would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. While claim 1 of issued U.S. Patent No. 12,033,372 includes additional limitations that are not set forth in the instant claim 1, the use of transitional term "comprising" in the instant claim 1 fails to preclude the possibility of additional elements, so that instant claim 1 fails to define an invention that is patentably distinct from claim 1 of issued U.S. Patent No. 12,033,372. Furthermore, the elements of instant claim 1 are fully anticipated by the patented claim, and anticipation is “the ultimate or epitome of obviousness (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 2-20 of the present application recite limitations which are in most cases word for word the same limitations as found in claims 2-19 respectively of issued U.S. Patent No.12,033,372.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,073,605. Although the conflicting claims are not identical, they are not patentably distinct from each other because both sets of claims are directed towards the common subject matter.
The claims in the present application define the invention differently from the claims in the issued U.S. Patent No. 12,073,605, however they are not patentably distinguishable from the claims in the other copending applications. In re White et al., 160 USPQ 417, In re Thorington et al., 163 USPQ 644.
For example, comparing representative claim 1 of the present application with representative claim 7 of issued U.S. Patent No. 12,073,605. Claim 1 of the present application recites: A non-transitory computer readable medium containing instructions that when executed by at least one processor cause the at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising (Claim 7 of issued U.S. Patent No. 12,073,605 recites: A non-transitory computer readable medium storing a software program comprising data and computer implementable instructions that when executed by at least one processor cause the at least one processor to perform operations for attributing aspects of generated visual contents to training examples, the operations comprising); receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (Claim 7 of issued U.S. Patent No. 12,073,605 recites: receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples is associated with a respective visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (Claim 7 of issued U.S. Patent No. 12,073,605 recites: receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples is associated with a respective visual content); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (Claim 7 of issued U.S. Patent No. 12,073,605 recites: receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning model using a plurality of training examples, each training example of the plurality of training examples is associated with a respective visual content); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (Claim 7 of issued U.S. Patent No. 12,073,605 recites: for each training example of the plurality of training examples, analyzing the respective visual content to determine one or more properties of the respective visual content); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (Claim 7 of issued U.S. Patent No. 12,073,605 recites: using the one or more properties of the first aspect of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first aspect of the first visual content to a first subgroup of at least one but not all of the plurality of training examples).
As the comparison shows the claims recite common subject matter, and the differences relate to variations of the claimed limitations, and the processing is carried out on the data and/or elements in no way affects how the data would be received from an input, processed and output within the context of the claims. Therefore, the substitution of the different variations would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. While claim 1 of issued U.S. Patent No. 12,073,605 includes additional limitations that are not set forth in the instant claim 1, the use of transitional term "comprising" in the instant claim 1 fails to preclude the possibility of additional elements, so that instant claim 1 fails to define an invention that is patentably distinct from claim 1 of issued U.S. Patent No. 12,073,605. Furthermore, the elements of instant claim 1 are fully anticipated by the patented claim, and anticipation is “the ultimate or epitome of obviousness (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 2-20 of the present application recite limitations which are in most cases word for word the same limitations as found in claims 2-19 respectively of issued U.S. Patent No. 12,073,605.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-19 of U.S. Patent No. 12,142,029. Although the conflicting claims are not identical, they are not patentably distinct from each other because both sets of claims are directed towards the common subject matter.
The claims in the present application define the invention differently from the claims in the issued U.S. Patent No. 12,142,029, however they are not patentably distinguishable from the claims in the other copending applications. In re White et al., 160 USPQ 417, In re Thorington et al., 163 USPQ 644.
For example, comparing representative claim 1 of the present application with representative claim 4 of issued U.S. Patent No. 12,142,029. Claim 1 of the present application recites: A non-transitory computer readable medium containing instructions that when executed by at least one processor cause the at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising (Claim 4 of issued U.S. Patent No. 12,142,029 recites: A non-transitory computer readable medium containing instructions for causing at least one processor to perform operations for attributing generated visual content to training examples, the operations comprising); receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (Claim 4 of issued U.S. Patent No. 12,142,029 recites: receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (Claim 4 of issued U.S. Patent No. 12,142,029 recites: calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (Claim 4 of issued U.S. Patent No. 12,142,029 recites: and basing the determination of the one or more properties of the first visual content on the result value of the calculated convolution of the at least part of the first visual content); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (Claim 4 of issued U.S. Patent No. 12,142,029 recites: for each training example of the plurality of training examples, analyzing the visual content associated with the training example to determine one or more properties of the visual content associated with the training example); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (Claim 4 of issued U.S. Patent No. 12,142,029 recites: using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples).
As the comparison shows the claims recite common subject matter, and the differences relate to variations of the claimed limitations, and the processing is carried out on the data and/or elements in no way affects how the data would be received from an input, processed and output within the context of the claims. Therefore, the substitution of the different variations would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention. While claim 1 of issued U.S. Patent No. 12,142,029 includes additional limitations that are not set forth in the instant claim 1, the use of transitional term "comprising" in the instant claim 1 fails to preclude the possibility of additional elements, so that instant claim 1 fails to define an invention that is patentably distinct from claim 1 of issued U.S. Patent No. 12,142,029. Furthermore, the elements of instant claim 1 are fully anticipated by the patented claim, and anticipation is “the ultimate or epitome of obviousness (In re Kalm, 154 USPQ 10 (CCPA 1967), also In re Dailey, 178 USPQ 293 (CCPA 1973) and In re Pearson, 181 USPQ 641 (CCPA 1974)).
Claims 2-20 of the present application recite limitations which are in most cases word for word the same limitations as found in claims 2-19 respectively of issued U.S. Patent No. 12,142,029.
Claim Rejections - 35 USC § 102
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 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 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 1-8, 10-13 and 15-20 are rejected under 35 U.S.C. §102(a)(1) as being anticipated by Wang et al. (U.S. Patent Application Publication No. US 2021/0019541 A1) (hereafter referred to as “Wang”).
With regard to claim 1, Wang describes a non-transitory computer readable medium containing instructions that when executed by at least one processor cause the at least one processor to perform operations for attributing generated visual content to training examples (see Figure 1 and refer for example to paragraph [0045]), the operations comprising receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (see Figure 2, element 206B and Fig. 3, element 206B and refer for example to paragraph [0013] where the Wang system describes generating the second image is based on a plurality of training facial images having different visual attributes, and to paragraphs [0079] through [0083] a machine learning algorithm which describe the training of a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0105], [0107], [0114], [0115] and [0116]); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0093], [0105], [0107], [0114], [0115] and [0116]); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (refer for example to paragraphs [0014], [0083] and [0093]); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (see Figure 7, element 704 and refer for example to paragraphs [0117] and [0118]);
As to claim 2, Wang describes determining that the at least one visual content associated with the training examples of the first subgroup is associated with a first at least one source (see Figure 7 and refer for example to paragraph [0117]); and for each source of the first at least one source, updating a data-record associated with the source based on the attribution (see Figure 8, element 810 and refer for example to paragraph [0139]).
In regard to claim 3, Wang describes using the one or more properties of the first visual content and the properties of the visual contents associated with the first subgroup to determine, for each training example of the first subgroup, a degree of attribution of the first visual content to the training example and for each source of the first at least one source, further basing the update to the data-record associated with the source on the degree of attribution associated with the source (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
With regard to claim 4, Wang describes wherein the determination of the one or more properties of the first visual content is based on an intermediate result of the generative model when generating the first visual content (see Figure 2 and refer for example to paragraphs [0055] through [0057]).
As to claim 5, Wang describes analyzing the first visual content to detect at least a first object and a second object depicted in the first visual content and basing the determination of the one or more properties of the first visual content on a location of the first object in the first visual content and on a location of the second object in the first visual content (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
In regard to claim 6, Wang describes analyzing the first visual content to determine a type of an object depicted in the first visual content; and basing the determination of the one or more properties of the first visual content on the type of the object (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
With regard to claim 7, Wang describes analyzing the first visual content to detect an event depicted in the first visual content and basing the determination of the one or more properties of the first visual content on the event (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
As to claim 8, Wang describes wherein the one or more properties of the first visual content are based on temporal relation between an appearance of a first object and an appearance of a second object in the first visual content (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
In regard to claim 10, Wang describes wherein the training of the machine learning algorithm to generate the generative model includes an iterative process, wherein in each iteration of the iterative process a training example of the plurality of training examples is analyzed and a loss function is updated, and wherein the one or more properties of the visual content associated with a particular training example are based on the update to the loss function in an iteration that includes analysis of the particular training example (refer for example to paragraphs [0099] through [0101]).
As to claim 11, Wang describes wherein the training of the machine learning algorithm to generate the generative model includes a first step of training using a first subgroup of the plurality of training examples to obtain an intermediate model and a second step of training using a second subgroup of the plurality of training examples and using the intermediate model for initialization to obtain the generative model, the second subgroup differs from the first subgroup, and wherein the operations further comprise comparing a result associated with the first visual content and the intermediate model with a result associated with the first visual content and the generative model and for each training example of the second subgroup, determining whether to attribute the first visual content to the training example based on a result of the comparison (see Figures 2, 7 and 8, and refer for example to paragraphs [0055] through [0057] and to paragraphs [0013], [0014], [0117] and [0139]).
With regard to claim 12, Wang describes wherein the one or more properties of a particular visual content associated with a particular training example of the plurality of training examples are determined based on a convolution of at least part of the particular visual content (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
As to claim 13, Wang describes wherein the one or more properties of a particular visual content associated with a particular training example of the plurality of training examples are based on temporal relation between an appearance of a first object and an appearance of a second object in the particular visual content (see Figures 2, 7 and 8, and refer to paragraphs [0013], [0014], [0117] and [0139]).
With regard to claim 15, Wang describes receiving a second visual content generated using the generative model, determining one or more properties of the second visual content, using the one or more properties of the second visual content and the properties of the visual contents associated with the plurality of training examples to attribute the second visual content to a second subgroup of at least one but not all of the plurality of training examples, the second subgroup includes at least one training example not included in the first subgroup, determining that the at least one visual content associated with the training examples of the second subgroup is associated with a second at least one source, the second at least one source includes one or more sources not included in the first at least one source, based on the second at least one source, forgoing usage of the second visual content and initiating usage of the first visual content (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
As to claim 16, Wang describes receiving a second visual content generated using the generative model, determining one or more properties of the second visual content; using the one or more properties of the second visual content and the properties of the visual contents associated with the plurality of training examples to attribute the second visual content to a second subgroup of at least one but not all of the plurality of training examples, the second subgroup includes at least one training example not included in the first subgroup, accessing a data-structure associating visual contents with amounts, using the data-structure to determine that the at least one visual content associated with the training examples of the first subgroup is associated a first total amount, using the data-structure to determine that the at least one visual content associated with the training examples of the second subgroup is associated a second total amount, based on the first and second total amounts, forgoing usage of the second visual content and initiating usage of the first visual content and further basing the updates to data-records associated with the sources on the first total amount (see Figures 2, 7 and 8, and refer to paragraphs [0013], [0014], [0117] and [0139]).
In regard to claim 17, Wang describes using the one or more properties of the first visual content to embed the first visual content in a mathematical space, for each training example of the plurality of training examples, using the one or more properties of the visual content associated with the training example to embed the visual content associated with the training example in the mathematical space, and using the mathematical space to select the first subgroup of at least one but not all of the plurality of training examples (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
With regard to claim 18, Wang describes using a parameter of the generative model and the properties of the visual contents associated with the plurality of training examples to attribute the parameter of the generative model to a second subgroup of at least one but not all of the plurality of training examples, determining that the at least one visual content associated with the training examples of the second subgroup is associated with a second at least one source, and for each source of the second at least one source, updating a data-record associated with the source based on the attribution of the parameter of the generative model (see Figures 2, 7 and 8, and refer for example to paragraphs [0013], [0014], [0117] and [0139]).
As to claim 19, Wang describes at least one processor (see Figure 1 and refer for example to paragraph [0045]) configured to perform the operations of receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (see Figure 2, element 206B and Fig. 3, element 206B and refer for example to paragraph [0013] where the Wang system describes generating the second image is based on a plurality of training facial images having different visual attributes, and to paragraphs [0079] through [0083] a machine learning algorithm which describe the training of a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0105], [0107], [0114], [0115] and [0116]); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0093], [0105], [0107], [0114], [0115] and [0116]); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (refer for example to paragraphs [0014], [0083] and [0093]); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (see Figure 7, element 704 and refer to paragraphs [0117] and [0118]).
In regard to claim 20, Wang describes receiving a first visual content generated using a generative model, the generative model is a result of training a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content (see Figure 2, element 206B and Fig. 3, element 206B and refer for example to paragraph [0013] where the Wang system describes generating the second image is based on a plurality of training facial images having different visual attributes, and to paragraphs [0079] through [0083] a machine learning algorithm which describe the training of a machine learning algorithm using a plurality of training examples, each training example of the plurality of training examples is associated with a visual content); calculating a convolution of at least part of the first visual content to thereby obtain a result value of the calculated convolution of the at least part of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0105], [0107], [0114], [0115] and [0116]); based on the result value of the calculated convolution of the at least part of the first visual content, determining one or more properties of the first visual content (see Figures 2, 5 and 6 and refer for example to paragraphs [0093], [0105], [0107], [0114], [0115] and [0116]); for each training example of the plurality of training examples, obtaining one or more properties of the visual content associated with the training example (refer for example to paragraphs [0014], [0083] and [0093]); and using the one or more properties of the first visual content and the properties of the visual contents associated with the plurality of training examples to attribute the first visual content to a first subgroup of at least one but not all of the plurality of training examples (see Figure 7, element 704 and refer for example to paragraphs [0117] and [0118]).
Allowable Subject Matter
Claims 9 and 14 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.
Relevant Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Souche, Guttmann (‘695) and (‘843) all disclose systems similar to applicant’s claimed invention.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Jose L. Couso whose telephone number is (571) 272-7388. The examiner can normally be reached on Monday through Friday from 5:30am to 1:30pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Bella, can be reached on 571-272-7778. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
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/JOSE L COUSO/Primary Examiner, Art Unit 2667
May 5, 2026
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