DETAILED ACTION
Claim(s) 24,25,26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of Kopparapu et al. (US 2022/0207875 A1):
Claim(s) 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1) as applied in claims 24,25,26:
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES):
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES) as applied in claims 5 and 17 further in view of YIN (CN 108133476 B) with Google Patents machine translation:
Claim(s) 9 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Gasper et al. (US 2022/0327119 A1):
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Rogers et al. (US 2021/0117859 A1):
Response to Amendment
The amendment was received 4/6/2026. Claims 1-26 pending.
Priority
Applicant’s claim for the benefit of a prior-filed application under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) is acknowledged. Applicant has not complied with one or more conditions for receiving the benefit of an earlier filing date under 35 U.S.C. 119(e) or under 35 U.S.C. 120, 121, 365(c), or 386(c) as follows:
The later-filed application must be an application for a patent for an invention which is also disclosed in the prior application (the parent or original nonprovisional application or provisional application). The disclosure of the invention in the parent application and in the later-filed application must be sufficient to comply with the requirements of 35 U.S.C. 112(a) or the first paragraph of pre-AIA 35 U.S.C. 112, except for the best mode requirement. See Transco Products, Inc. v. Performance Contracting, Inc., 38 F.3d 551, 32 USPQ2d 1077 (Fed. Cir. 1994).
The disclosure of the prior-filed application, Application No. 18/145,301 12/22/2022, fails to provide adequate support or enablement in the manner provided by 35 U.S.C. 112(a) or pre-AIA 35 U.S.C. 112, first paragraph for one or more claims of this application.
Application No. 18/145,301 12/22/2022 does not disclose claim 1’s:
“search configuration parameter”
“custom”:
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Claim Objections
Claims 24,25,26 objected to because of the following informalities:
Claim 24, last three line’s “the custom model” (twice claimed) is objected for truncating claim 24’s “custom 1 model” and thus does not give meaning and purpose to manipulative steps (MPEP 2111.04 I. : “(B) ‘wherein’ clauses”) with respect to the preamble’s processing and thus not having a limiting effect on claim 24:
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Thus “the custom model” is interpreted under the broadest reasonable interpretation as “the custom model” avoiding: (1) not giving meaning and purpose (MPEP 2111.04 I. : “(B) ‘wherein’ clauses”) with respect to the preamble’s processing and (2) not having a limiting effect on claim 24.
Thus claims 25,26 objected.
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 (detection) 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 (plural) described in the specification [0066] 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 (detection) function without the recital of structure, material, or acts (plural) in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification [0066] and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts (plural) for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts (plural) to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites (detection) function without reciting sufficient (computer) structure, material or acts (plural) to entirely perform the recited (detection) function.
This application includes one or more claim limitations that use the word “means” or “step” but are nonetheless not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph because the claim limitation(s) (prepositionally or adjectivally) recite(s) sufficient structure (a “computer” by definition (Dictionary.com) of claim 1’s “output”2 & “machine learning”3, each comprising “computer”), materials, or acts to entirely perform the recited (underlying) function, y(x). Such claim limitation(s) is/are:
--the custom model is configured to detect4 custom objects--
in claim 4;
--a text-to-visual content model trained to detect5 custom objects corresponding to the custom object label and to detect6 at least one predetermined object for which the at least one teacher model is trained to detect7--
in claim 5;
--the custom model is configured to detect custom objects--
in claim 16;
--the custom model is a text-to-visual content model trained to detect custom objects corresponding to the custom object label and to detect at least one predetermined object for which the at least one teacher model is trained to detect--
in claim 17.
Because this/these claim limitation(s) is/are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are not being interpreted to cover only the corresponding structure, material, or acts described in the specification as performing the claimed function, and equivalents thereof.
If applicant intends to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to remove the (“computer”) structure, materials, or acts that performs the claimed (underlying) function; or (2) present a sufficient showing that the claim limitation(s) does/do not recite sufficient (computer) structure, materials, or acts to perform the claimed (underlying) function (y(x)).
35 USC § 101 –
Positive Statement
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1,2,3,4,5,6,7,8,9,10,11 and 12 and 13,14,15,16,17,18,19,20,21,22,23 and 24,25,26 (i.e., claims 1-26) (filed 4/6/2026) not rejected under 35 U.S.C. 101 because the claimed invention is directed to improving machine learning technology not without significantly more (streamlined analysis):
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Response to Arguments
Claim rejections – 35 USC 103
Applicant's arguments pages 12-21 filed 4/6/2026 have been fully considered but they are not persuasive:
Claims 24-26
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 12, last para, last S: “for training”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicants state in page 14, penult para:
The set of example local defects does not include any of the defects among the examples of global defects, let alone a subset as would be required by the claim language (assuming that the local and global defects are comparable to the claimed domains, which applicant does not agree with or admit). That is, even assuming that the local defects represent a second domain and the global defects represent a first domain, Wei does not teach that the local defects (alleged second domain) are a subset of the global defects
(alleged first domain).
In response, the claimed “subset” is mapped in the current rejection of claim 24 to Wei’s “proper subset of candidate defect frames”, c. 11,ll. 10-15. Thus Wei teaches a whole set of defect frames and a proper8 subset of defect frames of the whole set of defect frames in the context of defects as identified by applicants. Thus applicant’s mentioning of “The set of example local defects” maps to the whole set of defect frames comprising the proper subset (i.e., not equal to the whole subset) of defect frames.
Applicants state in page 14, last para:
Applicant also notes that, although FIG. 2 depicts frame IDs 150A and 150B in the same box as the local defects 148A and global defects 148B, respectively, Wei does not teach that the frame IDS 150A and 150B are defects. As noted in column 6, lines 19-40 of Wei quoted above, the corresponding frame IDs identify locations of the respective defects and are not taught as being defects themselves. Applicant submits that locations of defects are not defects.
In response, the IDS 150A and 150B are not relied upon/not mapped in the current rejection of claim 24.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 15, 2nd para: “subsets of each9 other”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In contrast claim 24 says “the second domain is10 a subset of the first domain”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 15, 2nd para: “a domain which is a subset of another11 domain”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In contrast claim 24 says “the second domain is a subset of the first12 domain”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 14, penult para: “subsets of each other”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Applicants state on page 16, 3rd para:
Accordingly, Wei does not teach a second domain that is a subset of a first domain or that respective models are trained on such domains as recited in the amended claims.
The examiner respectfully disagrees since Wei teaches said proper subset of defect frames which is the claimed “second domain” of the whole set13 of defect frames, wherein the whole set is also a domain within which anything (e.g., defects) occurs, prevails, or dominates.
Applicant’s arguments, see remarks, page 16, 3rd para:
Accordingly, Wei does not teach a second domain that is a subset of a first domain or that respective models are trained on such domains as recited in the amended claims.
, filed 4/6/2026, with respect to the rejection(s) of claim(s) 24,25,26 under 35 USC 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of 35 USC 103:
Claim(s) 24,25,26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of Kopparapu et al. (US 2022/0207875 A1), wherein Wei teaches a defect detector machine learning (fig. 1:146) lacking the details for training thereof and Kopparapu teaches a detailed method (as indicated in fig. 5:502,504,506: “TRAINING”) to train machine learning with labels in the context of a high quality domain and a lower quality domain.
Applicants state in page 16, 4th para:
Bilenko does not teach these missing features. The office action points to Bilenko as allegedly teaching the claimed "potential features." Since the amended claims do not utilize this language, the portions of Bilenko cited for this feature are no longer applicable.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 17, 3rd para: “one domain is a subset of another14 domain”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In contrast claim 24 says “the second domain is a subset of the first15 domain”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 17, 4th para, 1st S: “other16 domain”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In contrast claim 24 says “the second domain is a subset of the first17 domain”.
In response to applicant's argument that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., page 17, 4th para, 2nd S: “another18 (first) domain”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). In contrast claim 24 says “the second domain is a subset of the first19 domain”.
Claims 1,2,3,4,7,8,11,12,13,14,15,16,19,20 and 23
Due to the amendment, claims 1,2,3,4,7,8,11,12,13,14,15,16,19,20 and 23 are rejected via said Kopparapu et al. (US 2022/0207875 A1), wherein Kopparapu teaches a detailed method to train machine learning with labels in the context of domains:
Claim(s) 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1) as applied in claims 24,25,26.
Claims 5 and 17
Due to the amendment, claims 5 and 17 are rejected via said Kopparapu et al. (US 2022/0207875 A1), wherein Kopparapu teaches a detailed method to train machine learning with labels in the context of domains:
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES).
Claim 6 and 18
Due to the amendment, claims 6 and 18 are rejected via said Kopparapu et al. (US 2022/0207875 A1), wherein Kopparapu teaches a detailed method to train machine learning with labels in the context of domains:
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES) as applied in claims 5 and 17 further in view of YIN (CN 108133476 B) with Google Patents machine translation:
Claims 9 and 21
Due to the amendment, claims 6 and 18 are rejected via said Kopparapu et al. (US 2022/0207875 A1), wherein Kopparapu teaches a detailed method to train machine learning with labels in the context of domains:
Claim(s) 9 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Gasper et al. (US 2022/0327119 A1):
Claims 10 and 22
Due to the amendment, claims 6 and 18 are rejected via said Kopparapu et al. (US 2022/0207875 A1), wherein Kopparapu teaches a detailed method to train machine learning with labels in the context of domains:
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Rogers et al. (US 2021/0117859 A1):
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically2021 disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole22 would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness (discussed below as “Factual Inquiry 4” in the rejection of claim 24).
Claim(s) 24,25,26 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of Kopparapu et al. (US 2022/0207875 A1):
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Re 24. (Currently Amended), Wei teaches A method for visual content processing23, (likewise) comprising24:
applying [“for visual content processing”] a custom 25 model (via fig. 3:308: “CUSTOM MODEL SYSTEM”) to a set of first media (player: fig. 1:120: “MEDIA PLAYER”) content (“delivery system 100”, c.4,ll. 50-55), wherein the custom model (via fig. 3:308: “CUSTOM MODEL SYSTEM”) is trained (via fig. 1:142: “TRAINED MODEL(S)”) using a set of second media content (“files 320”, c.9,ll. 25-30: fig. 3: 320: “TRAINING VIDEO FILE(S)”), wherein the set of second (training) media content (320) is generated (as “a proper subset of candidate defect frames”, c,11,ll. 10-15) by labeling a plurality of training candidates (or a to-be-inferred defect forming or “comprising at least one labeled defect”, c.11,ll 5-10) based on a plurality of predictions (i.e., machine learning, ML, inferences identifying said subset) output by a at least one teacher (“training by training service 332”26 “candidate”, c. 10,ll. 30-35: fig. 3: “MODEL TRAINING SERVICE”, designed to teach) model (fig. 1:142: candidate-teacher “TRAINED MODEL(S)”), wherein the plurality of (ML) prediction (defect) labels includes at least one instance of a custom (video) object (“custom model” “data objects (such as video files 318)”,c.9,ll. 23-25 & c.7,ll. 25-30) label (or “labeled” “Data 318”, c.9,ll. 5,6, “for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”), wherein the outputs of the custom model include at least one prediction for the set of first media content (as understood given ML’s “inference results 360B”, c.10,ll.40-45L fig. 3:360B: “INFERENCE RESP.”);
selecting [“for visual content processing”] (or extracting27) a (“features 140”, c.3,ll.60-65: fig. 2:140: “FEATURE(S) (E.G., PER FRAME)”) subset of the first media (player) content (fig. 4:408: “RECEIVING AN INFERENCE REQUEST FOR AN INPUT VIDEO FILE”) based on the at least one (inference-training) prediction for the set of first media (player) content output by the (training-inference) custom model (resulting in fig. 1:142: “TRAINED MODEL(S)”); and
providing [“for visual content processing”] (by preference) the (extraction) selected (“features 140”, c.3,ll.60-65: fig. 2:140: “FEATURE(S) (E.G., PER FRAME)”) subset of the first media content as inputs to an advanced machine learning model (fig. 2:146:“DEFECT DETECTOR(S)”), wherein the advanced machine learning model is trained (or likewise “one or more defect detector(s) 146…is a machine learning model trained… in a video file”, “c.4,ll.20-25) on2829a first domain (or likewise the whole set30 via “a proper31 subset” c. 2,ll. 35-40)32, wherein the is a set of features (or likewise “a proper subset of…features”, c.11,ll. 10-14) used by the advanced machine learning model (or likewise “defect detector(s) 146 (e.g., a local defect detector)… based33 on … feature categories 140A-140G, e.g., all of them” c.6,ll.25-30), wherein the custom model is trained on34 a second domain (or likewise “the trained model 142… based on … a first subset”, c. 5,ll. 55-60), wherein the second domain is a set of features (or likewise “a first subset of … a first category”, c.5, ll. 55-60) used by the custom model (or likewise “a trained (e.g., machine learning) model(s) 142…with one … feature category (e.g., any one of features 140A-140G)“, c.5,ll.50-55), wherein the second domain is a subset35 of the first domain.
Wei does not teach the single-word difference of claim 24 of:
--on3637 -- of:
(the advanced machine learning model is trained)38 on (i.e., in connection with) (a first domain)39.
Kopparapu teaches the difference of claim 24 of:
--on -- of:
(the advanced machine learning model is trained)40 on41 (i.e., in connection with) (a first domain)42 (or likewise “the machine learning model having been trained with43 a first set of images labeled based on image aesthetics, and further having been trained with44 second set of images labeled based on image quality, the first and second set of images being associated with45 different domains” [0142] last S).
Since46 Wei suggests building/training a machine learning model 146 but does not give the details for building/training the machine learning defect detector model 146:
c4,ll. 15-30:
Depicted detection system 136 includes one or more defect detector(s) 146 to detect one or more defects 148 in video file, e.g., based on the candidate defect frames 144. In certain embodiments, a defect detector is a machine learning model trained to determine probable defects 148 in a video file. Defect detector 146 may output a defect, e.g., along with an identification 150 of the one or more frames (e.g., a proper subset of the candidate defect frames 144) that correspond to that particular defect. In one embodiment, defect detector 146 may also analyze the entire video file in its original version, e.g., before it was encoded by encoder 110.
c.8,ll.55-65:
The defect detection service 302, in some embodiments, is a machine learning powered service that makes it easy for users to build and use trained model(s) 142, e.g., to build and use a trained model 142 that infers candidate defect frame(s) 144 based on an input into the trained model 142 of any combination of features (e.g., compression features), for example, features 140, and/or to build and use a defect detector model, e.g., as defect detector(s) 146.
one of skill in the art of machine learning would of looked to other teachings (e.g., Kopparapu et al. (US 2022/0207875 A1) for the details for building/training machine learning and thus can make Wei’s be as Kopparapu’s seeing in the change “approaches…to reduce the amount of labeling required for training the machine learning model 538 via the machine learning algorithm 508. For example, instead of requiring manual labeling (e.g., by human annotating) for a large number of videos (e.g., in millions), the weak labeling and self-training as described herein allow for manual labeling for a smaller number of videos (e.g., in the hundreds or low thousands). Reducing the amount of labeling may in turn reduce the resources (e.g., computer resources and/or manpower) needed to train a machine learning model.” , Kopparapu [0131] by:
a) using Kopparapu’s teaching at [0144][0145] of training of machine learning to train/build Wei’s machine learning defect detector 146:
--[0144] Training of the machine learning model may include performing a first stage of training the machine learning model [Wei’s 146] based on the first set of images [Wei’s fig. 3:320,322: “TRAINING VIDEO FILE(S)”, “LABELED DEFECT(S)”], providing the second set of images as input to the machine learning model [Wei’s 146] as trained in the first stage of training, in order to label the second set of images, generating a third set of images by decreasing image quality for the second set of images, and performing a second stage of training the machine learning model [Wei’s 146] based on the labeled second set of images and the third set of images. Decreasing image quality for the second set of images is based on at least one of down-sampling, adjusting exposure or Gaussian blurring of the second set of images.
[0145] Training of the machine learning model [Wei’s 146] may further include performing a third stage of training the machine learning model based on a labeled subset of images included within other videos stored in association with the messaging application, providing an unlabeled subset of images included within the other videos to the machine learning model [Wei’s 146] having been trained by the third stage of training, in order to pseudo-label the unlabeled subset of video frames, and performing a fourth stage of training the machine learning model [Wei’s 146] based on the labeled subset of images and the pseudo-labeled subset of images. The labeled subset of images may have been labeled based on a preference for at least one of a subject of the image being centered, the subject being larger, the subject being foregrounded, or the subject having a predefined expression or characteristic.—
; and
b) deploy Wei’s trained model 146 based on Kopparapu’s reduced labelling training of machine learning to Wei’s fig. 1:146: “DEFECT DETECTOR(S)”.
Re 25. (Original), Wei of the combination of Wei, Kopparapu teaches The method of claim 24, further comprising:
selecting the plurality of training (frame) candidates from among a set of third media content (comprised by video “frame 8”, c.4,ll.5-10) based on at least one search (“hyper”, c.9,ll. 15-20)parameter, wherein the at least one search (hyper)parameter defines criteria (comprised by said hyperparameter) for identifying the set of third(-frame) media content and for selecting samples (“for (e.g., automated) video quality monitoring”, c.1, 60-65) from among the third set of media content to be used as the training candidates, wherein the set of third media content includes a plurality of portions of media content showing a custom object (“such as video files 318”, c.7,ll. 25-30: fig. 3:318: “VIDEO FILE(S) (E.G., AND OTHER DATA)) corresponding to the custom object label (“for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”).
Re 26. (Original), Wei of the combination of Wei, Kopparapu teaches The method of claim 24, wherein each of the (categorization) custom machine learning model (fig. 1:142) and the (categorization) advanced machine learning model (fig. 1:146) is a classifier (via “Utilizing this categorization”, c.2, ll.40-45), wherein the custom machine learning model (142) is configured to output a plurality of first (“categorized…local defects”, c.2,ll.30-35) classes, wherein the advanced machine learning model (146) is configured to output a plurality of second (global) classes, wherein the plurality of first (local) classes is a subset (via a subset of candidates models and a subset of frames: fig. 2:142,144) of the plurality of second (global) classes, wherein each of the plurality of first (local) classes and the plurality of second (global) classes includes a custom object class corresponding to the custom object label47 (or custom defects labeled via “labeled defects 322) for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”).
Claim(s) 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1) as applied in claims 24,25,26:
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Claim 1 is rejected like claim 24:
Re 1. (Currently Amended), Wei teaches A method for visual content processing, comprising:
applying at least one teacher (“training service”-designed to teach--”candidate”, c. 10, ll.31-35) model (figs.1,2,3:142: TRAINED MODEL) to a set of training (“video file”, c.11,ll.10-15) candidates (or defects in fig. 3:318,320: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”) in order to output (fig. 2:arrows) a plurality of (local and global) instances (“for example”, c.2,ll. 15-20) of a custom object (“defects”, c.9,ll. 1-5) label (fig. 3:322: “LABELED DEFECT(S)”48: “for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”), wherein the set of training (video file) candidates (defects in 318,320) is (preferentially) selected (resulting in being “extracted”49, c.3,ll. 60-65: fig. 2:138: “FEATURE EXTRACTOR”) using a (“machine learning”, c.1,l..65 to c.2,l.2) student50 model (represented in fig. 2 as 138: “FEATURE EXTRACTOR”), wherein the (extractor) student model (138) is configured to select (i.e., extract) the (“sub”, c.1,l.65 to c.2,l. 2)set of training (“video”, c.11, ll. 10-15) candidates from among a set of media content (via fig. 1:134: “CONTENT PROVIDER(S)”) based on at least one (grid-)search configuration (“hyper”, c.9,ll. 15-25)parameter51, wherein the at least one (grid-)search configuration (hyper)parameter (exactly or specifically) defines (measuring) criteria for selecting (“video”, c.1,ll. 60-65) samples to be used as the set of training (defect) candidates;
generating (via “the defect detection system 136”, c.6,ll. 50-55: fig. 1: “DEFECT DETECTION SYSTEM”) a first set (“of manifests 116”, c.4,ll. 50-55: fig. 1:116: “MANIFEST IDENTIFYING VIDEO REPRESENTATION(S)”) of media content (via fig. 1:134: “CONTENT PROVIDER(S)”) by labeling (via fig. 3:322: “LABELED DEFECT(S)”) the set of training candidates based on the plurality of (example-feature-extraction) instances of the custom object label (fig. 3:322: “LABELED DEFECT(S)”: “for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”) output by the at least one teacher model (figs. 1,2,3:142: “TRAINED MODEL(S)”; “TRAINED MODEL”, “TRAINED MODEL(S)”);
training an advanced machine learning model, wherein the advanced machine learning model is trained on a first domain , wherein the first domain is set of features used by the advanced machine learning model;
creating a custom model (or likewise “creating a custom model”, c.9,ll. 20-25: fig. 1:142 via a “customer”, c.8,l. 66 to c.9,l. 6) using the at least one teacher model (fig. 1:142), wherein the (customer) custom model is a machine learning model trained (fig. 1:142: “TRAINED MODEL(S)”) using the first set of media content (via fig. 1:134: “CONTENT PROVIDER(S)”), wherein creating the custom model includes training the custom model on a second domain, wherein the second domain is a set of features used by the custom model, wherein the second domain is a subset of the first domain;
obtaining a (“representative”, c.1,l.65 to c.2, l. 2) subset of a second set of media content, wherein the (representative) subset of the second set of media content (via fig. 1:134: “CONTENT PROVIDER(S)”) is selected (i.e., extracted) based on outputs (fig. 2: arrows) of the (customer) custom model as applied to the second set of media content, wherein the (arrow) outputs of the (customer) custom model include at least one (inference) first prediction for the (representative) subset of the second set of media content (via fig. 1:134: “CONTENT PROVIDER(S)”); and
applying an advanced machine learning model (fig. 1:146: “DEFECT DECTECTOR(S)”) to the (representative) obtained subset of the second set of media content, wherein a (“local”, c.5,ll. 50-55) domain (fig. 2:200-210) used by the (customer) custom model (142) is a subset of a (“global”, c.6,ll. 20-25) domain used by the advanced machine learning model (fig. 1:146: “DEFECT DECTECTOR(S)”)52
Wei does not teach the difference of claim 1 of:
a) (the advanced machine learning model is trained)53 on (a first domain)…
b) using the at least one teacher model.
Kopparapu teaches/makes obvious difference “a)” in the rejection of claim 1.
The combination of Wei,Kopparapu does not teach the last difference “b)”:
b) using the at least one teacher model.
Da Silva teaches the last difference b) of claim 1:
Re 1., A method for visual content processing, comprising:
applying at least one teacher model (fig. 7:704a: “USE TEACHER MODEL TO SELECT ATTACKS”) to a (“new” [0042] 2nd S) set of (discriminator) training candidates (resulting in a “trained” “discriminator” [0043], 2nd S) in order to output a plurality of instances (via an “example” “cat image” [0026]: fig. 2:208,210:cats) of a (“same” [0025] last S) custom object (“object” “image captures” [0040] last S) label (fig. 1: “DOG”), wherein the (new) set of (discriminator) training candidates is selected (i.e., labeled54) using a (discriminator) student model (fig. 1:102: “STUDENT MODEL”), wherein the (discriminator) student model (102) is configured to select (i.e., label) the (new) set of (discriminator) training candidates from among a set of (“video”55 [0012]) media content based on at least one (“accepted” [0064]) search configuration parameter, wherein the at least one (accepted) search configuration parameter defines (accepted) criteria for selecting (i.e., labeling) samples (fig. 7:704a: “USE TEACHER MODEL TO SELECT ATTACKS”: fig. 1:104: SOURCE IMAGE + 108: PERTURBATIONS as labeled/selected by teacher) to be used (in the remainder of fig. 7) as the (new-adversarial) set of (discriminator) training candidates;
generating (“a new dataset” [0042] 2nd S: fig. 7:706a: “GENERATE ADVERSARIAL IMAGES”) a first (new) set of (video) media content by labeling the (new) set of (discriminator) training candidates (as “ ‘noise’ ” [0043]) based on the plurality of (cat) instances (being “misclassified” [0025] last S) of the (same) custom (dog) object label output (via fig. 1: arrows) by the at least one teacher model (figs. 1:7:703a: “LAYER COPIED FROM TEACHER” ; “USE TEACHER MODEL TO SELECT ATTACKS” being the basis for the segmentation/classification student model and also being a “similar, but distinct, task” [0021], 1st S, of student classification);
b) creating a custom (“accurate” [0021] 2nd S) model (fig. 1:102: “STUDENT MODEL” being the recipient of a customization “to further customize”, [0021] 3rd S, teacher models building the student model) using the at least one teacher model (“to create accurate”, [0021] [[2nd]] 3rd S, student models), wherein the custom (accurate) model is a machine learning model (“which may be referred to herein as a ‘discriminator’ “ [0040]: fig. 7:708a: “TRAIN DISCRIMINATOR”) trained using the first (new) set of (video) media content;
obtaining a subset of a second set of (video) media content, wherein the subset of the second set of (video) media content is selected (i.e., labeled) based on outputs (fig. 3:arrows) of the custom (accurate) model (fig. 3:304: “SEGMENTATION MODEL” that “comprises a student model” [0079]) as applied to the second set of (video) media content, wherein the outputs (fig. 3:arrows) of the custom (accurate) model include at least one first prediction (fig. 4,5: “PREDICTIONS”) for the subset of the second set of (video) media content; and
applying an advanced machine (“deep” [0020]) learning model to the obtained subset of the second set of (video) media content, wherein a (“different”, [0024] last S) domain used by the custom (accurate) model is a subset of a (different) domain used by the advanced machine (deep) learning model.
Since Wei of the combination of Wei,Kopparapu suggests training with tuning or the like, c.10,ll. 26-31, one of skill in the art of training can make Wei’s of the combination of Wei,Kopparapu be as de Silva’s transfer learning (i.e., transforming a teacher model into a student model) predictably recognizing the change creating “accurate models for specific tasks”, de Silva [0021] 2nd S, with remedial corrective action, such as creating accurate transfer learning machine learning models (i.e. discriminators) with the task of accurately categorizing conforming frames by type of defect with remedial corrective action of non-conforming noisy frames if need be:
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Re 2. (Original), Wei of the combination of Wei,de Silva, Kopparapu teaches The method of claim 1, further comprising:
training (via the above illustrated combination of figs. 3 & 7:708a: “TRAIN DISCRIMINATOR”) the custom (accurate-student) model (Wei’s 142,146 as modified via the combination, illustrated above) using the at least one teacher (accurate-teacher) model (142,146 as modified via the combination), wherein a (local) domain used by the custom (accurate) model (142,146 as modified via the combination) is a subset of a (global) domain used by the at least one teacher (accurate) model (142,146 as modified via the combination); and
sending (via arrows in fig. 3), from a second system (fig. 3:300:” PROVIDER NETWORK”) to a first system (fig. 3:306: “INTERMEDIATE NETWORK(S)”), the trained custom model (142) for deployment as the first machine learning (“hosted”, c.10,ll. 35-40) model (fig. 3:336: “HOSTED MODEL(S)”) at the first system (fig. 3:306: “INTERMEDIATE NETWORK(S)”), wherein the first system (fig. 3:306: “INTERMEDIATE NETWORK(S)”) is remote (via a “ ‘cloud’ provider network”, c.7,ll. 5-10) from the second system (fig. 3:300:” PROVIDER NETWORK”).
Re 3. (Originial), Wei of the combination of Wei,de Silva, Kopparapu teaches The method of claim 2, wherein creating the custom model using the at least one teacher model (i.e., transfer learning), further comprises:
labeling (Wei: fig. 3:322) at least a portion of the plurality of training candidates (fig. 3:318,320: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”) with the custom object (“custom model” “data objects (such as video files 318)”,c.9,ll. 23-25 & c.7,ll. 25-30) label (“for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”) based on the output plurality of instances (“for example”, c.2,ll. 15-20) of the custom object (“custom model” “data objects (such as video files 318)”,c.9,ll. 23-25 & c.7,ll. 25-30) label (“for use in creating a custom model”, c.9,ll. 25-30, via fig. 3:309: “USER”) in order to create labeled media content (fig. 3:318,320,322: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”; “LABELED DEFECTS”); and generating (expressed as arrows in fig. 3) the set (with a “representative”, c.1,l.65 to c.2, l. 2, subset) of second media content (via Wei: fig. 1:134: “CONTENT PROVIDER(S)”) based on at least a portion of the labeled media content (fig. 3:318,320,322: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”; “LABELED DEFECTS”).
Re 4. (Original), Wei of the combination of Wei,de Silva,Kopparapu teaches The method of claim 3, wherein the custom model (Wei: fig. 1:142 via a “customer”, c.8,l. 66 to c.9,l. 6 as modified by the combination) is configured to detect (via fig. 1:136: “DEFECT DETECTION SYSTEM”) custom objects (“custom model” “data objects (such as video files 318)”,c.9,ll. 23-25 & c.7,ll. 25-30: fig. 2:148,150A: “DEFECT(S)”; “FRAME(S) ID”) corresponding to the custom object label (“for use in creating a custom model”, Wei: c.9,ll. 25-30, via fig. 3:309: “USER”) when applied to (extracted) features of media content.
Re 7. (Original), Wei of the combination of Wei,de Silva, Kopparapu teaches The method of claim 3, wherein the second set of media content (via Wei: fig. 1:134: “CONTENT PROVIDER(S)”) includes exactly one sample (“for feature generation”, Wei: c.3,ll. 20-30) of previously labeled media content (via fig. 3:318,320,322: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”; “LABELED DEFECTS”).
Re 8. (Originial), Wei of the combination of Wei,de Silva, Kopparapu teaches The method of claim 3, further comprising:
selecting (i.e., labeling) the plurality of training candidates (fig. 3:318,320: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”) from among a third (“sub”-,c.3,l. 66 to c.4,l.5: I count four sets in total)set of media content (fig. 1:134: “CONTENT PROVIDER(S)”) based on at least one (“grid”, c.9,ll.15-20) search (hyper)parameter, wherein the at least one (grid-)search (hyper) parameter defines criteria for identifying the third (sub-)set (of four sets) of media content (fig. 1:134: “CONTENT PROVIDER(S)”) and for selecting (i.e., labeling) samples from among the third (sub-)set of media content (fig. 1:134: “CONTENT PROVIDER(S)”) to be used as the training candidates (fig. 3:318,320: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”) wherein the (sub-)set of third(-set) media content (fig. 1:134: “CONTENT PROVIDER(S)”) includes a plurality of (“segment”, c.4,ll. 60-65) portions of media content (fig. 1:134: “CONTENT PROVIDER(S)”) showing a (modeling) custom (video) object (i.e., fig. 3: 318: “VIDEO FILE(S) (E.G., AND OTHER DATA)”) see corresponding rejection of claim 24) corresponding to the custom object label (“for use in creating a custom model”, Wei: c.9,ll. 25-30, via fig. 3:309: “USER”).
Claim 11 is rejected similar to claim 26:
11 (Previuosly Presented(. The method of claim 1, wherein each of the first machine learning model and a second machine learning model is a classifier, wherein the first machine learning model is configured to output a plurality of first classes, wherein the second machine learning model is configured to output a plurality of second classes, wherein the plurality of first classes is a subset of the plurality of second classes, wherein each of the plurality of first classes and the plurality of second classes includes a custom object class corresponding to the custom object label.
Claim 12 is rejected similar to claims 1,24:
Re 12. (Currently Amened), Wei of the combination of Wei, DaSilva, Kopparapu teaches A non-transitory computer readable medium56 having stored thereon instructions for causing a processing circuitry to execute a process, the process comprising:
applying at least one teacher model to a set of training candidates in order to output a plurality of instances of a custom object label, wherein the set of training candidates is selected using a student model, wherein the student model is configured to select the set of training candidates from among a set of media content based on at least one search configuration parameter, wherein the at least one search configuration parameter defines criteria for selecting samples to be used as the set of training candidates;
generating a first set of media content by labeling the set of training candidates based on the plurality of instances of the custom object label output by the at least one teacher model;
training an advanced machine learning model, wherein the advanced machine learning model is trained on a first domain, wherein the first domain is a set of features used by the advanced machine learning model;
creating a custom model using the at least one teacher model, wherein the custom model is a machine learning model trained using the first set of media content, wherein creating the custom model includes training the custom model on a second domain, wherein the second domain is a set of features used by the custom model, wherein the second domain is a subset of the first domain;
obtaining a subset57 of a second set of media content, wherein the subset58 of the second set of media content is selected based on outputs of the custom model as applied to the second set of media content, wherein the outputs of the custom model include at least one first prediction for the subset59 of the second set of media content; and
applying an advanced machine learning model to the obtained subset60 of the second set of media content, wherein a domain used by the custom model is a subset61 of a domain used by the advanced machine learning model
Claim 13 is rejected similar to claims 24,1,12:
Re 13. (Currently Amended) Wei of the combination of Wei, DaSilva, Kopparapu teaches A system for visual content processing, comprising:
a processing circuitry; and
a memory, the memory containing instructions that, when executed by the processing circuitry, configure the system to:
apply at least one teacher model to a set of training candidates in order to output a plurality of instances of a custom object label, wherein the set of training candidates is selected using a student model, wherein the student model is configured to select the set of training candidates from among a set of media content based on at least one search configuration parameter, wherein the at least one search configuration parameter defines criteria for selecting samples to be used as the set of training candidates;
generate a first set of media content by labeling the set of training candidates based on the plurality of instances of the custom object label output by the at least one teacher model;
train an advanced machine learning model, wherein the advanced machine learning model is trained on a first domain, wherein the first domain is a set of features used by the advanced machine learning model;
create a custom model using the at least one teacher model, wherein the custom model is a machine learning model trained using the first set of media content, wherein creating the custom model includes training the custom model on a second domain, wherein the second domain is a set of features used by the custom model, wherein the second domain is a subset of the first domain;
obtain a subset of a second set of media content, wherein the subset of the second set of media content is selected based on outputs of the custom model as applied to the second set of media content, wherein the outputs of the custom model include at least one first prediction for the subset of the second set of media content; and
apply an advanced machine learning model to the obtained subset of the second set of media content, wherein a domain used by the custom model is a subset of a domain used by the advanced machine learning model
Claim 14 is rejected similar to claim 2:
Re 14 (Original). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 13, wherein the system is further configured to:
train the custom model using the at least one teacher model, wherein a domain used by the custom model is a subset of a domain used by the at least one teacher model; and
send, from a second system to a first system, the trained custom model for deployment as the first machine learning model at the first system, wherein the first system is remote from the second system.
Claim 15 is rejected similar to claim 3:
Re 15 (Original). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 14, wherein the system is further configured to:
label at least a portion of the plurality of training candidates with the custom object label based on the output plurality of instances of the custom object label in order to create labeled media content; and
generate the set of second media content based on the labeled media content.
Claim 16 is rejected similar to claim 4:
Re 16 (Original). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 15, wherein the custom model is configured to detect custom objects (“custom model” “data objects (such as video files 318)”,c.9,ll. 23-25 & c.7,ll. 25-30) corresponding to the custom object label when applied to features of media content.
Claim 19 is rejected similar to claim 7:
Re 19 (Original). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 15, wherein the second set of media content includes exactly one sample of previously labeled media content.
Claim 20 is rejected similar to claim 8:
Re 20 (Original). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 15, wherein the system is further configured to:
select the plurality of training candidates from among a third set of media content based on at least one search parameter, wherein the at least one search parameter defines criteria for identifying the third set of media content and for selecting samples from among the third set of media content to be used as the training candidates wherein the set of third media content includes a plurality of portions of media content showing a custom object corresponding to the custom object label.
Claim 23 is rejected similar to claim 11:
Re 23 (Previously Presented). Wei of the combination of Wei,de Silva, Kopparapu teaches The system of claim 13, wherein each of the first machine learning model and a second machine learning model is a classifier, wherein the first machine learning model is configured to output a plurality of first classes, wherein the second machine learning model is configured to output a plurality of second classes, wherein the plurality of first classes is a subset of the plurality of second classes, wherein each of the plurality of first classes and the plurality of second classes includes a custom object class corresponding to the custom object label.
Claim(s) 5 and 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES):
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Re 5. (Orignal), the combination of Wei and de Silva, Kopparapu teaches not The method of claim 4, wherein the custom model (Wei: fig. 1:142 via a “customer”, c.8,l. 66 to c.9,l. 6 as modified by the combination) is a text-to-visual content model trained to detect custom objects corresponding to the custom object label (“for use in creating a custom model”, Wei: c.9,ll. 25-30, via fig. 3:309: “USER”) and to detect at least one predetermined (video) object for which the at least one teacher model (142,146 as modified via the combination) is trained to detect (local & global defects via 35 USC 112(f), not invoked:
The combination of Wei and de Silva, Kopparapu does not teach the difference of claim 5:
--a text-to-visual content model trained to detect custom objects … and to detect at least one predetermined object for which the at least one teacher model is trained to detect--.
Acharya teaches the difference of claim 5:
5. The method of claim 4, wherein the custom model (“supervised by a large readily available pre-trained teacher model.”, pg. 13, 3rd para, penult S) is a text-to-visual content (“computer vision”, pg. 24, 2.2.3.1 Computer Vision 2nd S) model trained (via “knowledge distillation” pg. 24, 2.2.3.1 Computer Vision 1st S:pg. 24: fig. 2.1: “Generic KD Framework”) to detect custom objects (via “object detection” pg. 24, 2.2.3.1 Computer Vision 3rd S, 3rd listed item) corresponding to the custom object (“regularized”, pg. 13, 1st para, 1st S) label and to detect at least one predetermined object (via said objection detection) for which the at least one teacher model (fig. 1.4: “Teacher Model”: “Pre-Trained”) is trained to detect (an object).
MPEP 2143 Examples of Basic Requirements of a Prima Facie Case of Obviousness [R-01.2024]
I. EXAMPLES OF RATIONALES
C. Use of Known Technique To Improve Similar Devices (Methods, or Products) in the Same Way
To reject a claim (5) based on this rationale, Office personnel must resolve the Graham factual inquiries. Then, Office personnel must articulate the following:
a finding that the prior art (Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1), Kopparapu as applied to claims (1,2,3,4,7,8,11 and 12 and 13,14,15,16,19, 20, 23) above further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES)) contained a "base" device (method, or product-machine learning:“(e.g., deep) machine learning”, Wei, c.2,ll. 19-22;“deep learning”62, Acharya, pg. 12, 1.1 Background, 3rd para, 2nd S) upon which the claimed invention63 can be seen as an "improvement;"
a finding that the prior art (Archarya) contained a "comparable" (teaching-student) device (method, or product that is not the same as the base device) that has been improved in the same way (“maintaining a high accuracy of detection performance”, Wei, c.2,ll. 50-55; “the large deep model’s logits output…to precisely replicate the teacher model’s final prediction”, pg. 14, 1.2.1 Knowledge & pg.14: 1.2.1.1 Response-Based Knowledge, 2nd S ) as the claimed invention64;
(3) a finding (Archarya, pg. 19, 1.5 Significance Study, 2nd S) that one of ordinary skill in the art could have applied the known (teacher-student) "improvement" technique in the same way to the "base" (machine-learning) device (method, or product) and the results would have been predictable to one of ordinary skill in the art (such as expecting accurate deep learning teacher final predictions):
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; and
(4) whatever additional findings (no additional findings) based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness.
The rationale to support a conclusion that the claim would have been obvious is that a method of enhancing a particular class of devices (methods, or products) has been made part of the ordinary capabilities of one skilled in the art based upon the teaching of such improvement in other situations. One of ordinary skill in the art would have been capable of applying this known method of enhancement to a "base" device (method, or product) in the prior art and the results would have been predictable to one of ordinary skill in the art. "It's enough … to show that there was a known problem … in the art, that [another reference] … helped address that issue, and that combining the teachings of [the two references] wasn't beyond the skill of an ordinary artisan. Nothing more is required to show a motivation to combine under KSR." See Intel Corp. v. PACT XPP Schweiz AG, 61 F.4th 1373, 1380-81, 2023 USPQ2d 297 (Fed. Cir. 2023) (finding that both prior art references "address the same problem and that [the secondary reference’s] cache was a known way to address that problem is precisely the reason that there's a motivation to combine under KSR and our precedent.").
The Supreme Court in KSR noted that if the actual application of the technique would have been beyond the skill of one of ordinary skill in the art, then using the technique would not have been obvious. KSR, 550 U.S. at 417, 82 USPQ2d at 1396. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
Claim 17 rejected similar to claim 5:
17 (Orignial). The system of claim 16, wherein the custom model is a text-to-visual content model trained to detect custom objects corresponding to the custom object label and to detect at least one predetermined object for which the at least one teacher model is trained to detect.
Claim(s) 6 and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of ACHARYA (EVALUATE THE EFFECTIVENESS OF VARIOUS KNOWLEDGE DISTILLATION METHODS FOR SMALLER NEURAL NETWORK ARCHITECTURES) as applied in claims 5 and 17 further in view of YIN (CN 108133476 B) with Google Patents machine translation:
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Re 6 (Original), the combination of Wei and de Silva, Kopparapu and Acharya teaches The method of claim 5, wherein the at least a portion of the labeled media content (Wei: fig. 3:318,320, 322: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”; “LABELED DEFECTS”) is below a threshold proportion of a total number of the plurality of training candidates (Wei: in fig. 3:318,320: “VIDEO FILE(S) (E.G., AND OTHER DATA)”: “TRAINING VIDEO FILE(S)”).
The combination of Wei and de Silva, Kopparapu and Acharya does not teach the difference of claim 6: “a threshold proportion”.
YIN teaches the difference of claim 6:
6. The method of claim 5, wherein the at least a (“pixel”65, pg. 11, 1st txt blk) portion of the labeled (or “marked”66, pg. 8, 4th txt blk: last S, video display as a label) media content (i.e., “videos”, pg. 9, 6th txt blk-pixels) is (“otherwise”, pg. 13, 4th text blk) below a (“set”, pg. 13, 4th txt blk) threshold (“value”, pg. 13 4th txt blk) proportion (or “probability”67, pg. 13, 4th blk) of a total (“pixel”, pg. 9, 4th txt blk) number of the plurality of training candidates68 (or trained candidate objects via “trained” “candidate lung nodule image information”69, pg. 9, 4th txt blk).
Since Wei suggests the option of labeling training data, c.9,ll.1-5, one of skill in the art of labeling can make the combination of Wei’s be as Yin’s predictably recognizing the change providing an expected or looked forward to “detection scheme with higher accuracy”.
Claim 18 is rejected similar to claim 6:
18 (Original). The system of claim 17, wherein the at least a portion of the labeled media content is below a threshold proportion of a total number of the plurality of training candidates.
Claim(s) 9 and 21 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Gasper et al. (US 2022/0327119 A1):
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Re 9. (Previuosly presented), the combination of Wei and de Silva, Kopparapu teaches not The method of claim 1, further comprising:
enriching the subset (i.e., any one subset) of first media content based on the plurality of second (defect-detector) predictions (146) to create a set of enriched media content; and
sending (via fig. 1: arrows) the set of enriched media content to be used for populating a dashboard (“dashed borders”, c.23,ll. 25-30).
The combination of Wei and de Silva, Kopparapu does not teach the difference of claim 9 of:
--enriching the subset …based on the plurality of second predictions to create a set of enriched media content; and
sending the set of enriched media content to be used for populating a dashboard--.
Gasper teaches the difference of claim 9:
9. The method of claim 1, further comprising:
enriching the (“attribute” [0051]) subset of first media content based on the plurality of second (“match group” [0052] 3rd S) predictions to create a set (or “generate…datasets and portions thereof” [0052] 1st S & last S: fig. 1:106: “Content Graph Portion”) of enriched media content (“as enrichment data 147b” [0053] 4th S: figs. 1,13:147b,1347b: “Enrichment Data”); and
sending the set (106) of enriched (“YouTube” “video” [0043]) media content to be used for populating a dashboard (fig. 13:1313: “Dashboard/Visualization Data”).
MPEP 2143 Examples of Basic Requirements of a Prima Facie Case of Obviousness [R-01.2024]
I. EXAMPLES OF RATIONALES
F. Known Work in One Field of Endeavor May Prompt Variations of It for Use in Either the Same Field or a Different One Based on Design Incentives or Other Market Forces if the Variations Are Predictable to One of Ordinary Skill in the Art
To reject a claim (9) based on this rationale, Office personnel must resolve the Graham factual inquiries. Then, Office personnel must articulate the following:
(1) a finding that the scope and content of the prior art [Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1), Kopparapu as applied to claims (1,2,3,4,7,8,11 and 12 and 13,14,15,16,19, 20, 23) above further in view of Gasper et al. (US 2022/0327119 A1)], whether in the same field (same content processing):
applicant’s disclosure [002]:
--TECHNICAL FIELD
[002] The present disclosure relates generally to processing content such as images using machine learning, and more specifically to training and using machine learning models for tracking custom objects.—
Wei (US 11,445,168):
--BACKGROUND
Generally described, computing devices utilize a communication network, or a series of communication networks, to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as “data centers,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public. Service providers or content creators (such as businesses, artists, media distribution services, etc.) can employ one or more data centers to deliver content (such as web sites, web content, or other digital data) to users or clients.
Gasper:
FIELD
[0002] Various embodiments relate generally to data science and data analysis, computer software and systems, and data-driven control systems and algorithms based on graph-based data arrangements, among other things, and, more specifically, to a computing platform configured to receive and analyze datasets to implement a data model with which to identify relevant data catalog data derived from graph-based data arrangements, whereby a processor may be configured to cause implementation of subsets of programmatic executable instructions based on relevant data catalog70 data to perform an action (e.g., autonomously, semi-autonomously, or manually), at least one example of which may be configured to generate a concept interface portion relevant to a concept of interest, among other things.
of endeavor as that of the applicant’s invention or a different field of endeavor, included a similar or analogous (machine learning) device (method, or product);
(2) a finding that there were design incentives or market forces which would have prompted adaptation of the known (machine learning) device (method, or product):
Wei (US 11,445,168), c.1, ll.5-25: a business:
--BACKGROUND
Generally described, computing devices utilize a communication network, or a series of communication networks, to exchange data. Companies and organizations operate computer networks that interconnect a number of computing devices to support operations or provide services to third parties. The computing systems can be located in a single geographic location or located in multiple, distinct geographic locations (e.g., interconnected via private or public communication networks). Specifically, data centers or data processing centers, herein generally referred to as “data centers,” may include a number of interconnected computing systems to provide computing resources to users of the data center. The data centers may be private data centers operated on behalf of an organization or public data centers operated on behalf, or for the benefit of, the general public. Service providers or content creators (such as businesses, artists, media distribution services, etc.) can employ one or more data centers to deliver content (such as web sites, web content, or other digital data) to users or clients.
Gasper (US 2022/0327119 A1): marketing:
--[0004] While conventional approaches are functional, various approaches are not well-suited to significantly overcome the difficulties of data silos. Organizations, including enterprises, continue strive to understand, manage, and productively use large amounts of enterprise data. For example, consumers of enterprise organizations have different levels of skill and experience in using analytic data tools. Data scientists typically create complex data models using sophisticated analysis application tools, whereas other individuals, such as executives, marketing personnel, product managers, etc., have varying levels of skill, roles, and responsibilities in an organization. The disparities in various analytic data tools, reporting tools, visualization tools, etc., continue to frustrate efforts to improve interoperability and usage of large amounts of data.--;
(3) a finding that the differences between the claimed invention and the prior art were encompassed in known variations or in a principle known in the prior art:
Gasper (US 2022/0327119 A1) teaches the difference of claim 9:
9. The method of claim 1, further comprising:
enriching the (“attribute” [0051]) subset of first media content based on the plurality of second (“match group” [0052] 3rd S) predictions to create a set (or “generate …datasets and portions thereof” [0052] 1st S & last S: fig. 1:106: “Content Graph Portion”) of enriched media content (“as enrichment data 147b” [0053] 4th S: figs. 1,13:147b,1347b: “Enrichment Data”); and
sending the set (106) of enriched (“YouTube” “video” [0043]) media content to be used for populating a dashboard (fig. 13:1313: “Dashboard/Visualization Data”).;
(4) a finding that one of ordinary skill in the art, in view of the identified design incentives (said a business and marketing) or other market forces, could have implemented the claimed variation (of claim 9) of the prior art (Gasper (US 2022/0327119 A1)) by making Wei’s fig. 6:610: video-on-demand “VIRTUALIZATION SERVICES” be as Gasper’s fig. 13:1305: video-“content”, Gasper: [0045] 1st S, “Cloud Service(s)”:
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, and the claimed variation (of claim 9) would have been predictable to one of ordinary skill in the art (of machine learning by expecting improved “accuracy”, Gasper [0203] last S, of classification of dashboard data: Gaspar: figs. 1:134a,13:1330: “Classifier”, “Dataset Ingestion Controller”); and
(5) whatever additional findings (no other additional finding are presented) based on the Graham factual inquiries may be necessary, in view of the facts of the case under consideration, to explain a conclusion of obviousness.
The rationale to support a conclusion that the claimed invention would have been obvious is that design incentives or other market forces could have prompted one of ordinary skill in the art to vary the prior art in a predictable manner to result in the claimed invention. If any of these findings cannot be made, then this rationale cannot be used to support a conclusion that the claim would have been obvious to one of ordinary skill in the art.
Claim 21 is rejected similar to claim 9:
21 (Previuosly presented). The system of claim 13, wherein the system is further configured to:
enrich the subset of first media content based on the plurality of second predictions to create a set of enriched media content; and
send the set of enriched media content to be used for populating a dashboard.
Claim(s) 10 and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wei et al. (US 11,445,168 B1) in view of da Silva et al. (US 2024/0111868 A1) further in view of Kopparapu et al. (US 2022/0207875 A1), as applied in claims 24,25,26, as applied in claims 1,2,3,4,7,8,11 and 12 and 13,14,15,16,19,20,23 further in view of Rogers et al. (US 2021/0117859 A1):
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Re 10. (Previuosly presented), the combination of Wei and de Silva, Kopparapu teaches not The method of claim 1, wherein the first machine learning model (142) is applied by an edge device deployed locally with respect to a source (“video”,c.3,ll.15-20) of the (provider’s 134) media content, wherein the second machine learning model (146) is deployed remotely from the edge device.
The combination of Wei and de Silva, Kopparapu does not teach the difference of claim 10:
--edge device deployed locally with respect to…deployed remotely from the edge device--.
Rogers teaches the difference of claim 10:
10. The method of claim 1, wherein the first machine learning model (fig. 1:144: “Models”) is applied by an edge device (fig. 1:120: “Edge Server”) deployed (via “deploying a model” [0037] 1st S) locally (“obtained…and stored in a local directory” [0037] 3rd S) with respect to a source of the media content (fig. 1:160: “Media Source”), wherein the second machine learning model (fig. 1:128,144: “DL Model”, “Models”) is deployed remotely (via “a remote model store” [0038] 5th S: fig. 1:142: “Model Store”) from the edge device (120).
Since Wei suggests other “deployment ser-vices”, c.7,ll. 5-25, one of skill in the art of deployments can make the combination of Wei’s be as Rogers’ predictably recognizing the change reducing “latency”, Rogers [0020] last S.
Claim 22 is rejected similar to claim 10:
22 (Previuosly presented). The system of claim 13, wherein the first machine learning model is applied by an edge device deployed locally with respect to a source of the media content, wherein the second machine learning model is deployed remotely from the edge device.
Conclusion
The prior art “nearest to the subject matter defined in the claims” (MPEP 707.05) made of record and not relied upon is considered pertinent to applicant's disclosure.
The following table lists several references that are relevant to the subject matter claimed and disclosed in this Application. The references are not relied on by the Examiner, but are provided to assist the Applicant in responding to this Office action:
Citation
Relevance
Yan et al. (US 11,200,497 B1)
Yan teaches “machine learning model…training…from a…domain”, c.4,ll.15-20:
“To address the above problems, a machine learning model may be first trained based on general-purpose training data, and then fine-tuned using the limited training data collected from a task-specific domain.”
as the closest to the claimed “the advanced machine learning model is trained on a first domain” of claim 24.
CHI et al. (US 2024/0046107 A1)
CHI teaches “domain”-“subsets…as…source domain… partitioned…splits”:
[0008] In some embodiments, said obtaining the set of training samples from the one or more domains comprises: obtaining the set of training samples from a plurality of domains, the set of training samples comprises a plurality of subsets of training samples obtained from the plurality of domains; said using the set of training samples to query the plurality of AI models comprises: using each subset of training samples to query the plurality of AI models except an excluded AI model of the plurality of AI models; and the excluded AI models of the plurality of subset of training samples are different AI models.
[0131] The impact of private data is also tested. To simulate an environment as shown in FIG. 17, the training source domains are partitioned into two splits: private domains (custom-character.sup.pri) and public domains (custom-character.sup.pub). The private domains custom-character.sup.pri are used to train MoE models and the public domains custom-character.sup.pub are used for the subsequent meta-training. Since ARM and other methods only utilize the data as input, they are trained on custom-character.sup.pub. The testing is conducted to evaluate the impact of privacy regulations. Table 4 shows the testing results.
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as the closest to the claimed “the second domain is a subset of the first domain” of claim 24.
Geng et al. (Domain adaptive boosting method and its applications)
Geng teaches a target domain is subset of source domain, pg. 023038-03, section 3.1 Source-Domain Clustering, 1st para, 5th S:
“The left of the figure means that the target domain is a subset of the source domain, e.g., domain adaptation for personality.”
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as the closest to the claimed “the second domain is a subset of the first domain” of claim 24.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS ROSARIO whose telephone number is (571)272-7397. The examiner can normally be reached Monday-Friday, 9AM-5PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Henok Shiferaw can be reached at 571-272-4637. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/DENNIS ROSARIO/Examiner, Art Unit 2676
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
1 machine learning: Computers, Digital Technology. the capacity of a computer to process and evaluate data beyond programmed algorithms, through contextualized inference (often used attributively). (Dictionary.com); the strike-thru of the alternative “
2 output: Computers. to transfer (information) from internal storage to an external medium, wherein storage is defined: Computers. memory ( def 11 ), wherein memory is defined: Also called computer memory, Computers. the capacity of a computer to store information subject to recall (Dictionary.com)
3 machine learning Computers, Digital Technology. the capacity of a computer to process and evaluate data beyond programmed algorithms, through contextualized inference (often used attributively).
4 underlying detection function y(x)=y(configured), wherein x=configured is a single act and thus not acts
5 underlying detection function y(x)=y(trained), wherein x=trained is a single act and thus not acts
6 underlying detection function y(x)=y(trained), wherein x=trained is a single act and thus not acts
7 underlying detection function y(x)=y(trained), wherein x=trained is a single act and thus not acts
8 proper: Mathematics. (of a subset of a set) not equal to the whole set. (Dictinary.com)
9 each: every one of two or more considered individually or one by one. (Dictionary.com)
10 is: 3rd person singular present indicative of be, wherein be is defined: (used as a copula to connect the subject with its predicate adjective, or predicate nominative, in order to describe, identify, or amplify the subject). (Dictioanry.com)
11 another:1 being one more or more of the same; further; additional. 2 different; distinct; of a different period, place, or kind. (Dictionary.com)
12 BROAD CLAIM LANGUAGE: first: being before all others with respect to time, order, rank, importance, etc., used as the ordinal number of one, where etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
13 set: Mathematics. a collection of objects or elements classed together, wherein classed VERB (USED WITH OBJECT) is defined: to place or arrange in a class; classify, wherein place VERB (USED WITH OBJECT) is defined: to put or set in a particular place, position, situation, or relation, wherein place is defined: space in general, wherein space is defined: the unlimited or incalculably great three-dimensional realm or expanse in which all material objects are located and all events occur, wherein realm is defined: the region, sphere, or domain within which anything occurs, prevails, or dominates. . (Dictionary.com)
14 another:
1 being one more or more of the same; further; additional.
2 different; distinct; of a different period, place, or kind. (Dictionary.com)
15 BROAD CLAIM LANGUAGE: first: being before all others with respect to time, order, rank, importance, etc., used as the ordinal number of one, where etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
16 other: additional or further (Dictionary.com).
17 BROAD CLAIM LANGUAGE: first: being before all others with respect to time, order, rank, importance, etc., used as the ordinal number of one, where etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
18 another:
1 being one more or more of the same; further; additional.
2 different; distinct; of a different period, place, or kind. (Dictionary.com)
19 BROAD CLAIM LANGUAGE: first: being before all others with respect to time, order, rank, importance, etc., used as the ordinal number of one, where etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
20 identically: Derived word form of identical (Dictionary.com)
21 MPEP 2131 Anticipation — Application of 35 U.S.C. 102 [R-08.2017], 2nd para, 2nd to last S:
The elements must be arranged as required by the claim, but this is not an ipsissimis verbis test, i.e., identity of terminology is not required. In re Bond, 910 F.2d 831, 15 USPQ2d 1566 (Fed. Cir. 1990),
wherein arranged is defined: to place in proper, desired, or convenient order; adjust properly, wherein adjust is defined: to change (something) so that it fits, corresponds, or conforms; adapt; accommodate, wherein change is defined: to substitute another or others for; exchange for something else, usually of the same kind, wherein substitute is defined: to take the place of; replace, wherein replace is defined: to assume the former role, position, or function of; substitute for (a person or thing); and
wherein identity is defined: Logic. an assertion that two terms refer to the same thing, wherein same is defined: identical with what is about to be or has just been mentioned. (Dictionary.com)
22 “the claimed invention as a whole” is further discussed below in the section THE CLAIMED INVENTION AS A WHOLE in the rejection of claim 24
23 MPEP 2111.02 Effect of Preamble [R-07.2022]
The determination of whether a preamble limits a claim [claim 24 is limited by the preamble’s “for visual content processing”] is made on a case-by-case basis in light of the facts in each case; there is no litmus test defining when a preamble limits the scope of a claim. Catalina Mktg. Int’l v. Coolsavings.com, Inc., 289 F.3d 801, 808, 62 USPQ2d 1781, 1785 (Fed. Cir. 2002). See id. at 808-10, 62 USPQ2d at 1784-86 for a discussion of guideposts that have emerged from various decisions exploring the preamble’s effect on claim scope, as well as a hypothetical example illustrating these principles
24 BROAD CLAIM LANGUAGE: -ing (of “comprising”): a suffix of nouns formed from verbs, expressing the action of the verb or its result, product, material, etc. (the art of building; a new building; cotton wadding ), wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted)., wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
25 Struck-out “” (not a deletion) does not limit the scope of a claim [claim 24] under the broadest reasonable claim interpretation” via MPEP 2143.03 All Claim Limitations Must Be Considered [R-01.2024], 3rd para:
As a general matter, the grammar (e.g., coordinate adjectives [Nordquist (Coordinate Adjectives: Definition and Examples)], wherein coordinate is defined: Grammar. of the same rank in grammatical construction, as Jack and Jill in the phrase Jack and Jill, or got up and shook hands in the sentence He got up and shook hands. (Dictionary.com)) and ordinary meaning of terms as understood by one having ordinary skill in the art used in a claim will dictate whether, and to what extent, the language (e.g., coordinate adjectives: “custom machine learning”) limits the claim scope (i.e., “model”). Language (“custom machine learning model”) that suggests or makes a feature or step optional (coordinate adjectives is alternative language meaning that each of “custom” AND “machine learning” coordinates with the other (such as Jack AND Jill went up the hill, To fetch a pail of water) equally and individually modify “model”) but does not require that feature or step does not limit the scope of a claim under the broadest reasonable claim interpretation. In addition, when a claim requires selection of an element from a list of alternatives (“custom” AND “machine learning”), the prior art teaches the element if one of the alternatives is taught by the prior art. See, e.g., Fresenius USA, Inc. v. Baxter Int’l, Inc., 582 F.3d 1288, 1298, 92 USPQ2d 1163, 1171 (Fed. Cir. 2009).
26 train: intr to do exercises and prepare for a specific purpose the athlete trained for the Olympics; wherein exercise is defined: noun a set of movements, questions, tasks, etc, designed to train, improve, or test one's ability in a particular field, wherein train is defined: tr to guide or teach (to do something), as by subjecting to various exercises or experiences (Dictionary.com)
27extract: to take or copy out (matter), as from a book, wherein take is defined: (of a person or thing) to win favor or acceptance, wherein favor is defined: preferential treatment (Dictionary.com)
28 CLAIM SCOPE regarding the claimed “on” via applicant’s disclosure: (not to limit the scope of any and all aspects):
--[007] A summary of several example embodiments of the disclosure follows. This summary is provided for the convenience of the reader to provide a basic understanding of such embodiments and does not wholly define the breadth of the disclosure. This summary is not an extensive overview of all contemplated embodiments, and is intended to neither identify key or critical elements of all embodiments nor to delineate the scope of any or all aspects. Its sole purpose is to present some concepts of one or more embodiments in a simplified form as a prelude to the more detailed description that is presented later. For convenience, the term "some embodiments" or "certain embodiments" may be used herein to refer to a single embodiment or multiple embodiments of the disclosure.—:
wherein nor is defined: (used to continue the force of a negative, as not, no, never, etc., occurring in a preceding clause). (Dictionary.com)
wherein delineate is defined: to portray in words; describe or outline with precision, wherein outline is defined: the line by which a figure or object is defined or bounded; contour, wherein bounded is defined: having bounds or limits,
where scope is defined: Linguistics, Logic. the range of words or elements of an expression (claim 24) over which a modifier (e.g., a patent examiner) or operator (e.g., me) has control. (Dictionary.com) .
29 In view of said above footnote--CLAIM SCOPE regarding the claimed “on”--, “on” is defined: in connection, association, or cooperation with; as a part or element of. (Dictionary.com) and thus is “taken” under the broadest reasonable interpretation via MPEP 2111 III. "PLAIN MEANING" REFERS TO THE ORDINARY AND CUSTOMARY MEANING GIVEN TO THE TERM BY THOSE OF ORDINARY SKILL IN THE ART , 4th para 1st two Ss:
Any meaning of a claim term (“on”) taken from the prior art must be consistent with the use of the claim term in the specification and drawings (applicant’s figures 1-8: line/arrow connections). Moreover, when the specification is clear about the scope and content of a claim term (“on”), there is no need to turn to extrinsic evidence (Dictionary.com) for claim interpretation.
30 set: Mathematics. a collection of objects or elements classed together, wherein classed VERB (USED WITH OBJECT) is defined: to place or arrange in a class; classify, wherein place VERB (USED WITH OBJECT) is defined: to put or set in a particular place, position, situation, or relation, wherein place is defined: space in general, wherein space is defined: the unlimited or incalculably great three-dimensional realm or expanse in which all material objects are located and all events occur, wherein realm is defined: the region, sphere, or domain within which anything occurs, prevails, or dominates. . (Dictionary.com)
31 proper: Mathematics. (of a subset of a set) not equal to the whole set. (Dictionary.com)
32 BROAD CLAIM LANGUGE: domain: a region characterized by a specific feature, type of growth or wildlife, etc., wherein region is defined: an area of interest, activity, pursuit, etc.; field, wherein area is defined: extent, range, or scope, wherein etc. is defined: and others; and so forth; and so on (used to indicate that more of the same sort or class might have been mentioned, but for brevity have been omitted), wherein so is defined: likewise or correspondingly; also; too, wherein forth is defined: out, as from concealment or inaction; into view or consideration. (Dictionary.com)
33 base VERB (tr foll by on or upon) to use as a basis (for); found (on) (Dictionary.com)
34 In view of said above footnote--CLAIM SCOPE regarding the claimed “on”--, “on” is defined: in connection, association, or cooperation with; as a part or element of. (Dictionary.com) and thus is “taken” under the broadest reasonable interpretation via MPEP 2111 III. "PLAIN MEANING" REFERS TO THE ORDINARY AND CUSTOMARY MEANING GIVEN TO THE TERM BY THOSE OF ORDINARY SKILL IN THE ART , 4th para 1st two Ss:
Any meaning of a claim term (“on”) taken from the prior art must be consistent with the use of the claim term in the specification and drawings (applicant’s figures 1-8: line/arrow connections). Moreover, when the specification is clear about the scope and content of a claim term (“on”), there is no need to turn to extrinsic evidence (Dictionary.com) for claim interpretation.
35 The claimed “subset” is very suggestive of being a subset of the claimed “set of features”; however, claim 24 does not claim that the claimed “subset” is a subset of the claimed “set of features”.
36 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com): this semantic sense of “on” is “taken” under the broadest reasonable interpretation via MPEP 2111 III. "PLAIN MEANING" REFERS TO THE ORDINARY AND CUSTOMARY MEANING GIVEN TO THE TERM BY THOSE OF ORDINARY SKILL IN THE ART , 4th para 1st two Ss:
Any meaning of a claim term (“on”) taken from the prior art must be consistent with the use of the claim term in the specification and drawings (figures 1-8: line/arrow connections). Moreover, when the specification is clear about the scope and content of a claim term (“on”), there is no need to turn to extrinsic evidence (Dictionary.com) for claim interpretation.
37 THE CLAIMED INVENTION AS A WHOLE:
The problem is via applicant’s disclosure:
[003] With the rapid adoption of computerized monitoring technologies, the amount of media content being captured and processed has exploded in recent years. With this explosion of media content that needs to be analyzed and processed, the need for efficient ways to process this vast amount of data is more acute than ever. Video monitoring technologies are being used for many different purposes, such as home monitoring (e.g., video doorbells which watch for activity outside of a door), vehicle monitoring (e.g., systems which monitor video for parking or other vehicle-related violations), hospitality (e.g., video monitoring inside of hotels), and many more. Although software can be installed locally where the media content is captured, processing such large amounts of media content presents a challenge and not all sites are equipped with the computing resources to handle such processing.
The solution is:
[0029]The disclosed embodiments may be utilized for image processing in situations where a large amount of image data is continuously collected in order to perform basic analysis of those images using the custom model deployed at an edge device and to only perform advanced analyses of the images when the basic analysis yields predictions indicating that those images are potentially interesting, for example, when the custom model outputs predictions indicating that certain images show the custom object. To this end, in accordance with various disclosed embodiments, the custom model may be trained on a subset of the feature domain used by the teacher models such that applying the custom model created as described herein requires less processing than applying teacher models or other more advanced models. Accordingly, the disclosed embodiments can reduce total processing of content by limiting the amount of analysis performed using heavier models (e.g., models which have larger feature domains, more granular analysis, more kinds of outputs, otherwise require more processing, etc.) as enabled by a lighter model.
Factual Inquiry 4: Considering objective evidence present in the application indicating obviousness: The lack in claim 24 of [0029]’s-- limiting the amount of analysis performed using heavier models (e.g., models which have larger feature domains, more granular analysis, more kinds of outputs, otherwise require more processing, etc.) as enabled by a lighter model.—is an indication of obviousness.
38 (italics) represent claim limitations already taught
39 ellipses (…) represent claim limitations already taught
40 (italics) represent claim limitations already taught
41 on: in connection, association, or cooperation with; as a part or element of. (Dictionary.com): this semantic sense of “on” is “taken” under the broadest reasonable interpretation via MPEP 2111 III. "PLAIN MEANING" REFERS TO THE ORDINARY AND CUSTOMARY MEANING GIVEN TO THE TERM BY THOSE OF ORDINARY SKILL IN THE ART , 4th para 1st two Ss:
Any meaning of a claim term (“on”) taken from the prior art must be consistent with the use of the claim term in the specification and drawings (figures 1-8: line/arrow connections). Moreover, when the specification is clear about the scope and content of a claim term (“on”), there is no need to turn to extrinsic evidence (Dictionary.com) for claim interpretation.
42 ellipses (…) represent claim limitations already taught
43 with: at the same time as or immediately after; upon, wherein upon is defined: on (in any of various senses, used as an equivalent of on with no added idea of ascent or elevation, and preferred in certain cases only for euphonic or metrical reasons). (Dictionary.com).
44 with: at the same time as or immediately after; upon, wherein upon is defined: on (in any of various senses, used as an equivalent of on with no added idea of ascent or elevation, and preferred in certain cases only for euphonic or metrical reasons). (Dictionary.com).
45 with: at the same time as or immediately after; upon, wherein upon is defined: on (in any of various senses, used as an equivalent of on with no added idea of ascent or elevation, and preferred in certain cases only for euphonic or metrical reasons). (Dictionary.com).
46 since: because; inasmuch as, wherein because is defined: for the reason that; due to the fact that. (Dictionary.com)
47 label: a word or phrase indicating that what follows belongs in a particular category or classification (Dictionary.com)
48 label: a piece of paper, card, or other material attached to an object (or defect) to identify it or give instructions or details concerning its ownership, use, nature, destination, etc; tag (Dictionary.com)
49 extract (used with object): to take or copy out (matter), as from a book, wherein take is defined: to pick from a number; select: (Dictionary.com)
50 student: any person who studies, investigates, or examines thoughtfully, wherein person is defined: an individual of distinction or importance, wherein individual is defined: a distinct, indivisible entity; a single thing, being, instance, or item (Dictionary.com)
51parameter: Statistics. a variable entering into the mathematical form of any distribution such that the possible values of the variable correspond to different distributions, wherein value is defined: Mathematics. magnitude; quantity; number represented by a figure, symbol, or the like, wherein quantity is defined: an exact or specified amount or measure, wherein measure is defined: (7) any standard of comparison, estimation, or judgment (Dictionary.com)
52 i.e., subset set
53 (italics) represent claim limitations already taught
54 label: to mark with a label, wherein mark is defined: tr to select, designate, or doom by or as if by a mark (Dictonary.com)
55 video: a program, movie, or other visual media product featuring moving images, with or without audio, that is recorded and saved digitally or on videocassette (Dictionary.com)
56 Applicant’s disclosure: OK:
[0098]The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units ("CPUs"), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit Page 23 of and a printing unit. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal.
57 “subset” is part of applicant’s disclosed large data solution, but this “subset” is not clearly in the context of training.
58 “subset” is part of applicant’s disclosed large data solution, but this “subset” is not clearly in the context of training.
59 “subset” is part of applicant’s disclosed large data solution, but this “subset” is not clearly in the context of training.
60 “subset” is part of applicant’s disclosed large data solution, but this “subset” is not clearly in the context of training.
61 “subset” is part of applicant’s disclosed large data solution, but this “subset” is not clearly in the context of training.
62 deep learning: an advanced type of machine learning that uses multilayered neural networks to establish nested hierarchical models for data processing and analysis, as in image recognition or natural language processing, with the goal of self-directed information processing. (Dictionary.com)
63 Applicant’s disclosure:
[0027]The disclosed embodiments allow for custom defining objects which may not be represented in the initial training data used to initially configure the teacher machine learning model. This, in turn, allows users to define custom classes to be utilized by machine learning models without requiring explicit programming or otherwise providing a large amount of text for training. Moreover, various disclosed embodiments provide techniques which obtain content to be used for training which does not require multiple initial samples showing the custom-defined objects in order to train the resulting machine learning models to accurately identify the custom-defined objects.
64 Applicant’s disclosure:
[0027]The disclosed embodiments allow for custom defining objects which may not be represented in the initial training data used to initially configure the teacher machine learning model. This, in turn, allows users to define custom classes to be utilized by machine learning models without requiring explicit programming or otherwise providing a large amount of text for training. Moreover, various disclosed embodiments provide techniques which obtain content to be used for training which does not require multiple initial samples showing the custom-defined objects in order to train the resulting machine learning models to accurately identify the custom-defined objects.
65 pixel: Computers, Television. the smallest element of an image that can be individually processed in a video display system. (Dictionary.com)
66 mark: to furnish with figures, signs, tags, etc., to indicate price, quality, brand name, or the like, wherein tags is defined: a descriptive word or phrase applied to a person, group, organization, etc., as a label or means of identification (Dictionary.com)
67probability: statistics a measure or estimate of the degree of confidence one may have in the occurrence of an event, measured on a scale from zero (impossibility) to one (certainty). It may be defined as the proportion of favourable outcomes to the total number of possibilities if these are indifferent (mathematical probability ), or the proportion observed in a sample ( empirical probability ), or the limit of this as the sample size tends to infinity ( relative frequency ), or by more subjective criteria ( subjective probability ) (Dictionary.com)
68 candidate: a person or thing regarded as suitable or likely for a particular fate or position, wherein thing is defined: an object, fact, affair, circumstance, or concept considered as being a separate entity (Dictionary.com)
69 information Computers. important or useful facts obtained as output from a computer by means of processing input data with a program, wherein facts is defined: something known to exist or to have happened, wherein thing is defined: a material object without life or consciousness; an inanimate object (Dictionary.com)
70 catalog: a list of the contents of a library or a group of libraries, arranged according to any of various systems. (Dictionary.com)
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