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 .
Election/Restrictions
Restricting to one of the following inventions is required under 35 U.S.C. 121:
I. Claims 1-8 and 15-20, drawn to a method and non-transitory computer-readable medium storing executable code for training a generative AI model to produce style-guide compliant content from input text that is not compliant with a company’s specific style guide, classified in G06N20/00.
II. Claims 9-14, drawn to a system of managing a plurality of software solutions, each software solution useful for constructing or operating a large-scale industrial facility of a company, classified in G06Q30/0624.
The inventions are independent or distinct, each from the other because:
Inventions I and II are related as process of making and product made. The inventions are distinct if either or both of the following can be shown: (1) that the process as claimed can be used to make another and materially different product or (2) that the product as claimed can be made by another and materially different process (MPEP § 806.05(f)). In the instant case, the product as claimed can be made by another and materially different process. The system of Invention II only requires a style-guide compliant generative artificial intelligence (AI) to produce style-guide compliant content, and does not require the generative AI to have been trained in a specific manner.
Restriction for examination purposes as indicated is proper because all the inventions listed in this action are independent or distinct for the reasons given above and there would be a serious search and/or examination burden if restriction were not required because one or more of the following reasons apply:
The inventions require a different field of search. Invention I requires a search for training a pre-trained generative AI model using style-guide compliant text. Invention II requires a search for systems that ask a user a dynamic sequence of questions from a hierarchical tree to select software solutions for constructing or operating an industrial facility, and build a report from the answers.
Applicant is advised that the reply to this requirement to be complete must include (i) an election of an invention to be examined even though the requirement may be traversed (37 CFR 1.143) and (ii) identification of the claims encompassing the elected invention.
The election of an invention may be made with or without traverse. To reserve a right to petition, the election must be made with traverse. If the reply does not distinctly and specifically point out supposed errors in the restriction requirement, the election shall be treated as an election without traverse. Traversal must be presented at the time of election in order to be considered timely. Failure to timely traverse the requirement will result in the loss of right to petition under 37 CFR 1.144. If claims are added after the election, applicant must indicate which of these claims are readable upon the elected invention.
Should applicant traverse on the ground that the inventions are not patentably distinct, applicant should submit evidence or identify such evidence now of record showing the inventions to be obvious variants or clearly admit on the record that this is the case. In either instance, if the examiner finds one of the inventions unpatentable over the prior art, the evidence or admission may be used in a rejection under 35 U.S.C. 103 or pre-AIA 35 U.S.C. 103(a) of the other invention.
Applicant is reminded that upon the cancelation of claims to a non-elected invention, the inventorship must be corrected in compliance with 37 CFR 1.48(a) if one or more of the currently named inventors is no longer an inventor of at least one claim remaining in the application. A request to correct inventorship under 37 CFR 1.48(a) must be accompanied by an application data sheet in accordance with 37 CFR 1.76 that identifies each inventor by his or her legal name and by the processing fee required under 37 CFR 1.17(i).
During a telephone conversation with THOMAS TUYTSCHAEVERS (Reg No. 42,190) on 09/17/2026, a provisional election was made without traverse to prosecute the invention of Group I, claims 1-8 and 15-20. Affirmation of this election must be made by applicant in replying to this Office action. Claims 9-14 are withdrawn from further consideration by the examiner, 37 CFR 1.142(b), as being drawn to a non-elected invention.
Priority
Acknowledgment is made of applicant’s claim to domestic benefit of provisional application 63/545,818 filed on 10/26/2023.
Claim Objections
Claim 7 is objected to because the claim recites “and the style guide specifies us of the trademark only as an adjective”. It appears that the claim should recite “and the style guide specifies use of the trademark only as an adjective”. Appropriate correction is required.
Claims 16-18 are objected to because the claims recite “code for code for”. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an 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, 8, and 15 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by THEVENIN (US 20240394511 A1), hereinafter THEVENIN.
Regarding Claim 1:
THEVENIN teaches:
providing a plurality of examples of style-guide compliant text, which text is compliant with a company’s specific style guide; (THEVENIN [0028] teaches: “Process 200A may be utilized to generate synthetic data 204, 210 based on input data 202, 208. During process 200A, generative modeling engine 120, implementing generative AI model 206, may be configured to receive input data 202, 208, or both. In an aspect, input data 202, 208 may be received via a communication interface (e.g., communication interface 122 of FIG. 1), such as from a user device (e.g., computing device 130 of FIG. 1).” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As an example, the one or more rules of the set of rules included in input data 202 may include or correspond to content guidelines. The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., examples of style-guide compliant text, which text is compliant with a company’s specific style guide) and a negative example representing a piece of synthetic data 210.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided (i.e., providing a plurality of examples) to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. […] As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example.” THEVENIN [0044] teaches: “For instance, input prompt 216 may include or correspond to text (e.g., a sentence, a paragraph, etc.) that may or may not comport with one or more rules in an organization's manual of usage or style (i.e., company’s specific style guide).”)
providing a pre-existing, trained generative artificial intelligence, the pre-existing, trained generative artificial intelligence not customized to produce text compliant with the company’s specific style guide; (THEVENIN [0006] teaches: “A system of the present disclosure, such as a system implementing or instantiating a generative AI model (i.e., providing a pre-existing, trained generative artificial intelligence), may generate synthetic data for use in training or tuning the generative AI model.” THEVENIN [0023] teaches: “Examples of LLMs include GPT-3, LaMDA, ESMFold, Gato, and WuDao 2.0 (i.e., the pre-existing, trained generative artificial intelligence not customized to produce text compliant with the company’s specific style guide).” THEVENIN [0043] teaches: “Utilizing the above processes 200A and 200B, a tuned generative AI model 214 may be generated. Tuned generative AI model 214 may be configured to generate content that complies with the entire set of rules, such as the set of rules used to generate input data 202. Exemplary details regarding tuned generative AI model 214 are described in more detail below with reference to FIG. 2C. It is noted that if the set of rules changes, processes 200A and 200B may be performed to update the tuned generative AI model 214 (e.g., allow the tuned generative AI model 214 to learn the updated set of rules).” Examiner’s note: The examples of LLMs provided in THEVENIN [0023] are pre-trained models, and processes 200A and 200B generate a fine-tuned model for rewriting text to comport with one or more rules in an organization’s manual of usage or style.)
customizing the pre-existing, trained generative artificial intelligence by training the pre-existing, trained generative artificial intelligence using the plurality of examples of style-guide compliant text to produce a style-guide compliant generative artificial intelligence configured to produce style-guide compliant content from input text that is not compliant with the company’s specific style guide. (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model. For example, during tuning (i.e., customizing), the first and second synthetic data may be used to train the generative AI model (i.e., the pre-existing, trained generative artificial intelligence by training the pre-existing, trained generative artificial intelligence) to generate content that complies with the set of rules.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., using the plurality of examples of style-guide compliant text) and a negative example representing a piece of synthetic data 210.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. Thus, during tuning, a piece of synthetic data 210i may be provided to generative AI model 206 as an input with a paired piece of synthetic data 204i representing the desired output to be generated by the generative AI model 206. Generative AI model 206 may then process the negative example to produce an output corresponding to the positive example. As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example. By providing each of the matched pairs of synthetic data 204 (positive examples) and synthetic data 210 (negative examples) to generative AI model 206, generative AI model 206 learns the entire set of rules, which are used to create the synthetic data 204, without providing the entire set of rules to generative AI model 206 in a single prompt.” THEVENIN [0043] teaches: “Utilizing the above processes 200A and 200B, a tuned generative AI model 214 may be generated (i.e., to produce a style-guide compliant generative artificial intelligence). Tuned generative AI model 214 may be configured to generate content that complies with the entire set of rules, such as the set of rules used to generate input data 202 (i.e., configured to produce style-guide compliant content from input text that is not compliant with the company’s specific style guide).” THEVENIN [0044] teaches: “For instance, input prompt 216 may include or correspond to text (e.g., a sentence, a paragraph, etc.) that may or may not comport with one or more rules in an organization's manual of usage or style.”)
Regarding Claim 8:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
receiving a suite vector comprising text that is not compliant with the company’s specific style guide; (THEVENIN [0044] teaches: “For example, tuned generative AI model 214 may receive input prompt 216 (i.e., receiving a suite vector). Input prompt 216 may include or correspond to an instruction to generate an output. In response to receipt of input prompt 216, tuned generative AI model 214 may be configured to generate output that conforms to one or more rules, such as a set of content or style guidelines. For instance, input prompt 216 may include or correspond to text (e.g., a sentence, a paragraph, etc.) that may or may not comport with one or more rules in an organization's manual of usage or style (i.e., comprising text that is not compliant with the company’s specific style guide).”.)
providing the suite vector to the style-guide compliant generative artificial intelligence; (THEVENIN [0046] teaches: “In this manner, output data 218, generated by tuned generative AI model 214, is more likely to conform to the rules even when tuned generative model 214 is provided with input prompt 216 that includes input material that deviates from the rules.”)
using the style-guide compliant generative artificial intelligence to transform the suite vector into a style-guide compliant report. (THEVENIN [0044] teaches: “In response to receipt of input prompt 216, tuned generative AI model 214 may be configured to transform such content included with input prompt 216 (i.e., the suite vector) into compliant output data 218 (i.e., a style-guide compliant report).” THEVENIN [0053] teaches: “The output of the tuned generative AI model may then be provided to the computing device 130 where the user may use the output to publish the content (e.g., send the e-mail, post the blog article or press release, etc.).”)
Regarding Claim 15:
The claim recites similar limitations as corresponding claim 1 and is rejected for similar reasons as claim 1 using similar teachings and rationale. THEVENIN further teaches:
A non-transitory computer-readable medium having computer executable code thereon, the computer executable code, when executed by a computer system, causing the computer system to perform a method, the code comprising: (THEVENIN [0010] teaches: “In an aspect, a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for tuning a generative artificial intelligence (AI) model using synthetic data is disclosed.”)
code for obtaining, from a computer memory, a plurality of examples of style-guide compliant text, which text is compliant with a company’s specific style guide; (THEVENIN [0010] teaches: “In an aspect, a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for tuning a generative artificial intelligence (AI) model using synthetic data is disclosed.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below. Additionally, the memory 114 may be configured to store data and information in database 118 (e.g., one or more databases) (i.e., code for obtaining, from a computer memory).” THEVENIN [0028] teaches: “Process 200A may be utilized to generate synthetic data 204, 210 based on input data 202, 208. During process 200A, generative modeling engine 120, implementing generative AI model 206, may be configured to receive input data 202, 208, or both. In an aspect, input data 202, 208 may be received via a communication interface (e.g., communication interface 122 of FIG. 1), such as from a user device (e.g., computing device 130 of FIG. 1).” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As an example, the one or more rules of the set of rules included in input data 202 may include or correspond to content guidelines. The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., examples of style-guide compliant text, which text is compliant with a company’s specific style guide) and a negative example representing a piece of synthetic data 210.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided (i.e., […] obtaining […] a plurality of examples) to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. […] As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example.” THEVENIN [0044] teaches: “For instance, input prompt 216 may include or correspond to text (e.g., a sentence, a paragraph, etc.) that may or may not comport with one or more rules in an organization's manual of usage or style (i.e., company’s specific style guide).”)
code for providing a pre-existing, trained generative artificial intelligence, […] (THEVENIN [0006] teaches: “A system of the present disclosure, such as a system implementing or instantiating a generative AI model (i.e., providing a pre-existing, trained generative artificial intelligence), may generate synthetic data for use in training or tuning the generative AI model.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 (i.e., code for providing) that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below.” THEVENIN [0023] teaches: “Examples of LLMs include GPT-3, LaMDA, ESMFold, Gato, and WuDao 2.0 (i.e., a pre-existing, trained generative artificial intelligence).” THEVENIN [0060] teaches: “If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a computer-readable medium. […] Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer-readable medium, which may be incorporated into a computer program product.”)
code for customizing the pre-existing, trained generative artificial intelligence […] (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model. For example, during tuning (i.e., customizing), the first and second synthetic data may be used to train the generative AI model (i.e., the pre-existing, trained generative artificial intelligence) to generate content that complies with the set of rules.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 (i.e., code for customizing) that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below.” THEVENIN [0060] teaches: “If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a computer-readable medium. […] Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer-readable medium, which may be incorporated into a computer program product.”)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a 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 2-3 and 16-17 are rejected under 35 U.S.C. 103 as being unpatentable over THEVENIN, as applied to claims 1 and 15 respectively above, and further in view of TOSHEVSKA (“A Review of Text Style Transfer using Deep Learning”), hereinafter TOSHEVSKA.
Regarding Claim 2:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
wherein training the pre-existing, trained generative artificial intelligence using the plurality of examples of style-guide compliant text comprises: (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model (i.e., training the pre-existing, trained generative artificial intelligence). For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As an example, the one or more rules of the set of rules included in input data 202 may include or correspond to content guidelines. The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., using the plurality of examples of style-guide compliant text) and a negative example representing a piece of synthetic data 210.”)
THEVENIN is not relied upon for teaching, but TOSHEVSKA teaches: training the pre-existing, trained generative artificial intelligence using a generative adversarial network. (TOSHEVSKA [pages 1-2, section I. Introduction] teaches: “Pivotal in this review are the studies that address the automatic adjustment of the style of a written text. […] The evaluation of how successful a particular model is on the task of style transfer has two objectives: to measure how well the semantic content in the generated sentence was preserved and to assess the quality of rewriting the sentence in a new (target) style.” TOSHEVSKA [pages 6-7, section E. Generative Adversarial Networks] teaches: “Generative Adversarial Network (GAN) [148] is a deep neural network architecture comprised of two networks – generator and discriminator (i.e., training […] using a generative adversarial network). These two networks are trained simultaneously in a two-player minimax game. The generator network (i.e., the pre-existing, trained generative artificial intelligence) aims to learn (i.e., training) the distribution of the training data and to generate samples from the learned distribution. The discriminator network determines whether a sample is from the data distribution or from the model distribution, by maximizing the probability of assigning the correct label to samples from the training data as well as from the generated data. The objective of the generator is to generate samples that are indistinguishable from the training samples, by maximizing the opposite objective of the discriminator. GANs have been applied in many natural language generation tasks [149, 150, 151] including machine translation [152], text summarization [153] and question answering [154].”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and TOSHEVSKA before them, to include TOSHEVSKA’s GAN training in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination given the promising success of adversarial-based methods for the style transfer task (TOSHEVSKA [page 12, section C. Deep Style Transfer]).
Regarding Claim 3:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
wherein training the pre-existing, trained generative artificial intelligence using the plurality of examples of style-guide compliant text comprises: (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model (i.e., training the pre-existing, trained generative artificial intelligence). For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As an example, the one or more rules of the set of rules included in input data 202 may include or correspond to content guidelines. The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., using the plurality of examples of style-guide compliant text) and a negative example representing a piece of synthetic data 210.”)
THEVENIN is not relied upon for teaching, but TOSHEVSKA teaches: training the pre-existing, trained generative artificial intelligence using a variational autoencoder. (TOSHEVSKA [pages 10-11, section B. Sentence Generation] teaches: “AttrControl [34] is an encoder-decoder based model that employs a Projection Discriminator [162] to generate realistic and style compatible sentences. The discriminator determines whether the generated sentence is real or fake, based on a style embedding and output sentence obtained by a GRU decoder. Controlled Text Generation (CTGen) [31] is built upon VAE architecture. […] A new framework named Pre-train and Plug-in Variational Autoencoder (PPVAE) was proposed by Duan et al. [106] with a realistic system in mind that can mitigate the problem of starting from scratch whenever we need to learn a new style. The framework PPVAE is composed of two variational autoencoders: the PretrainVAE, which learns to represent and reconstruct a sentence in its original style and the PluginVAE, which learns the conditional latent space for each style. The role of PluginVAE as a lightweight easily-trained network is to transform the conditional “style-specific” latent space into the global latent space learned by PretrainVAE and vice versa.”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and TOSHEVSKA before them, to include TOSHEVSKA’s PluginVAE training in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination in order to mitigate the problem of starting from scratch whenever we need to learn a new style (TOSHEVSKA [pages 10-11, section B. Sentence Generation]).
Regarding Claim 16:
THEVENIN teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 2 and is rejected for similar reasons as claim 2 using similar teachings and rationale. THEVENIN further teaches:
wherein the code for customizing the pre-existing, trained generative artificial intelligence comprises code for code for […] (THEVENIN [0010] teaches: “In an aspect, a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for tuning a generative artificial intelligence (AI) model using synthetic data is disclosed.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below.”)
Regarding Claim 17:
THEVENIN teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 3 and is rejected for similar reasons as claim 3 using similar teachings and rationale. THEVENIN further teaches:
wherein the code for customizing the pre-existing, trained generative artificial intelligence comprises code for code for […] (THEVENIN [0010] teaches: “In an aspect, a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for tuning a generative artificial intelligence (AI) model using synthetic data is disclosed.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below.”)
Claims 4 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over THEVENIN, as applied to claims 1 and 15 respectively above, and further in view of REID (“DiffusER: Discrete Diffusion Via Edit-Based Reconstruction”), hereinafter REID.
Regarding Claim 4:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
wherein training the pre-existing, trained generative artificial intelligence using the plurality of examples of style-guide compliant text comprises: (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model (i.e., training the pre-existing, trained generative artificial intelligence). For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As an example, the one or more rules of the set of rules included in input data 202 may include or correspond to content guidelines. The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content.” THEVENIN [0041] teaches: “For example, as shown in Table 1 below, the synthetic training data 212 may include n synthetic data pairs (i.e., plurality of examples), where each pair includes a positive example representing a piece of synthetic data 204 (i.e., using the plurality of examples of style-guide compliant text) and a negative example representing a piece of synthetic data 210.”)
THEVENIN is not relied upon for teaching, but REID teaches: training the pre-existing, trained generative artificial intelligence using a diffusion model. (REID [page 1, Abstract] teaches: “In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios. We look to address this, with DIFFUSER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models – a class of models that use a Markov chain of denoising steps to incrementally generate data. DIFFUSER is not only a strong generative model in general, rivalling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for.” REID [page 7, Text Style Transfer] teaches: “We train two separate, style-specific (e.g. positive and negative) DIFFUSER models on the style-specific data. We then perform transfer at test time, feeding text from each style into the model trained to edit in the opposite style (e.g. positive text → negative DIFFUSER model; negative text → positive DIFFUSER model).”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and REID before them, to include REID’s diffusion-based generative model in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination because models that generate text one token at a time lack the ability to revise existing text (REID [page 1, Abstract]), and using a diffusion-based generative model shows improvement in style transfer (REID [page 10, section 6 Conclusion]).
Regarding Claim 18:
THEVENIN teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 4 and is rejected for similar reasons as claim 4 using similar teachings and rationale. THEVENIN further teaches:
wherein the code for customizing the pre-existing, trained generative artificial intelligence comprises code for code for […] (THEVENIN [0010] teaches: “In an aspect, a non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations for tuning a generative artificial intelligence (AI) model using synthetic data is disclosed.” THEVENIN [0022] teaches: “Software configured to facilitate operations and functionality of the content generation device 110 may be stored in the memory 114 as instructions 116 that, when executed by processor 112, cause processor 112 to perform one or more operations described herein with respect to content generation device 110, as described in more detail below.”)
Claims 5 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over THEVENIN, as applied to claims 1 and 15 respectively above, and further in view of HUYNH (US 11961168 B1), hereinafter HUYNH.
Regarding Claim 5:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
wherein the company has a graphical company logo and the style guide specifies rules for proper use of the graphical company logo […]; (THEVENIN [0030] teaches: “The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo (i.e., the company has a graphical company logo) to be included in a particular location of the image (i.e., and the style guide specifies rules for proper use of the graphical company logo […]), use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0031] teaches: “The one or more instructions provided to generative AI model 206 may include or correspond to a command to generate text, an image, audio content, or a combination thereof.”)
the plurality of examples of style-guide compliant text includes examples of proper use of the graphical company logo […] as defined by the company’s specific style guide; (THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo (i.e., the graphical company logo) to be included in a particular location of the image (i.e., proper use of the graphical company logo), use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Each piece of synthetic data 204 (i.e., the plurality of examples) may include or correspond to output that conforms to the portion of the set of rules (i.e., of style-guide compliant text includes examples of proper use of the graphical company logo […] as defined by the company’s specific style guide) included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.” Examiner’s note: THEVENIN [0033] teaches that synthetic data 204 include examples of outputs that conform to the portion of the set of rules included in the corresponding input data 202. The input data 202 containing the rules include rules for generating text and images in combination.)
the style-guide compliant generative artificial intelligence is trained to produce text that complies with the rules for proper use of the graphical company logo. (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model. For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo (i.e., the graphical company logo) to be included in a particular location of the image (i.e., proper use of the graphical company logo), use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Generative AI model 206 may generate synthetic data 204 for each based input data 202. Each piece of synthetic data 204 may include or correspond to output that conforms to the portion of the set of rules included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. Thus, during tuning, a piece of synthetic data 210i may be provided to generative AI model 206 as an input with a paired piece of synthetic data 204i representing the desired output to be generated by the generative AI model 206. Generative AI model 206 may then process the negative example to produce an output corresponding to the positive example. As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example. By providing each of the matched pairs of synthetic data 204 (positive examples) and synthetic data 210 (negative examples) to generative AI model 206, generative AI model 206 learns the entire set of rules, which are used to create the synthetic data 204, without providing the entire set of rules to generative AI model 206 in a single prompt.” Examiner’s note: Under BRI, the style-guide compliant generative artificial intelligence is trained to produce text that complies with the rules for proper use of the graphical company logo can be interpreted as tuning the generative AI model 206 to generate outputs that comport with the rules given in input data 202 based on positive examples 204, which can include positive image use examples such as logos, according to THEVENIN [0030].)
THEVENIN is not relied upon for teaching, but HUYNH teaches: […] rules for proper use of the graphical company logo with text; (HUYNH [column 8, lines 28-43] teaches: “Templates generated in this fashion may be used to quickly generate content that conforms to specific rules for visual presentation, providing for more aesthetic image generation. For example, consider the logo shown in the bottom-right region of various box scores illustrated in FIG. 1 and FIG. 2—it may be the case that the logo changes one day in a manner that changes the shape—for example, a new circular logo that is wider may replace the rectangular logo. The added width of the new logo may make it so that simply replacing the old logo with the new logo may cause the text for some teams with long names to overlap with the new logo. However, by using techniques described herein, templates may be used to quickly generate updated images and replace long team names with abbreviated team names to conform to element rules that prohibit overlapping of team name text element with the league logo element (i.e., […] rules for proper use of the graphical company logo with text).”)
[…] proper use of the graphical company logo with text […]; (HUYNH [column 8, lines 28-43] teaches: “Templates generated in this fashion may be used to quickly generate content that conforms to specific rules for visual presentation, providing for more aesthetic image generation. For example, consider the logo shown in the bottom-right region of various box scores illustrated in FIG. 1 and FIG. 2—it may be the case that the logo changes one day in a manner that changes the shape—for example, a new circular logo that is wider may replace the rectangular logo. The added width of the new logo may make it so that simply replacing the old logo with the new logo may cause the text for some teams with long names to overlap with the new logo. However, by using techniques described herein, templates may be used to quickly generate updated images and replace long team names with abbreviated team names to conform to element rules that prohibit overlapping of team name text element with the league logo element (i.e., […] proper use of the graphical company logo with text).”)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and HUYNH before them, to include HUYNH’s element rules that prohibit text elements overlapping with logo elements in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination in order to keep text from overlapping when the logo changes (HUYNH [column 8, lines 28-43]).
Regarding Claim 19:
THEVENIN teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 5 and is rejected for similar reasons as claim 5 using similar teachings and rationale.
Claims 6-7 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over THEVENIN, as applied to claims 1 and 15 respectively above, and further in view of International Trademark Association (INTA) (“A Guide to Proper Trademark Use For Media, Internet, and Publishing Professionals”), hereinafter INTA.
Regarding Claim 6:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
the company has a company […] and the style guide specifies rules for proper use of the company […]; (THEVENIN [0030] teaches: “The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0031] teaches: “The one or more instructions provided to generative AI model 206 may include or correspond to a command to generate text, an image, audio content, or a combination thereof.”)
the plurality of examples of style-guide compliant text includes examples of proper use of the company […] as defined by the company’s specific style guide; (THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Each piece of synthetic data 204 may include or correspond to output that conforms to the portion of the set of rules included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.”)
the style-guide compliant generative artificial intelligence is trained to produce text that complies with the rules for proper use of the company […]. (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model. For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Generative AI model 206 may generate synthetic data 204 for each based input data 202. Each piece of synthetic data 204 may include or correspond to output that conforms to the portion of the set of rules included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. Thus, during tuning, a piece of synthetic data 210i may be provided to generative AI model 206 as an input with a paired piece of synthetic data 204i representing the desired output to be generated by the generative AI model 206. Generative AI model 206 may then process the negative example to produce an output corresponding to the positive example. As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example. By providing each of the matched pairs of synthetic data 204 (positive examples) and synthetic data 210 (negative examples) to generative AI model 206, generative AI model 206 learns the entire set of rules, which are used to create the synthetic data 204, without providing the entire set of rules to generative AI model 206 in a single prompt.” Examiner’s note: Under BRI, the style-guide compliant generative artificial intelligence is trained to produce text that complies with the rules for proper use of the company […] can be interpreted as tuning the generative AI model 206 to generate outputs that comport with the rules given in input data 202 based on positive examples 204.)
THEVENIN is not relied upon for teaching, but INTA teaches: […] a company trademark expressed in text and the style guide specifies rules for proper use of the company trademark; (INTA [page 2, Adjective Examples] teaches: “Trademarks and service marks are proper adjectives. Not nouns. Not verbs. A mark should always be used as an adjective qualifying a generic noun that defines the product or service. A mark is a company brand name, not a product or service itself. As adjectives, marks should not be used as plurals or in the possessive form, unless the mark itself is plural or possessive (such as 1-800-FLOWERS, MCDONALD’S or LEVI’S).” Additionally, INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences (i.e., a company trademark expressed in text and the style guide specifies rules for proper use of the company trademark).)
[…] examples of proper use of the company trademark with other text […] (INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences (i.e., proper use of the company trademark with other text).)
[…] text that complies with the rules for proper use of the company trademark. (INTA [page 2, Adjective Examples] teaches: “Trademarks and service marks are proper adjectives. Not nouns. Not verbs. A mark should always be used as an adjective qualifying a generic noun that defines the product or service. A mark is a company brand name, not a product or service itself. As adjectives, marks should not be used as plurals or in the possessive form, unless the mark itself is plural or possessive (such as 1-800-FLOWERS, MCDONALD’S or LEVI’S).” Additionally, INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences (i.e., text that complies with the rules for proper use of the company trademark).)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and INTA before them, to include INTA’s rules for using trademarks as adjectives in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination because trademarks are important and valuable assets for both businesses and consumers (INTA [page 2, What is a trademark?]) and misuse can turn a mark into a “ghost-mark”, which serves as a historic and costly reminder of what can happen to marks if the public comes to regard a brand name as the generic name of a product (INTA [pages 3-4, Adjective]).
Regarding Claim 7:
THEVENIN teaches the elements of claim 1 as outlined above. THEVENIN further teaches:
the company has a company […] and the style guide specifies us of […] (THEVENIN [0030] teaches: “The content guidelines may include or correspond to an organization's guidelines governing the style of written, visual, or audio content, the subject matter of written, visual, or audio content, or combinations thereof. For instance, the content guidelines may include or correspond to a manual of usage or style governing the grammar, style, subject matter, or combinations thereof of written content. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0031] teaches: “The one or more instructions provided to generative AI model 206 may include or correspond to a command to generate text, an image, audio content, or a combination thereof.”)
the plurality of examples of style-guide compliant text includes examples of proper use of the company […] as defined by the company’s specific style guide; (THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Each piece of synthetic data 204 may include or correspond to output that conforms to the portion of the set of rules included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.”)
the style-guide compliant generative artificial intelligence is trained to produce text that includes proper use of the company […]. (THEVENIN [0007] teaches: “The first synthetic data and the second synthetic data may form a set of training data that may be used to tune the generative AI model. For example, during tuning, the first and second synthetic data may be used to train the generative AI model to generate content that complies with the set of rules.” THEVENIN [0030] teaches: “The one or more rules of the set of rules included in input data 202 may include rules for generation of textual content, rules for generation of image content, rules for generation of video content, rules for generating audio content, or combinations thereof. As another example, the one or more rules of the set of rules corresponding to content guidelines may include rules governing the artistic style, subject matter, or both of images (e.g., such as requiring a logo to be included in a particular location of the image, use of certain color schemes (e.g., specific colors used by a company in branding materials), acceptable content that may be included in the image, or other content guidelines or rules), a size of the image, and the like.” THEVENIN [0033] teaches: “Generative AI model 206 may generate synthetic data 204 for each based input data 202. Each piece of synthetic data 204 may include or correspond to output that conforms to the portion of the set of rules included in the corresponding input data 202 and may contain content based on the instructions of the prompt. […] As another example, if input data 202 includes or corresponds to a set of content guidelines governing the subject matter, artistic style, or both of images, the content outputs generated based on the input data 202 may include an image, a fragment of an image, or both that conform to content guidelines constituting input data 202.” THEVENIN [0042] teaches: “Synthetic training data 212 may be provided to generative AI model 206 to tune generative AI model 206, thereby resulting in a tuned generative AI model 214. To illustrate, a positive example of synthetic data may represent the desired output that is to be generated by generative AI model 206 when presented with a negative example. Thus, during tuning, a piece of synthetic data 210i may be provided to generative AI model 206 as an input with a paired piece of synthetic data 204i representing the desired output to be generated by the generative AI model 206. Generative AI model 206 may then process the negative example to produce an output corresponding to the positive example. As generative AI model 206 rewrites the negative example to conform to the positive example generative AI, model 206 learns the one or more rules associated the portion of the set of rules corresponding to the positive example. By providing each of the matched pairs of synthetic data 204 (positive examples) and synthetic data 210 (negative examples) to generative AI model 206, generative AI model 206 learns the entire set of rules, which are used to create the synthetic data 204, without providing the entire set of rules to generative AI model 206 in a single prompt.” Examiner’s note: Under BRI, the style-guide compliant generative artificial intelligence is trained to produce text that includes proper use of the company […] can be interpreted as tuning the generative AI model 206 to generate outputs that comport with the rules given in input data 202 based on positive examples 204.)
THEVENIN is not relied upon for teaching, but INTA teaches: […] a company trademark expressed in text and the style guide specifies us of the trademark only as an adjective, and (INTA [page 2, Adjective Examples] teaches: “Trademarks and service marks are proper adjectives. Not nouns. Not verbs. A mark should always be used as an adjective qualifying a generic noun that defines the product or service. A mark is a company brand name, not a product or service itself. As adjectives, marks should not be used as plurals or in the possessive form, unless the mark itself is plural or possessive (such as 1-800-FLOWERS, MCDONALD’S or LEVI’S).” Additionally, INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences (i.e., a company trademark expressed in text and the style guide specifies us of the trademark only as an adjective).)
[…] text includes examples of proper use of the company trademark only as an adjective with other text […] (INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences.)
[…] text that includes proper use of the company trademark as an adjective. (INTA [page 2, Adjective Examples] teaches: “Trademarks and service marks are proper adjectives. Not nouns. Not verbs. A mark should always be used as an adjective qualifying a generic noun that defines the product or service. A mark is a company brand name, not a product or service itself. As adjectives, marks should not be used as plurals or in the possessive form, unless the mark itself is plural or possessive (such as 1-800-FLOWERS, MCDONALD’S or LEVI’S).” Additionally, INTA [page 2, Adjective Examples] teaches correct and incorrect examples on how to use trademarks as adjectives in sentences.)
Accordingly, it would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention, having the teachings of THEVENIN and INTA before them, to include INTA’s rules for using trademarks as adjectives in THEVENIN’s enhanced generative AI model tuning method. One would have been motivated to make such a combination because trademarks are important and valuable assets for both businesses and consumers (INTA [page 2, What is a trademark?]) and misuse can turn a mark into a “ghost-mark”, which serves as a historic and costly reminder of what can happen to marks if the public comes to regard a brand name as the generic name of a product (INTA [pages 3-4, Adjective]).
Regarding Claim 20:
THEVENIN teaches the elements of claim 15 as outlined above. Additionally, the claim recites similar limitations as corresponding claim 6 and is rejected for similar reasons as claim 6 using similar teachings and rationale.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
SRINIVASAN (US 20210264109 A1) relates to a method for rewriting text in the writing style of a target author by using a trained language model.
TAO (US 20190325626 A1) relates to a graphic design system for dynamically generating content, such as brand-compliant content and/or other creative content.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Alvaro S Laham Bauzo whose telephone number is (571) 272-5650. The examiner can normally be reached Mon-Fri 7:30 AM - 11:00 AM | 1:00 PM - 5:30 PM ET.
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/A.S.L./Examiner, Art Unit 2146
/USMAAN SAEED/Supervisory Patent Examiner, Art Unit 2146
