DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 11/19/2024, 02/25/2025, and 06/09/2026 have been considered by the examiner.
Claim Objections
Claim 11 is objected to because of the following informalities:
In claim 11, line 5, “storing in a memory of the computing device…” should read “storing in a memory of [[the]] a computing device…”.
Appropriate correction is required.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
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 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 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 function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
Claims 1, 10-12, 19, and 21 recite limitations that use words like “means” (or “step”) or similar terms with functional language and do invoke 35 U.S.C. 112(f):
Claim 1, 11; recite the limitation, “executing by a processing unit…,” [Lines 6, 8].
Claim 10; recites the limitation, “executing by the processing unit…,” [Line 1].
Claim 12; recites the limitation, “a processing unit for: determining…,” [Line 3].
Claim 19; recites the limitation, “processing by the processing unit…,” [Line 2].
Claim 21; recites the limitation, “the processing unit executes…,” [Line 1].
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
After a careful analysis, as disclosed above, and a careful review of the specification the following limitations in claim 9:
“processing unit” (Fig. 2, #310 called processing unit, Paragraph [0047] – “The computing device 300 comprises a processing unit 310. The processing unit 310 comprises one or more processor (not represented in Figure 2 for simplification purposes) capable of executing instructions of computer program(s) for implementing functionalities of the computing device 300 (e.g. receiving data, processing the received data, generating data, transmitting the generated data, etc.). Each processor may further have one or more core.” Thus, a processing unit does have sufficient structure associated with it wherein it is a processor.
If applicant does not intend 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 avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 101
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 1-21 are rejected under 35 U.S.C. 101 because:
Regarding independent claim 1 and its dependent claims 2-10,
Step 1 Analysis: Claim 1 is directed to a process (method), which falls within the four statutory categories.
Step 2A Prong 1 Analysis: Claim 1 recites, in part:
“determining a content approval indicator based on inputs…”
“indicating whether a content of the at least one image is approved…”
The limitations as drafted above, are processes that, under broadest reasonable interpretation (BRI) cover the performance of the limitation in the mind which falls within the “mental processes” grouping of abstract ideas. The limitations above are steps, under BRI, that a human can also perform through mental processes such as observation, evaluation, judgement, and opinion, as it is merely reciting steps of approval of media content based on an input (ex: an image).
Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: This judicial exception is not integrated into a practical application. In particular, the claim recites the following additional elements –
“using machine learning for performing digital content approval…”
“storing in a memory of a computing device a model of a machine learning algorithm…”
“determining metadata related to at least one image…”
“executing by a processing unit of the computing device the machine learning algorithm…”
“the machine learning algorithm using the model for determining a content approval indicator based on inputs…”
“…the inputs comprising the metadata.”
The additional element of “using machine learning for performing digital content approval…” is part of the preamble reciting an intended use for a generic well-known neural network model recited at a high level of generality, without limiting further, in details, on how the neural network/machine learning model(s) function to arrive at such an outcome. The limitation of “determining metadata related to at least one image…” recites an insignificant extra-solution activity of data gathering. The limitations of “storing in a memory of a computing device a model of a machine learning algorithm…” and “executing by a processing unit of the computing device the machine learning algorithm…” are features of generic computers and computer components recited at a high level of generality to perform generic well-known functions such as a processor processing instructions stored in a memory, etc. The limitation of “the machine learning algorithm using the model for determining a content approval indicator based on inputs…” simply recite an intended use for a generic well-known neural network model at a high level of generality, without limiting further, in details, on how the neural network/machine learning model(s) function to arrive at such an outcome. The additional limitation of “…the inputs comprising the metadata” further recites a clause of merely further specification of the element which it depends on, therefore not an indication of an integration of the abstract ideas into a practical application nor considered significantly more.
These additional elements are recited as a mere attempt to implement the abstract ideas/judicial exceptions using generic neural network/machine learning models.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim as a whole is directed to an abstract idea. Please see MPEP §2106.04.(d).III.C.
Step 2B Analysis: there are no additional elements, such as for these additional elements as indicated above, that amount to significantly more than the judicial exception. Please see MPEP §2106.05. The claim is directed to an abstract idea.
For all of the foregoing reasons, claim 1 does not comply with the requirements of 35 USC 101.
Accordingly, the dependent claims 2-10 do not provide elements that overcome the deficiencies of the independent claim 1.
Claims 2-7 and 9 recite, in part, wherein clauses of merely further specification of the elements that they depend on, therefore, not an indication of an integration of the abstract ideas into a practical application not considered significantly more.
Claim 8 recites, in part, “the determination of the metadata comprises a processing of the at least one image” which is an insignificant extra-solution activity of data gathering; “with another machine learning algorithm” recites a generic well-known neural network model at a high level of generality, it is in the claim as a mere attempt to implement the abstract ideas/judicial exceptions using a generic neural network model without further limiting how, in detail, the model works to arrive at such an outcome.
Claim 10 recites, in part, “executing by the processing unit of the computing device” which are features of generic computer and computer components recited at a high level of generality to perform generic well-known functions such as a processor processing instruction stored in a memory, etc; “the machine learning algorithm with another model, the machine learning algorithm using the other model for determining a content category based on inputs” ” recites a generic well-known neural network model at a high level of generality, it is in the claim as a mere attempt to implement the abstract ideas/judicial exceptions using a generic neural network model without further limiting how, in detail, the model works to arrive at such an outcome; “the content category identifying a category of the content of the at least one image, the inputs comprising at least some of the metadata” further recites a clause of merely further specification of the element which it depends on, therefore not an indication of an integration of the abstract ideas into a practical application nor considered significantly more.
Accordingly, the dependent claims 2-10 are not patent eligible under 35 U.S.C. 101.
Regarding independent claim 11,
The independent claim 11 recites analogous limitations analogous limitations to the independent claim 1. Hence, these analogous limitations are not 35 U.S.C. 101 eligible for the reasons above in the claim 1 analysis. Furthermore, claim 11 recites some additional features such as “a non-transitory computer-readable medium storing instructions executable by a processing unit of a management platform, the execution of the instructions by the processing unit of the management platform providing for using machine learning…” which are features of generic computers and computer components recited at a high level of generality to perform generic well-known functions such as a processor processing instructions stored in a memory, etc.
Regarding independent claim 12,
The independent claim 12 recites analogous limitations analogous limitations to the independent claim 1. Hence, these analogous limitations are not 35 U.S.C. 101 eligible for the reasons above in the claim 1 analysis. Furthermore, claim 12 recites some additional features such as “computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit …” which are features of generic computers and computer components recited at a high level of generality to perform generic well-known functions such as a processor processing instructions stored in a memory, etc.
The dependent claims 13-21 each recite analogous limitations to the dependent claims 2-10, hence, these analogous limitations are not 35 U.S.C. eligible for the reasons in the analysis above. Accordingly, the dependent claims 13-21 do not provide elements that overcome the deficiencies of the independent claim 12.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of pre-AIA 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 3, 4, 10, 11, 12, 14, 15, and 21 are rejected under 35 U.S.C. 102(a)(1)/(a)(2) as being anticipated by RAO (US 20210174089 A1), hereinafter referenced as RAO.
Regarding claim 1, RAO teaches a method using machine learning for performing digital content approval (Fig. 4, Paragraph [0077] - RAO discloses FIG. 4 is a flow chart of an example process 400 for utilizing machine learning models to identify context of content for policy compliance determination.), the method comprising:
storing in a memory (Fig. 3, #330 called memory and/or #340 called storage component, Paragraph [0069] - RAO discloses device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340.)
of a computing device (Fig. 3, #300 called device, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. In some implementations, client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240 may include one or more devices 300 and/or one or more components of device 300.)
a model of a machine learning algorithm (Figs 3-4, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. Paragraph [0057] - RAO further discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. See also Paragraphs [0074-0075].);
determining metadata related to at least one image (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.);
and executing by a processing unit (Fig. 3, #320 called processor, Paragraph [0069] - RAO discloses device 300 may include a bus 310, a processor 320.)
of the computing device (Fig. 3, #300 called device, Paragraph [0074] - RAO discloses device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.)
the machine learning algorithm (Fig. 4, Paragraph [0022] - RAO discloses the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like. Paragraph [0080] - RAO discloses the device (e.g., using computing resource 224, processor 320, storage component 340, and/or the like) may process the image data, with an image analysis model, to determine an image compliance score associated with the image data, as described above.),
the machine learning algorithm using the model for determining a content approval indicator based on inputs (Fig. 1D, Paragraph [0022] - RAO discloses the image analysis model may determine an image compliance score associated with image data (e.g., which indicates whether an image is compliant or non-compliant), as described below. The image compliance score may be associated with a policy and with context of the image data. In some implementations, the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like.),
the content approval indicator indicating whether a content of the at least one image is approved (Fig. 1J, Paragraph [0036] - RAO discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. In some implementations, the policy may include a policy defined by an entity associated with the server device (e.g., a social media service provider) and may specify rules for content that is non-compliant (e.g., and should be rejected) and rules for content that is compliant (e.g., and should be approved).),
the inputs comprising the metadata (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like. Paragraph [0036] - RAO further discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. See also Paragraph [0057].).
Regarding claim 3, RAO teaches the method of claim 1,
RAO further teaches wherein the at least one image is part of a video (Fig. 4, Paragraph [0079] – RAO discloses process 400 may include identifying image data associated with the video data, wherein the image data corresponds to a frame of the video (block 410).).
Regarding claim 4, RAO teaches the method of claim 1,
RAO further teaches wherein the metadata comprise textual metadata (Fig. 1A, Paragraph [0014] – RAO discloses the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.).
Regarding claim 10, RAO teaches the method of claim 1,
RAO further teaches further comprising executing by the processing unit of the computing device (Fig. 3, #300 called device, Paragraph [0074] - RAO discloses device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.)
the machine learning algorithm with another model (Fig. 2, Paragraph [0057] – RAO discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. In some implementations, contextual analysis platform 220 may be designed to be modular such that certain software components may be swapped in or out depending on a particular need. Paragraph [0068] – RAO further discloses the number and arrangement of devices and networks shown in FIG. 2 are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in FIG. 2.),
the machine learning algorithm using the other model for determining a content category based on inputs (Fig. 1C, Paragraph [0019] – RAO further discloses contextual analysis platform may identify categories (e.g., scenarios and/or use cases that indicate non-compliant content) and context for the tags applied to the objects. The apriori model may identify context in images by determining associations in tags for a particular scenario (e.g., association of the tags child, alcohol, drinking implies non-compliance with respect to underage drinking). The apriori model may thus create a repository of context (e.g., or rules) for each of the categories (e.g., drink and drive, drinking and pregnancy, underage drinking, and/or the like).),
the content category identifying a category of the content of the at least one image (Fig. 1C, Paragraph [0019] – RAO discloses shown in FIG. 1C, and by reference number 115, the contextual analysis platform may process the tags applied to the objects, with a first machine learning model, to determine associations between the tags. The contextual analysis platform may identify categories (e.g., scenarios and/or use cases that indicate non-compliant content) and context for the tags applied to the objects. The apriori model may identify context in images by determining associations in tags for a particular scenario (e.g., association of the tags child, alcohol, drinking implies non-compliance with respect to underage drinking). The apriori model may thus create a repository of context (e.g., or rules) for each of the categories (e.g., drink and drive, drinking and pregnancy, underage drinking, and/or the like).),
the inputs comprising at least some of the metadata (Fig. 1A, Paragraph [0014] – RAO discloses as further shown in FIG. 1A, and by reference number 105, the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.).
Regarding claim 11, RAO teaches a non-transitory computer-readable medium (Fig. 3, #340 called storage component, Paragraph [0071] - RAO discloses storage component 340 stores information and/or software related to the operation and use of device 300. For example, storage component 340 may include a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optic disk, and/or a solid-state disk), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cartridge, a magnetic tape, and/or another type of non-transitory computer-readable medium, along with a corresponding drive.)
storing instructions executable by a processing unit of a management platform (Fig. 3, Paragraph [0074] - RAO discloses device 300 may perform these processes based on processor 320 [wherein processor 320 is a processing unit] executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.),
the execution of the instructions by the processing unit of the management platform providing for using machine learning to perform digital content approval (Figs 3-4, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. Paragraph [0057] - RAO further discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. See also Paragraphs [0074-0075].) by:
storing in a memory (Fig. 3, #330 called memory and/or #340 called storage component, Paragraph [0069] - RAO discloses device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340.)
of the computing device (Fig. 3, #300 called device, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. In some implementations, client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240 may include one or more devices 300 and/or one or more components of device 300.)
a model of a machine learning algorithm (Figs 3-4, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. Paragraph [0057] - RAO further discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. See also Paragraphs [0074-0075].);
determining metadata related to at least one image (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.);
and executing by a processing unit (Fig. 3, #320 called processor, Paragraph [0069] - RAO discloses device 300 may include a bus 310, a processor 320.)
of the computing device (Fig. 3, #300 called device, Paragraph [0074] - RAO discloses device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.)
the machine learning algorithm (Fig. 4, Paragraph [0022] - RAO discloses the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like. Paragraph [0080] - RAO discloses the device (e.g., using computing resource 224, processor 320, storage component 340, and/or the like) may process the image data, with an image analysis model, to determine an image compliance score associated with the image data, as described above.),
the machine learning algorithm using the model for determining a content approval indicator based on inputs (Fig. 1D, Paragraph [0022] - RAO discloses the image analysis model may determine an image compliance score associated with image data (e.g., which indicates whether an image is compliant or non-compliant), as described below. The image compliance score may be associated with a policy and with context of the image data. In some implementations, the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like.),
the content approval indicator indicating whether a content of the at least one image is approved (Fig. 1J, Paragraph [0036] - RAO discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. In some implementations, the policy may include a policy defined by an entity associated with the server device (e.g., a social media service provider) and may specify rules for content that is non-compliant (e.g., and should be rejected) and rules for content that is compliant (e.g., and should be approved).),
the inputs comprising the metadata (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like. Paragraph [0036] - RAO further discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. See also Paragraph [0057].).
Regarding claim 12, RAO teaches a computing device (Fig. 3, #300 called device, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. In some implementations, client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240 may include one or more devices 300 and/or one or more components of device 300. See also Paragraph [0067].) comprising:
memory (Fig. 3, #330 called memory and/or #340 called storage component, Paragraph [0069] - RAO discloses device 300 may correspond to client device 210, contextual analysis platform 220, computing resource 224, and/or server device 240. RAO further discloses device 300 may include a bus 310, a processor 320, a memory 330, a storage component 340.)
storing a model of a machine learning algorithm (Fig. 3, Paragraph [0057] - RAO discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. Paragraph [0023] - RAO discloses the contextual analysis platform may train the image analysis model with historical data (e.g., the determined associations between the tags) to generate a trained image analysis model.);
and a processing unit (Fig. 3, #320 called processor, Paragraph [0069] - RAO discloses device 300 may include a bus 310, a processor 320. Paragraph [0074] - RAO discloses device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.) for:
determining metadata related to at least one image (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.);
and executing the machine learning algorithm (Fig. 4, Paragraph [0022] - RAO discloses the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like. Paragraph [0080] - RAO discloses the device (e.g., using computing resource 224, processor 320, storage component 340, and/or the like) may process the image data, with an image analysis model, to determine an image compliance score associated with the image data, as described above.),
the machine learning algorithm using the model for determining a content approval indicator based on inputs (Fig. 4, Paragraph [0022] - RAO discloses the image analysis model may include a logistic regression model, a gradient boost model, a random forest model, a multinomial naïve Bayesian model, a neural network model, and/or the like. Paragraph [0080] - RAO discloses the device (e.g., using computing resource 224, processor 320, storage component 340, and/or the like) may process the image data, with an image analysis model, to determine an image compliance score associated with the image data, as described above.),
the content approval indicator indicating whether a content of the at least one image is approved (Fig. 1J, Paragraph [0036] - RAO discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. In some implementations, the policy may include a policy defined by an entity associated with the server device (e.g., a social media service provider) and may specify rules for content that is non-compliant (e.g., and should be rejected) and rules for content that is compliant (e.g., and should be approved).),
the inputs comprising the metadata (Fig. 1A, Paragraph [0014] - RAO discloses the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like. Paragraph [0036] - RAO further discloses the contextual analysis platform may process the image data, with the image analysis model, to determine an image compliance score associated with the image data, with a policy, and with context of the image data. See also Paragraph [0057].).
Regarding claim 14, RAO teaches the computing device of claim 12,
RAO further teaches wherein the at least one image is part of a video (Fig. 4, Paragraph [0079] – RAO discloses process 400 may include identifying image data associated with the video data, wherein the image data corresponds to a frame of the video (block 410).).
Regarding claim 15, RAO teaches the computing device of claim 12,
RAO further teaches wherein the metadata comprise textual metadata (Fig. 1A, Paragraph [0014] – RAO discloses the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.).
Regarding claim 21, RAO teaches the computing device of claim 12,
RAO further teaches wherein the processing unit (Fig. 3, Paragraph [0074] - RAO discloses device 300 may perform one or more processes described herein. Device 300 may perform these processes based on processor 320 executing software instructions stored by a non-transitory computer-readable medium, such as memory 330 and/or storage component 340.)
executes the machine learning algorithm with another model (Fig. 2, Paragraph [0057] – RAO discloses contextual analysis platform 220 includes one or more devices that utilize machine learning models to identify context of content for policy compliance determination. In some implementations, contextual analysis platform 220 may be designed to be modular such that certain software components may be swapped in or out depending on a particular need. Paragraph [0068] – RAO further discloses the number and arrangement of devices and networks shown in FIG. 2 are provided as an example. In practice, there may be additional devices and/or networks, fewer devices and/or networks, different devices and/or networks, or differently arranged devices and/or networks than those shown in FIG. 2.),
the machine learning algorithm using the other model for determining a content category based on inputs (Fig. 1C, Paragraph [0019] – RAO further discloses contextual analysis platform may identify categories (e.g., scenarios and/or use cases that indicate non-compliant content) and context for the tags applied to the objects. The apriori model may identify context in images by determining associations in tags for a particular scenario (e.g., association of the tags child, alcohol, drinking implies non-compliance with respect to underage drinking). The apriori model may thus create a repository of context (e.g., or rules) for each of the categories (e.g., drink and drive, drinking and pregnancy, underage drinking, and/or the like).),
the content category identifying a category of the content of the at least one image (Fig. 1C, Paragraph [0019] – RAO discloses shown in FIG. 1C, and by reference number 115, the contextual analysis platform may process the tags applied to the objects, with a first machine learning model, to determine associations between the tags. The contextual analysis platform may identify categories (e.g., scenarios and/or use cases that indicate non-compliant content) and context for the tags applied to the objects. The apriori model may identify context in images by determining associations in tags for a particular scenario (e.g., association of the tags child, alcohol, drinking implies non-compliance with respect to underage drinking). The apriori model may thus create a repository of context (e.g., or rules) for each of the categories (e.g., drink and drive, drinking and pregnancy, underage drinking, and/or the like).),
the inputs comprising at least some of the metadata (Fig. 1A, Paragraph [0014] – RAO discloses as further shown in FIG. 1A, and by reference number 105, the contextual analysis platform may receive, from the client devices and/or the server devices, historical image data that includes images, and historical text data that includes text. In some implementations, the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 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 identically disclosed as set forth in section 102 of this title, if the
differences between the claimed invention and the prior art are such that the claimed
invention as a whole 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.
Claims 2, 5, 8, 13, 16 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over RAO (US 20210174089 A1), hereinafter referenced as RAO in view of GABALE (US 20220129911 A1), hereinafter referenced as GABALE.
Regarding claim 2, RAO teaches a method of claim 1,
RAO fails to explicitly teach wherein the at least one image comprises a digital signage content.
However, GABALE explicitly teaches wherein the at least one image comprises a digital signage content (Fig. 1, Paragraph [0034] - GABALE discloses the media content includes at least one of an image of an asset, a video of an asset, a shelf brand display, a point of sale brand display, a digital advertisement display or an image, a video or a three-dimensional model of at least one of a physical retail store environment, a digital retail store environment, a virtual reality store environment, a social media environment or a web page environment.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having wherein the at least one image comprises a digital signage content.
Wherein RAO’s method wherein the at least one image comprises a digital signage content.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Regarding claim 5, RAO teaches the method of claim 4,
RAO further teaches wherein the textual metadata (Fig. 1A, Paragraph [0014] – RAO discloses the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.) comprise at least one of the following:
a word or a group of words represented in the at least one image (Fig. 1A, Paragraph [0017] – RAO discloses the contextual analysis platform may utilize one or more text processing techniques to preprocess the text metadata associated with the images of the historical image data and/or the text of the historical text data. The one or more text processing techniques may include a technique to convert text to lowercase, a technique to remove punctuation from text, a lemmatization technique, a stemming technique, a stop words removal technique, and/or the like.),
Although RAO teaches and a type of content represented in the at least one image (Fig. 1A, Paragraph [0015] – RAO discloses for each type of category violation (e.g., as defined by the one or more content policies), the contextual analysis platform may annotate the historical image data and/or the historical text data as compliant or non-compliant. In some implementations, for every image of the historical image data, the contextual analysis platform may retrieve tags that describe objects, scenes, context, and/or the like in the image based on computer vision techniques (e.g., an object detection technique, a scene recognition technique, and/or the like).).
RAO fails to explicitly teach a logo represented in the at least one image, a color used in the at least one image,
However, GABALE explicitly teaches a logo represented in the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses image capturing device 104 captures a media content from the environment. The brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.),
a color used in the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses image capturing device 104 captures a media content from the environment. The brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having a logo represented in the at least one image, a color used in the at least one image.
Wherein RAO’s method wherein the textual metadata comprise at least one of the following: a logo represented in the at least one image, a color used in the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Regarding claim 8, RAO teaches the method of claim 1,
RAO fails to explicitly teach wherein the determination of the metadata comprises a processing of the at least one image with another machine learning algorithm.
However, GABALE explicitly teaches wherein the determination of the metadata comprises a processing of the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses image capturing device 104 captures a media content from the environment.) with another machine learning algorithm (Fig. 1, Paragraph [0034] – GABALE discloses the brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having wherein the determination of the metadata comprises a processing of the at least one image with another machine learning algorithm.
Wherein RAO’s method wherein the determination of the metadata comprises a processing of the at least one image with another machine learning algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Regarding claim 13, RAO teaches the computing device of claim 12,
RAO fails to explicitly teach wherein the at least one image comprises a digital signage content.
However, GABALE explicitly teaches wherein the at least one image comprises a digital signage content (Fig. 1, Paragraph [0034] - GABALE discloses the media content includes at least one of an image of an asset, a video of an asset, a shelf brand display, a point of sale brand display, a digital advertisement display or an image, a video or a three-dimensional model of at least one of a physical retail store environment, a digital retail store environment, a virtual reality store environment, a social media environment or a web page environment.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having wherein the at least one image comprises a digital signage content.
Wherein RAO’s computing device wherein the at least one image comprises a digital signage content.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Regarding claim 16, RAO teaches the computing device of claim 15,
RAO further teaches wherein the textual metadata (Fig. 1A, Paragraph [0014] – RAO discloses the historical image data may include images associated with compliant content in accordance with one or more content policies, images associated with non-compliant content in accordance with the one or more content policies, text metadata associated with the images, and/or the like.) comprise at least one of the following:
a word or a group of words represented in the at least one image (Fig. 1A, Paragraph [0017] – RAO discloses the contextual analysis platform may utilize one or more text processing techniques to preprocess the text metadata associated with the images of the historical image data and/or the text of the historical text data. The one or more text processing techniques may include a technique to convert text to lowercase, a technique to remove punctuation from text, a lemmatization technique, a stemming technique, a stop words removal technique, and/or the like.),
Although RAO teaches and a type of content represented in the at least one image (Fig. 1A, Paragraph [0015] – RAO discloses for each type of category violation (e.g., as defined by the one or more content policies), the contextual analysis platform may annotate the historical image data and/or the historical text data as compliant or non-compliant. In some implementations, for every image of the historical image data, the contextual analysis platform may retrieve tags that describe objects, scenes, context, and/or the like in the image based on computer vision techniques (e.g., an object detection technique, a scene recognition technique, and/or the like).).
RAO fails to explicitly teach a logo represented in the at least one image, a color used in the at least one image,
However, GABALE explicitly teaches a logo represented in the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses image capturing device 104 captures a media content from the environment. The brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.),
a color used in the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses image capturing device 104 captures a media content from the environment. The brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having a logo represented in the at least one image, a color used in the at least one image.
Wherein RAO’s computing device wherein the textual metadata comprise at least one of the following: a logo represented in the at least one image, a color used in the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Regarding claim 19, RAO teaches the computing device of claim 12,
RAO fails to explicitly teach wherein the determination of the metadata comprises a processing by the processing unit of the at least one image with another machine learning algorithm.
However, GABALE explicitly teaches wherein the determination of the metadata comprises a processing by the processing unit of the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses brand visibility and compliance metric quantification system 106 includes a memory and a processor. The image capturing device 104 captures a media content from the environment. The brand visibility and compliance metric quantification system 106 generates a database of media content associated with the environment.)
with another machine learning algorithm (Fig. 1, Paragraph [0034] – GABALE discloses the brand visibility and compliance metric quantification system 106 determines at least one attribute of the at least one determined object associated with the brand within the environment using a deep neural networking model. The at least one object may include at least one of a brand name, a brand logo, a text, a product or a brand-specific object. The at least one attribute may include a color, a color contrast, a location of the object, a text size, or a number of words in the text.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having wherein the determination of the metadata comprises a processing by the processing unit of the at least one image with another machine learning algorithm.
Wherein RAO’s computing device wherein the determination of the metadata comprises a processing by the processing unit of the at least one image with another machine learning algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
Claims 6, 7, 17, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over RAO (US 20210174089 A1), hereinafter referenced as RAO in view of KUSCH (US 20240420262 A1), hereinafter referenced as KUSCH.
Regarding claim 6, RAO teaches the method of claim 4,
RAO fails to explicitly teach wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
However, KUSCH explicitly teaches wherein the model is a Natural Language Processing (NLP) model (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting. Paragraph [0038] – KUSCH further discloses any of the models described herein may use natural language processing.)
and the machine learning algorithm is an NLP algorithm (Fig. 1, Paragraph [0038] – KUSCH discloses any of the models described herein may use natural language processing. Statistical NLP combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of KUSCH of having wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
Wherein RAO’s method wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency and further optimizes complex tasks, since both RAO and KUSCH relate to methods and systems that utilize machine learning to assess documents and/or media, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and KUSCH relates generally to the field of applied Artificial Intelligence, and more specifically to the field of training and applying machine learning models, neural network models, deep learning models, or Natural Language Processing such as Large Language Models to contract and/or request for proposal prioritization and/or proposal drafting; unsupervised learning can leverage large amounts of unlabeled data, supervised learning can fine-tune the model for specific tasks with high precision, and reinforcement learning can optimize for complex, multi-step tasks where the best action depends on the context. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and KUSCH (US 20240420262 A1), Paragraph [0006, 0042].
Regarding claim 7, RAO in view of KUSCH teach the method of claim 6,
RAO fails to explicitly teach wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
However, KUSCH explicitly teaches wherein the model is a Large Language Model (LLM) (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting.)
and the machine learning algorithm is an LLM algorithm (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting. Paragraph [0025] – KUSCH further discloses process 100 may further include predicting the probability of winning a contract using the trained machine learning algorithm.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO in view of KUSCH of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of KUSCH of having wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
Wherein RAO’s method wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency and further optimizes complex tasks, since both RAO and KUSCH relate to methods and systems that utilize machine learning to assess documents and/or media, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and KUSCH relates generally to the field of applied Artificial Intelligence, and more specifically to the field of training and applying machine learning models, neural network models, deep learning models, or Natural Language Processing such as Large Language Models to contract and/or request for proposal prioritization and/or proposal drafting; unsupervised learning can leverage large amounts of unlabeled data, supervised learning can fine-tune the model for specific tasks with high precision, and reinforcement learning can optimize for complex, multi-step tasks where the best action depends on the context. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and KUSCH (US 20240420262 A1), Paragraph [0006, 0042].
Regarding claim 17, RAO teaches the computing device of claim 15,
RAO fails to explicitly teach wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
However, KUSCH explicitly teaches wherein the model is a Natural Language Processing (NLP) model (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting. Paragraph [0038] – KUSCH further discloses any of the models described herein may use natural language processing.)
and the machine learning algorithm is an NLP algorithm (Fig. 1, Paragraph [0038] – KUSCH discloses any of the models described herein may use natural language processing. Statistical NLP combines computer algorithms with machine learning and deep learning models to automatically extract, classify, and label elements of text and voice data and then assign a statistical likelihood to each possible meaning of those elements.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of KUSCH of having wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
Wherein RAO’s computing device wherein the model is a Natural Language Processing (NLP) model and the machine learning algorithm is an NLP algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency and further optimizes complex tasks, since both RAO and KUSCH relate to methods and systems that utilize machine learning to assess documents and/or media, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and KUSCH relates generally to the field of applied Artificial Intelligence, and more specifically to the field of training and applying machine learning models, neural network models, deep learning models, or Natural Language Processing such as Large Language Models to contract and/or request for proposal prioritization and/or proposal drafting; unsupervised learning can leverage large amounts of unlabeled data, supervised learning can fine-tune the model for specific tasks with high precision, and reinforcement learning can optimize for complex, multi-step tasks where the best action depends on the context. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and KUSCH (US 20240420262 A1), Paragraph [0006, 0042].
Regarding claim 18, RAO teaches the computing device of claim 17,
RAO fails to explicitly teach wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
However, KUSCH explicitly teaches wherein the model is a Large Language Model (LLM) (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting.)
and the machine learning algorithm is an LLM algorithm (Fig. 1, Paragraph [0011] – KUSCH discloses FIG. 1 illustrates a high-level embodiment of an example system that applies a first machine learning model, a first large language model (LLM), and a second LLM for contract prioritization and proposal drafting. Paragraph [0025] – KUSCH further discloses process 100 may further include predicting the probability of winning a contract using the trained machine learning algorithm.).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO in view of KUSCH of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of KUSCH of having wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
Wherein RAO’s computing device wherein the model is a Large Language Model (LLM) and the machine learning algorithm is an LLM algorithm.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency and further optimizes complex tasks, since both RAO and KUSCH relate to methods and systems that utilize machine learning to assess documents and/or media, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and KUSCH relates generally to the field of applied Artificial Intelligence, and more specifically to the field of training and applying machine learning models, neural network models, deep learning models, or Natural Language Processing such as Large Language Models to contract and/or request for proposal prioritization and/or proposal drafting; unsupervised learning can leverage large amounts of unlabeled data, supervised learning can fine-tune the model for specific tasks with high precision, and reinforcement learning can optimize for complex, multi-step tasks where the best action depends on the context. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and KUSCH (US 20240420262 A1), Paragraph [0006, 0042].
Claims 9 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over RAO (US 20210174089 A1), hereinafter referenced as RAO in view of GABALE (US 20220129911 A1), hereinafter referenced as GABALE, further in view of ARORA (US 20240037368 A1), hereinafter referenced as ARORA.
Regarding claim 9, RAO teaches the method of claim 1,
Although RAO further teaches wherein the inputs further comprise at least one of the following: data related to a time of display of the at least one image (Fig. 1M, Paragraph [0043] – RAO discloses the image data may be associated with a first timestamp (e.g., a one minute mark of the video), the text data may be associated a second timestamp, and the first timestamp of the image data may be within a threshold time period from the second timestamp of the text data.),
RAO fails to explicitly teach data related to a location of display of the at least one image,
However, GABALE explicitly teaches data related to a location of display of the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses brand visibility and compliance metric quantification system 106 determines a location of a plurality of assets and a type of each of the plurality of assets within the media content associated with the environment. The brand visibility and compliance metric quantification system 106 includes a deep neural networking model to determine a brand from each of the plurality of assets and determines at least one object from the brand associated with each of the plurality of assets.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having data related to a location of display of the at least one image.
Wherein RAO’s method wherein the inputs further comprise at least one of the following: data related to a location of display of the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
RAO in view of GABALE fail to explicitly teach and data related to a screen used for displaying the at least one image.
However, ARORA explicitly teaches and data related to a screen used for displaying the at least one image (Fig. 1, Paragraph [0053] – ARORA discloses input to the model 111 can additional include feature values determined from other information about the display device, such as a feature value indicating a location of the device, a type of the device, a mode that the device is intended to be operating in, etc. Input feature values can also include values determined from the received device information, to indicate characteristics of the current status of the device (e.g., the device type or device model, the presence or absence of error codes, indication of which software is running, indications of which versions of software or firmware is used, indications of hardware settings or software settings, etc.).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a method using machine learning for performing digital content approval, the method comprising: storing in a memory of a computing device a model of a machine learning algorithm; determining metadata related to at least one image; and executing by a processing unit of the computing device the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of ARORA of having and data related to a screen used for displaying the at least one image.
Wherein RAO’s method wherein the inputs further comprise at least one of the following: data related to a time of display of the at least one image, data related to a location of display of the at least one image, and data related to a screen used for displaying the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed, efficiency, and accuracy, since both RAO and ARORA relate to methods and arrangements for pattern recognition or machine learning in image or video data, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and ARORA relates to managing display devices using machine learning; the system can also improve the scale of detection and remediation; the system can also be leveraged to provide customized monitoring or customized models for particular networks or sites, which can further improve the accuracy and effectiveness of the models. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and ARORA (US 20240037368 A1), Paragraph [0036].
Regarding claim 20, RAO teaches the computing device of claim 12,
Although RAO further teaches wherein the inputs further comprise at least one of the following: data related to a time of display of the at least one image (Fig. 1M, Paragraph [0043] – RAO discloses the image data may be associated with a first timestamp (e.g., a one minute mark of the video), the text data may be associated a second timestamp, and the first timestamp of the image data may be within a threshold time period from the second timestamp of the text data.),
RAO fails to explicitly teach data related to a location of display of the at least one image,
However, GABALE explicitly teaches data related to a location of display of the at least one image (Fig. 1, Paragraph [0034] – GABALE discloses brand visibility and compliance metric quantification system 106 determines a location of a plurality of assets and a type of each of the plurality of assets within the media content associated with the environment. The brand visibility and compliance metric quantification system 106 includes a deep neural networking model to determine a brand from each of the plurality of assets and determines at least one object from the brand associated with each of the plurality of assets.),
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of GABALE of having data related to a location of display of the at least one image.
Wherein RAO’s computing device wherein the inputs further comprise at least one of the following: data related to a location of display of the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed and efficiency, since both RAO and GABALE relate to methods and systems for determining compliance metrics for media content, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and GABALE relates to a system and method for quantifying brand visibility and compliance metric for the brand in the environment based on the analysis of the assets; such level of information helps to quantify the brand visibility and compliance effectively. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and GABALE (US 20220129911 A1), Paragraph [0003].
RAO in view of GABALE fail to explicitly teach and data related to a screen used for displaying the at least one image.
However, ARORA explicitly teaches and data related to a screen used for displaying the at least one image (Fig. 1, Paragraph [0053] – ARORA discloses input to the model 111 can additional include feature values determined from other information about the display device, such as a feature value indicating a location of the device, a type of the device, a mode that the device is intended to be operating in, etc. Input feature values can also include values determined from the received device information, to indicate characteristics of the current status of the device (e.g., the device type or device model, the presence or absence of error codes, indication of which software is running, indications of which versions of software or firmware is used, indications of hardware settings or software settings, etc.).).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date the claimed invention was made to combine the teachings of RAO of having a computing device comprising: memory storing a model of a machine learning algorithm; and a processing unit for: determining metadata related to at least one image; and executing the machine learning algorithm, the machine learning algorithm using the model for determining a content approval indicator based on inputs, the content approval indicator indicating whether a content of the at least one image is approved, the inputs comprising the metadata, with the teachings of ARORA of having and data related to a screen used for displaying the at least one image.
Wherein RAO’s computing device wherein the inputs further comprise at least one of the following: data related to a time of display of the at least one image, data related to a location of display of the at least one image, and data related to a screen used for displaying the at least one image.
The motivation behind this modification would have been to provide an enhanced method of using machine learning to perform content approval that improves speed, efficiency, and accuracy, since both RAO and ARORA relate to methods and arrangements for pattern recognition or machine learning in image or video data, wherein RAO relates to a contextual analysis platform that utilizes machine learning models to identify context of content for policy compliance determination; the contextual analysis platform may improve the accuracy of the image analysis model and/or the text analysis model in processing image data and/or text data associated with videos, which may improve speed and efficiency of the image analysis model and/or the text analysis model and may conserve computing resources, networking resources, and/or the like, and ARORA relates to managing display devices using machine learning; the system can also improve the scale of detection and remediation; the system can also be leveraged to provide customized monitoring or customized models for particular networks or sites, which can further improve the accuracy and effectiveness of the models. Please see RAO (US 20210174089 A1), Paragraph [0011, 0052], and ARORA (US 20240037368 A1), Paragraph [0036].
Conclusion
Listed below are the prior arts made of record and not relied upon but are considered pertinent to applicant’s disclosure.
BROUDOU et al. (US 20210081566 A1) - A system for a computer useable medium, the system having a set of executable code is provided including a first set of computer program code adapted to receive at least a portion of a document comprising at least one classifiable distinct marker, a second set of computer program code adapted to analyze the distinct marker and assign a classifier thereto, and a third set of computer program code adapted to assess the potential risk of the distinct marker and calculate a first risk value associated with the distinct marker as it relates to the classifier and display the first risk value to a user of the system....… Fig. 1, Abstract.
NELSON et al. (US 20200293605 A1) - Artificial intelligence is introduced into document review to identify content suggestions from input to generate suggested annotations for the reviewed document. An approach is provided for receiving an electronic document that contains original content from an original electronic document for review and electronic mark-ups provided by a first user. One or more electronic mark-ups that represent content suggestions proposed by the first user are identified from the electronic document. For each electronic mark-up of the one or more electronic mark-ups identified a document portion of the original content that corresponds to the electronic mark-up is identified, and an annotation is generated for the electronic mark-up comprising the electronic mark-up and a first user ID for the first user and associating the annotation to the document portion identified. The original content with one or more annotations generated from the one or more electronic mark-ups is displayed, in electronic form, within a display window....… Fig. 1, Abstract.
MERKULOV et al. (US 20230252544 A1) - Technologies for machine learning-based product classification and approval are described. In some embodiments, a product data record is received and analyzed by machine learning models. The data record includes at least a product description, a product category, and a source organization. A first trained machine learning model is applied to the product description. The first trained machine learning model generates a product type. A second machine learning model is applied to the data record. The second machine learning model produces a classification that includes a confidence score. A decision rule is applied to the classification and the product type. An approval status is generated for the data record.....… Fig. 1, Abstract.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BEZAWIT N SHIMELES whose telephone number is (571)272-7663. The examiner can normally be reached M-F 7:30am-5pm.
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, Chineyere Wills-Burns can be reached at (571) 272-9752. 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.
/BEZAWIT NOLAWI SHIMELES/Examiner, Art Unit 2673
/CHINEYERE WILLS-BURNS/Supervisory Patent Examiner, Art Unit 2673
Prosecution Insights